Spatial Computing

The MIT Press Essential Knowledge Series

A complete list of the titles in this series appears at the back of this book.

Spatial Computing

Shashi Shekhar and Pamela Vold

The MIT Press | Cambridge, Massachusetts | London, England

© 2019 Massachusetts Institute of Technology

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

This book was set in Chaparral Pro by Toppan Best-set Premedia Limited.

Library of Congress Cataloging-in-Publication Data

Names: Shekhar, Shashi, 1963- author. | Vold, Pamela, author.

Title: Spatial computing / Shashi Shekhar and Pamela Vold.

Description: Cambridge, MA : The MIT Press, [2019] | Series: The MIT Press essential knowledge series | Includes bibliographical references and index.

Identifiers: LCCN 2019013947 | ISBN 9780262538046 (pbk. : alk. paper)

Subjects: LCSH: Geographic information systems.

Classification: LCC G70.212 .S538 2019 | DDC 910.285—dc23 LC record available at https://lccn.loc.gov/2019013947

10 9 8 7 6 5 4 3 2 1

d_r0

To Ibha, Will, Henry, Hirsh, and Hugo

Contents

Series Foreword

Preface

Acknowledgments

1 What Is Spatial Computing?

2 Where Am I? Positioning, Outdoors and Indoors

3 What’s There? Remote Sensing

4 Geographic Information Systems and Cartography

5 Spatial Databases

6 Spatial Data Science

7 The Future of Spatial Computing

Glossary

Bibliography

Further Reading

Index

Series Foreword

The MIT Press Essential Knowledge series offers accessible, concise, beautifully produced pocket-size books on topics of current interest. Written by leading thinkers, the books in this series deliver expert overviews of subjects that range from the cultural and the historical to the scientific and the technical.

In today’s era of instant information gratification, we have ready access to opinions, rationalizations, and superficial descriptions. Much harder to come by is the foundational knowledge that informs a principled understanding of the world. Essential Knowledge books fill that need. Synthesizing specialized subject matter for nonspecialists and engaging critical topics through fundamentals, each of these compact volumes offers readers a point of access to complex ideas.

Bruce Tidor

Professor of Biological Engineering and Computer Science

Massachusetts Institute of Technology

Preface

How could a technology used by billions of people around the world not have an accessible guide to describe it to a broad audience? This was the unique position we were in as we set out to write this book about spatial computing. While there are books about some of the individual concepts included within this field, such as GPS, as of this printing, texts for nonscientists or specialists are missing. This book is our attempt to address the gap between individuals curious about science and computer science professionals.

In this book, we have set out to briefly describe the recent spatial computing revolution, how it has improved our lives, and where it is headed. This book introduces five spatial computing capabilities: positioning (GPS); remote sensing; geographic information systems (GIS); spatial database and management systems; and spatial data science. Using maps, historic and current use cases, and success stories, we demonstrate what these technologies are and how they are used. Navigation apps, ride-sharing apps for smartphones, and precision agriculture each use many spatial computing technologies.

It is important to acknowledge the growing ethical debate around spatial computing applications, the greatest one surrounding privacy. We attempt to recognize the debate without taking a side. Recent court decisions have invalidated gerrymandered electoral district maps, and our society is actively debating the privacy of our location traces and the risks of social feedback loops related to crime hotspot identification and analysis. We anticipate that in the coming years, we will see new regulations and social norms emerge from this ongoing debate.

It is our ultimate hope that this book conveys the excitement of spatial computing, not only due to its tremendous impact on our daily lives but also its potential to keep transforming the ways we live, work, communicate, and perform research. Because this book is not intended to provide an exhaustive coverage of the growing body of knowledge in spatial computing, we have provided a list of resources including textbooks, monographs, encyclopedias, and journals for a deeper and broader exploration of spatial computing.

Acknowledgments

First and foremost, we owe gratitude to our families as this book would not have been completed without their support and encouragement. Most importantly, they encouraged and helped us recover after many busy days and weeks developing this book. Further, Ibha and Hirsh helped review and choose spatial computing success stories by acting as our audience. Special thanks to Will Crain who brought his copyediting expertise to refine and substantially improve an earlier draft, and Henry and Hugo for their curiosity about the project.

In this book, we incorporate information sources from different points of view such as government (e.g., Congressional Research Service, the National Academies), civil society (e.g., the New York Times, the British Broadcasting Corporation), businesses (e.g., the McKinsey Global Institute, the Economist), and academia (e.g., the Association for Computing Machinery, the University Consortium for Geographic Information Science, the Institute on Navigation, the International Society for Photogrammetry and Remote Sensing). These helped us “triangulate” the spatial computing accomplishments and trends across diverse interest groups.

The Computing Research Association and the Computing Community Consortium were especially helpful. The multisector workshop “From GPS and Virtual Globes to Spatial Computing” identified several major accomplishments in spatial computing and indicated future directions for the work, and also encouraged us to speak at the US congressional reception on Deconstructing Precision Agriculture (March 2015) and at the annual meeting of the American Association for the Advancement of Sciences (February 2018). These experiences helped us understand the need to share the key computing accomplishments and opportunities with wider audiences such as policy makers, professionals, and other curious minds.

Thank you to the current and former members of the spatial computing research group in the department of Computer Science and Engineering at the University of Minnesota for a variety of contributions including literature surveys, research, and general assistance. Special thanks go to Yan Li, who helped create many illustrations for the book. We would also like to thank students enrolled in Shashi’s courses “From GPS and Virtual Globes to Spatial Computing” (Csci 5715) and “Spatial Data Science Research” (Csci 8715), as they helped review and refine examples, pictures, and stories used in this book.

As we have learned a lot about spatial computing via research, we would like to acknowledge and thank those sponsors for making the work possible, including the United Nations Development Programme, the National Science Foundation, the National Geospatial-Intelligence Agency, the National Institute for Food and Agriculture, the Advanced Research Projects Agency—Energy, the National Institute for Health, the National Aeronautical and Space Agency, the Army Geospatial Center, the Oak Ridge National Laboratory, the Ford Motor Company University Research Program, Microsoft Bing, the Minnesota Department of Transportation, and many spatial research centers at the University of Minnesota, such as the Minnesota Population Center, the Center for Transportation Studies, and the Institute on the Environment).

Finally, special thanks are due to the generous team at the MIT Press for helping us refine the content and quality of this book, including Stephanie Cohen and Christine Savage (assistant acquisitions editors), Deborah Cantor-Adams (production editor), and all those whose help went unseen by Shashi and Pamela. In particular, we would like to thank Marie L. Lee (executive editor, computer science and engineering), who read the spatial computing cover article in the Communications of the ACM (January 2016) and considered the potential to bring this content to a wider audience, then persuaded us to undertake this project and guided us through the journey.

1

What Is Spatial Computing?

Hundreds of times a week the following scene plays out: An individual attends an event at a large stadium and uses a ride-sharing app, such as Uber or Lyft, to summon a ride home. Because hundreds of others are exiting the stadium at the same time as this person, the exit is crowded, making it impossible for the driver of the car to meet the rider there. The crowd of people flowing out of the stadium and spilling over nearby roads also makes it difficult for the ride-sharing driver to reach roads adjacent to the stadium for pickup. So the rider walks to the next block, where there is less traffic and the driver is accessible. The driver and rider connect and move on to their next location.

If you have either been the driver or the rider in that scenario, you are one of billions people around the world using maps and making maps with nonspecialized software—a radical change from the previous century and from all of history. This leap in the creation and adoption of technology is part of the science of location, or spatial computing.

Spatial computing is a set of ideas and technologies that transform our lives by understanding the physical world, knowing and communicating our relation to places in that world, and navigating through those places. Uber, Google Maps, and Global Positioning System (GPS) devices are applications of spatial computing that most people will recognize, but the applications and technologies have a reach much wider than these consumer applications. Spatial-computing technologies have changed our society and infrastructure in profound ways.

Spatial computing allows scientists and researchers to map the bottoms of the oceans and find the evidence of water on planets far away. Data scientists can quickly and easily create election maps in real time and air them on live news broadcasts to illustrate voting results.

Epidemiologists can predict the spread of diseases such as the Zika virus or track down the origins of food-borne illnesses such as salmonella, helping to prevent more people from becoming sick.

Biologists can track endangered species to better understand their behavior and how they interact with their environments. Farmers use GPS and remote sensing in precision agriculture to increase crop yields while reducing costs.

Spatial computing is a set of ideas and technologies that transform our lives by understanding the physical world, knowing and communicating our relation to places in that world, and navigating through those places.

Protesters can form impromptu events via “check-in” models used by social media platforms. Google Earth is used in classrooms to teach children about their neighborhoods and the world in a fun, interactive way.

A hiker in Yellowstone National Park, a schoolgirl in Washington, DC, a drone over a Japanese rice farm, and an Uber driver in London know precisely where they are, what points of interest are nearby, and how to reach their destinations.

All of these examples are part of an incredible historical shift in the way we access, create, and use maps and the location technology that we explore in this book. Thirty years ago, the majority of the world’s population used paper maps for route planning. Most of our homes still contain these tools, now relics of different sizes and scales describing places near and far: a highway map to navigate across the state, a national road atlas to travel across the country, a local metropolitan map with an index of streets and their corresponding pages, but we have largely abandoned them in favor of digital devices. Today, roughly 2 billion GPS receivers are in use in smartphones, vehicles, and sensor networks—the so-called Internet of Things—with the ability to store all of that information and more, making it accessible at any time around the globe.

We aren’t just accessing location information; we are creating it as well. In our ride-sharing scenario, the driver and rider could not connect at the exact location of the original pickup request. Every moment a driver is not providing a ride means lost revenue for the driver. To minimize wait time as much as possible for both customers and drivers, the ride-sharing app takes the location input gathered from other points of connection and suggests them as pickup points for future ride requests. When the rider walked a block away from the event location, the driver and rider connected and created a new pickup point that was then reported to the ride-sharing app, adding information to the app’s database, enriching the information for the app, drivers, and users.

In addition to ride-sharing apps that are creating maps, dozens of apps on our smartphones are using our location and providing that information to app developers. Navigation and ride-sharing apps in the United States and around the world, such as Waze, Uber, Ola, and Didi Chuxing estimate traffic speeds, choose less-congested routes, and estimate arrival times at destinations from smartphone location traces. Our cell phone companies are constantly accessing our location using cellular technology, Wi-Fi, and Bluetooth and analyzing those data to improve the accuracy of the information provided by the apps on our phones.

Why Spatial?

So why do we call this technology spatial when all of these examples seem to refer to geographic locations? Although the most widely known and used applications of spatial computing work on the surface of Earth, new areas of this work expand its scope beyond geography and into both larger and smaller domains. The definition of spatial thus includes not only physical space but also time.

More than 100 international soccer teams use Catapult, a wearable technology. Players are equipped with GPS, accelerometers, magnetometers, and gyroscopes. The devices track the players’ movements across the field, monitoring speed, direction, and distance as well as body temperature and heart rate. These data are gathered during matches and accessed by the coaches and trainers on the field during the match via tablet computers. The analysis helps the team leaders determine when players are showing signs of fatigue and are more likely to make mistakes.

Match data are compared with training data from the weeks and months prior to the match. Using player data obtained during training prior to the match, coaches and trainers compare player acceleration and deceleration as well as the number of sprints a player makes during the game to the intensity and duration of the drills the players completed. They also review the spaces on the field that a player frequently occupies, mapping the athlete’s movements to determine how well it matches the strategies directed to the player prior to the game.

We likely don’t think of a soccer field as geography, nor would we describe it as a geographic feature. The soccer field is a space, however. The data reviewed by the coaches are explicitly linked to the space and time of the match. The match is linked to the athletes’ performances and informed by their training routines.

Let’s consider another example of space. It’s unlikely that we would consider the inside of our homes as geography, yet household tools use spatial-computing technologies for cleaning. High-end models of Roomba, the robotic vacuum, are equipped with sensors to identify the locations of our walls and furniture. These sensors prevent the vacuum from hitting those items and allow the machine to clean more efficiently—if the vacuum doesn’t collide with our furniture, it uses less battery power, allowing it to cover more territory in the home. To maximize productivity, the computer in the vacuum creates a map of the home, identifying and recording the number of rooms and their contents, including the size, location, and placement of the furniture. In doing so, the computer is able to identify the function of each room—a dining room has a set of chairs of uniform size around a table. The living room has a couch, and the child’s room is the one where the vacuum is constantly bumping into toys scattered across the floor. The Roomba maps our private spaces and via its Wi-Fi connection reports those data to the company.

We can shrink the domain of spatial technologies even further to our own bodies. Neurosurgeons at Brigham and Women’s Hospital in Boston face an incredible challenge. All human brains look similar in size, shape, and color, yet every brain is different. Each area of the brain controls a different set of functions or multiple sets of functions. Slight disturbances from surgical instruments during an operation can damage nerves or affect a patient’s ability to process memories or control bodily functions. The surgeons want to reduce the likelihood of damage as much as possible. To do this, prior to the surgery the team creates a three-dimensional model of the neural tracts delivering nerve impulses for the patient. This map is different for each person, so each map of tracts is unique. Once the neural tracts are mapped, the surgeons can plan the path of the surgery and determine which connections might be disturbed during the procedure. If their planned path has the potential to disrupt a critical language connection, they use the map to plan an alternative route, not unlike the way an app such as Waze can help you avoid a traffic jam.

During the surgery, the instruments are equipped with a tracking capability providing surgeons with information on their position within the brain. The operating-room team monitors the instruments as they travel along the planned route to the internal destination and tracks them as they move out again, quickly and efficiently.

The human body, the home, a soccer field—these examples better describe space more than geography. Spatial computing began as a way to represent Earth, but the work of today and tomorrow will continue to evolve beyond the surface of our planet.

Significant Applications

In this book, we describe some of the most significant applications of spatial computing that serve diverse industries and audiences. As we noted, GPS is the application most familiar to readers. In only 40 years and most significantly in the past 10 years, GPS has changed the way humans navigate the world. We have already mentioned some of those uses, and we delve into more of them. You are likely very familiar with some other spatial technologies, even if you have never heard many of the names or technical terms associated with them. Unmanned aerial vehicles (UAVs), or drones, equipped with cameras and used by professionals and hobbyists, are remote-sensing tools to survey roads, bridges, and land to detect changes such as congestion, bridge scour (a form of erosion), and floods. Remote-sensing applications provide us information about the icy surface of Jupiter’s moons and the depth of Earth’s oceans without ever coming into physical contact with either of them.

Geographic information systems (GISs) fuse geographic and map data from multiple different sources. A GIS can store, analyze, and manage the data and then provide visualizations of those data in the form of a map. Spatial database management systems (SDBMSs) provide the backbone of many of these technologies. These databases are specifically equipped to handle spatial data types and structures—spatial operations and algorithms to calculate paths, distances, and the locations of objects in a way that is fast and reduces manual programming. SDBMSs are used with GPS to provide us with directions and to help us find goods and services near us.

Spatial data science digs into very large data sets to find meaningful patterns and thus provide us with new descriptive and predictive information. Spatial statistical tests are routinely used for forecasting the weather, detecting outbreaks of infectious diseases, and anticipating where those outbreaks will travel and at what speed.

Spatial-computing technologies work together frequently. Drones use GPS to orient themselves and steer clear of restricted airspaces. They use SDBMSs to help chart their courses, while their on-board cameras and sensors detect what is around them. The data that drones collect can be used in a GIS for visualization or in spatial statistical tests to mine the information for patterns.

Autonomous vehicles require several spatial-computing technologies as well. Like many of us, autonomous vehicles use GPS for navigation. However, the GPS accuracy needed to navigate an autonomous vehicle requires a level of positioning accuracy that is far greater than what is used on our phones. Our phones use maps with relatively low levels of detail and provide our position with a buffer from a few meters up to 30 meters. That means our consumer devices may say we are at a coffeehouse when we are across the street at a pharmacy. To navigate an autonomous vehicle, however, the positioning must be much more exact, and the maps must be much more detailed. These maps need to indicate not just a road, but street signs, traffic signals, pedestrian crosswalks, and overpasses so the vehicle can understand which surroundings are part of the landscape and which are foreign objects it needs to navigate around. In some cases, those objects even include trees and power lines. These static maps should be stored in an on-board database to allow the computer aboard the vehicle to reduce the workload on the vehicle’s sensors. The car or truck sends out a pulse of light not visible to human eyes and measures how long it takes for the signal to be returned. A faster return means the signal has bounced off an object nearby.

Light detection and ranging, or lidar, an adapted version of this system, is already being used in many cars with adaptive cruise control. When cruise control is engaged, the system detects objects in front of the vehicle (other traffic) and adjusts its speed based on the distance and speed of the other vehicle. This is a remote-sensing system.

Lidar lasers send out signals millions of times per second, meaning that an autonomous vehicle’s computer is receiving new data millions of times a second. All of those data need sophisticated, fast data science algorithms to mine the information for patterns or aberrations in patterns to inform the vehicle’s navigation system of its environment.

The realization of autonomous vehicles requires all of the technologies we cover in this book—GPS, GIS, remote sensing, spatial databases, and spatial data science.

As we describe these technologies and their applications, you will find that the relationship between space and time is sometimes explicit and sometimes implicit. The positioning accuracy of GPS is explicitly tied to an ultraprecise time-keeping system such as an atomic clock. GPS also is a major source of time information for a large number of clocks, such as those in smartphones, wall clocks, wrist watches, computers, all of our interconnected electronic devices, electric grids, and more. Remote-sensing applications and spatial-statistics patterns need data about time to make them useful for detecting changes in hot spots and outbreaks. Some professionals and researchers refer to this combination as spatiotemporal computing. We are seeing a trend in research and reporting where the relationship between space and time is being included in the definition of spatial computing, and we consider time essential to our concept of spatial computing as well.

There are more spatial-computing technologies that we do not cover in depth in this book. In the summer of 2016, Pokémon Go, one of the first games featuring augmented reality, was creating headlines around the world. The wildly popular smartphone game imposed fictional Pokémon characters on a real-world map of the user’s location. News reports circulated stories of players traveling long distances and crossing national borders, falling off cliffs, and crashing cars while playing the game. At one point that year, the app was more popular than the dating site Tinder. Although the game’s popularity peaked that year, the technology has not. The applications for augmented reality are growing. In addition to games, augmented reality is used in car rearview camera imagery to add isodistance lines from the vehicle’s rear view to aid drivers. Spatial instructions are superimposed on physical objects to guide human action. American football game broadcasts add computer-generated lines to the playing field to indicate the lines of scrimmage.

Spatial interfaces using human gestures to command computers is another growing area of spatial computing. Beyond mouse clicks, touch screens, and voice commands, spatial interfaces include gesture-based control in gaming platforms such as Sony PS4, Xbox Kinect, and Nintendo Wii gaming systems. Autonomous vehicle-navigation systems recognize the gestures of pedestrians and other drivers and use eye contact or hand signals to negotiate traffic. In-home technology is being created now using cameras to recognize gestures and actions to start home appliances such as the coffee machine or to switch the lights on and off.

In 2013, the City of Los Angeles took on an ambitious plan to improve traffic flow. Engineers synchronized 4,500 traffic signals across the city. Using cameras to visually monitor traffic, the city reported it had decreased delays by 12 percent at major intersections and increased the average flow of traffic across the city by 16 percent. The system has the capability to extend green lights in bus lanes to keep buses on schedule, so public-transportation passengers as well as individual drivers can benefit. The change represents another emerging spatial technology called extreme spatial collaboration among location-aware objects. In the future, a system of self-driving/connected cars may negotiate a road intersection without a traffic light while improving traffic flow and reducing collisions. During rush hours, road construction, sporting events, and other times of high traffic density, all vehicles on the highway would coordinate with each other to improve traffic flow and reduce congestion, causing less stress for the drivers, fewer accidents, and a lower amount of vehicle emissions entering the atmosphere. The work in these areas is growing. As these systems mature, their impact may be as wide as the impact of the current technologies we cover in detail in this book. Although the applications described here are significant, affecting millions of people, the technologies we describe have billions of users and are pivotal to supporting the basic infrastructure of our modern economy.

For each of these spatial-computing applications, we touch on their historical context to understand how they are affecting our lives today. We provide information on how they work and on their applications across a variety of uses and audiences. We also work to understand some of the current limitations to the technology and where the work is moving in the future.

Although spatial computing may seem like a highly niche sector of work, the opposite is true. Major technology employers such as Facebook, Google, Apple, Uber, IBM, Microsoft, and Oracle as well as many government agencies related to public health, public safety, and transportation seek people with spatial-computing knowledge and skills. Job opportunities are growing and diversifying as spatial-computing applications in existing and new technologies are expected to be incorporated in ever more areas.

The technologies we describe have billions of users and are pivotal to supporting the basic infrastructure of our modern economy.

Spatial Data and Algorithms

Our goal is to walk through spatial-computing concepts, technologies, and applications in a manner that requires the reader to have little to no scientific background. Rather, it is our hope to inspire more people to consider how this technology touches our lives every day, multiple times each day, and to reveal the ways it has transformed our modern lives and continues to do so. When we discuss how the technology works, we focus on broad concepts that are essential to understanding their functions rather than dwell on the details of individual computations. That noted, some key terms and concepts are mentioned at various points in the upcoming chapters.

Spatial data and nonspatial data are fundamental to all spatial-computing technologies rather than specific to one. Nonspatial data are numeric data and text strings. Numeric nonspatial data can include items such as a city’s population and the date it was founded. A nonspatial text string includes nonnumeric data such as the name of the city. Nonspatial data are loaded and stored in a database until they are needed. Spatial data are data that are geographically referenced. A city center’s specific longitude, latitude, and elevation are spatial data. These data are used to make familiar spatial computations on demand, such as How close am I (or how close is the city center) to the nearest city park? This question is then answered using the data point at the city center and the location of the nearest identified city park. City footprints and zip codes, with outer boundaries, are spatial data. A zip code’s numerals indicating the city center are not.

There are three types of spatial data, all of which you access and use on your smartphone on a daily basis: raster data, graph data, and vector data. The photos and videos you take with your phone or digital camera are made of raster data. These images are broken into rows and columns, dividing up the space into a grid. Each grid cell, or pixel, has a property. The properties of the pixels on your camera are dictated by their reflectance of light, but raster data can store many different types of data, such as temperature, rainfall, elevation, or location identification in latitude and longitude. Climate-data scientists use raster data extensively to monitor temperature, rainfall, and forest cover from satellite imagery.

Digital road maps in smartphone navigation apps such as Google Maps often use graph representations. The road intersections are nodes, and road segments connecting adjacent road intersections are edges. Directions on the edges provide information for one-way and two-way streets. Graph representations are helpful in computing the shortest path between two nodes, such as your current location and the nearest city park. Beyond road data, graphs are helpful in depicting information about other transportation networks, such as subway systems, utility networks, electric grids, gas pipelines, and drinking-water distribution networks.

Many navigation apps such as Google Maps also contain vector data. Vector data represent important geographic features in points, lines, and polygon-shaped footprints and are used in geometric analysis. A map rendered in vector data would show buildings as squares and rectangles or simple polygons and the roads as lines. Vector data also contain points of interest. A bus stop or a pickup point is represented as a point in a vector-data map. Unlike a raster map, where each square contains a property, vector maps model only things with names. A vector map of San Francisco would note streets as lines and buildings as polygons. Although there are many hills in San Francisco, only the taller hills, such as Mount Sutro, may be worthy of note on the vector map. A vector map contains customized, simplified information for a specific purpose, thus reducing information and storage overload. This means a visitor’s map of San Francisco would likely contain information of interest to tourists—such as locations of restaurants, gas stations, and train stations—and fewer geographic features.

Data scientists sometimes use a combination of data formats. The maps produced by Google Maps are reference maps and contain many layers of data. One layer may be the graph representation; another layer is the vector map with points of interest; and additional layers provide information as needed by the user.

The National Weather Service uses raster data to monitor snow accumulation and melt in the flood-prone Red River Valley at the border of Minnesota and North Dakota. Data scientists studying weather data are also able to compare the elevation of one pixel to the elevation of the neighboring pixels to calculate slope and gradient to determine the direction of water flow. They then model the terrain to determine which areas are more likely to flood during spring snow melts or unusually heavy rainfalls. Data scientists create a graph of water flow on the raster map. They may also use vector data to identify houses, villages, and towns that may be affected by the predicted flood. The types of data and combinations of data used by scientists and programmers depend largely on the problems and issues they are working to resolve.

Spatial computing uses many computational algorithms, or processes and sets of computations. We occasionally refer to some of the most well-established algorithms—for example, shortest-path determination—as we explain how the applications of spatial computing work, but we avoid discussing them at length. That information is well described in numerous textbooks and professional journals and by the robust research communities and professional organizations for each of the application areas we discuss here.

Transforming through Speed and Scale

It may seem like hyperbole to refer to spatial computing as transformational; our goal, however, is to demonstrate that it is not hyperbole. Location is important. It exerts an incredible amount of influence over our lives, playing a fundamental role in our access to basic necessities such as food, water, and health care. This influence is borne out beyond the comparison of countries that are considered economically poor or rich; a growing body of work shows how within the United States our zip code has a greater effect on personal health than biology does.

We are beginning an era in which we can predict the transmission of Zika virus across continents over months and locate ground zero of a salmonella outbreak within days. In the future, we may one day be able predict the spread of inflammation within our bodies and treat it with precision. In many cases, we are close to having the spatial technology for these possibilities; we await the biotechnology to keep up.

The speed and scale of what is possible is changing in unprecedented ways. Lewis and Clark needed a team of men and two years to create more than 100 maps describing their route from St. Louis to the Pacific Ocean—a series of maps that few people would have access to once they were created. Today, a simple Internet search can within seconds provide a number of re-created maps describing Lewis and Clark’s route, images of their actual maps, as well as current satellite imagery of the same area. Those maps are available to nonexperts using nonspecialized technology. A new satellite or aerial image of the terrain covered in the explorers’ journey can be re-created in minutes or hours.

This availability of location technology has changed us as consumers as well. As the possibilities using these technologies have been revealed to us, our expectations have arisen. We no longer want just a map that shows us the route from one place to another; we want that information tailored to the way we prefer to travel. Do you prefer to take the freeway but are frustrated by traffic? Then you want to know what traffic is like on your preferred route, and you want that information to update and change in real time to help you reach your destination more quickly. Maybe you want a route that avoids freeways for a more scenic route, but you also have a time limit. What about a bicycle commuter who wants to travel on the safest routes for cyclists? What about commuters who take buses or other public-transit options? Besides information about routing and navigation, we expect quick delivery of rides, prepared food, groceries, and shopping packages near our current locations or homes.

It’s now possible to track every bus and train in a transit system and predict their arrivals at each station. Although this ability is an incredible change over the past 20 years, many consumers still take a moment to grumble when those systems provide predictions that are inaccurate because of weather, traffic, or accidents.

We are speeding to a reality where all software needs to be spatially aware and where every user is a participant in updating the location information presented with that software. With every edit suggested to Google Maps, every pickup from Uber, every check-in on Facebook, and every restaurant review on Yelp, we are participating in the transformation of the way we create, access, and consume spatial information. Those examples are only a few that most consumers know about and have access to. Business and government applications of these technologies are highly specialized but no less transformational, with new applications being developed all the time. Spatial data science is the science of where, and it is the science of now.

2

Where Am I? Positioning, Outdoors and Indoors

This app would like to use your location. Does this phrase look familiar? If you have a smartphone, you use GPS on a daily basis. Your phone has dozens of apps using your location. The app that provides information about movies shows only theaters near you. The search engine customizes your searches based on products and services near your location. An activity-tracking app tracks your bike routes and when you rode them. The news app provides headlines that are localized to your area. The weather app shows you weather near you. The camera tags photos with their date, time, and location so that you don’t have to remember when and where you took them, and the reminders app uses your location to notify you to pick up your dry cleaning when you are within a half-mile of your home. This is only a small selection of the consumer applications for GPS that the average consumer with a smartphone has access to each day, all day, at any location where he or she has a clear sightline to a satellite.

Billions of people around the world are active users of the most widely known technology of spatial computing, global navigation satellite systems (GNSSs). Most people are more familiar with the name of the American GNSS, the Global Positioning System, or GPS. Each app asking to use our location is accessing GPS to improve the utility of numerous applications on our smartphones and tablet computers throughout our day. Mapping and navigation systems such as Google Maps and Apple Maps are the most familiar to consumers, but GPS is being used in research, military, industrial, and agricultural settings.

GPS tells us two things: where we are and the current time. These two pieces of information make GPS a revolutionary tool for an accurate, reliable, inexpensive, and accessible global navigation system. Beyond the apps on our phones, GPS is critical for myriad other systems, such as electrical grids and technologies that all people routinely rely upon.

History and Background: Where Am I?

As far back as we humans have been traveling, we have sought to answer the question “Where am I?” To create a route from point A to point B, we need to know where each of these points is and where we are along these routes. Are we at point A? Or are we a bit farther on the route? It may be surprising to learn that accuracy and reliability in understanding our location globally have been achieved only in the past 20 years with the invention and the adoption of GNSSs.

Way-finding tools and devices have been around for millennia. In books and movies, characters consult maps that provide an image of a place, but not the individual’s location within the picture. These characters check the stars and nautical charts, use compasses, examine horizons with sextants, or consult an astrolabe. All of these things were useful tools, but they weren’t up to the task of providing exact location. That is primarily due to the difficulty of determining longitude.

Our global position on Earth’s surface is determined by a geographic coordinate system using two numbers measured in degrees: latitude and longitude. The concept of the system dates back to the ancient Greeks. Longitude is measured using imaginary lines running north to south, from pole to pole. Latitude is measured using imaginary lines running east to west parallel to the equator.

Navigators throughout history had a fairly accurate way to measure latitude, using the sun or stars and the other devices previously mentioned. Hawaiian and Chinese navigators were particularly dependent on the distance of the Southern Cross constellation from the horizon to measure latitude. As the navigators traveled south, the stars of the Southern Cross appeared higher in the sky; as they traveled north, those stars were lower. In the Northern Hemisphere, Ursa Major (the Big Dipper) and Cassiopeia were used to find Polaris, or the North Star. There is a significant drawback to using stars for navigation, of course: they are visible only at night and only if the skies are clear.

Determining longitude was much more difficult for navigators, particularly at sea. For travelers on land, maps could note landmarks that travelers could use to identify the correct course, as could talking with the locals (if they shared a language). Traveling across large bodies of water, where there are no landmarks or people, made this process more difficult.

To calculate longitude, we break up the 360-degree rotation of the earth into 24 hours. Each hour represents 15 degrees longitude. We can calculate our local time by observing the location of the sun in the sky. But in order to calculate our longitude, we need to be able to compare the time at our location with the time in another location—Greenwich, England, for example. An international agreement in 1884 chose Greenwich as the location of the Zero Meridian. That city is located at 51° N (latitude) and 0° W (longitude). Historically, the difficult part was finding the time in another location. Pendulum clocks were not invented until the seventeenth century, and even then the swaying movement of a ship or a wagon disturbed the swing of a pendulum.

As a result, many ship routes hugged coastlines, using lighthouses and other beacons for way finding. When traveling in open water, seafarers calculated their approximate locations using dead reckoning—measurements using the ship’s compass heading, the ship’s starting point, and estimates of how quickly the seafarers had traveled and how long they had traveled at that speed. Using this system, one error in the calculation of distance, speed, winds, and currents could set off weeks of cumulative errors, landing travelers far from their intended destination.

To avoid the problems of dead reckoning, some mariners traveled in right angles. A ship would move north or south to the latitude of its destination, then turn in the direction of the mariners’ destination (east or west) and follow the line of latitude until reaching that destination. Because this form of navigation did not take into account currents and was not a direct path, it might add days and weeks to travel time.

Longitude posed such a dilemma to Europeans that multiple countries, including Spain, Britain, and the Netherlands offered cash prizes to inventors who could solve the problem. The British Longitude Act of 1714 set a cash prize of £20,000, a sum equivalent to more than $1 million today, for the inventor who could calculate longitude within half a degree, or two minutes, of time. Ultimately, a clockmaker named John Harrison was successful. He invented the first ship’s clock, the chronometer. It was the first reliable and precise way to tell time that didn’t rely on pendulum movement.

The chronometer was a major achievement. It worked well both at sea and on land. However, once the age of air travel began, the chronometer didn’t work as well. Chronometers were too large for the tiny cockpits of the airplanes of the era. Navigation technology moved to using radio frequencies. Airplane navigators plotted their courses via radio signal. Radio towers in fixed positions on land emitted similar signals at identical intervals, allowing the pilots to plot their route based on the time difference between the signals. The US Army created the long-range navigation (LORAN) system using this concept. This system still had its drawbacks because it was dependent on the quality of the signal and the skill of the operator transmitting it. Nevertheless, radio-signal technology remained in use in the United States until the Space Age. Once the first satellites were launched with radio signals to tell us more about space and orbits, the governments of the United States and the Soviet Union found other uses for radio signals.

In the early 1960s, the US Navy had six satellites orbiting the poles and used them to track missile-carrying US submarines. Using the signals from the satellites, navy engineers were able to locate the submarines in minutes, though the technology was not perfect. Locating a sub could sometimes take hours when a satellite was not in the vessel’s sightline. Building on the navy’s work, the US Department of Defense (DOD) prioritized satellite navigation in the 1970s, envisioning a system with 24 satellites continuously orbiting the globe, ensuring there would always be at least three satellites in view at all points on the surface. The three satellites could then be used to determine location at any point. The DOD launched its first NAVSTAR satellite, the first US version of GPS, in 1978. The full system of 24 satellites became fully operational in 1993. The system was initially restricted to government use, primarily for defense purposes. Over time, however, the US government understood that the technology could be beneficial for civilian use as well. Some GPS signals were opened for civilian use to help aircraft follow planned paths. In 1998, Vice President Al Gore announced that the government would broadcast additional radio signals that would be available for civilian use. Today, GPS maintains several signals for government use and one for civilian use.

A Working GPS System

Today’s GPS is a system of at least 24 satellites that circle Earth on one of six pole-to-pole orbits twice a day, providing continuous global coverage. GPS satellites travel at a consistent height of 20,350 kilometers above the planet. The satellites must keep traveling at a consistent speed, or Earth’s gravitational pull will cause them to fall, so they balance propulsion via on-board thrusters with gravity to travel in their orbits. The US Air Force operates the satellites and keeps six or seven additional satellites in the sky, ready for deployment, so they may be pulled into the system should others require maintenance.

Figure 1 GPS orbits. Source: US Air Force.

Each GPS satellite broadcasts electromagnetic waves that travel through Earth’s atmosphere at the speed of light. The signals are transmitted on two or more frequencies. The air force specifically selects the frequencies used to broadcast the signals to allow for precise measurements while also being accessible and able to be understood by simple GPS receivers for civilian use. The signals must also be strong enough to be mostly impervious to weather phenomena such as wind and rain.

Each signal broadcasts three types of information: a pseudorandom code identifies the satellite to the receiver unit; ephemeris data provide the satellite’s position in the sky as well as date and time via on-board atomic clocks; and almanac data provide information on the satellite’s orbit. A device on (or near) Earth’s surface receives the satellite’s signals, noting the exact time of arrival. The GPS receiver calculates its distance from each satellite by measuring the time the initial signal was broadcast by the satellite and the time it was received by the unit on the surface.

Once the GPS unit on the surface has received information from four or more different satellites, the GPS device uses a simple geometry calculation called multilateration, or triangulation or trilateration in popular parlance, to determine the user’s precise location.

When the device receives a signal from a single satellite, it determines the user’s distance from that satellite, but it only knows that the receiver’s position is somewhere on a sphere radiating in all directions from that satellite. The radius represents the exact distance from the satellite. When the device receives a second signal, it identifies a second sphere. The user’s location is somewhere on the circle where the two spheres intersect with each other. When we add the third satellite, all three spheres intersect at two points, and with the fourth satellite we have one point describing the user’s exact location at that time in latitude, longitude, and altitude. On a two-dimensional surface such as a plane or Earth’s surface, we can use a simpler trilateration process by measuring three distances (trilateration) or three angles (triangulations) from well-known landmarks such as satellites or Wi-Fi routers, lighthouses or mountaintops.

Figure 2 Source: US Air Force.

There is some room for error as the signal travels. Although the electromagnetic waves travel at the speed of light, Earth’s atmosphere can cause a slight alteration in the waves’ velocity or direction. There is also some delay as the signal works through the different segments of the atmosphere. Most of the alterations are slight, and mathematical modeling in the ground receiver adjusts for the resulting errors. For other errors, engineers compare the delays between the two carrier frequencies of the GPS signal to determine the cause of the delay and account for it in modeling.

GPS requires more than satellites and receivers to work. Another part of the system, the Control Segment, is on the ground, monitoring, adjusting, and accounting for the health of the satellites as well as their orbit, signals, clocks, and the information they pass down to the receiver. As the creator and primary user of GPS, the DOD, via the US Air Force, maintains the health and control of the system. There is a master control station, which monitors the health of the constellation of satellites, and there are back-up master control stations in other locations.

GPS receivers are just that: receivers of information. They do not send information back to the satellite. All information traveling to a satellite is completed by the Control Segment via a series of four ground antennas sending messages and adjustments to the satellites. They synchronize the atomic clocks aboard each satellite within nanoseconds of each other. Each satellite has an internal orbit model, and the Control Segment also monitors each satellite’s ephemeris, or position, while in orbit, making adjustments as necessary.

If a GPS receiver is coupled with a communication device, such as a smartphone, the phone or an app on the phone communicates location completed by the GPS receiver to the app’s remote servers. No information is collected or stored by GPS satellites; that work is completed by the apps that use information, such as Google Maps, Facebook, and Uber, among many others.

Figure 3 GPS Control Segment Map. Source: US Air Force.

Although the technology on our smartphones is able to calculate location within a few meters, more sophisticated GPS receivers have access to augmentation systems that can dramatically improve accuracy. The Federal Aviation Administration developed the Wide Area Augmentation System (WAAS) to improve the accuracy of GPS signals and to be able to use them for precise information for landing at any airport with WAAS coverage. Some consumer devices have access to WAAS, which improves the accuracy of location detection to about one meter. Differential GPS, operated by the US Coast Guard, uses beacon transmitters and requires a differential beacon receiver to use. This signal can improve location accuracy to within a few decimeters or centimeters or better based on the speed of the receiver.

Other GNSS Systems

The United States was the first country to create a GNSS, but it is not the only one to do so. There are three other global systems and two regional systems. The Russian system is called Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS). Development of GLONASS happened somewhat in tandem to the development of GPS. Although satellite launches began in the early 1980s, the system did not become fully operational until 1996 with 24 satellites. The early success of GLONASS was short-lived. Once the Soviet Union collapsed in 1989, the Russian government did not maintain the system. Ten years later, only seven satellites remained operational, and at that time the Russian government began reinvesting in the system. Full capability of GLONASS resumed in 2011.

The Galileo system is not operated by a single country, but by the European Commission and the European Space Agency. This initiative is much more recent. Galileo satellites began launching in 2011, and the system began transmitting signals in December 2016. The agency plans for the system to reach full capability by 2020. Unlike GPS and GLONASS, which are operated by the governments of the United States and Russia, Galileo is under civilian control.

China’s BeiDou System began as a regional satellite-based navigation system in 2003. It is unique in placing its satellites in orbits of different heights while still achieving global coverage.

Japan has approached its regional system in a different manner, planning for multiple satellites to orbit. The goal is to maintain constant coverage directly above Japan so that signals will not be blocked by tall buildings—which create urban canyons—in the country’s cities. The Japanese signal will also be much more resistant to blockage from natural structures such as mountains. Service launched in November 2018.

Navigation with Indian Constellation (NavIC, formerly the Indian Regional Navigation Satellite System, or IRNSS) is the regional system created by the Indian government to serve that country and some surrounding areas. Like GPS, NavIC offers two types of services, a restricted service that is encrypted and primarily for government use and an open service for civilian and business use.

Because GPS is the oldest system in continuous use, GPS satellites and equipment are older than those of other GNSS systems. Twelve operational satellites in the GPS system have outlasted the seven-and-a-half-year lifespan for which they were designed. Twelve new satellites were launched within the past 10 years. The US Air Force is currently in the middle of a modernization project called GPS III. New GPS III satellites, launched in 2018, will have better accuracy and security as well as a longer lifespan than the current satellites. These newer satellites will also broadcast a compatible signal for other GNSS systems to improve user experience for civilians.

Applications for GPS in the United States and around the World

GPS may serve only one function—identifying location and time—but that single function is used in thousands of applications. There are two major categories of navigation devices, personal and augmented or professional. Personal navigation devices and in-car navigation systems dominate the market, making up 90 percent of the global core revenue for chip sales. Augmented and specialized GNSS/GPS equipment used for agriculture, surveying, timing, and other transportation systems (aviation, rail, and maritime) supports less than 10 percent of global chip sales.

At the beginning of this chapter, we mentioned several applications on our smartphones and tablets that use location. Smartphone applications and in-car navigation systems are the most well-known applications for GPS technology. Finding directions has become synonymous with the usage of smartphones. In 2015, Pew Research found that more than 80 percent of adults of all ages were using their phones for location-based information. For adults between the ages of 18 and 29, that number rose to 95 percent. In 2011, that number was 55 percent. A June 2017 report from MarketsandMarkets™ anticipated the GPS device market would grow from US$1.25 billion in 2016 to US$2.89 billion by 2023.

GPS may serve only one function—identifying location and time—but that single function is used in thousands of applications.

Globally, most countries are using GNSS systems for similar purposes, with some novel applications that have not yet hit the United States. We mentioned that Japan is using and adapting its regional signal to overcome barriers blocking its satellites’ signals. It is also building a signal that is strong enough to pass through walls, providing for location accuracy and way finding in large buildings.

Other uses of this technology are perhaps less welcome to the average consumer. Russian authorities have used spatial computing as a way to allocate taxes. Automakers there are making more fuel-efficient vehicles, leading to a decline in the collection of gas taxes that fund road maintenance, so Russia devised a way to combat this declining tax using GLONASS. Under the country’s new highway tax system, the user’s fee is based on his or her use of highways and is collected via the Platon Electronic Toll Collection (ETC) system. Truckers, whose large, heavy vehicles cause the most wear and tear on highways, have two options for paying a toll. They can either provide their planned route to a tollbooth and obtain a prepaid toll ticket, or they can install an on-board unit. When the vehicle with an on-board unit enters a highway, the ETC system determines the truck’s position using GNSS and sends the data to a processing center. During the journey, the truck’s data are updated from time to time, following its route. When the truck exits the highway, the total distance via highway is determined by the ETC system, and the corresponding fee is calculated and charged to an account associated with the vehicle. The process is not without controversy; long-haul truckers in Russia have stopped work twice over the past five years over the road tax, which they say is too high.

Nearly all major transportation systems use GPS: planes, trains, boats, and automobiles. Airplanes, as we mentioned, use GPS during all phases of flight. Because GPS is able to track location in three dimensions, pilots use it to calculate plane position during takeoff, en route, and on arrival. Once at the airport, pilots even use GPS to navigate through airport runways to find their gate.

Long gone are the days of boats being lost at sea. Marine search-and-rescue teams use GPS and LORAN in open water to instantly find their own location and the site of ocean-going vessels needing assistance. Ocean-going vessels use GPS to measure their speed and accurately navigate their routes, not only across open water but also in congested waterways. Rail systems use GPS to monitor train operations, track maintenance systems, and avoid collisions. Railways in many countries use a system called Point Train Control to automatically reroute rail traffic and track capacity on different lines.

GPS is a standard tool for surveying. The GPS receivers used by surveyors are far more precise than those used by smartphones; these tools can accurately identify position down to the millimeter. This incredible precision makes it possible to observe very small changes in Earth’s surface over time. It can even measure changes in glaciers. Scientists and researchers performing environmental monitoring use specialized GPS tools to monitor changes in a glacier’s location and mass over time. Researchers place receiver units on top of the glacier, and as gravity pulls the mass downslope to the sea, the receivers travel with it. By continually monitoring the unit’s progress as it moves, scientists track the direction and rate of flow as well as the elevation of the unit and the glacier at different stages on the unit’s journey.

Researchers can also track the mass of a glacier over time by placing GPS units on solid ground near a glacier. Glaciers lie on the tectonic plates of Earth’s crust. These plates maintain a buoyant equilibrium with the asthenosphere, a part of Earth’s mantle located just under the plates. The asthenosphere is viscoelastic, meaning it flows with long-term stress, not unlike Silly Putty. When mass is added to the glaciers, there is a small change in the elevation of Earth’s surface as added mass causes the plates to descend. The distance is so slight that it is measured in millimeters. When the glacier is thinner, the elevation of Earth’s crust rises. Over time, researchers can track the cumulative changes in this rise and fall, year after year, in the glaciers as they add and lose mass.

GPS is also used to measure drought and groundwater levels through a similar process. Lakes and rivers push down on the tectonic plates in the same way glaciers do. The land rebounds when the water dries away. Scientists using GPS to observe the Sierra Nevada in drought-ravaged California are finding that the mountains are gradually rising over time as they accumulate less mass from snowfall.

In California’s Central Valley, we see an opposite trend. As groundwater aquifers lose water, the soil and sediment compact. The lack of space between the grains of soil and sediments makes it more difficult for the wells to recharge. More water is being pumped out of the ground than is recharged each year, depleting the total water supply. This depletion causes the elevation at the surface to gradually descend over time. Tracking these changes in elevation, based in movements of millimeters per year, is important to determine the overall health of particular places on the planet.

One of the first commercial uses of GPS was in precision agriculture. Many years before the technology was used in smartphones and cars, farmers added differential GPS to farm equipment in order to map soil quality and optimize crop yields. You have likely seen news coverage about companies such as Google and Tesla developing self-driving cars and the emerging controversies that arise when they appear on city streets, but farmers have been using self-driving tractors for several years. Harvesting machines equipped with GPS are able to steer more accurately than humans, which lowers fuel costs and allows the farmers to complete harvesting more swiftly. Farmers are also able to use data collected by sensors that monitor crop performance and yield.

Farmers also utilize location information for mapping soil quality, fertilizer needs, property boundaries, water features such as streams and ponds, roads, and irrigation systems. They use GPS to track problem areas where crops perform poorly, to adjust seed density, or to apply pesticides precisely. GPS is used to help determine precisely which fields are watered, fertilized, and sprayed, tracking the measurements to the centimeter. This precise application system minimizes the use of unnecessary fertilizers and chemicals and reduces negative effects of farming on the environment.

One growing area of applying GPS to agriculture is in the use of UAVs, or drones, to monitor crops. The UAVs are equipped with GPS to precisely determine where to take aerial photos so that farmers can monitor crop areas over time and apply fertilizers only to areas that need it or pesticides only to bug-infested areas.

Amazon has discussed the option of UAVs to deliver packages in residential areas, and at least one startup business used drones as wait staff, delivering food to patrons. For individuals who are uncomfortable with the idea of drones flying over their property, GPS can also be used to keep UAVs out. The company NoFlyZone.org allows individuals to register their address into a database that will prevent a drone from flying over it, thus creating a virtual dog fence in the sky.

The US DOD was, of course, the first user of GPS. As we previously noted, the US military uses a different signal than civilians, one that is more precise. All of the US Armed Forces use GPS for navigation as well as in combat for weapon targeting and in guiding unmanned systems. GPS-like technology can be used to locate sound as well as personnel. The military and police can accurately locate the source of gunfire using a network of sound sensors (e.g. microphones) and then target that location for countermeasures. According to the military, the use of GPS for precision targeting has reduced friendly-fire accidents and collateral civilian injuries in combat zones.

Finally, you may have noticed that your phone never needs to have its clock changed. This is because your phone’s clock is set via GPS. The ultraprecise atomic clocks aboard the GPS satellites (which are periodically synchronized with a reference atomic clock such as the NIST-F1 in Boulder, Colorado) are used to update timing systems from phones to computer networks, clock radios to school systems.

Limitations and Concerns

In the months before January 1, 2000, there was a great panic around the world as computer programmers warned of a sort of digital apocalypse because computers were not prepared to understand that date. President Bill Clinton held a press conference, and a special task force was created to help address the problem. A thriller movie, Y2K, came out, imagining a doomsday scenario. But when New Year’s Day of 2000 finally arrived, all was calm. Disaster was averted. A similar disaster scenario is possible with GPS. GPS sets the time for our phones and computer systems. Without GPS, many systems would shut down or malfunction, affecting airports, banks, and public utilities. And it has happened.

You likely don’t remember the morning of January 16, 2016, but it was almost a day of disaster. That day the US Air Force decommissioned one of the GPS satellites and zeroed out its database, but in doing so it introduced errors, causing the satellite to send out bad timing data that threw off several systems dependent on the satellites for hyperaccurate time reporting. The error was fixed within a day, but it underscores how fragile the system is. Twenty-four satellites are needed to maintain GPS, and the US Air Force keeps additional units in the sky for repair and maintenance, but the total number is only at or near 31 total satellites, not a large redundancy. These units provide information for 2 billion GPS receivers, a number that is expected to reach 7 billion by 2022. Keeping our banks, airplanes, airports, and computer systems safe depends on keeping our GPS satellites safe.

The GPS signal is also at risk because the signal is not very secure. Jamming devices, which intentionally block or interfere with the signal, are illegal, but they are also inexpensive and easy to procure via the Internet, and tens of thousands of GPS jamming incidents have been reported near highways and a few airports. In the United States, violators caught jamming a signal are fined amounts upward of $10,000 and risk imprisonment. Researchers have been able to veer ships off course by intercepting GPS signals and replacing them with false signals. A US military surveillance drone was brought down in Afghanistan in 2011 when its signal was intercepted. In 2017, reports surfaced that Russia was investigating and testing GPS spoofing devices when more than 20 ships in the Black Sea were directed off course. Galileo is one of the first GNSS systems to add antispoofing measures, and other systems continue to work on maintaining security for civilian and government use.

Other applications of positioning technology may not be violating the law but nonetheless raise ethical issues. There are several models of wearable GPS trackers for children, allowing parents to monitor their location for their safety. Some employers use employee IDs containing radio-frequency identification (RFID) tags that can monitor when an employee enters and leaves his place of employment, thus pinpointing when the employee begins and ends his day and the duration of his breaks. Taxi and limousine services can track their employees, monitoring where a driver goes when not in service and whether she is using a fleet vehicle for unauthorized purposes. Some groups call this type of tracking a violation of privacy.

The GPS signal is also at risk because the signal is not very secure. Jamming devices, which intentionally block or interfere with the signal, are illegal, but they are also inexpensive and easy to procure.

The US Supreme Court has weighed in on the use of GPS in some cases. In United States v. Jones (565 US 400 [2012]), the Court ruled that police must obtain a warrant before using GPS to track someone’s vehicle and that failure to do so violates the Fourth Amendment’s protection from unreasonable search and seizure. In other cases, however, the Court has admitted GPS data as evidence. In the high-profile case of Scott Peterson, who was charged with killing his pregnant wife, Laci, GPS tracking of Peterson’s vehicle was admitted in court. The prosecution introduced data showing that Peterson’s vehicle had made numerous trips to the San Francisco Bay, near the site where his wife’s body was found, between the time his wife was reported missing and the date Peterson was eventually arrested.

As a society, we have not determined guidelines and regulations regarding how much data collection from GPS we need, the extent to which it should be used, and in which settings. For the GPS consumer, privacy is important and must be respected, as advocated by the recent enactment of the General Data Protection Regulation in the European Union. Industry or business considers consumer privacy important but also argues that the consumer must agree to trade some of that privacy in order to receive the services that those businesses provide. This trade-off is commonly part of the terms of agreement in your apps and devices. As the industry regulator and creator of policy, the government is the body that regulates the balance among national security, emergency services, industry, and consumer.

In some cases, the government has created limited policies for GPS use. The E911 system in the United States allows cell phone users to access emergency services. To provide these services, GPS is used to determine the user’s location. The Wireless Emergency Alert system accesses our phones for public-safety emergencies such as severe weather, terror threats, child abductions, and emergency chemical spills. The alerts are localized to the areas affected by the emergency.

The conversation around policy, regulation, and privacy continues. Congress has introduced bills, such as the Geolocation Privacy and Surveillance Act of 2011, to set guidelines for GPS-enabled devices. The House of Representatives did not vote on that bill and has not voted on any of the variations of it introduced since then, though the bills have received support from several organizations, including the American Civil Liberties Union, the Digital Liberty Organization, and the Electronic Frontier Foundation. Because technology moves much faster than policy, the lag time in creating policy leaves some of the decision making and responsibility in determining appropriateness to the consumer.

Future Directions

Each fall a new crop of incoming students arrives on a university campus ready for their first day of class. Their class schedule provides them with the name of the course and where the class will be held. Their phones can help them find the buildings where those courses are held. They may also help track the amount of time spent in classrooms, library, gym, and other places for self-reflection or counseling if privacy issues can be resolved. But once students are inside the building, the information stops.

GPS is adept at finding our location on Earth’s surface, but the next area of work is moving indoors to help users such as the new students on campus. Developers have recently begun to work on indoor location services that use Wi-Fi inside university halls, hospitals, and high-rise buildings.

The technology is still under development, but Wi-Fi is already used to find the location of smartphones. The next time you are in an area where there are many Wi-Fi signals, try moving your phone to airplane mode, making sure Wi-Fi is still on, then open one of your mapping apps. Does it still work? Airplane mode turns off your cellular connection and your phone’s GPS. If your mapping app still works, it is because you are being located by the Wi-Fi signal on your phone, even if you have not yet selected a Wi-Fi network.

Wi-Fi can be used to overcome the physical barriers to a GPS signal in urban canyons where tall buildings can block a satellite signal but there are ample Wi-Fi signals. The same triangulation used by GPS receivers for satellites can be used with smartphones and Wi-Fi routers, with signal strength serving as a proxy for distance. The first iPhones were not equipped with GPS and relied upon Wi-Fi for positioning. This worked well in urban settings but faltered in rural areas. Unlike the GPS satellites, there is no government database of Wi-Fi routers, and in order to provide continuity of services providers still need to know the locations and reliability of the Wi-Fi transmitters. To resolve this issue, researchers and commercial developers are developing a database of Wi-Fi routers and using them to supplement GPS positioning.

At the Mall of America in Bloomington, Minnesota, kiosks allow shoppers to enter the name of the store they are seeking and then provide directions from the kiosk to the store, along with an estimate of how long it will take to walk there. Large universities and medical centers are using similar indoor-positioning technology to help guests and visitors find their way.

Indoor-positioning tools use a combination of technologies, including Bluetooth, Wi-Fi, infrared sensors, and pedestrian dead reckoning. They are a growing market that is expected to reach US$40 billion by 2022.

In our introduction, we discussed how neurosurgeons are using GPS-like positioning tools for mapping the human brain and planning the route of their surgeries. Mapping the human body’s networks of nerves, veins, and other natural pathways is an area of work that can help us improve how we perform arthroscopic surgeries, insert stents in arteries, and deliver medicines with precision.

Other parts of Earth need location help as well. Finding location below Earth’s surface is still difficult. GNSS satellite signals do not pass through water, soil, or rocks to determine position underwater or underground. Researchers are working on ways to solve this problem.

Conclusion

The first 911 call was placed in February 1968, and in the 50 years since then, the 911 call system has become a critical tool in providing help and safety to people throughout the United States. For many years, calls to 911 were placed on landlines and handled by the local phone company. Today, 80 percent of calls to 911 come from cell phones, and the government infrastructure to handle them has not kept up with the changes in technology. Cell phones trying to connect with 911 face outages and suffer from poor location awareness in high-rise buildings using current GPS and Wi-Fi router databases.

Indoor-positioning tools use a combination of technologies, including Bluetooth, Wi-Fi, infrared sensors, and pedestrian dead reckoning. They are a growing market that is expected to reach US$40 billion by 2022.

Individuals, businesses, and government agencies are increasingly dependent on GPS as part of the country’s core infrastructure, and the US government has committed to continue supporting the technology. A modernization effort is in progress, replacing existing satellites with newer models that have improved capabilities and longer life spans. The signals are being improved as well to reduce susceptibility to jamming and improve user security. There is more work to be done in the infrastructure on the ground as we modernize the systems that use GPS.

We have described only a few examples of how the public and private sectors are using this technology. As we write this book, new applications incorporating time and location information are being created. Market forecasters predict that the market for GPS will continue to grow as it serves new industries and pairs with other applications of spatial computing.

3

What’s There? Remote Sensing

On a fall night in 1983, Lieutenant Colonel Stanislav Petrov was on duty at Serpukhov-15, a bunker where the Soviet Union monitored the satellite Oko. Oko’s job was to detect nuclear missiles launched by the United States and its Cold War allies. Just after midnight, an alarm went off. This was not a rare instance; the system experienced occasional false alarms. This time, however, once the alarm sounded, it kept going. The Soviet satellite reported the United States had launched five ballistic missiles that would make impact in a matter of minutes.

Petrov had clear instructions for this situation. It was his duty to alert his superiors so they could prepare a large-scale counterattack. But this is not what Petrov did. He took a moment to pause and think rather than jumping to respond. He took time to interpret the data he had available. An attack from the United States would not necessarily have been unexpected. Only three weeks earlier, in a high-profile incident the Soviet Union had taken down a South Korean commercial airliner, killing 269 people, including a US congressman. Tensions were high, and North Atlantic Treaty Organization allies had been running military exercises ever since.

However, Petrov was confused by the small number of missiles that the response system was reporting. He had been trained to believe that an attack from the United States would happen at a far larger scale than five missiles. Oko, the satellite reporting this missile launch, was also somewhat new. As Petrov reviewed satellite images of the US launch site for evidence of missiles, he noted how shadows from the sunset obscured the sites in the images, providing no new information. Ground radar used by the Russians at the time to perform additional monitoring of air activity did not extend beyond the horizon, providing no additional information. As Petrov waited, crucial time was lost for a Soviet counterattack.

Petrov’s final piece of highly unscientific data was the funny feeling he noted in his stomach. He moved forward with his report, declaring a false alarm, though he had no factual verification. As time crawled by, no evidence came in from ground radar. Eventually, the time that an impact would have occurred, had the threat been real, passed without incident, confirming Petrov’s suspicion that the alarm was false.

Petrov later learned that high-altitude clouds had reflected sunlight directly into the satellites’ sensors, alerting the system to missiles.

The incident, a narrow escape from nuclear war on a massive scale, was not widely known until after the fall of the Soviet Union. In the past 10 years, Petrov has been hailed as a hero.

The satellite system that the Soviets were using was a remote-sensing system. Remote sensing is a way to monitor and collect data about something without the data-collection instrument coming into contact with the object or objects it is monitoring. Remote sensing has been used for decades by the military and is used in hundreds of applications that go far beyond defense. The story of Stanislav Petrov illustrates both the critical role and the constraints of remote-sensing operations. Although the sensors used today are more sensitive and more advanced than those used in 1983, these complex systems for gathering data about and monitoring areas from a distance still have limitations. The data collected by remote-sensing operations continue to require processing, review, and interpretation by both humans and computers.

Background

The invention of the camera in the 1840s was the first step in remote-sensing technology. Photographers began mounting cameras to other objects, including ladders to buildings, to take photos from a distance and from greater heights. In the 1850s, the French photographer and balloonist Gaspard-Felix Tournachon brought a camera on his aerial balloon to take photos of the natural landscape from above. During the First World War, cameras were mounted on airplanes and even on pigeons to gain intelligence of enemy operations.

The idea of taking cameras into space came before there were rockets to take them. In 1896, Alfred Nobel filed a patent for a method of obtaining aerial maps by attaching cameras to rockets. But the rush to develop the technology for remote-sensing efforts from space did not come along until more than 50 years later, after the Soviet Union launched the satellite Sputnik I in 1957.

Sputnik marked the beginning of the space race between the Soviet Union and the United States. Jarred into action by the Sputnik launch, the United States created the National Reconnaissance Office (NRO), prioritizing the creation of a satellite that could provide remote visual information about the Soviet Union’s missile facilities. At that time, deep in the Cold War, Americans knew little about the capabilities of their primary adversary. The Soviet government controlled access to nearly all sources of information about the country, from maps to phone books, letting very little reach beyond its borders. Americans had virtually no hard information about the Soviet military and its weapons capabilities. Obtaining information via traditional intelligence methods was also difficult because visitors were not frequently let into the Soviet Union. When they were allowed to enter, they were not invited to interact with citizens, and most were actively prevented from doing so.

In order to gain greater intelligence, American reconnaissance planes flew over the Soviet Union with high-resolution cameras, providing some information. In spite of flying at a much higher altitude than other airplanes, U-2 spy plane missions over enemy airspace were dangerous. In 1960, a U-2 was shot down over the Soviet Union, further degrading the relationship between the two adversaries.

In the United States, the NRO was busy working on a plan to build and launch a satellite that would take photos of the Soviet Union and return the film to Earth’s surface for intelligence use. The plan was known as the Corona program, and the technology for its complex system of launch, orbit, and recovery was so new and challenging that many of the early attempts to complete it were unsuccessful. Today, we take for granted the easy transmission of visual imagery from a satellite to the ground, but in 1960 getting visual imagery from space meant retrieving physical film canisters that were dropped from the satellite. A film canister had to travel through the atmosphere and then launch a parachute to slow its descent so that it could be snagged in the sky via a grappling hook attached to an airplane and delivered back to the ground. Given these complex steps and sensitive timing, the Corona program’s first 12 missions failed. The thirteenth test mission, with a canister containing no actual film, was a success. In August 1960, the first successful Corona satellite mission launched and delivered 3,000 feet of high-resolution film of Soviet territory a week later, more film than all of the previous U-2 missions combined.

The military intelligence provided by Corona was highly valuable. Because of the program, American intelligence personnel learned that the Soviets did not have more ballistic missile capabilities than the United States. Originally intended as a short-term solution, the program continued for 12 years, until 1972.

In 1965, with the Corona program going strong, the director of the US Geological Survey (USGS) suggested using satellites to gather more information about natural resources on our planet. By that time, the American space program was rapidly progressing, and astronauts on the Mercury, Gemini, and Apollo missions had taken photographs of Earth from orbit. Spurred on by the USGS, Landsat, a new program created expressly to monitor Earth’s landmasses, launched its first satellite in 1972.

The United States was not the only country to consider using satellites for this purpose; the Soviet Union launched Kosmose 771 around the same time as Landsat 1. In the late 1970s, India launched its first satellite for remote sensing. In the 1980s, the French followed suit. The 1990s saw several countries and international organizations launch satellites, including Japan, Canada, China, South Korea, and the European Space Agency.

In the late 1980s, satellite imagery made the transition from film to digital imagery with additional sensors to provide a variety of data. In the 1990s, resolution of digital imagery improved greatly along with the computer-processing capabilities. Today, there are hundreds of Earth-observation satellites in space from dozens of countries.

The Landsat program is still going today, with Landsat 7 and Landsat 8 in orbit and a new satellite, Landsat 9, planned for launch in 2020. The program now has more than 40 years of consistently archived visual imagery of Earth’s surface. In 2008, the US secretary of the interior made the imagery available to the public. Today, scientists around the world use Landsat images to study changes to Earth’s surface over time, monitoring the effects of climate change and urban sprawl as well as other large-scale developments in agriculture, forests, coastal zones, and wildlife habitats.

Today, scientists around the world use Landsat images to study changes to Earth’s surface over time, monitoring the effects of climate change and urban sprawl as well as other large-scale developments in agriculture, forests, coastal zones, and wildlife habitats.

A Working Remote-Sensing System

In the previous chapter, we mentioned how the same location technology can be used in many ways, all based on identifying location. With remote sensing, the question we seek to answer is not “Where am I?” but “What is there?” In our look at GPS, we saw how once developers had determined how to find location, they found many ways to apply that technology. With remote sensing, the question is far broader, and so are the technologies. Whereas GPS uses one technology, remote-sensing systems use many.

Remote sensing is often associated with satellite imagery, but that is just one application. Other applications are much closer to home. At its most simple, remote sensing needs three essential items. First, we need an object to observe. Then we need an instrument, or sensor, that is used to observe the object. Sensors detect sound and electromagnetic radiation including light rays not visible to humans. Finally, we need a platform to hold the instrument used for sensing. A baby monitor in an infant’s room is a remote-sensing system: the object being observed is the infant, the instrument is the camera or microphone in the room, and the platform is the table used to hold the monitor.

Whether a weather satellite or a baby monitor, a remote-sensing system provides a way to collect data about something without the data-collection instrument coming into contact with the object being observed. The goal of any remote-sensing system is to obtain information about the object. The way that information is stored and used is also important. In the case of the baby monitor, the instrument is used to allow a caregiver to monitor the child’s safety or wakefulness while freeing the caregiver to do other things. When the caregiver receives data showing the child in distress, he or she then interprets the data to determine the next appropriate steps. In this chapter, we explore remote-sensing systems capturing data by airplane, drone, and satellite.

A baby monitor in an infant’s room is a remote-sensing system: the object being observed is the infant, the instrument is the monitor in the room, and the platform is the table used to hold the monitor.

We can divide sensors into two large groups, passive and active. Passive sensors detect and observe electromagnetic radiation that is absorbed, reflected, or scattered from objects, and they use an external energy source from the physical environment, such as the sun. An active sensor provides its own energy source, which is directed at the object. The sensor then measures the response to the object. When we use a camera without a flash, the camera is a passive sensor, observing and recording light from the sun reflected from the objects in the camera’s lens. A camera that uses a flash provides its own light for the object and then measures and records the light that is reflected off of the object. Our eyes are passive sensors measuring natural or artificial light reflected off the world around us. That reflection has information about shape, size, color, and movement. But there are limits to the amount of information that the human eye can provide about an object because it can see only a small section of the electromagnetic spectrum.

The electromagnetic spectrum has an extensive range of wavelengths that are grouped into spectral bands. The human eye has the ability to perceive wavelengths in a small section called the visible band. X-rays, which have short wavelengths, are part of the ultraviolet band. Infrared light, which is associated with heat, comprises the longer wavelengths of another band. The instruments used in remote sensing go far beyond the spectrum visible to humans and provide critical information about the objects we monitor.

Figure 4 The electromagnetic spectrum. Source: NASA.

Our eyes can discern different landscape features, such as water, sand, roads, and vegetation. Beyond what our eyes can see, however, these features reflect or absorb electromagnetic energy from the sun. Scientists have developed sensors to measure the interaction between electromagnetic energy and the features of Earth’s landscape. We can now measure the reflected energy, sunlight, or near infrared, coming from the objects on Earth’s surface. Scientists measure how much of the energy is reflected and express this amount as a percentage: a high percentage means high reflection; a low percentage means little light is reflected. The reflectance values for each feature are plotted across a range of wavelengths. Just as water, forests, and deserts have unique chemical compositions, they also have unique plots called spectral signatures.

Scientists use a broad swath of the electromagnetic spectrum—ultraviolet, visible, near infrared, midinfrared, thermal infrared—to classify minerals, soils, rocks, liquids, plants, microorganisms, and man-made materials. The USGS Spectral Library has collected samples and spectra of all of these materials to help scientists and researchers detect them in observations of Earth.

Just as our eyes have limited capacity and range, sensors have limited capacity as well. Sensors are not programmed to take information from all spectral bands. They are instead preset to measure energy at specific wavelengths. For example, Landsat 8 measures the red-channel wavelengths between 640 to 670 nanometers, while a sensor on another satellite might measure different wavelengths in the red channel. If the wavelengths are adjacent, this is called a spectral band. It is possible to also measure multiple spectral bands with one sensor. The spectral bands necessary for measuring ground vegetation are different from the bands needed to find archaeological sites. Each satellite’s sensors have their own set of spectral bands to measure the response to the object they observe. Some are multispectral, meaning they measure from 3 to 20 narrow bands. Hyperspectral sensors have hundreds of narrow bands, which can gather more detailed information, but they also require increased data processing to synthesize the input gathered.

Spatial resolution is the term used for the minimal size of the area that can be measured (usually in pixels from digital sensors). Spatial resolution can vary greatly. Sensors are similar to our eyes in that their accuracy is best when objects are close. Peripheral vision allows us to take in a limited amount of information. With peripheral vision, our eyes can detect movement and general shapes, but the accuracy is not nearly as great as it is when we see objects directly in front of us. Sensors work in much the same way. In the Landsat system, the area directly below the satellite is known as the nadir. The information in a sensor’s nadir is highly accurate, and the resolution is very high. Information outside of the nadir becomes less accurate the farther away it is from the nadir, and the spatial resolution of that area is reduced.

Because remote sensing is most useful when it provides input over time, the sensor’s temporal resolution, or how frequently the sensors revisit and obtain information from the same area, is also important. The more frequent the visits, the higher the temporal resolution. A routine of daily visits and data captures is considered high temporal resolution for nongeostationary satellites. A weekly visit is considered low temporal resolution.

Any remote-sensing system must make trade-offs between each type of resolution due to constraints in data acquisition and processing. Data obtained from a satellite, plane, or drone is limited by the frequency of data collection. Does the satellite make a pass of the area once every two weeks or once a day? Does the plane fly this route once a week or once a day?

Historically, data storage was an issue for remote-sensing systems. With the advent of cloud computing, however, it is less of a concern, but data with high spatial resolution may be constrained by processing ability.

All the data collected are in raster-data format—each pixel has a value for each band, as with the digital camera on your cell phone.

The data gathered from several spectral bands are often combined to form an index, or a single number summary for the target. The index is based on the spectral band measurements for feature construction or detection, such as an index to identify green vegetation or water. In the same way that the Dow Jones Industrial Average is used as a measure of the overall health of the stock market or the Consumer Price Index is used as a measure of inflation, remote-sensing indexes gather selected data and summarize them in a single number.

The most well-known spectral index is the Normalized Difference Vegetation Index (NDVI). The NDVI measures the difference between the visible and near-infrared sunlight reflected by vegetation. Chlorophyll in plants absorbs visible light to use for photosynthesis. The cell structure of a green leaf reflects near-infrared light. The more green leaves a plant has, the more it absorbs or reflects these kinds of light. The NDVI metric is in a range between −1 and 1. The closer the measured value is to −1, the more likely it is that the ground is covered in water. The closer that value is to 1, the more likely it is that the ground is covered in dense green leaves. The NDVI can also be used to identify drought. A healthy plant absorbs visible light for photosynthesis during the growing season. The less healthy and productive the plant, the less sunlight it absorbs. By reviewing NDVI over time, plant scientists and farmers can monitor the health of the plants and crops around the world.

The NDVI looks at two spectral bands, the near-infrared and blue bands, which are more or less sensitive to different readings depending on the time of day. A key challenge is the variation in intensity of blue and infrared bands throughout the day. If the satellite sensor goes over the plant early in the morning, the response is subdued due to the low light. The response is greater at midday when the light is brightest. To remove the variation in blue and infrared bands across hours of a day, the NDVI measure is normalized by dividing the energy difference between two bands by the sum of energy across two bands. This calculation provides a numeric value between −1 and 1, with a higher value indicating a higher amount of chlorophyll, a measure of plant health.

The NDVI is just one index among dozens that are used to measure vegetation. Researchers, farmers, geologists, and others use specialized indexes to measure objects on Earth’s service that provide information for numerous applications.

Applications

During the Second World War, military forces used a relatively new technology to track enemy aircraft and missiles. This new system, known as radar, or radio detection and ranging, is widely known today, but the technology was seriously flawed. Cloud cover often obscured the radio waves directed at airplanes. After the end of the war, the US Navy donated 25 radar devices to the National Weather Service, which modified them to measure the signals reflecting off cloud formations in order to track the movement and intensity of storms. These adjusted devices were installed in different locations around the country, and radar systems were installed once again in aircraft. This time, however, rather than detecting enemy craft, the radar systems helped pilots avoid bad weather, leading to safer air travel. Modern radar sensors are still used for air-traffic control. These systems work with GPS to help aircraft navigate around rough weather and steer clear of restricted airspace, thus reducing the chance of midair collisions.

The sensors used for weather today can measure light, heat, and radio waves to forecast weather events and inform us of current events. Most Americans are familiar with the weather radar seen on TV news broadcasts and on weather websites. The National Weather Service measures weather events using Doppler radar over wide regions of land. These systems also monitor long-term climate change and its impact on forests, wetlands, glaciers, lakes, and underground water resources.

Police also use radar guns to monitor vehicle traffic for speeding. Naval vessels use marine radar in concert with GPS devices to monitor water traffic. The fate of the Titanic would have been much different if the navigators had access to radar devices such as the Canadian Coast Guard’s Ice Hazard Radar to help them avoid icebergs.

Light detection and ranging, or lidar, is an active sensor that uses laser pulses to measure distance. The laser, usually mounted on an airplane or helicopter, is designed to measure and map land. Lidar can also measure lake and ocean floors and riverbeds by using green light, which can pass through water. The National Oceanic and Atmospheric Administration uses lidar extensively to map coastlines for commercial and public use. Lidar is also used in navigating autonomous vehicles, as we mentioned in the introduction. Professional golfers frequently use distance-measuring devices equipped with a lidarlike technology to help them select which club to use for a certain shot. Some devices measure distance, and others measure slope and weather conditions as well.

Synthetic-aperture radar (SAR) is an active sensor mounted on either aircraft or spacecraft. SAR pulses microwaves at frequencies that are not vulnerable to weather or light, making this type of radar useful for intelligence and defense purposes as well as for monitoring land use and polar ice.

After Hurricane Maria hit Puerto Rico in September 2017, NASA used SAR images from the European Space Agency’s Sentinel-1A and Sentinel-1B satellites to create before-and-after images of the island to help the agency understand the extent of the damage caused by the storm. The images were then passed along to the US Federal Emergency Management Agency (FEMA) team working in Puerto Rico to help direct resources to residents. FEMA also used the images to determine which routes were open and safe to use in transporting resources to residents spread out around the island. Similarly, NASA used SAR imagery after the earthquake in Haiti in 2010 to find the fault line where the earthquake occurred. In both situations, relief organizations found people who needed help by looking at the SAR imagery for blue pixels, which indicated the blue tarps used by the local population to temporarily fix damaged or broken roofs.

Figure 5 White dots are tarps seen from satellite imagery. Source: NASA.

Remote-sensing technologies are important in managing risk for farmers, insurance companies, and commodities traders. Sensors attached to tractors, drones, airplanes, and satellites scan soil and crops to monitor soil quality and crop health, allowing farmers to better target applications of fertilizer and to customize planting so that seed density is suited for local soil quality. This process makes crops more productive, allows farmers to apply fertilizer and water based on the plant’s health, reduces the amount of fertilizer runoff from unnecessary applications, and thus helps farmers increase the profitability of the land while reducing their environmental impact.

Several national and international groups monitor agricultural land use for food supply, both nationally and internationally. The United Nations Food and Agriculture Organization established the Global Information and Early Warning System in 1975 as a check on the global food supply and demand in preparation for the possibility of regional food shortages. The group reviews satellite information of land cover and land use, field reports, and meteorological data to devise and report an international forecast of the anticipated global supply. Like the Global Information and Early Warning System, the International Soil Resource and Information Centre collects data on soil quality around the globe and reports on the health of the land for farming.

Figure 6 Landsat images taken over Syria show evidence of drilling for water in underground aquifers. Center pivot irrigation provides distinct circular patches of farmland in the middle of a desert. Source: NASA.

The private company Geosys provides satellite remote-sensing services to farmer co-ops, banks, and farm insurers. The company, which has contracts with a variety of satellite systems, calibrates and compares data from multiple sources and reports its findings to its clients. By using many satellites for input, Geosys is able to correct for images that might be obscured by cloud cover. The use of multiple sources comes with a trade-off for the company, though, because the greater amount of data requires heavier work in processing that information.

Geologists use a form of ground-penetrating radar to measure Earth’s crust and study rock formations, groundwater, and ice. In some cases, radar is used to unveil lost history. In 2011, Sarah Parcak, an archaeologist at the University of Alabama, used radar to find previously undocumented pyramids in Egypt. By studying small changes in plants and soil, Parcak found more than 1,000 burial sites, 17 pyramid-like structures, and more than 3,000 lost settlements in Egypt. Since that successful venture, Parcak has helped launch GlobalXplorer, an online tool that allows anyone to review satellite imagery of Peru, to help identify other possible archaeological sites.

Satellite remote-sensing technologies are used traditionally for defense purposes, however. For many years, the United States has used remote satellite imagery for military intelligence purposes, and the DOD heavily monitors conflict zones. Satellite imagery of North Korea has uncovered many government “re-education” camps or prisons. The images of the camps have been detailed enough to provide visual evidence of dormitory-style housing, watchtowers, and security enclosures such as gates and fencing.

The DOD also uses hyperspectral sensing, a technology in which larger portions of the electromagnetic spectrum are used to scan objects for their unique reflectance properties, called their spectral signature. For defense purposes, hyperspectral imaging can find landmines and improvised explosive devices by noting differences in the soil. Soil layers near Earth’s surface form horizontal layers, with each layer having its own spectral signature. Irregularities in those layers can reveal soil disturbances from vehicle traffic or foreign objects buried within the layers. Hyperspectral sensing has many civilian uses as well.

To provide greater consumer transparency over its supply chain, the food and beverage giant Nestlé has been creating its own satellite-monitoring program as part of their pledge to not contribute to global deforestation. They monitor their suppliers’ forests in Indonesia, where they source palm oil. Satellite imagery allows them to distinguish between the palm oil plantations from other crops, including rubber trees. The company also uses the imagery to identify new sections of forest that have been cleared. They then contact their suppliers, who must verify they are not responsible for clearing trees, or risk losing contracts with the company.

Limitations and Trends

With applications that cover places ranging from the depths of our oceans to faraway planets, the potential for remote sensing seems limitless, but that is certainly not the case. One of the great trade-offs that we make with remote-sensing technologies is between space and time. These monitoring technologies are not able to be everywhere all the time. Fundamentally, the user must choose between sample areas and coverage. With each location, be it Mars or a camp in Kabul, the user must prioritize what he or she would like to cover at what times with the available technology, tools, and budget. A sensor aboard a Polar-orbiting satellite will be able to scan only a single strip of Earth’s surface at a time as it makes its rotation around the globe. The same sensor aboard a geostationary satellite can continuously monitor one selected area as the satellite travels with Earth’s rotation.

The other constraint is the level of detail required in the data gathered. Remote sensors attached to drones or tractors scan sections of soil to find detail down to the centimeter to monitor the health of the land. By contrast, sensors used in space exploration to seek information about the possibility of water on other planets cannot provide the same level of detail. Much of the satellite imagery from space exploration is not readable by the human eye. The images require processing in order to translate those data into something that is easily understandable.

One project seeking to continuously view a large area in fine detail is the Autonomous Real-Time Ground Ubiquitous Surveillance Imaging System (ARGUS-IS), created by the Defense Advanced Research Projects Agency (DARPA) and launched in 2013. The 1.8 gigapixel camera (created from 368 five-megapixel cell phone cameras), mounted on a drone flying at 20,000 feet, is able to view an object as small as six inches. This “persistent-stare” technology provides continuous viewing of an area the size of Manhattan twenty-four hours a day, seven days a week. The Baltimore Police Department is one of the first nonmilitary organizations to use the technology.

Continuous coverage and a high level of detail present another set of limitations related to storage and processing power. Once the sensing mechanism has been created to collect the data, there must be a system to store and mine the data for significant findings. The ARGUS-IS transmits 600 gigabits of data per second and creates 6,000 terabytes of data per day, which can quickly overwhelm the storage, communication, and computing capacity of local computer systems for many organizations. This challenge is addressed in part by cloud computing efforts such as Google Earth Engine, AWS Earth, and NASA Earth Exchange, which now host large amounts of remote-sensing information and provide computing power.

Another challenge in remote sensing arises from deliberate denial and deception, particularly in contested or adversarial locations. Many governments place dummy tanks and airplanes at military installations to confuse adversaries with remote-sensing capabilities. Although the technologies are powerful, they are not perfect and have limitations. The sensors can malfunction and deteriorate. In the case of Stanislav Petrov discussed earlier, the sensor read a flash of light as a missile launch. Petrov himself provided the most valuable data processing in the situation.

Remote-sensing technologies provide us imagery but generally don’t provide details about the area other than what is contained in the image. A satellite image of a clear-cut forest in Oregon provides information about land cover, but not about land use or land ownership. There is no information about the landowner’s intent. There is other social information that may not be conveyed through image alone. If we look at a picture of the world at night, much of North America is distinctly well lit with electric lights. The picture looks darker closer to the Arctic Circle because very few people live there. Much of Africa is dark like the northernmost areas of North America. If we assume that lights indicate population, we might infer that few people live in Africa, but in fact more than a billion people make their home there. In this case, the sensor does not provide enough information or enough of the right kind of information for us to make an accurate assumption about population size.

In 1992, the police in Florence, Oregon, suspected that Danny Kyllo was growing marijuana in his home. Without enough evidence to obtain a search warrant, they could not proceed any further until they had an idea that a substantial growing operation would need significant lighting for the plants. Testing this theory, police trained a thermal-imaging device at the site, hoping to find evidence of unusual lighting without entering Kyllo’s home. The devices revealed high levels of thermal radiation coming from the walls and roof of the home. Police used this information to get a search warrant that later revealed some 100 plants and heat lamps. After being charged with illegally growing marijuana, Kyllo filed to have the infrared evidence thrown out, arguing that it constituted an unreasonable search in violation of his rights under the Fourth Amendment to the US Constitution. The case went all the way to the Supreme Court in Kyllo v. United States (533 US 27 [2001]). The court ruled in favor of Kyllo, saying that the infrared camera used by the police was not in general use by the public and thus constituted an intrusion into Kyllo’s reasonable expectation of privacy.

Kyllo v. United States presents just one concern related to remote-sensing technologies. As these technologies become more abundant, privacy will become a greater concern. Should Google or the US government or any other large entity have the ability to look at or into anyone’s personal property? As in Kyllo v. United States, we as a society may need to renegotiate the terms of privacy when we have sensors that can see through walls. Those concerns go beyond the borders of our country. What right do we have to information about Brazilian farms or rain forests? US satellites can discover information about Russian forest cover, but the Russian government may consider this spying. Should it be considered spying? Crop information gained from GEOGLAM, a global crop-monitoring initiative, can be used to monitor the global food supply of four major crops, but commodities traders can also use the information for financial gain. As the technologies evolve, our conversations about their use continue to evolve with them.

Future Directions

A Research and Markets report issued in 2016 used data from 2013, 2014, and 2015 to estimate that the global remote-sensing technologies market will reach nearly US$19.3 billion by 2025. To get there, analysts predict the market for UAVs and wide-area motion imagery will grow. As satellites become smaller and cheaper to produce, they will be easier to launch for specific remote-sensing purposes. Within 5 to 10 years, the cost of remote-sensing tools and systems may drop significantly enough to monitor all of Earth all of the time. With those tools in reach, a store such as Target can monitor which stores have high traffic by counting cars in the parking lots. One company, Orbital, has already begun tracking more than 260,000 parking lots across the country, selling the data to retailers so they can monitor and estimate their competitors’ retail foot traffic.

Nanosatellite technologies are also on the horizon. Although nanosatellites are already going up in orbit, some companies requiring precise imagery at regular intervals have suggested that the technology is not currently reliable enough.

Only 10 years ago, NASA had specialized centers where the data collected from satellites was stored. For researchers who wanted to use Landsat data, accessing and storing huge amounts of information were difficult and required niche tools. The advent of cloud-based storage systems and the adoption of the technology have removed the burden of storage and transport of data for NASA and other agencies.

Google Earth, the free program that offers satellite images from all around the globe, was launched in 2001. It paved the way for programs such as NASA’s Solar System Exploration, where anyone can see three-dimensional imagery of planets and moons in the solar system and learn more about their features. Amazon now has a similar tool. The competition for the use of free imagery continues to grow.

One debate happening now in the research community for remote sensing surrounds the idea of deep learning. Data acquired from any sensor must be cleaned for relevant information. Programmers and data scientists create classification algorithms for object detection, sorting the information in each pixel. Artificial intelligence assists in the process, and with enough examples the machine learns and forms summary information. The algorithms correctly classify 90 percent of the material. After that, manual effort is required to clean up the last 10 percent of errors by writing small programs to address repeated errors or by adjusting the algorithm to look for additional information.

With deep learning, some data scientists and programmers believe that they can build a computer that can make its own decisions given enough examples. The most well-known sample scenario is Google’s neural network, which was able to recognize individual human faces, human body parts, and cat faces with accuracy after being exposed to 10 million thumbnail images from YouTube videos. In time, with enough exposure to satellite imagery, these deep neural networks may be able to create their own indexes, like NDVI, to better classify land cover.

Conclusion

Jacqui Kenney is a prolific international photographer. In a single day, she captured photos from Mexico in the morning and Kyrgyzstan in the afternoon. In one year, she collected 26,000 shots from around the world while she stayed at home by using Google Street View. In her project Agoraphobic Traveler, her photos include warm sandy deserts and snowy mountain peaks, a mother and child crossing a rural field, and a scooter traveling through a busy urban center. Jacqui suffers from anxiety when leaving home, but through this project she is able to experience the world. Her work has been reported by National Geographic and the New Yorker—and it is all made possible without leaving the house, using tools that were not intended for her purpose and were unavailable twenty years ago. From looking at the volcanoes on the moon Io to making art from travel photos taken from our couches, our knowledge of locations near and far is expanded through the use of remote-sensing systems.

4

Geographic Information Systems and Cartography

Hurricane Harvey rampaged through Texas in the summer of 2017, cutting a wide swath of destruction, including massive flooding in thousands of homes in the Houston metropolitan area. Some areas received more than 60 inches of floodwater. Tens of thousands of people in the area were displaced, and the damage carried a total estimated price tag of $200 billion in damage.

The National Flood Insurance Program (NFIP) was able to provide financial assistance to the owners of the many homes and businesses affected by Harvey, except in one instance. One housing community investigated by the New York Times, the Woodlands, received substantial flooding, but the homeowners there were caught unprepared—their homes were not designated as being located in a floodplain, so they did not have flood insurance and were not eligible for government assistance from NFIP.

When reporters from the Times researched records, however, they found that in the recent past the neighborhood had been designated as well inside the floodplain and that someone had changed the designation. Economic growth in the Houston area had spurned new home construction. Developers, eager to feed the demand for more housing in the area, knew that if they built housing on a floodplain, they would need to disclose that information. This would make the property more difficult to sell, and the developers’ profits would take a hit. Any buyer would be forced to buy flood insurance—which can run $5,000 per year—enough to make many buyers look for a home in a different neighborhood. So the developers of the Woodlands hauled in trucks full of soil to raise the property to an elevation just higher than the level that carried liability for floodplain disclosure and insurance.

By filling in the Woodlands with soil, the developers were able to alter FEMA’s mapping of floodplains, including the lots of at least 6,000 properties that were damaged by Harvey. Unfortunately, although the new soil was enough to technically change the floodplain status of the Woodlands, it wasn’t enough to actually stop the neighborhood from flooding.

Although these developers’ actions may be unsavory, they were not illegal. Citizens and developers are allowed to petition changes to FEMA’s maps if they hire surveyors to correct elevations and build levees and drainage channels. Hauling in tons of soil to raise the elevation of the Woodlands provided more cost benefit than other options to render the topography of the area good for building and owning a home. However, homebuyers in the area were not informed that the area had once been a floodplain, and few of them took out flood insurance. In the wake of Hurricane Harvey, many of the area’s homeowners were hit with disastrous economic consequences.

The story illustrates the incredible power of maps. They are used simply to describe, but they hold authority.

Maps are designed and tailored to present the information that they need to convey. In the case of FEMA’s map of the Woodlands, the New York Times made the case that the map was gerrymandered. A particular developer established an economic advantage for itself by manipulating the floodplain map’s boundaries.

Maps like FEMA’s are made via geographic information systems (GISs). A GIS can store, analyze, manage, and visualize spatial data collected via remote sensing, manual surveys, and digitized paper maps. Two primary elements are part of a GIS. The first element is database storage, where the GIS combines map data from multiple different sources to create the most accurate possible global data. These systems understand hundreds of coordinate reference frames used to specify locations on Earth’s surface. The second element of a GIS is the variety of color coding, symbol libraries, and map-generalization tools that help combine spatial data from multiple sources and create easy-to-understand cartographic visualizations, including maps. A GIS is often a gateway to the spatial database management systems and spatial-statistics libraries we discuss in the next two chapters.

Background

Maps have been around for millennia, and their roles have changed over time. One of the oldest surviving maps is carved in a stone the size of a smartphone. This map, the Babylonian Map of the World, is around 2,600 years old. It describes a large area but not in great detail. Babylon is at its center. There are wide, carved lines for rivers and the ocean, and a few additional areas are labeled as being “beyond the flight of birds.” Mythological beasts surround the map. The map, scholars say, was not used for navigation but as a way to describe the known world, with Babylon at the center. They believe it was a way to establish some sort of order and understanding of the world.

Claudius Ptolemy used more mathematical rigor in his mapping, including one of the first coordinate systems, latitude and longitude. He also charted terrestrial landmarks using celestial navigation. His projection map from the second century no longer exists, but he described it with such detail in his book Geography that it was re-created in the thirteenth century and was used until the seventeenth century. Some scholars even credit it with helping the expansion of the Roman Empire. Though the Greeks and Romans traveled more extensively in the second century than the Babylonians did, Ptolemy’s “world” did not include the entire globe, but it did include Europe, Africa, and a large part of Asia, with detail on some 10,000 locations.

The greatest contribution to mapmaking next to Ptolemy came from Gerardus Mercator, a Dutch cartographer who in 1569 created the Mercator projection. Map projections were not new; Ptolemy described several in his book. Mercator’s projection was novel for its contribution to navigation. Sea voyages in the sixteenth century were becoming longer, but the existing nautical charts were better suited to regional trips that crossed no more than a few lines of latitude. For a transatlantic voyage, they offered almost no help. Scholars have theorized that poor navigation is one of the reasons that Christopher Columbus landed as far south in the Caribbean Islands as he did. Translating a sphere (such as Earth) to a flat plane (a paper map) without distorting its shape was already considered an impossible task. Mercator realized that a particular distortion that created lines of longitude that did not intersect at the poles would make those lines cross at right angles, which would allow navigators to plot courses in a straight line on a flat map using a compass. Mercator’s distortion preserved direction, making it a useful tool that is still widely used in modern mapping applications. Yet although it is useful in preserving direction, it does not preserve area or distances.

The Mercator projection is still one of the most commonly seen maps of the globe today, but the effect of its distortion means land located closer to the poles appears far larger than it actually is.

Early cartography was often a combination of scholarly work and artistic expression. Many maps featured heavily ornamented borders, topographic features, mountains and rivers far out of scale but in exaggerated detail. A Korean map from 1402 looks more like a modern map than many of the maps created in medieval Europe. The map, known as Kangnido, shows the outlines and boundaries of an oversize Korea, Japan, and China, with Europe and Africa much smaller and to the far west. Rivers are shown as small lines. In contrast, Pieter van den Keere’s map of the world from 1611 is less map and more visual storybook with geography in its center. Scholars theorize that cartographers experienced horror vacui, a fear of leaving any blank areas. The size distortions and flourishes may have served to hide the cartographers’ lack of knowledge because there was certainly little known about the interior of the Americas in the seventeenth century, and extensive artistry provided value to the patron who commissioned the work.

Throughout most of history, mapmaking was a time-intensive and expensive undertaking. The Cassini map of France, made of nearly 200 separate sheets, was the first undertaking by that country to map every meter of its territory. The project was so ambitious that work started under Louis XIV took four generations of the Cassini family to complete. Cartographers worked at the pleasure of the aristocrats or government leaders who hired them. To display a map in a home was to announce a certain level of wealth, and the level of artistry displayed on a map often reflected the largesse of the mapmaker’s patrons.

Though the Mercator projection had advanced navigation, cartography was regarded as more of an artistic pursuit than a scholarly one until the middle of the eighteenth century, when mapping became a key asset in warfare. George Washington was a land surveyor throughout his life, working professionally early in his career, during his military work, and later in his life measuring the land on his plantation, Mount Vernon. During the Revolutionary War, British troops were at a huge disadvantage because they had no maps of the interior of North America. Although the eastern coast was rendered in detail on their maps, the British knew very little about the area just a few miles inland. During the French and Indian War (1754–1763), General Edward Braddock was dealt a crushing blow when he realized that the journey from Alexandria to modern-day Pittsburg was not 45 miles, but rather 240 miles. That journey included crossing the Allegheny Mountains. Braddock’s troops were soundly beaten in an ambush attack by much smaller forces just outside of their intended destination. Later on, John Mitchell’s map of North America helped in the negotiations for the Treaty of Paris, which ended the Revolutionary War.

The US government has had a large role in the mapping of the country. Understanding the advantage of mapping, the brand-new US Congress authorized the appointment of geographers and surveyors to map the interior of the country. This work, consisting of more than a hundred different mapping expeditions, lasted until the Civil War. In 1879, the USGS was established to study the nation’s landscape and natural resources. Over the years since then, government intelligence agencies and the DOD have had their own mapmaking divisions. The US military is still the largest mapmaking body in the country. Because its work focuses on areas where there are people and borders, some of the least-mapped places are at the poles. That is now beginning to change because the poles are being re-examined for their use in environmental monitoring and for possible shipping routes during the ice-free summers made possible by climate change.

Until the twentieth century, mapmaking continued to be a time- and labor-intensive pursuit. Maps were drawn by hand. Computing moved mapmaking into a new era. In the 1960s, the Harvard Graphics Lab created a computerized mapping tool that allowed users to display geographic data, such as census tables, from digitized archives. Though these maps could be made more quickly and less expensively than hand-drawn maps, they were somewhat crude. Nevertheless, they allowed the user to vary the inputs of the map and see the results of modeling. The computers were huge machines that cost millions of dollars, keeping the power of these new tools out of the hands of everyone except the universities and government agencies that could afford them.

When computer-processing power increased in the 1980s and the price of computing hardware decreased, the usage of computers in mapmaking expanded, launching an industry for the use of GIS software that went beyond the military and academic settings. One of the first major systems for GIS software, ArcInfo, created by the software vendor ESRI, Inc., is still a major force in GIS today. Access to this technology transformed the work of cartographers as mapmaking went digital. Although these maps were still rendered simply, they made up for a lack of detail with speedy production. They also allowed for greater flexibility in map creation because changing a map’s scale or projection was no longer a major undertaking. This also impelled a fundamental shift in the maps themselves. Historically, maps had served as both a database of information and the display of that database. All available information (or lack of information) was presented in the map. In the digital age, we have physically and conceptually separate aspects of handling geographic data, and only the necessary features are displayed on the map to serve the map’s purpose.

The early 1990s saw another technological shift as Internet-based GIS made it even easier to access GIS technologies. MapQuest, one of the first Internet mapping services, delivered 130 million digital maps to consumers in late 1990s. GIS technologies have continued to shift in the past 20 years. Maps that were once viewed from a desktop computer are now accessed via mobile devices using cloud-based storage systems for their geographic databases.

Geographic Information Systems—How They Work

In 2003, the Economist ran an article about North Korea’s missile power. The accompanying map, based on the Mercator projection, showed a set of concentric circles to indicate the range of missiles in production at the time. The smallest circle on the map is very small in relation to the next two circles, which reach to Alaska to the east of North Korea and Africa to the west. Unfortunately, the illustration was wrong. The creators of the illustration used circles on a flat map to designate the area within a given distance of a missile launch site. Of course, Earth is not flat. It is not even a sphere, but rather an irregularly shaped ellipsoid. Earth’s continuous spinning makes it bulge at the equator, so that the radii near Earth’s poles are around 5 percent less in length than the radius near the equator. Over a short distance, this difference in calculations between a flat plane and an ellipsoid shape doesn’t make a great deal of difference, but for a missile with a range of up to 10,000 kilometers, the calculation created a large-scale error.

GIS are equipped to tackle exactly this sort of calculation with mapping output. The systems can hold and understand a large number of map projections and coordinate systems used by data producers, including ellipsoid representations of Earth. When the data from the Economist’s article were placed in a GIS, the corrected map relayed very different information. The revised map no longer had three concentric circles but isodistance curves representing areas reachable by the missiles. One missile’s range encompassed areas so large that only South America was out of range.

A GIS, as we mentioned, captures, stores, analyzes, manages, and visualizes spatial data. It also supports a variety of cartographic conventions (e.g., color coding, symbol libraries, and map-generalization tools) to help combine spatial data from multiple sources to create easy-to-understand cartographic visualizations, including maps.

The input into GIS includes both spatial and nonspatial data. Spatial data are geographically referenced, or geocoded, using a unique identifier, so that a location is understood by all the people working with the information. A place-name, such as “San Francisco,” is one way of specifying a location. But many cities and places around the world are named “San Francisco.” When we add “California” after “San Francisco,” we have a much more specific identifier, but it includes an area that is a little more than 47 square miles. Geocodes can range from those that are very specific, defining an infinitely fine space, such as coordinates in latitude and longitude, to those that are very large and varied, such as city names—for example, “San Francisco.”

The street address is one of the most well-known, exact location specifications that we work with every day. In the United States, a street address usually includes a building number, the street name, city, state, and zip code. The lowest-numbered and highest-numbered addresses are located on the ends of a road segment between two adjacent road intersections. Each number is specific to a single mailbox location. Street addresses were created to deliver mail, but they are used for many other purposes. People and businesses around the globe use an address as a location specification for smartphone apps to find where they are on a map, nearby facilities, and directions to reach them. For those without access to a GPS device, addresses in the United States are often numerically sequential. Many cities have numbered streets or avenues as well so that we can understand that if we are on First Street and going to Twelfth Street, the block names will follow sequentially, making it easier for us to find our destination. This standardization of street names and address conventions is not found everywhere in the world.

Geocodes can range from those that are very specific, defining an infinitely fine space, such as coordinates in latitude and longitude, to those that are very large and varied, such as city names—for example, “San Francisco.”

Addresses work well in urban areas, where a driveway or a home is not far removed from the road segment. In other areas, addresses are less helpful. There are no street addresses for the hiking trails in the Superior National Forest, a large natural recreation area in northern Minnesota. The US Forest Service lists locations for various access points to the trails, but an emergency crew trying to find a lost hiker or fighting a forest fire in a nearly 4-million-acre recreation area needs a more specific way of finding location.

For this challenge, we use coordinate systems. The most well-known coordinate system is one that we have already discussed, the latitude-and-longitude system. Coordinate systems assign a pair of numbers to a location measured as a distance from two axes drawn at right angles—the way latitude and longitude do at the equator. Using latitude and longitude, we have a system to provide global coverage that works in fine detail. Each line of longitude is broken up into 60 minutes, and each minute into 60 seconds, though computers usually store those minutes and seconds as decimals of a degree.

Some coordinate systems are based on a flat plane. We know that Earth is not flat, and so to translate a coordinate system to a flat plane we use projections. The Mercator projection is one cylindrical projection, but GISs make several hundred others available for use. These projections can account for a variety of variables and are better for presenting certain kinds of data, but projected coordinates can cause errors in data analysis when the projection is not taken into account. If we calculate the distance between London and Amsterdam using a flat Earth model, the distance is 567 kilometers. Using a spherical Earth model, the distance is 358 kilometers. In the missile-launch example we discussed earlier, the Economist ran into trouble when it calculated distance on a flat projection to make its map.

A GIS is capable of converting and combining data sets that use different coordinate systems, or datums. There are hundreds of different datums, created using satellite imagery and measurements, to represent the size and shape of Earth, from spheres to ellipsoids. Google Maps uses one datum, WGS 84, and many countries in Europe use the ETRS89 datum, which provides a more accurate geographic model for that part of the world. A given value of latitude and longitude across two different coordinate systems may not map to a common point on Earth’s surface. Furthermore, the points represented by a fixed value of latitude and longitude across different coordinate systems may be more than 100 meters apart on Earth’s surface.

The input into a GIS consists of both spatial and nonspatial data; the output of a GIS is a map or a series of maps. These systems are good at creating a variety of maps, which come in a wide variety of styles. If you are able to break your data into different groups on a table, GIS tools can regroup those data into zones, as on a weather map.

We can group the maps from GISs into two large categories, reference maps and thematic maps. Reference maps are used to convey information about location and navigation, so they include road atlases, mobile maps, and the kiosk maps at shopping malls that direct you to different shops. Thematic maps, in contrast, are not interested in conveying navigation or orientation but rather communicate the spatial distribution of geographic attributes. Some of the most common thematic maps show how each state voted in federal elections. The Centers for Disease Control (CDC) map that shows the spread of flu each season is a thematic map. Media organizations frequently use thematic maps to communicate ideas quickly.

There are trade-offs in deciding what information we convey in any map that we make. Consider the reference map your mobile phone displays when you are in St. Paul, Minnesota, and looking for directions to a football stadium in Minneapolis. Depending on how much you have zoomed in or out, the map shows notable road names, neighborhoods, larger landmarks, a few universities, schools, the Mississippi River, and green areas for parks. The river is described visually as a space of blue with a space of green next to it. The map does not relay the depth of the Mississippi or the direction it flows. Those pieces of information aren’t relevant to getting to the stadium. The data in the map must conform to a single scale and projection, while allowing for some distortion of features. If we zoom out far enough to a view of the whole state of Minnesota, the cities of St. Paul and Minneapolis will eventually merge into a single dot that sits either on top of or to one side of the Mississippi River rather than being shown as two dots, one on each side of the river. This abstraction makes it easier to read the map as a whole, even if it presents information that is not accurate. Map abstractions are usually done for a few reasons. In making any map, we must choose what information is most important, what we must preserve. Maps simplify information, providing only a limited amount of detail about the world. They also classify information, providing information on only certain types of features—for example, when the road map provides only an outline of the river to show how it affects a route. Maps also use symbols to represent features of the area, as in the dots that represent cities. Cities, of course, are not geographically circular but are often represented as dots or circles on national maps.

We have to make many design choices in creating thematic maps as well. In a choropleth (area or region) map depicting quantitative data, each spatial unit is given a single color from a range to help the reader understand the spatial variation of the attribute. A CDC map showing influenza cases by state or region is a choropleth map. It might be parsed by county or census tract, assigning a color to each area by flu-activity intervals. Such a map usually displays a lighter color shade for less activity and a darker shade of the same color for high activity. In national presidential election maps, the data are usually delivered by showing how each state votes by color, red for Republican or blue for Democrat.

Figure 7 Choropleth map of H1N1 cases. Source: US Coast Guard.

Maps simplify information, providing only a limited amount of detail about the world.

Quantitative data, as in a map of times zones, are usually rendered in varying colors rather than different intensities of a single color. In using GIS to make choropleth maps, color choice and color ranges are important because they can make it either easier or harder to accurately convey the information in the map.

Another way to show the spatial distribution of information is via a graduated proportional symbol map. In these maps, the size of a symbol, often a circle, is proportionate to the amount of data at that location. The symbols can overlap and obscure some of the data, as seen in figure 8.

Figure 8 Proportional symbol map. Source: Minnesota Spatial Computing Group.

Cartograms are another type of map that is familiar from election reporting. The size of the geographic areas they describe are as large or small as the volume or density of the attribute they represent. In describing election data, a state such as California is larger in order to indicate its large number of electoral votes, whereas Wyoming, with fewer electoral college votes, appears smaller, distorting the map extensively. Because of this, cartograms can be confusing. They rarely appear alone and are usually paired with another map to show the nondistorted area to illustrate the geography more correctly.

Figure 9 Cartogram US presidential election map. Source: Minnesota Spatial Computing Group.

Flow maps show attributes that connect two places. Flow maps are good for showing immigration from one country to others or the flow of goods from one place to others.

There are other maps that GIS is not equipped to create but that are effective on their own. A topological map, such as a transit map or subway map, is beyond the scope of GIS. These reference maps present a graphic simplification of bus and subway lines. They are rarely to scale, but they reveal stops, direction, and transfer points to other lines, cutting out any other geographic features.

GIS is also not equipped to create maps that are not location based or spatial. A diagram of the human body is a kind of map, and when we consider the future of medical research, certainly it will focus on how medications and treatments travel within the body. Charles Menard’s historic map of Napoleon’s march to Moscow and back provides an interesting visual representation of how many men traveled there and how few returned, but this type of nonspatial representation cannot be created using GIS.

GIS provides the user with computer or smartphone displays that can be easy to browse, zoom in on, and change scale. Some allow for animation with the addition of time data, so that we can see changes over time. Every map, even those that are preset as part of GIS, require thought about design and the information that is presented in the map. They all must take into account the limits of human cognition and what viewers can perceive easily, so mapmakers must consider colors and symbols that are pleasant to view and easy to understand. The benefit of GIS is that we can change and update those settings to make them more readable for our audience. With many features to use, we have to remember that we need to limit them to those that are most well equipped to convey the information needed.

Figure 10 Historic flow map, 1869. Source: Charles Joseph Minard.

Applications

Between 2005 and 2008, the cost of key grains for the world’s food supply spiked dramatically. Wheat and corn prices tripled, and there was a fivefold increase in the price of rice. This increase in prices caused extreme hardship for the world’s food-importing countries, particularly for the poorest communities, in which more than half of monthly income goes to food costs. Although food production was adequate, the world was not producing enough to stockpile reserves in the event of a crop failure. Several world leaders saw the need to collaborate to monitor global food supplies to prevent such global upheaval. At the G20 Summit in Paris in 2011, 20 agricultural ministers from around the world made an agreement to coordinate satellite observations over Earth (along with other measures) to generate accurate and reliable measurements of the world’s crop supply. The initiative, GEOGLAM, is now going strong with more than 80 partner agencies all over the world that share the work of monitoring the world’s food supply.

When these agencies place the data and models into a GIS, the output is the map that they use to create a complex report on the current status and annual predictions for global crop yields.

The current applications for GIS, including this application and many others, are widespread and growing. Government agencies have had a hand in developing GISs and a long history of using them, and such systems are widely used by private companies and nongovernmental organizations as well.

With the easy input of climate and time data, GIS is frequently used to visualize climate change and rising sea levels. NASA has created a map visualizing projected global temperature and rainfall changes through 2100. These systems can also easily alter those predictive maps assuming changes to reduce greenhouse gases. In one such map, National Geographic created a visualization of the globe with all of the polar ice melting.

State and city agencies use GIS for emergency management. The City of San Diego has an online tool called “Know Your Hazards” that allows residents to enter their home address and see area risk maps for tsunami, flooding, earthquake, and wildfire. The tool also provides emergency-preparedness activities that residents can perform based on their risk-map results. Police crime data have been plotted in the map in figure 11 by census tract. Maps like these are used by county and state agencies to illustrate where crime hotspots are and to help allocate appropriate funding and staffing for crime prevention.

When wildfires blazed across California in the fall and winter of 2017 and in the summer of 2018, GIS tools were used not only to monitor and predict the spread of those fires but also to plan for evacuation zones, model road closures and traffic congestion related to moving large groups of people away from danger, and display help resources, such as hospitals, shelters, and so on. Programs such as Monitoring Trends in Burn Severity, run by the USGS, the US Department of the Interior, and US Forest Service, use GIS track-and-learn fire-management strategies from all large fires in the United States over the past 30 years.

After reviewing research that high school students performed better academically when school started later in the mornings, the Saint Paul Public School District reconsidered the start times of its high schools. Although the school board, teachers, and parents supported the change, one key issue in adjusting start times was bussing students to school. The school district staggers the start time of the schools, with high school students starting their days at 7:30 a.m. and other schools starting at 8:30 a.m. or 9:30 a.m. The staggered start times allowed the system to use fewer buses to transport students because drivers could do the job in three different shifts. However, a plan to make all high school students start later meant that the bus routes would need to be reconsidered. GIS is equipped to easily demonstrate the consequences of and help plan alternatives for exactly these scenarios and in this situation could be used to present the proposed changes to the school board, administrators, and parents. GIS is also used in security and defense scenarios. The system maps are equipped to address variances in terrains and visibility from certain vantage points. When the Secret Service is planning for a presidential motorcade passing through a city, for example, a GIS would account for and supply information about which windows and rooftops provide sight lines to the president’s vehicles and should be secured. Some armies use visibility analysis, also called terrain analysis, to select border post locations that are easier to defend in hilly terrain, mountain tops, and ridges. Cell phone infrastructure providers use visibility analysis in choosing cell tower locations to cover streets and buildings in populated areas while reducing infrastructure cost. Tourism and travel industry professionals use visibility analysis to select sites for scenic hikes and drives along ridges and mountainsides or through canyons and valleys. GIS can be used to plan transportation routes and for mass transit because it can look at commuter routes and commute times and visualize how adding more mass-transit options may affect a community’s access to amenities such as shopping and parks.

Figure 11 City of Minneapolis. Source: Minnesota Spatial Computing Group.

Limitations and Concerns

Because GIS is a visualization tool, it can also be used to represent data inaccurately. For example, a tool such as Google Maps will represent a road and will often flag construction sites if that information has been input into its database. However, it does not relay the size or the quality of that road. A rural dirt road may be represented in the same manner as a paved suburban street. There are also concerns about the information that is included on a map. When you are using a mapping tool such as Google Maps to seek out a public space, you will see only certain nearby businesses because they have paid Google a fee to be included in the map. Meanwhile, other public information, such as whether a public park allows dogs or camping, is not displayed. This lopsided presentation raises questions about access to information via these tools—that is, what information is not being relayed due to the monetization of information. We can already see through mapping tools that richer places with more commercial interest are mapped first, while other less economically advantaged but high-density areas (when seen via satellite image) lack mapping.

Maps can be used to intentionally confuse and misrepresent information. We started this chapter explaining how one housing community was effectively gerrymandered out of a flood zone. Political gerrymandering is a great concern in GIS. Partisan gerrymandering of US congressional districts to favor one political party over another has sparked numerous court cases in state supreme courts. Politicians, analysts, and many others still hotly debate the most effective and fair methods of redrawing these congressional districts. Currently in most states, state lawmakers decide how to draw district maps. When partisan lawmakers are in charge of drawing maps, there is an inherent conflict of interest, and we see partisan maps as a result. The maps reinforce the political party in power. In the 2012 federal election, Republicans seized control of the House of Representatives in spite of Democratic House candidates receiving more than 1 million more votes than Republican candidates.

Maps can visually distort information as well. When we look at a map of how each state voted in the 2012 presidential election, with states that supported the Republican candidate, Mitt Romney, in red and states that supported the Democratic candidate, President Barack Obama, in blue, most of the map is visually red. Visually, the maps conveys a win for Mitt Romney because many of the so-called red states are large in area. But these states are low in population, so, for example, the geographic size of Wyoming overrepresents its electoral power (only three votes), while the greater electoral power of the geographically small state of Massachusetts (11 votes) is visually underrepresented in blue. Other choropleth maps can distort information based on interval choices. By grouping a large amount of data in one interval, we can over- and underrepresent data to mislead our audience. The data display choices have a huge effect on how the data that we represent are interpreted. We know that too many colors in a choropleth map can alter the audience’s ability to understand the information presented. The same effect can come about through unfortunate choices of other symbols, outlines, and colors. The choice of how we display information is as important as the data being displayed, and with many choices in GIS there are many way to alter the representation of the data.

Future Directions

GIS continues to transform the role of technology in farming beyond global crop monitoring. Farmers are dependent on the health of their land and the land of their neighbors. What one farmer plants or does not plant can have consequences on decisions another farmer makes on his or her land about fertilization, irrigation, tillage, and pest control as well as which crops to plant or fields to leave fallow. Computer-supported collaborative-mapping tools are beginning to become available. These tools provide maps with a touchscreen interface, which allows stakeholders and farmers to alter the maps together, partition and specify alternative uses for planting areas, and see the modeled effects of these decisions displayed on the map. This ability creates a social awareness of each party’s needs and constraints, helping the community make appropriate plans for crop rotation and density. In all of these situations, GIS is a visualization tool that can be used to evaluate alternative decisions to optimize outcomes and create consensus.

To date, the displays for GIS have been primarily two dimensional. That is beginning to change as we see more spherical displays on globelike computer monitors, which resolve many issues of distortion in transposing three-dimensional planets to two-dimensional displays.

GIS is being incorporated in mapping infrastructure as well. Sewer systems in the United States were designed based on historic rainfall data. As climate change continues, so does the rainfall in the country. Sewer systems, designed to receive three inches of rainfall from a single weather event, are now being overwhelmed by multiple weather events each year that produce five inches of water and more. We live on a dynamic planet. As the climate changes, polar ice melts, coastlines are reformed, and the direction and flow of rivers are changing. GIS is an important tool in visualizing predictive data from climate change on a global scale as we examine not a single change but cumulative changes.

GIS is a visualization tool that can be used to evaluate alternative decisions to optimize outcomes and create consensus.

Figure 12 Collaborative mapping tool. Source: U-Spatial, University of Minnesota.

The market for GIS is only increasing, with GIS software companies such as Esri bringing in $1 billion in revenue per year. The software, once the domain of academics and government agencies, is being introduced to new users. New audiences are being introduced in private industry to help visualize business market opportunities and in schools to teach students mapping visualization.

Conclusion

In the movie The Englishman Who Went up a Hill but Came Down a Mountain (Christopher Monger, 1995), two English cartographers come to a Welsh village to measure its mountain. Their work creates a furor among the locals when the cartographers determine the feature they have measured qualifies as a hill, not a mountain. The offended locals band together and bring buckets of earth to the top of the mountain, raising the hill’s height until it qualifies as a mountain once more. In doing so, the villagers regain a symbol of their community and identity. Though the actions of these fictitious Welsh men and women are the same as the actions taken by the real estate developers in the Woodlands in Houston—dumping earth to alter a map—the intent and the outcomes are very different. Both show the power of maps.

5

Spatial Databases

In the fall of 2017, Amazon announced that the company was seeking a site for a second headquarters and put out a request for proposals (RFP) from cities vying for the opportunity to host the second Amazon domestic home base. More than 200 cities responded, lured by the economic prospects and prestige of hosting a major corporation. The company promised an investment of $5 billion in construction alone, one of the largest such investments in recent memory.

Amazon narrowed the competition down to 20 sites before selecting two winners. The retail giant had a very specific set of requirements in its RFP. Some requirements were more general, economic concerns, such as a qualified and ready labor source, economic incentives from the city, and at least 500,000 square feet of existing buildings on a greenfield site of approximately 100 acres. Others were less easy to quantify, such as a cultural community that is a “good fit” and a city with an overall high quality of life.

The most interesting details of the company’s proposal that relate to this book were the many requirements based on location. For example, the proposed location needed to be within 30 miles of a population center and within 45 minutes driving distance of an international airport. At the same time, the new site had to be not more than a mile or two from a major highway and to have direct access from the site to mass-transit options. Perhaps most specifically, the company indicated a preference for a site similar to the Seattle headquarters, close to recreation sites such as mountains and bodies of water.

Amazon’s RFP provides an example of a question that spatial database management systems (SDBMSs) are specially equipped to handle. SDBMSs are used to organize, store, and retrieve spatial data and efficiently address complex questions involving different data types, operations, algorithms, and spatial data structures. The databases are able to parse and optimize geographic types and functions.

In the case of Amazon, a relatively simple query or question was posed to a database to identify sites, formally called land parcels, that would satisfy the desired properties defined by the RFP from among hundreds of millions of candidates in the land-parcel databases. These databases were originally compiled for tracking land ownership and property tax. Searching for criteria such as desired size in square feet or acres, access to a strong cellular and fiber network (another requirement), or location near a major airport can be accomplished quickly using modern relational database management systems, assuming the properties (e.g., area, access to high-bandwidth communication networks, travel time to a major airport) have been precomputed and stored for each land parcel.

Searching for other criteria, however, can easily overwhelm relational database management systems. For example, we can assume that the land-parcel database does not store data about distance to major recreation sites such as mountains, rivers, lakes, and oceans. A search request for a site within 100 miles of major recreation facilities is not as simple. That is a geometry query, which requires a programming language that is able to handle geographic types (e.g., points, polygons, line strings) and functions (e.g., distance, inside, slope or gradient) to find an answer. Relational databases were not initially equipped to handle these types of spatial data and relationships. For example, to identify land parcels near lakes or oceans, we must compute distance between polygons representing land parcels, lakes, and oceans. To identify land parcels near rivers and streams, we need to calculate distances between land-parcel polygons and line strings representing the center lines of rivers and streams. Given that each polygon and line string representing a lake or river or stream has thousands of points, it would take substantial computation time to test hundreds of millions of land parcels for their proximity to water bodies.

To identify land parcels nears mountains, we need to process elevation maps to identify land-parcel polygons close to elevation gradients and much higher- or lower-elevation points that represent mountain peaks, waterfalls, and vista points. We would also look for line strings representing ridges, hiking, and biking trails as well as for polygons representing canyons, valleys, high-elevation lakes, parks, and ski-resorts. Elevation maps are often represented in raster format, which is similar to pictures taken from digital cameras, with millions of pixels. Each pixel describes the average elevation of a small (e.g., 10-by-10-meter) area. It takes specialized algorithms to estimate slopes and gradients (i.e., slope in steepest direction) and to estimate locations of mountain tops, ridges, valleys, and canyons from raster elevation data.

Spatial queries require calculations that combine different types of geographic data such as points, line strings, polygons, rasters, and more. SDBMSs store and represent these multiple forms of geographic data, which then allow us to reduce programming effort and receive faster responses to these complex questions. All of the tools that we have discussed so far require a spatial database to function. While GPS, GIS, and remote-sensing systems use additional tools beyond a spatial database, at their core a database is essential to their function. Spatial databases hold the maps used in GIS and the images obtained via remote-sensing tools. As these technologies grow, the importance of SDBMSs will continue to grow with them.

Background

The US Census is considered the mother of modern databases due to its size and history of recording information of the nation’s inhabitants. Conducted every ten years and inclusive of the entire population, the US Census is used to allocate congressional seats, distribute state and federal dollars, and track changes to the population over time. The founders of the United States considered it such a vital tool that they wrote it into the Constitution (Art. 1, sec. 2) in 1787. The US Census Bureau is also responsible for some of the major advancements in computing.

The idea of counting the population was not new when the US Census was instituted, but the countries that conducted censuses and the populations that were counted did not grow as quickly as the United States did a hundred years after its founding. The US federal government had no idea how difficult it would be to continue to count a growing population by hand over long distances. At the end of the nineteenth century, immigrants moved to the United States in tremendous numbers, lured by the economic growth of cities around the country. This growth proved to be a major challenge for hand-counting the population. It took seven years to hand-count the 1880 census. The labor-intensive process had officials concerned as they considered the current rate of growth. The Census Bureau believed that the 1890 census would not be counted within ten years, making the division and assignment of congressional representation required by the US Constitution impossible.

In response to this challenge, Herman Hollerith, an engineer who had worked on the lengthy 1880 census, created a way to automate the process. Inspired by train conductors and their punch cards, Hollerith created durable paper cards, each card representing one individual. His or her responses to the census questionnaire were transferred to a card with holes punched to indicate attributes and characteristics such as age, sex, marital status, city of birth, and city of residence. In one location on the card, a hole indicated male; in another location a hole indicated female; and so on. When cards were fed into a tabulating machine, the hole would allow the connection of an electrical contact, and the machine registered and counted the information on the card, based on the number and location of the holes punched in it. The counts were represented on a series of dials, a sort of dashboard. Hollerith’s tabulating machine was approved for use by the US government and installed in time to count the 1890 census, which was completed ahead of schedule and under budget.

Figure 13 A Hollerith punch card, ca. 1895.

Based on his success with the census, Hollerith expanded his company and went on to build faster machines able to handle more complicated data. His company merged with several others over time to form International Business Machines, or IBM, in 1924.

While the Census Bureau continued to use forms of the punching machines until the 1960s, electronic digital computer databases started during the Second World War when the early DOD (then called the War Department) needed to process ballistic information. The Electronic Numerical Integrator and Computer (ENIAC) is considered one of the first computers. Data were still stored on punch cards, but engineers could program and reprogram the machine to calculate artillery tables for the army. The speed of these machines surpassed the older, nondigital tabulators such as the Hollerith machine, and the engineers who worked on them saw the possibility for use in many nonmilitary domains.

The UNIVersal Automatic Computer I (UNIVAC I) was designed by the same team that created the ENIAC, but for commercial use, though the very first unit was still delivered to a government agency, the Census Bureau. IBM continued to evolve data storage and data processing. During the 1950s, it moved data storage from punch cards to magnetic tapes and created the first mass-produced computer.

The magnetic tapes were an improvement from the cards but still posed issues for users. Just as with audiocassette tapes, which listeners had to rewind or fast-forward to find their favorite songs, similar delays were present in processing data recorded on magnetic tapes. Even with embedded tools, the process was fussy. Users created indexes to mark locations of different items on tapes and sorted files, which sped the retrieval of a group of related data items without the need to scan all data items. But it often took users hours to prepare a summary report from data stored on tapes. The requested reports were usually combined together for processing the night before they were needed, a practice called batch processing.

The invention of random-access magnetic disks in the 1960s transformed the speed of data access by allowing programmers and data users to retrieve individual data items of interest much more quickly—similar to more recent CD-ROMs and DVDs. Computer software evolved to include special file structures to take advantage of magnetic disks. Hash files allowed users to retrieve in milliseconds a single data item from a collection of billions of items, including historic census records. The ultrafast searches provided by hash files were magical to users in the 1960s. Although it is tempting to think this type of file was named after marijuana-smoking counterculture, many believe that in fact the name reflected the dictionary meaning of the term hash, “to chop or make a mess out of something.”

Hardware and software innovation in the 1960s ushered in a new era of interactive computing, raising users’ expectations for interactive responses to queries using a new generation of data-management software named Database Management Systems.

The term database rose into prominence in the 1960s as a way to describe the information that is stored, changed, and used independently of the machine in which it is housed. The 1960s saw the rise of various data languages and the establishment of the common business-oriented language, known as COBOL, developed by the DOD to incorporate standards such as Conference/Committee on Data Systems Languages (CODASYL) that could be used consistently among databases. The databases at the time were considered “navigational”—the user traveled around the database to view sequentially linked records via commands such as <<get next>> or <<get prior>>.

In the early 1970s, Edgar Codd theorized a new way to access data that bypassed all of that navigating by the user: the relational data model. It provided the idea for a high-level query language that could be used by decision makers without knowledge of procedural programming languages such as COBOL. Rather than moving through a database record by record to find relevant information, the user could command a single set of high-level operations for an entire data set without the need to understand the physical data organization such as hash files, sorted files, and unsorted files, among others.

Relational databases organize data in tables, called relations, with one table generally used to describe an entity type, such as a book in a library. The table has unique keys to identify each row, and columns called attributes are used to describe the entity—such as copyright date, price, and library of origin for the book in question. At a library, a typical transaction table would include the patron (with their own table) checking out the book with their name and address. Another table would describe the transaction between the patron and the library, the book that was checked out, the date, and the date it was due to return to the library. Though the library’s database contains millions of transactions, the librarian can access a view of that database that is limited to the needs of that individual librarian. Relational databases use the high-level structured query language (SQL) for access and data gathering, which is still very popular today and often considered intergalactic dataspeak.

The invention and implementation of relational databases was a major breakthrough instigated by a DOD DARPA challenge similar to the recent project concerning self-driving cars. These well-organized databases are much easier to view and understand, and they include metadata—information that describes the data—with rich information on its own about where the data came from, its meaning, and how it was collected. Each day we interact with relational databases dozens of times a day, from ordering our coffee in the morning to accessing the contacts in our phones, getting cash at the cash machine, and checking our bank accounts.

The data sets that didn’t work particularly well with relational databases are the ones we focus on here—spatial data sets. The conceptual data models in classical relational database management systems assume that the relationships between entities in the database will be few. However, spatial entities have numerous implicit relationships such as distance and direction that create a cluttered conceptual model. In addition, when we look at the data in the tables for our library example, they include a limited set of data types, primarily numbers (e.g., cost) or text strings (e.g., book title). It is very cumbersome to use such simple data types and operations to represent spatial data (e.g., polygon, raster) and queries (e.g., find land parcels near mountains and water bodies). This difficulty is called a semantic gap, a difference between the data and how they are described for spatial querying, as in Google Maps or GPS devices.

Furthermore, the popular file structure, the sorted file, assumed that data could be sorted like numbers in an ordered list or like words in a dictionary. This kind of sorting is not natural for spatial data because those data are embedded in a multidimensional space and may have a large footprint. For example, latitude and longitude, city name, street name, and house number are all different ways of describing the location of your home, as discussed in the previous chapter. Simple latitude and longitude coordinates may be reasonable for describing the location of your home, but large metropolitan area such as New York City would need a polygon to describe its spatial footprint.

Sorting polygons to preserve proximity relationships is a significant task. Even if we represent each city by the location of the point representing its city center, how do we sort the (latitude and longitude) coordinates for city centers? We may sort them by either latitude or longitude or a concatenation of two; however, none of these approaches will preserve all spatial-proximity relationships needed to speed up nearest neighbor queries. For example, sorting by latitude will put Minneapolis closer to Rome than to Houston, while sorting by longitude will place Stockholm closer to Cape Town than to Moscow. Extending hash files to spatial data faces similar challenges, and it is a significant computational task to design proximity-preserving hash files to support spatial queries such as “nearest neighbor.” As a consequence, traditional databases struggle to answer spatial queries, and the tasks can take hours or days to compute, an inadequate time frame for the expectation of interactive responses. This issue is sometimes referred to as the impedance mismatch problem, using an analogy from the electrical equipment field and detailed in the next section. To support interactive spatial queries, we need newer ideas. To efficiently implement all of the technologies we describe in this book requires ideas for database, semantics, libraries, and spatial computer languages.

How It Works

If you have ever tried to do electrical wiring in your home or even played with electric circuitry sets, you have some experience with the concept of impedance. Impedance is the opposition presented to the alternating current flowing through the circuit. In your home, you have a circuit box to protect the outlets in your home from a power surge that could happen from lightning. Without the circuit box to provide opposition to the energy flowing into your home, during a storm with lightning all of the outlets and power circuits in your home would fry from the excess of power coming in. The circuit box is there to protect your home connections and safely provide the right supply of power to each part of your house. If you have ever tripped a switch in your home box, you know that you have exceeded the power that can flow through that switch. You need to adjust your usage for that switch, which corresponds to a room or an area of your home.

Impedance also exists with spatial data and relational databases. There is a mismatch between the way spatial data are organized and how they can be queried in a relational database. For example, if you were to search for all pet food stores near you, that simple query would take a long time. The query might run through every house address to find one most closely matching yours by sorting the house numbers in numeric order.

If the query involves an area such as a city block, that query is even more inefficient. We can simplify the idea of a city block by describing it as a rectangle with four corners. In standard relational table design (formally normal forms), however, it must be described using points and edges in several different tables: one for edges, one for points, one describing connections, another for starts or ends, and then a boundary table to connect the edges. To place a query about this square, we then need to connect all five of these tables, an onerous and inefficient process for a programmer.

Now database vendors, in the most current version of SQL, support user-defined data types, where an existing data type, such as a square, is built into the system and the user can customize and extend it for her use. This object-oriented programming simplifies all those previous tables into a single table describing the polygon and thus simplifying the query. In addition, computer scientists working in industry and research have created standards to manage space, expressed as geometries, in relational databases. For instance, the first standard proposed for the Open Geodata Interchange Standard, widely adopted today, is Simple Features, which includes several spatial data types and operations, bypassing the need for programmers to translate data types. Simple Features has half-a-dozen new data types, points, line strengths, and polygons as well as two dozen operations, such as distance or area of the polygons. Major database vendors such as ESRI, Oracle, and IBM support these features. All of these operations allow us to make many of the calculations needed for the technologies described in all of the other chapters of this book.

In addition to having a representation of multidimensional data, SDBMSs have new indexes and algorithms based on computational geometry, which can competently work in two to three dimensions. For example, many commercial and public-domain spatial databases use R-trees to speed up spatial queries. The R-tree hierarchies organize and sort spatial data objects within bounding boxes, usually expressed as the minimum-size box needed to contain the object. The box is named, and the object is named. The bounding boxes may contain a single object (the leaf of a tree), multiple objects (a tree branch), or a collection of objects (the trunk). The goal is to group and organize objects such as cities and census blocks in order to sort them and make calculations to answer spatial queries such as nearest neighbor or all land parcels overlapping with the site of a new school, factory, bridge, pipeline, or roadway.

Figure 14 R-tree hierarchies.

Suppose we wish to identify all land parcels overlapping with the footprint of a new school. For a simple illustration, let’s assume that the school site has a rectangular footprint represented by R7 in the R-tree example. A baseline algorithm, often called linear search, may answer this query by comparing R7 with all other rectangles (R1, R2, R3, R4, R5, and R6) one by one. This algorithm may be able to provide a reasonable response time to process a small data set with very few other land parcels. However, it will take hours or days to compute larger data sets such as the US land-parcel database, with 150 million units. To process larger data sets quickly, we can leverage the R-tree by first comparing the query rectangle R7 with the information available in the root of the R-tree, which contains bounding rectangles D1, D2, and D3 for its children nodes. Since R7 overlaps with only D3, we only need to compare R7 with rectangles (R6 and R7) contained in D3 to find the answer. Because R7 does not overlap with D1 and D2, we do not need to examine rectangles R1, R2, R3, R4, and R5 contained in D1 and D2, thus preventing a large amount of irrelevant computation by linear search. Interested readers may carry out a similar exercise for query rectangle R6 to notice that the computation savings are smaller but still possibly substantial.

Another common query called spatial join asks for pairs from two spatial data sets. For example, we may ask for land parcels adjacent to lakes (pairs of land parcels and lakes with an adjacency relationship) if we are given a list of lake polygons and land-parcel polygons. This query may be addressed by a popular geometric algorithm called plane sweep. A plane-sweep algorithm sorts the objects using a vertical sweep line moving across a plane. We can illustrate it using a simple example of two sets of rectangles. The objects are ordered by their left side, beginning from the left and moving to the right. Once the sweep line crosses an object, it is added to the sweep structure and checked for intersections with other objects, as in A1 and B1 in figure 15.

Figure 15 After the object is sweeped, it is removed from the sweep structure because it can no longer intersect with other objects.

Figure 16 Sweep structures.

If we think of A1 as a lake and B1 as a land parcel, then we see that we have a match for a land parcel adjacent to a lake. Plane-sweep algorithms can be refined to sweep in different directions and to work faster. They eliminate unnecessary computations, such as expressing objects as sets of point and comparing the complete sets of points. By refining a series of queries, you can answer more complex spatial questions such as “overlap” and “inside” involving polygons and richer spatial relationships.

Applications

The Amazon second-headquarters site-selection problem is an example of a decision support system or business intelligence use case for databases to support long-term strategic or tactical purposes. Although these use cases involve summarization of a large amount of data, they make few changes to the databases. Another major use case for databases is to support the operational bookkeeping needs of a large number of business transactions, which may update the database. A transaction is any business exchange where one party pays another at the end. A debit card purchase of food at the grocery store is a transaction. An airline reservation, a bank deposit or a withdrawal, and even many cash transactions have a digital footprint attached to them. If you pay with cash at a Target store, you will still receive a receipt recording the details of your purchase, including the items purchased, your method of payment, date, time, store location, cashier, and even the particular terminal at which the transaction happened. Many transactions involve spatial information, including real estate sales, customs/immigration at border crossings, hotel or Airbnb reservations, and ride-sharing requests to Uber, Lyft, or Didi Chuxing.

Prior to databases, such transactions were recorded on paper. The recording of those transactions was limited to the paper available and how much the recorder was able to record. This process was slow and prone to error. For example, early credit card issuers lost millions of dollars due to lost or invalid paper slips. Now these bookkeeping functions are performed by online transaction-processing systems built around databases, which are equipped to handle millions of transactions per minute. In the spring of 2018, the New York Stock Exchange averaged 1.6 billion shares traded a day and China’s ride-sharing company, Didi Chuxing, processed 50 billion routing requests per day, resulting in about 30 million rides per day. The numbers are even higher for keeping track of the locations of about half a billion smartphones (or 2 billion GPS receivers), which may update their location every second to service providers (Verizon, AT&T, Sprint, T-Mobile) and platforms (Apple iOS, Google Android). The sheer volume of business transactions taking place today is possible only because of modern database-management systems.

These transactions are not limited to retail and financial use. Electronic medical records provide a digital receipt of our health record, the medical interventions we received, the medications we have used, and even the environment we are exposed to along our daily routes and locations—all information that is available from smartphone location-based services. Thirty years ago this information resided on paper charts that were in your doctor’s office of clinic. Sharing that information with another doctor meant requesting paper files to be sent to another office. Today, the process is becoming much easier as a more comprehensive digital record can be shared easily between doctors and clinics. The data collected from all of these transactions provides rich information that businesses mine for consumer information to help with strategic planning. Large grocery chains make decisions about which items to stock in certain regions or stores based on their consumer transaction information. Sales volumes calculated by databases help aid in budget forecasting for business.

The sheer volume of business transactions taking place today is possible only because of database-management systems.

Businesses track the products growing in popularity, items purchased only at certain times of the year, and others purchased more frequently. Stores such as Target and CVS use the databases to create personalized coupons for customers based on their buying habits as a way to bring them back to the store. They also customize the product offerings to individual cities, states, and seasons based on the sales data. To maximize efficiency, they can use these data to determine staffing based on the volumes of transactions happening at each store at which days and times of the week. Credit card companies use this information to detect anomalous behavior and thus fraudulent charges. A restaurant charge in Romania that is recorded an hour after an individual purchases gas at a service station in Chicago will trigger a notice from the bank. This capability has led to the rise of business intelligence and the field of data science and predictive analytics, which we discuss further in the next chapter.

With their vital role in determining business strategy, data have become a valuable business asset that must be protected. It is far easier and faster to back up and recover digital data than the paper records of old. It’s ironic to note that the first semidigitized US Census records of 1890, mentioned at the beginning this chapter, are the only ones in US history that are no longer available because the data—stored on those paper cards—were badly damaged in a fire in 1921. As business and government data became digitized over the years, data were backed up and stored on disks stored off-site at a remote data center. Today, that data backup can be updated and stored in the cloud.

Online transaction-processing (OLTP) systems built around databases also offer services for protecting data. They are popular for businesses because it is considered poor customer service to lose customer orders due to power outage or even hurricanes, earthquakes, and other catastrophes. In fact, OLTP systems promise great durability. Once the transaction is committed, the OLTP system will remember the effects of that transaction forever. After the attacks on September 11, 2001 (9/11), this service, sometimes called disaster recovery or business-continuity planning, became a niche business and not only for Fortune 500 companies. Small and medium-size businesses can choose from among a variety of services providing protection and recovery services for business data. That protection includes coverage for natural disasters such as floods, hurricanes, tornadoes, and fire as well as for human errors or actions. In 2001, when the World Trade Center was attacked and destroyed, many companies lost data centers located there. However, perhaps because of lessons learned from an earlier bombing in 1993, the businesses at the World Trade Center had not only regular remote secure data backups (performed every few hours) but also remote backup data centers with computing equipment. After the 9/11 attacks, these data centers were working again in days and in some cases a matter of hours. Today, data recovery lag time may be only minutes because of recent developments such as cloud computing for continuous backup of data as well as computing resources. Post-9/11 requirements encourage a separation of at least 50 miles between the main data center and the backup data centers to protect against terrorism. Many companies pursue triple continental redundancy, with backup data centers on different continents, to protect against hurricanes, earthquakes, and other large geographic disasters.

The expansive use of data and databases has also given rise to the shared-software business to support them. In the 1960s, when computers were first introduced commercially, each company had a different set of files with its own method for naming, storing, and accessing the files. But as computers grew in popularity and were installed in more businesses and in different departments of a business, those machines and databases were set up differently, so that, for example, the computers, software, and databases used by the staff in human resources were very different from the computers, software, and databases used by those in the finance department. There was no way for those systems to talk to one another. Data would have to be extracted from one system and entered into another system before they could be shared.

That problem still exists to some extent today. When you buy a sandwich at a fast-food restaurant, the computer used by the cashier is a specialized machine very different than what would be used at the corporate headquarters or even in the restaurant manager’s office, but those machines run on standard software that allows all the machines to share information with each other.

All of these database uses and applications include location information, spatial data. Photos taken with your cell phone are geotagged. Each grocery store receipt includes the store location and even the location within the store (the register terminal) where the items were purchased. Each medical record contains information of who that patient was seen by and at which location in the health system. Airline tickets contain departure and destination information. Online purchases must be delivered to a geographic location. Have you ever been traveling and had your bank call you to verify a purchase? Your bank keeps track of the locations of your purchases to help prevent fraud.

Because near things are more related than distant things, all databases need to be optimized for storing and querying spatial data to enable the mining of that data for relevant business insights and for use by both consumers and other technologies. Each chapter of this book explains technologies that are leveraging spatial data. In order for those data to be used, they must be stored and coded before they can be used by those technologies. Cable companies use location to black out the broadcast of NFL games that are not sold out within 75 miles of the home market.

When you think back to some of your recent Internet searches, you may note that they are often related to three basic queries: “Where am I?” “What is around me?” “How do I get there?” To provide the most relevant information, the search engine uses your location. Your cell phone consistently makes connections to the closest cell phone tower to provide you with uninterrupted cellular service. That means that your provider knows the location of your phone—and you—almost all the time.

That knowledge is giving rise to applications that provide unprompted information to you, such as directions home with real-time traffic updates or to the location of your nearest appointment, and an estimate of how long it will take to travel there. We have described some of these applications in other chapters. Those requests are not demanded by you but are triggered based on your location and other stored data about you. The AMBER Alert system that we mentioned in our discussion of GPS provides wireless emergency alerts that are relevant to you, as they arise, based on your location.

Limitations and Concerns

Even the most affluent political campaigns need to make decisions about where their funds are best spent to target voters. In US presidential elections, this means that citizens in swing states such as Ohio, Pennsylvania, and Virginia are deluged with campaign ads for candidates, while residents of California and Washington see far fewer pieces of advertising. The campaign managers of both major parties will elect to spend far less money advertising their candidate in California, a state that has a long record of supporting Democratic candidates. When Facebook’s Cambridge Analytica scandal came to international attention in early 2018 for violating its users’ privacy, the same sort of campaign targeting was happening digitally based on user location gathered with other rich Facebook user data.

Those user data, spatial and otherwise, are shared by its users voluntarily—in geotagged photos, restaurant check-ins, product and service recommendations, birthdays, and information about children, vacations, hometowns, and schools. One of the authors of this book has never added her birthday, hometown, or cities of residence to the site, but Facebook nevertheless offers suggestions for events, goods, services, social groups, and affinity groups based on her posts, the friends in her profile, where her friends have gone to school, their age, and where they live now.

In early 2018, news came out that Facebook had violated the privacy of 87 million users in the United States—nearly half of all users in the nation. The company and its affiliates had the ability to target users with advertising and bots with very specific location accuracy. Russian groups, among others, were planting fake news on Facebook to sway US voters in the 2016 presidential election. Immediately after the election, amid the furor that the fake news had contributed to the outcome, Facebook chief executive Mark Zuckerberg was quoted as saying that the fact that these stories had influence on a presidential election was a “pretty crazy idea.”

The world later learned later that, crazy idea or not, foreign agents had indeed targeted people based on the rich data available to them. Data-broker companies such as Acxiom, a database marketing company that collects, analyzes, and sells information, purchased that information from Facebook via its Partner Categories feature. When Facebook announced it would end the feature in March of 2019, Acxiom lost a third of its value. It subsequently struggled to regain its lost value and reputation.

Facebook illustrates the power and value of data as well as their susceptibility to being stolen or abused. In spite of efforts to protect data, nearly every company is vulnerable to a data breach., which can result in a loss of confidence in the company and loss of value.

There are examples of a middle ground, however. The US Census has long protected the confidentiality of individual citizens while still making summary data available to researchers for demographic research. Personally identifiable information is made public after 72 years. Companies such as Facebook could be providing aggregated data to its partner companies protecting the information of its users. With time, money, and access to talent, Facebook and other large tech companies like it have ample resources and incentive to protect their users.

Without that protection, companies face a backlash as the average user-consumer struggles to understand the limits of the technology and its ability to share information and both overestimates and underestimates the extent of the data collection and dissemination currently occurring.

Future Directions

The European Commission, the legislative body for the European Union, is recognizing the power and value of data. In 2016, it adopted the General Data Protection Regulation (GDPR) to provide its citizens with more control over the personal information that is collected, stored, and used by any company offering goods or services to customers in the European Union. GDPR went into effect at the end of May 2018. Since then you may have noticed new pop-up windows as you visit new sites informing you of privacy and user-agreement policies that detail the kinds of data that the site collects from you during your visit, how it plans to use them, and options that allow you not to be tracked or how to opt out of some of the data sharing.

This disclosure is mandatory only for sites serving residents of the European Union, but Internet users around the world are seeing it. The disclosure includes special segments that are specific to residents of the user’s country. Although there are ways to differentiate which machines are in a certain location, it is far easier—and legally safer—to place a blanket statement on a website that serves all users. In this way, the European Commission is helping to shine a light on many companies’ data-collection and use practices. Of course, the other side of that coin is that few people read those notifications. Instead, they simply click through to access the sites and services that they began their search to see. To the average user, the notifications are long, wordy, and vague, and declining the terms of service means no longer being allowed access to the tool he or she is using.

The opposite of this trend occurs in China and the Middle East. In those regions, the national governments require that a company with data moving through a data center in that country must provide the government access to view it. Much like airport Transportation Security Administration agents looking through your luggage at the airport, local governments have the authority to review the data that come from or travel through the country. In early 2018, after Apple announced that it would be opening a data center in China, human rights activists became concerned that Chinese government officials would use the data to track down dissidents. During the Arab Spring, protesters in Egypt used social media to organize rallies and events to unseat the president, prompting the government to shut down the nation’s Internet. People around the world will continue to examine these questions of who has access and rights to data.

As for how the data themselves are handled by spatial databases, among programmers and data scientists there is still a need to created standardization in spatial operations and ontologies. The way Google Maps labels road networks or calculates the shortest path is not the same as other mapping services. This lack of standardization means the tech giant has a competitive advantage in the short term because no other group knows or has access to its superfast and accurate algorithms. In the long term, that lack of standardization can limit the growth of the field of spatial computing because fewer programmers have access to view and improve upon the company’s work.

Conclusion

In The Net (Irwin Winkler), a thriller from 1995, Sandra Bullock plays a cybersecurity specialist who becomes privy to a top-secret government application that controls banks, stock markets, and what seems like every major company in the country. Soon after she makes the discovery, cyberterrorists steal her identity and replace it with that of a wanted criminal. Her bank accounts are frozen, her home and assets sold, until she is left with nothing.

Although the film’s plot and technology now seem both dated and laughable—in minutes, Bullock’s character brings down the bad guys by using a floppy disc and a dial-up modem connection to upload a program into a computer during a bustling computer trade show—the film does serve as a reminder of how much information about us is stored in databases, some of it without our control.

Today our devices, appliances, and machines are increasingly connected with each other in a network called the Internet of Things. This connectivity offers us an increasingly personalized experience, possible only due to the data that are collected, stored, and made relevant for us. Location is an important part of this calculation. As we mentioned in the introduction and examine further in the next chapter, everything is related to everything else, but near things are more related than distant things. Spatial database management systems help make this relatedness possible.

6

Spatial Data Science

Cholera was ravaging London in the 1850s, causing tens of thousands of deaths and untold suffering, with no end in sight. As a science, epidemiology was in its infancy, and experts misunderstood the way the disease was spread. Among the general population and even among doctors, it was believed that people contracted the disease from unclean air, something London had in abundance. A doctor named John Snow did not believe this prevailing “miasma theory,” however. Snow had a different theory, and to test it he spoke with locals and created a map of all of the deaths from cholera in the city, putting dots on the location of each of the cases. If the disease spread through the air, posited Snow, then the deaths from cholera should be widely distributed throughout the city. That is not what he found in his map. Snow saw a distinct pattern. The deaths were concentrated in one area, and at the center of this area was a water pump where locals retrieved water for cooking and washing. Snow hypothesized that contaminated water drawn from this local water pump was the source of the epidemic.

He convinced the city officials to take away the pump’s handle, thereby disabling the pump and forcing the locals to get water from other pumps. Once the city enacted his plan, cholera cases subsided. Snow’s actions led to a new theory for the spread of cholera and the beginning of germ theory, a turning point in biological sciences. It also spurred industrial cities to construct sewer and sanitation systems to protect drinking water from waste as well as to begin construction of green and open spaces to improve public health.

Snow’s map illustrated one of the most popular and well-known patterns in spatial data science: hot spots. A spatial hot spot is a geographic area inside which the concentration of something is much higher than it is on the outside of that area. Snow was a proto-spatial data scientist, and his story is helpful in defining and illustrating what spatial data science is and is not today.

Spatial data science is the process of discovering patterns in geographic or spatial data that are interesting, useful, and nontrivial and that come from very large data sets.

Spatial data science is the process of discovering patterns in geographic or spatial data that are interesting, useful, and nontrivial and that come from very large data sets.

Background

Today, we frequently hear the term data science along with data analytics, data mining, big data, artificial intelligence, and machine learning. Data scientists, researchers, and programmers, in their work and in scientific literature, define all of these things distinctly. We define data science as a set of techniques used for informed decision making by means of technology. Unlike instinct, informed decision making relies on tools that provide us with additional information to guide us. This information can take the form of technical reports, transaction-log databases, and sensor-data output as tools for researchers and corporations. We have already discussed the data that retailers accumulate on their customers. Data science algorithms mine through that vast catalog of information and put it to use. During your most recent purchase from an online retailer, you likely browsed for the item you had in mind before making your selection and completing your purchase. While you are shopping, data science algorithms recommend other items to you based on data collected from your browsing and purchase history as well as from other individuals’ browsing for and purchasing of similar items.

Spatial data science applies the techniques of data science to geographic or spatial data sets. GPS on our smartphones provides cell phone companies and some applications with continuously updated information on our location. Those location data are then stored in spatial databases and analyzed by those companies for insights in user behavior.

In the past, humans did this retail analysis via hand calculations, looking for interesting patterns in the data and then selecting some for further review, similar to the way Snow reviewed data on cholera cases in London. With billions of smartphones and billions of database transactions currently happening each day, the rate of data collection is far too large for humans to handle efficiently. The volume of information requires humans to use some sampling and automation to identify patterns that are more relevant for further research.

In 1936, the Literary Digest polled its readers to predict the outcome of the presidential election. The magazine had polled its readers for the previous five presidential elections and had correctly predicted the winners. The poll in 1936 was huge, identifying nearly 2.5 million individuals from phone numbers, vehicle registrations, and the magazine’s list of subscribers and collecting data from them. The magazine predicted that the Republican candidate, Governor Alfred Landon of Kansas, would win by a majority. It was wrong: the vote went to Franklin Roosevelt in 46 states, a huge error for the publication. Meanwhile, George Gallup’s American Institute of Public Opinion had a much smaller sample size for its poll, using only 50,000 respondents, but correctly predicted the winner. As the Literary Digest reviewed its polling method, researchers found that its selection of participants—people who owned vehicles, subscribed to magazines, and owned phones during the height of the Great Depression—had skewed the results. Gallup’s smaller sample size was more representative of the US population as a whole, and his results were more accurate and cost less than larger convenience sampling. Gallup’s results fueled the rise of weighted, representative sampling in statistics, assuming that data samples are drawn independently of each other and from identical populations.

Unfortunately, the independence and identical distribution (i.i.d.) assumption is often violated by spatial data sets. For example, the temperature and rainfall at nearby locations are often similar—that is, not independent. Many have poked fun at this mismatch between independence assumptions and our geospatial world. In an episode of the television show The Big Bang Theory, a group of scientists sits around a room, and one character has news to share. He asks his friends to guess the name of a person who has been chosen to do a special task. One of the scientists, Sheldon, responds, “Mohammed Lee.” When his colleague asks who Mohammed Lee is, he responds, “‘Mohammed’ is the most common first name in the world, and ‘Lee’ is the most common last name. Since I didn’t know the answer, I thought the name ‘Mohammed Lee’ would give me a mathematical edge.” We know, however, that “Mohammed Lee” is a rare name due to geography. The geographic distribution of the two names is not random; in fact, the two names have relatively little overlap, geographically speaking, though they may be the most common first and last names in the world. The punch line works because the combination of the two names strikes us as bizarre.

In the late 1960s, geographer and cartographer Walter Tobler proposed the First Law of Geography: “All things are related, but nearby things are more related than distant things.” This idea, that things near to each other are more similar than things that are far away from each other, is fundamentally important to spatial data science. It is especially helpful in understanding how specialized spatial data science tools are both necessary and different from necessary traditional statistical tools.

In statistics, one of the main tenets is the i.i.d. principle, or independent and identically distributed data samples. In a world where the i.i.d. principle works absolutely, when we are given a set of data such as common names, classical statistics assumes that these data samples are independent of each other, that they don’t interact, and that they came from identical populations—and so we get responses such as “Mohammed Lee.” Geographic data are not independent, however—the first and last names are distinctly tied to places. The problems with identical distribution are similarly easy to detect.

All things are related, but nearby things are more related than distant things.

Imagine a farm that raises two kinds of livestock, chickens and cows. There is an equal number of each kind of animal. If you were asked, “What is the average number of legs of the animals on this farm?” the answer would be three. We know none of the animals on the farm has three legs, but the property of identical distribution assumes that many of the animals would have this number of legs. To summarize these data better, we would need to divide the two populations of animals and calculate the average number of legs in cows and the average number of legs of chickens, so that our data summary would be more informative. In geographic data, the i.i.d. assumption leads to data with fewer real insights.

Individuals interact with their environments in myriad ways that affect their lives. The real world, spatial data in particular, rarely conforms with the i.i.d. assumption. Even so, the assumption has been a helpful tool. Prior to computers or calculators, statisticians had to hand-calculate these complex statistical math problems. By incorporating the i.i.d. assumption, the work could be completed by fewer people in less time. Statisticians knew there were problems with the system at the time it was created, but they lacked the fast, sophisticated computing abilities we have today. In fact, it was not until the 1980s, with the advent of broad access to computers, that a new generation of statistical computing could move beyond problems that violate the i.i.d. assumption and enjoy the spatial data science that we do today.

In the early 1900s, many researchers started noticing the discrepancy between classical the statistical i.i.d. assumption and geographic data from agriculture, mining, climate, and weather. For example, soil properties vary smoothly across an agricultural field or farm. This phenomenon is formally called spatial autocorrelation. The climate and weather vary dramatically across climatic zones, affecting the productivity of different seeds and cultivars, a phenomenon formally called spatial heterogeneity. These phenomena violate the i.i.d. assumption. The US Department of Agriculture found classical statistics, including sampling theory, inadequate for a planned agriculture census, and so it sponsored research in Iowa and North Carolina to generalize statistical theories for spatial data. Other research probed new statistical methods to design agricultural field experiments to evaluate new seeds in face of smoothly varying soil properties. Mining practitioners exploited the smooth variation to analyze data from wells and borings to localize mineral “nuggets,” underground areas of concentration of minerals such as iron ore. These efforts around the world led to the birth of spatial statistics, whose methods are used widely today in agriculture, mining, weather prediction, and climate modeling.

How It Works

Spatial data science has the unique ability to predict future occurrences based on past data. To understand how this works, consider the queries we discussed in the previous chapter. Queries (spatial or otherwise) are submitted only to a closed database. A query asks for a summary or a selection from the stored data. When we ask Google Maps for the nearest Starbucks, it delivers a subset of stored data such as roadmaps and business directories, then takes the information from the GPS receiver of our location, and makes a nearest-neighbor calculation.

Now imagine Google Maps takes what information it has in its database about Starbucks cafes in the United States and your current location in a midsize American city and predicts there are three cafes within one and a half miles of your current location, with another one coming in the next two years. That response would miss the mark for your question but is an incredibly helpful skill for other queries, such as predicting the weather.

Using spatial data science, data scientists can look at the stored rainfall data as well as at other events that occurred around the rainfall data, such as length or intensity of a storm or air flow, and use a prediction model to determine how much rainfall is likely to occur in the next few hours or days based on current conditions. Weather predictions are often inaccurate, especially when they make predictions about events further in the future. Weather-prediction models that forecast the next few hours are more accurate because they have more relevant data to use in forecasting.

A spatial query put to a weather database can look only at the past set of temperatures and precipitation rates and answer precise questions about what types of weather events happened on specific dates in the past, such as looking for the average rainfall in the month of June in 2018 in Minneapolis, Minnesota.

We defined spatial data science as discovering interesting, useful, and nontrivial patterns in large data sets. Data contain many patterns, but not all patterns are interesting, meriting further study. Examining the weather patterns for the neighboring cities of Minneapolis and Saint Paul, we see very similar weather patterns; this information is not a surprise, nor is it terribly interesting. However, if we examine the patterns of the El Niño weather system and find that there is a relationship between El Niño and warmer winter weather in Minneapolis, this is new information to many people, though not to climate scientists. Recent analysis of weather data has revealed increasing frequency and intensity of extreme events such as droughts and floods. And the same revelation of a nontrivial pattern from spatial data science happened in the 1850s: John Snow certainly revealed new information about the source of cholera, its transmission, and who in London was more likely to get it.

Spatial data science patterns must be useful for an end user such as a government agency or business or consumer. During the cholera outbreak, the municipal government was the intended audience. Today, corporations, government agencies, consumer groups, and research institutes benefit from spatial data science. Public-health and public-safety organizations frequently carry out hot-spot analysis to identify geographic concentration of diseases such as cancer or to help in crime detection.

The search for patterns in spatial data science must be significant. Each data set is a very large set of information that can be searched for interesting and useful patterns. Finding those useful patterns requires the help of efficient algorithms and computers. The US Food and Drug Administration must identify the source of outbreaks of foodborne illness as part of its work. It may have to examine tens of thousands of possible sources, including restaurants, food trucks, grocery stores, food-processing facilities, and domestic as well as international food producers to narrow down the number of possible sources in order to perform more exact genetic tests. Spatial data science can identify interesting patterns from that extensive information.

Spatial data science excludes nonspatial data patterns. When Amazon uses data patterns to predict the items that may be of interest to individuals as they shop online, those patterns are considered spatial data science only if the data include shopping trends for individuals in certain parts of the country or selected cities. John Snow’s research, however, was spatial data science in that his data set showed a clear spatial pattern in deaths from cholera in London: the disease was not dispersed with air currents, nor were the deaths distributed by random chance—they were clearly localized to a specific geographic area.

Spatial data science also excludes hypothesis testing using primary data analysis of the type practiced by activist Erin Brockovich in examining cancer rates in the town of Hinkley, California, in the early 1990s. However, if she had examined the incidence of certain types of cancer for all cities and counties in the entire state of California or the United States, the possible locations would be large enough to be considered data science.

Data scientists use dozens of spatial patterns every day to solve complex societal problems. Most people are familiar with four well-established spatial patterns that have many applications and significant uses—the location-prediction model, hot spots, spatial outliers, and colocation.

Location-Prediction Model

In the early eighteenth century, European settlers settled on the (relatively) high crescent of land near the mouth of the Mississippi River that was inhabited by the Chitimach tribe. This area, now the city of New Orleans, was an ideal spot to control shipping in the area. Over the next century, settlers used improved drainage techniques and created levees to expand the habitable area of the city to a shallow bowl of terrain, some of which lies as much as 13 feet below sea level.

In 2005, Hurricane Katrina hit New Orleans and overwhelmed the levees. Although many neighborhoods were flooded, the French Quarter, the oldest part of New Orleans, located in the original slim crescent of land, was spared.

If we were to build a model to predict where people would want to live in that part of the world, it’s easy to predict that they would want to live in a place like the French Quarter. That strip of land was inhabited first because it was high and dry in a low, wet area. It provided a good lookout and easy access to the river. If we were to identify all of the best, most habitable areas of land in coastal Louisiana near the river, the French Quarter would make it to the top of the list. The low-lying areas right next to it would be nowhere near the top, yet people eventually made their homes there.

Climate and elevation are only two factors that individuals consider when choosing places to live. In choosing a home, the consumer takes into account the home’s proximity to economic opportunities, schools, family, quiet streets, and roadways for daily commutes. Spatial models for location prediction take into account the ideas of the First Law of Geography, using spatial autoregression analysis to take those desirable neighborhood features into account.

Location-prediction models are used in finding the future neighborhoods of human homeowners. They are also used to predict nesting-worthy places for endangered birds. Researchers at the CDC working with infectious diseases use location-prediction models to predict the spread of viruses such as West Nile. The researchers monitor crows and blue jays, which carry the virus and then spread the virus to humans via mosquitoes. In studying the migration patterns of the birds and peak mosquito seasons, the researchers are able to determine the future spread of West Nile.

The National Weather Service frequently uses location-prediction models to predict hurricane landfall and to help plan evacuation routes. Data scientists can use these models to find new places to drill for oil, new agricultural areas for corn production, and the habitats of endangered species. The tool is well known for predicting the effects of climate change. In Minnesota, researchers have used these tools to predict the resurgence of the emerald ash borer, a beetle that feeds on ash trees. Minnesota is home to more than one 1 billion ash trees, which grow in wet areas. The state relies on its harsh winters to help keep the population of this nonnative invasive beetle species from destroying the ash forests. As global warming changes the local climate, workers at the state’s Department of Natural Resources are especially interested in predicting the spread of emerald ash borers and preventing the beetles from altering the landscape of the state through the destruction of ash trees as well as the animals and insects that thrive in them. Location-prediction models are useful in many cases where we need to examine climates, habitats, and conditions that can make future events possible.

Hot Spots

John Snow was the pioneer of spatial clustering, or hot spots, 150 years ago. Today hot spots remain one of the most relevant spatial patterns in spatial data science. As we noted at the beginning of the chapter, a spatial hot spot is a geographic area inside which the concentration of something is much higher than it is on the outside of that area. Snow was able to complete his analysis very easily using a map, his list of cholera cases, and their locations. That simple visualization, using dots to depict locations of cholera cases on a map and comparing the dots with locations of another urban topographic feature, city wells, revealed a pattern. It became visually clear that the cholera cases were concentrated in an area around the Broad Street water pump.

We can visualize spatial clustering either as a map or as a table. As an example, we can use a group of employees participating in an office wellness plan. Suppose the company’s human resources department wants to find out if there are any patterns that can help it predict which employees are more likely to participate in wellness plans based on their age or years of service with the company. With only these two attributes (age and years of service), it is easy to create a table and visually find patterns in employee participation. From a quick glance at figure 18, you can easily see that newer employees are more likely to participate in a wellness plan.

Figure 17 John Snow’s map of cholera cases in London, 1850s. Source: Wikimedia Commons.

Figure 18

In the Snow example and in the employee-participation example, a simple plot of our data allowed for easy analysis, but these cases are somewhat unusual. One of the most common usages for spatial clustering is in finding hot spots of crime.

A large city such as Chicago might have more than 100,000 different crime reports in a year describing a hundred different types of crimes of different classes, ranging from violent crimes such as assault to property crimes such as vandalism. If you were to create a different map or table for each type of crime at different times of the day and different times of the year, you may end up with a thousand different maps. But spatial data science tools can be used not only to seek out meaningful patterns in the crime data but also to better predict where crimes are likely to happen in the future. This ability allows city police departments to make staffing and patrolling decisions. Data scientists now working with hot spots are going beyond simple circular hot-spot patterns to illegal-activity clusters with more sophisticated shapes, such as a doughnut shape surrounding a bar after bar closing time or a line of crime that follows a transit line.

Environmental criminologists theorize that serial criminals don’t commit crimes right next to their homes because their neighbors may recognize them. They venture farther away, but not too far away because the costs of transportation prevent them from traveling long distances. The result is a doughnut or ring pattern of criminal activity around the serial criminal’s home. New work is also being done in finding noncircular hot spots around geographic features such as rivers and roads. In urban areas, road accidents or pedestrian deaths are concentrated near frequently traveled roads.

Agencies such as the CDC have well-established internal teams to look for hot spots of virus disease occurrence and other illnesses. When Snow was doing his analysis of cholera in London, he was lucky on multiple fronts: he was analyzing only a few hundred cases of cholera, and he had a small geographic footprint for the illness. Health agencies today are expected to intervene at a much earlier stage even while monitoring a much larger geographic region. In the fall of 2016, when there was an outbreak of Legionnaires’ disease in Minnesota, state health officials began investigating when a single case was reported and released information to the press when five cases were reported in the state. The infection source, a bottling plant in Hopkins, Minnesota, was identified some six weeks later, and the outbreak was contained to 24 total cases.

Global Outliers and Spatial Outliers

Two types of outliers are particularly interesting to data scientists: spatial outliers and global outliers. A global outlier is an event that is inconsistent with the rest of a data set. In a data set made up of skyscrapers around the world, the Burj Khalifa in Dubai is a global outlier at 2,717 feet tall, more than 700 feet taller than the second-tallest skyscraper and almost 1,500 feet taller than New York’s Empire State Building. Recent gerrymandering court cases used data analysis of global outliers as a basis to request redrawing selected existing electoral districts, which vary greatly from possible district boundaries.

A spatial outlier is something that stands out or is inconsistent with its neighbors. In the Macalester Groveland neighborhood of Saint Paul, Minnesota, most of the housing stock is between 80 and 100 years old. Casually driving through the neighborhood, you will notice that the houses have similar characteristics. With slight variations in home color and finish, they are nearly all the same size. In recent years, some of the old homes in the neighborhood have been remodeled to double their original size by adding height or increasing the home’s footprint so that they more closely resemble larger homes in the suburbs. These remodeled houses are not like their neighbors and thus are spatial outliers. They aren’t radically different from all houses, but they are anomalies in the neighborhood. Geologists studying the layers of rock in the Grand Canyon noticed a spatial anomaly between one sedimentary layer of rock in the cliffs and the dark metamorphic rocks below that layer. That discontinuity in rock layers has come to be known as the Great Unconformity. Scientists now believe the Great Unconformity to be a global glaciation event, when huge amounts of ice and snow effectively bulldozed away massive amounts of Earth’s surface.

Identifying outliers depends on the data set. The average American man is around 5 feet 9 inches tall, whereas the average NBA player is around 6 feet 7 inches tall. NBA players are global outliers in the data set of all American males, but a 5-foot-9-inch-tall male basketball player is a spatial outlier in a data set of NBA players.

An average means that not every man is the same height, and we expect some degree of variation among individual males, but we expect that variation to be somewhat smooth. Some men are slightly taller, and some are slightly shorter, but 99 percent of American males are less than 6 feet 4 inches tall. NBA players are outliers because they are on average not just slightly taller but much taller than most of the male population in the United States. In statistical terminology, we attach a 99 percent confidence to the assertion that a 6-foot-4-inch-tall person is an outlier. A similar analysis can be used to reduce the occurrence of chance patterns in hot-spot analysis. For example, the National Cancer Institute performs geographic surveillance of the disease, seeking clusters in spatial and space–time data, looking for significant trends. It uses SatScan software and reports only on events that have 95 percent confidence for reported hot spots.

Anytime there is an exception to the First Law of Geography, it is interesting to data scientists. Spatial outliers are a signal for further investigation. Examining a data set for outliers is only the first step in analysis. We know that the NBA believes that height is an asset in playing basketball, and so it recruits tall men to play, but we don’t know why those men are tall. It’s possible they come from families where there is a strong gene for height, and so they are not outliers in their own families. We cannot and should not make assumptions about them—outliers are only flags for further investigation. One of this book’s authors led a team of graduate students who used spatial-outlier detection in examining data from the Minnesota Department of Transportation’s freeway traffic sensors. In their review, the input from one sensor was dramatically different, detecting heavy traffic even after midnight. That sensor was located only a mile away from the next sensor, which showed no traffic after midnight. When the Department of Transportation flagged and investigated that spatial outlier, staffers identified a faulty sensor and replaced it. A similar mismatch of readings between consecutive upstream and downstream water-quality sensors led environmental scientists to look for a pollution source between the sensor locations.

Data scientists also use outlier detection in tandem with hot spots in seeking spikes in crime rates and influenza outbreaks.

Colocations and Co-occurrences

Imagine yourself on a car trip driving down a freeway in an unfamiliar area. One of your companions complains of being hungry, and you realize that you could use some gas in the car and a cup of coffee. Soon you see a sign for a gas station and a McDonald’s, but your companion complains that he or she doesn’t want food from McDonald’s. Would you take the exit and stop? If you stop, you are likely assuming that if there is a gas station and a McDonald’s, there will be other food offerings around it. This is the spatial relationship we call colocation.

In the 1950s, before awarding a new franchise, the corporate office at McDonald’s conducted statistical analysis of geographic areas to predict the economic viability of prospective locations for their restaurants. The company reviewed income and population density and the demographic makeup of the area, looking for its target market, children and teens. For a new location to be profitable, the corporation needed assurance that there were enough kids and teens in the area. Over time, other food and convenience companies knew that McDonald’s performed this analysis, and they would colocate after verifying adequate business volume by monitoring the parking lots and drive-through lanes at the McDonald’s restaurants.

The idea of colocation is not limited to businesses. Spatial colocation can refer to any events or trends that happen under the same conditions. The Egyptian Plover and the Nile crocodile colocate due to their symbiotic relationship. The bird feeds by cleaning the teeth of the crocodile, which in turn derives dental health benefits from the natural flossing. In theory, an environment that is conducive to crocodile habitats is likewise conducive to plovers. Colocation can address human behavior as well. Police departments and city councils have noted that in midsize to large downtown areas with bars, bar closings are followed by a rise in misdemeanor activities.

Spatial colocation is built around the association rule, an if–then statement that discovers interesting relationships in a large data set. A hardware store in Minnesota has a database of retail transactions. It snows frequently in the state, and transactions are seasonal. If a consumer buys a shovel, then there is a 70 percent chance he or she will also buy de-icer (to melt the ice on the sidewalk). The data are informed by a measure called support. Support for a subset of items, in this case shovels and de-icer, is the fraction of transactions in which they occur together. If we examine a set of four transactions and ask for support for shovels and de-icer, we look for that pair in the set of four transactions.

Walmart used a strategy like this in determining which supplies went to which store in 2005. At the time, Walmart noticed that in hurricane-prone areas some items were frequently purchased together before a storm: bottled water, tarps, flashlights, generators, and another less obvious item—strawberry Pop-Tarts. Dan Phillips, Walmart’s vice president for information systems, explained that the latter purchase was most likely made because Pop-Tarts stay fresh until opened and appeal to entire families. When the National Weather Service predicted that Hurricane Katrina would land east of New Orleans (instead of in Florida’s Panhandle), Walmart redirected trucks full of water, flashlights, generators, and Pop-Tarts to the area.

We can mine data for similar patterns in geographic data. For instance, in figure 19 there are several geographic features: a dry tree, a house, a blue bird, an owl, a fire, and a green tree. Visually, it is easy to see which items go together in this small data set. Using traditional statistical tools, it is not as simple. Although it seems as though the association rule should work to mine the data for pairs, it doesn’t. The rule has limitations because it is transactional. Spatial data have no similar transactions; they are embedded in a continuous space. It is possible to force a transaction by defining a space via city or county boundaries, zip codes, or census tracts. Data scientists and programmers find, however, that this forced application of association often leads to erroneous patterns. Items tend to be undercounted or double-counted, and substantial manual programming effort must then be made to look for geographic patterns.

Figure 19 Colocation patterns—sample data.

A group of researchers focused on this problem in trying to understand if the rate of a certain childhood cancer colocated with two different birth defects. After they examined the data for associations using boundaries to count each type of disease within counties, the results were poor, showing two diseases located close together but separated by county boundary lines. After this type of analysis, the diseases remained unlinked in spite of their proximity. But when researchers analyzed the same areas using a spatial colocation model with point data, they could make the appropriate links between cases.

Imagine we wanted to ask for the average annual rainfall in Portland, Oregon. Although it is possible to measure that rainfall, we know that the rain does not stop at the boundary of the city. Adjacent cities are affected by rainfall as well, and the surrounding geography of Portland contributes to how much rainfall it gets each year. To predict weather for Portland, we need to examine areas that are much larger than the city boundaries for events that occur together.

Similar to city and county boundaries, calendar months impose artificial boundaries on time. Snow shovels are stocked and sold during winter, but in some parts of the country snow arrives much earlier than the first day of winter, and in some places it extends its stay until much later. If retail analysts review only the consumer data between December 20 and March 20, they would likely undercount the total number of shovels sold in Alaska, the Dakotas, and several other states.

More applications for colocations are being created all the time. Data scientists watching a soccer match or an American football game examine the types of players who frequently move together in the defensive and offensive positions. Any situation where we are able to study the ways people, animals, objects, or systems appear in time and space is a potential set of data to study colocation.

Limitations and Concerns

Computing support is the way to test our confidence in a set of items that collocate. Support is one of many sanity checks that data scientists use to help inform them that their methods are reliable. Statistical confidence measures are another way of testing the accuracy of our work. If 99 percent of all American males are shorter than 6 feet 4 inches tall, in statistical terminology we would have a 99 percent confidence that a 6-foot-4-inch-tall person is an outlier. A similar analysis is used to reduce the occurrence of chance patterns in hot-spot analysis. As previously mentioned, SatScan, used by the National Cancer Institute and other agencies, requires a confidence level of 95 percent in many cases before hot spots can be reported.

Not all checks are done by software. In some cases, data scientists need to check with domain experts, who can review the results of spatial data science to look for inconsistencies or use their expertise to inform those results. When one of this book’s authors and his team looked at the traffic sensors and found an anomaly, they presented their findings to the staff at the Minnesota Department of Transportation for their review. Based on this review, staff members there were able to confirm that the findings were not within a normal range for traffic flows in the area where the anomaly was detected. When that anomaly was confirmed, the staff did yet another check—they sent people to the site of the traffic sensor. We call that ground truthing or on-the-scene reporting. The department staffers were able to confirm that the sensor at that site was not working. Ground truthing is often used as a final check due to its expense in time and labor.

Ground truthing and other checks are all part of the scientific cycle for data science work. One industry standard used by many analysts working with big data sets is the Cross Industry Standard Process for Data Mining, or CRISP-DM. This process includes understanding the business case for the work, collecting data, and preparing the data for modeling tools. The data science patterns that we have discussed are part of the modeling and evaluation phases. It is only after all of these stages that the work is prepared and presented to the end customer in the deployment phase.

Not all spatial patterns are real. Some events happen near other events but are not otherwise related. They are called false positives or chance patterns in spatial clustering. This happens when random events and occurrences are perceived as hot spots. In 2008, Google officials believed that they could identify flu outbreaks based on search requests. Google staff theorized that search engine users’ searches reflected their immediate concerns because when people are sick with flu, they seek out flu-related information such as symptoms, treatments, and medications. When there was a significant increase in Google searches for flu-related information, Google was provided with a notification of flu outbreaks days or weeks before the CDC could obtain the data via officially reported cases from physicians. This earlier notification could save lives. The product developed from the correlation, Google Flu Trends, produced great predictions in the first year but only good predictions in its second year, and then it began failing, until it missed the peak of the 2013 flu season by 140 percent. Google Flu Trends provides a cautionary tale for data scientists and analysts, who are wary of reporting false positives due to their possible social costs.

Reports of Zika virus in Miami, Florida, affected the city’s economy negatively. In July 2016, state and federal health officials in Florida confirmed that local mosquitoes were spreading Zika virus. As early as September 2016, hotel bookings in Miami were down, and businesses were reporting steep losses from lack of tourism to the area. In January 2017, the CDC posted a map of selected Miami-Dade County neighborhoods and recommended that pregnant women avoid or postpone travel to those areas. Residents in the area became worried about the impact that the CDC’s actions regarding the disease would have on the local housing market. Individuals living in Zika-infected areas reported experiencing stigma. Although the reports of Zika were true, a false report could mean substantial economic consequences for the new location.

Reporting actual data patterns requires sensitivity as well. In identifying crime hot spots, police departments and other agencies may unintentionally emphasize racial or socioeconomic biases. After identifying a high-crime area, a police department might assign more staff to the neighborhood, which means more arrests are made, thus reidentifying the neighborhood as a high-crime neighborhood and reinforcing the cycle rather than making the neighborhood safer for residents.

Future Directions

In the previous chapter, we discussed how GIS is used to visualize global crop production monitored through GEOGLAM. That same global initiative uses almost all of the spatial data science elements and tools that we have introduced in this chapter, from data collection to ground truthing. The GEOGLAM partners receive their raster data from satellites monitoring crop health. When those data come in, they must be processed and prepared because there may be holes in the satellite images either from cloud cover or dust in the air. Holes in the images mean holes in the data, and these missing data are outliers that must be investigated. Data scientists use spatial prediction models to try and account for the holes in the data by comparing how the crops were performing historically before the cloud cover and after it or by examining how adjacent crops are faring. In looking at the data, they can look for hot spots, where crops are doing better or worse than others. If there are areas where some crops are faring worse, data scientists can look for other environmental factors colocated with those crop failures, such as lack of water or insect infestation. All of these methods and models come together to create a complex report on the current status and end-of-season predictions for crop yields that help world agencies monitor the planet’s food supply and take timely actions to avoid food shortages in critical areas.

Dozens more spatial data science patterns are being used today, and even more are emerging. Data scientists monitor the changes in global forest cover or the growth of urban footprints in change detection. They also look more at hot spots, not only to identify them but to examine how they grow, shrink, or move over time. With teleconnection or telecoupling, data scientists look at environmental phenomena that are far away from each other yet affect each other, as in the way dust transfers from the Sahara Desert to Brazilian rain forests or El Niño weather patterns have an impact on areas at a distance. Many more such patterns are being investigated, with processes that are in the early stages but that have the possibility to affect our lives on the same scale.

Conclusion

What if we could predict not only the spread of disease between humans but also the spread of disease to humans? The CDC estimates that 60 percent of all existing human infectious diseases are zoonotic—they spread between animals and humans. Dr. Kevin Olival is a researcher at the EcoHealth Alliance. He studies bats, carriers of deadly viruses that can infect people. By swabbing the bats’ blood, saliva, and excrement, Olival identifies the viruses they carry in global locations where the animals come into close contact with humans. He and his colleagues theorize that tracking the viruses on animals in these hot spots makes it possible to predict the outbreaks of severe acute respiratory syndrome (SARS), Ebola, Middle East respiratory syndrome (MERS), and other diseases in humans. Spatial statistical tools and analysis will be key in taking on the task. Coupling these tools with human and animal biologic data, researchers and epidemiologists may someday be able to prevent epidemics before they start.

7

The Future of Spatial Computing

Making maps, using maps, finding our locations as well as spatial patterns, telling us what is in a space without us being physically present there, and storing and making useful huge amounts of data related to location—this is spatial computing. In each chapter, we have examined where the work for each technology is moving and the applications to come for GPS, GIS, remote sensing, spatial databases, and spatial statistics. But it is also important to step back and look at the direction of the work as a field to find general trends in the science of where. We can group those trends into three categories: trends in platforms, trends in space and time, and trends in use cases.

Some of the future directions for spatial-computing technologies rely on advancements of other elements of the systems in which they work, their platforms, and the way humans will access technology in the future. At the turn of the millennium, touchscreen technology was very new. Most people were used to interacting with computers through a separate keyboard and mouse. Today, the screen is how we interact with computers. Yet numerous reports show that extended time in front of the computer screen is bad for the health of children and adults. Computer and phone companies are receiving backlash as consumers and users seek ways to wean themselves off screens.

Using a screen is not always practical and is even dangerous in situations such as cooking or driving. Further, for those who are visually impaired, screens are useless. Voice-based devices such as Siri and Alexa are changing the way people access the Internet, music, movies, and more. That technology has its limitations as well, though. It is not particularly easy to convey the rich information contained in a map via voice. Mapping applications provide directions, telling us where to go and when to turn to find our destinations. They aren’t particularly good at providing context or at relaying all of the information that a map does to reinforce or augment our knowledge. “Turn left in 200 feet. Your destination is on the right” is helpful, but so is “Turn left at the church, your destination is a blue, two-story house in the middle of the block on the right.” As programmers, developers, and designers look at the way we access and consume information, they will need to adapt the technology for spatial data.

GPS and remote sensing face a limitation: they are still expensive and power hungry in many uses. If you have ever tried to use your phone to navigate a lengthy road trip on your battery power alone, you have likely watched the charge drain before your eyes. Many advancements in remote sensing and GPS will need to reduce both the financial and the power costs of those technologies. Companies such as Tile create Bluetooth-enabled trackers that can be attached to your keys or computer so that you can track and never lose them. Those individual products still run around $20 each and require a connection to your phone. That cost is low enough to use on a selected few items in your home, but certainly not all. Now imagine never losing your shoes, your child’s backpack, or your hairbrush. With a small, inexpensive RFID tag that attaches to all of your items, you can always know the item’s location. The technology could also be applied to food to track foodborne illnesses. Imagine tracking every tomato and head of lettuce and being able to identify the source of a salmonella outbreak. If the cost of GPS-enabled devices were brought down to pennies, we can envision a truly ubiquitous location system where we can track individual items in our homes or businesses—not only our car keys and computers but also our eyeglasses, items that go with us to the gym, and individual pieces of equipment in a surgery room. For people with Alzheimer’s disease, this capability would ease the burden of keeping track of personal items by providing an inventory of their things and a map of where they are at all times.

It’s also possible that almost all future computing hardware, including sensors, computing nodes, and software, will be location aware. This location awareness can improve cybersecurity and customize user experience to local environment and culture. By associating machines and Internet protocol addresses with a physical location, we can screen messages by location and follow their route in the same way we track packages to our homes, noting the route of the messages and identifying untrusted locations. Documents could someday know their locations and lock themselves when they are in an untrusted location. This ability could reduce incidents of espionage or increase the security of hospital patient information.

Computing power, computer storage, remote-sensing tools—all are continuing to improve. Our computers are becoming smaller and more powerful, and much of the storage and processing of computer imagery is done in cloud computing systems. Nanosatellites are becoming smaller, cheaper, and more precise in monitoring their locations, allowing all of the platforms associated with remote sensing to become more powerful and increasing the richness of the platform as a whole. Cloud storage and processing are improving access to spatial data.

When Landsat images first became available for public use, the researchers and groups who wanted the images faced another hurdle to access—processing power. The images created by the satellites were huge compilations of data. Transferring the data to a machine took a huge amount of time, and the machines needed to be powerful enough to handle the data load. Today, groups such as Google Earth Engine, Amazon Earth on AWS, and NASA Earth Exchange store satellite data in the cloud and provide computing power there, thus reducing the individual’s need for powerful computing systems and increasing access. Fees to use the site are based on how much data the user is accessing.

As the other elements of remote-sensing systems improve, so will the sensors—becoming more precise. Remote-sensing imagery today can identify an apple orchard from space; future UAVs will obtain resolution high enough to count the apples in the orchard. Another frontier is night imagery. Most satellite imagery is done during the daytime. The image in figure 20 is one of very few nighttime images collected from a satellite, but night imagery has the potential to be useful—for example, we could discover how well homes are insulated by tracking their heat signatures on cold nights and determining the amount of heat escaping from them.

All of these advancements in computing platforms will make it the second trend in spatial computing possible—reaching new time frames for information. In our chapter on remote sensing, we covered how these systems have a trade-off: you can choose either extensive coverage of a small area or less-frequent coverage of a large area. Our current remote-sensing satellite systems make passes of Earth once or twice a day. Some agricultural clients receive detailed information twice a week. New startup companies are providing visual satellite imagery at high resolution every day. China is collecting satellite imagery of some areas every few hours. To the other extreme, the Baltimore Police Department uses UAV surveillance to monitor roughly 30 square miles up to ten hours a day.

Figure 20 Source: NASA/NOAA.

If satellite information is available to use within 24 hours, that is considered a very good time frame. A constellation of nanosatellites equipped with a large collection of sensors and combined with fast computing power could radically change the rate we access information from around the globe. If we can launch, arrange, and position those satellites in a way to provide global coverage, we could receive a complete snapshot of Earth every minute of every hour.

Current remote-sensing technologies are not able to be everywhere all the time. The user must choose between sampling frequency and area of coverage.

Improved tools and platforms can also mean obtaining imagery of places we cannot yet access, such as indoor spaces and underground tunnels. Subsurface structures and cavities such as city water pipes and sewer systems also need monitoring. We have maps of those systems, but not the tools needed to monitor their health and safety consistently because today’s remote-sensing systems are largely surface geographic applications. As ground soil settles and trees grow, the pipes for our drinking water and sewer systems underground are moved. Intense rainfall and toxic substances in the drainage systems can also affect the health of the pipes and the health of the water runoff that drains to lakes and rivers. Oil companies use barrels equipped with sensors to travel down pipelines to monitor the health of those systems.

Once those systems are monitored, we have to consider the future of how we display that information. Maps are mostly two dimensional, but water and sewer systems are three dimensional. The way we map and display these systems is beginning to change.

In the introduction, we discussed brain surgeons using a GPS-like tool to position medical instruments during surgery. Positioning inside the human body cannot easily be described by a coordinate system such as latitude and longitude. The body is not rigid, either. Most of it is made of tissues that move and flex, and each body is different according to age, sex, health, and heredity. Visualization of nongeographic spaces will continue to adapt to use more immersive three-dimensional computer graphics, allowing views that will go beyond a flat map to an interactive experience that allows users to explore inside a human body or a water-distribution system and direct their view to different points and orientations in navigation.

The advances in spatial computing are sandwiched between two forces that direct the work: the applications of the technology and their platforms. When either of those forces shifts, the technology innovates and adjusts to new opportunities. The applications can direct the innovations in platform and vice versa.

Policy and Power

In each chapter, we have also dug into the applications for these technologies that are on the horizon, but there are larger trends in use cases that cross all of these areas of work. The United Nations’ sustainable-development goal has identified a set of global priorities that are geospatial in nature, such as reducing poverty and hunger and increasing health and well-being. In the decades ahead, there will be continued growth in use of spatial-computing technologies to monitor not only global crops but also surface water and energy waste from poorly insulated homes and buildings.

There are other directions for the use of spatial work. As a society, we are increasing our demand for fairness, accountability, and transparency from the technologies we use and the companies that own and run them. The Baltimore Police Department began using UAVs to monitor activity in the city during the trial of Caesar Goodson, who in 2016 was charged with the death of Freddie Gray. Gray died in police custody in an incident that caused racial tensions to mount in the city. The police department began surveilling citizens but neglected to inform them of the activity, causing further deterioration in the relationship between the city’s inhabitants and the police force. Rather than feeling that the cameras were there for their security, citizens felt watched, which exacerbated the tensions between the police and the people. Future spatial methods and use cases will increasingly need to address these social feedback loops.

As the speed, scale, and power of these technologies increase, so do the risks associated with them. Today, many people own personal drones for recreation, flying them almost like kites. The Federal Aviation Administration has rules for owning and operating drones, and owners are required to follow these rules, which include avoiding secure and restricted areas such as airports and airspace over stadiums. The drones come with software to prevent them from flying in those areas. Owners are required to periodically update this software, but there is always a risk that they will not do so and thus possibly compromise the safety and security of others.

For those of us who prefer not to have our homes watched by someone else’s aerial cameras, how can we enforce that right to privacy? As we move to a future in which we can locate anyone anytime and anywhere, privacy becomes a larger issue. The idea of never having to worry about losing a child at a crowded event is comforting, but what about the idea of never being able to slip away and buy a gift for a loved one unnoticed?

Many retailers receive pushback from consumers when the companies’ consumer algorithms provide recommendations for products based on previous purchases that seem to violate the individual’s privacy. When news reports stated that the retailer Target was able to predict when a teen was pregnant based on her purchase history and that this information was important to the company because pregnancy causes a spike in consumer spending and consumer loyalty, the company received negative attention from the public.

Spatial statistical methods are currently used to predict traffic conditions, forecast the weather, and map the spread of wildfires based on environmental conditions. In the future, these methods will be refined to predict more phenomena with greater accuracy and applied to different domains. Spatial data science models may be used in medicine to predict when and how chronic diseases such as arthritis will cause inflammation within the human body and where it will spread.

Technology often moves across a spectrum of prosperity, national security, and civil society. The business community, of course, is most interested in prosperity, the business model for using technology, and technology’s economic impact when brought to consumers. The government is most concerned with national security—the ways we use technologies to keep our country secure and safe from our adversaries and from natural disasters. Civil society looks at whether technology is being used for fairness, goodwill, and respect for citizens.

One country’s use of technology might be more dominant in one segment than in another. For example, the governments of China and Russia are more concerned with maintaining national security and monitoring their citizens. In the United States, culture and policy tend to focus more on prosperity, prioritizing the economic benefits of technology. Spatial-computing technologies have become part of the critical infrastructure in both business and government, and the public feels the infrastructure needs to be protected from outages that can disrupt modern life. In June 2018, the United States announced the formation of a Space Force to monitor and protect the growing number of satellites in low orbit used for positioning and remote sensing. We could also argue that northern European and Scandinavian countries tend to be more interested in understanding how technology can be used to reinforce the values of a civil society. Over time, if there is an imbalance in the way the technology is being used that does not reflect government or societal priorities, the system adjusts. The Arab Spring and the protests that removed Egyptian president Hosni Mubarak from power were fueled by access to the Internet and social media. Once the authoritarian government understood the organizing power of those tools, they took steps to control access to them.

In the United States, there is an ongoing debate about privacy and transparency. The companies that gather and distribute our personal location information lack transparency in the ways they use that information. In July 2018, Walmart announced a newly patented surveillance technology that the corporation intended to use to record audio conversations between employees and customers with the intent to use the information to improve the employees’ performance in assisting customers. The company did not announce how the audio recordings would be gathered, used, or stored or for how long they would be archived.

The companies that do attempt to provide transparency in how they gather and use consumer information often do so by providing a lengthy terms-and-conditions announcement, which most users just click through and agree to without reading. Those who do not agree to the terms and conditions are left without access to the technology and the opportunities the technology provides. Many medium and large employers recruit and hire employees through an application on company websites. If the prospective employee does not agree to the terms and conditions for the storage and use of their personal information, they are denied the opportunity to apply for a job.

Spatial-computing technologies travel along the spectrum as well. All five of the areas of spatial computing that we have covered in this book started out as initiatives by the federal government. For instance, GPS was created by the federal government in the 1970s, then opened up for public use in the 1990s. Today, there is discussion about data sharing, privacy, and regulation. As the market for GPS devices and public use continues to grow, that conversation is shifting once again to security—for instance, maintaining the security of our satellites.

Remote sensing began in the 1960s with the launch of the Landsat satellites. These technologies are more mature in government use but are now gaining real momentum in commercial markets. As the same time, there are also discussions of how we can use these tools and still maintain privacy and provide transparency. More than ever before, there is a need both to balance and to reconcile the needs and desires of consumers, businesses, and government. Because technology moves much faster than public policy, consumers often have a chance to effect change faster than policy makers. As this happens, and as the technologies and their uses continue to grow, adapt, change, and mature, they move and occupy different portions of this spectrum of use.

Not only are the spatial technologies described in this book constantly being adapted and used in different applications that affect our lives, but their applications also change and update constantly with more information and increased precision. The rate at which consumers are adopting the technologies is increasing, too. After the telephone was introduced, it took decades for more than 50 percent of Americans to have one in their homes, yet the smartphone took only a decade to reach 40 percent of homes. Twenty years after opening up GPS for civilian use, there are more than 2 billion receivers in use around the world, and that number accounts for only a single spatial technology.

One of the challenges of writing a book about a growing technology is keeping up with the rate of change and trying to detect where those changes will occur. How will spatial computing affect the way we live and work in the future? We have tried to point to the directions of change and what is to come and to the ways these changes will affect people and policies. Although the popularity of a game such as Pokémon Go represents only a moment in time, spatial-computing technologies are ubiquitous in our present, even as they lead us into the future.

Glossary

active sensors

A sensor that provides its own energy source, which is directed at an object in a remote-sensing system

algorithm

A set of processes and/or set of computations

batch processing

The process of a computer working through sets or batches of jobs at one time without stopping between sets

BeiDou System

The satellite navigation system created and used by China

chance pattern

A spatial pattern which is spurious and which may occur at random

colocation

Located nearby in space or time; also co-occurrence

Control Segment

Part of the global positioning system that monitors and controls the health and accuracy of satellites; also referred to as the Ground Segment or Operational Control System

co-occurrence

Located nearby in space or time; also colocation

data science

A set of techniques used for informed decision making with technology by discovering patterns in data

Galileo

The satellite navigation system created and used by the European Space Agency

geographic information system (GIS)

A system that stores, analyzes, manages, and visualizes spatial data collected via remote sensing, manual surveys, and digitized paper maps

geotag

An electronic tag with digital information labeling location

global navigation satellite system (GNSS)

A navigation system using satellites in space and ground receivers to provide digital location detection and navigation

Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS)

The satellite navigation system created and used by Russia

global outlier

An event that is inconsistent with the rest of a data set

Global Positioning System (GPS)

The satellite navigation system created and used by the United States

graph data

A data structure made up of nodes or points, joined or connected by edges, arcs, and lines

ground truthing

Collecting data on site to verify findings, also called “on-the-scene reporting”

hash function

A function to quickly map a search key to the location of matching data

hash file

A data organization leveraging a hash function

hot spot

A geographic area inside which the concentration of something is much higher than it is on the outside of that area

impedance mismatch

A discrepancy between queries and data organization leading to very large computational cost

independence and identical distribution (i.i.d.)

Applied to data samples and used in math or statistics to indicate that the variables are not related and have the same distributions

land parcel

A plot of land that is owned

Landsat

A set of Earth-observing satellites managed by NASA and the US Geological Survey

lidar

Light detection and ranging, a remote-sensing tool used to measure distance by pulse light and the distance the light travels

long-range navigation (LORAN)

A navigation system using radio-transmission pulses to calculate location

nadir

The point directly under a satellite

Navigation with Indian Constellation (NavIC)

The satellite navigation system created and used by India, formerly known as the Indian Regional Navigation Satellite System, or IRNSS

NAVSTAR

The network of satellites, owned and maintained by the US government, that provide location and positioning (GPS) services

nearest-neighbor query

A type of spatial query to identify the nearest objects to a point in space

nonspatial data

Data that are not geographically referenced—for example, numeric data and text strings

Normalized Difference Vegetation Index (NDVI)

An index used to measure the health of vegetation

passive sensors

Sensors that detect and observe electromagnetic radiation that is absorbed, reflected, or scattered from objects using an external energy source from the physical environment

pixel

A cell, part of a grid or matrix of cells, each containing information

plane sweep

A set of computations using a sweep line to move across a plane to find notable changes in information

query

A question posed to a database

raster data

A matrix of rows and columns divided into cells, or pixels, with each cell or pixel containing information

spatial

Related to space

spatial data

Geographically referenced or geocoded data using a unique identifier so that a location is understood by all the people working with the information

spatial join

An operation that joins the attributes from one feature to another feature in a spatial perspective

spatial outlier

An event or item that stands out or is inconsistent with its neighbors

support

The fraction of transactions that contain the subset of items

synthetic-aperture radar (SAR)

Moves radar antenna over a target region to provide finer spatial resolution

trilateration

A process to determine location using the geometry of circles or spheres

unmanned aerial vehicle (UAV)

Drone

vector data

Data representing geographic features in points, lines, and polygon-shaped footprints

Wide Area Augmentation System (WAAS)

A system of ground stations working with satellites to improve the accuracy of determining location

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Shekhar, Shashi, and Sanjay Chawla. Spatial Databases: A Tour. London: Pearson, 2003.

Shekhar, Shashi, Sanjay Chawla, Siva Ravada, Andrew Fetterer, Xuan Liu, and Chang-Tien Liu. “Spatial Databases: Accomplishments and Research Needs.” IEEE Transactions on Knowledge and Data Engineering 11 (1) (January–February 1999): 45–55.

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Further Reading

Campbell, James B., and Randolph Wynne. Introduction to Remote Sensing. 5th ed. New York: Guilford Press, 2011.

Ceruzzi, Paul E. GPS. Cambridge, MA: MIT Press, 2018.

DeMers, Michael N. GIS for Dummies. Hoboken, NJ: Wiley, 2009.

Johnson, Steven. The Ghost Map: The Story of London’s Most Terrifying Epidemic—and How It Changed Science, Cities, and the Modern World. Riverhead, NY: Riverhead Books, 2007.

Kaplan, Elliott D., and Christopher J. Hegarty, Understanding GPS/GNSS: Principles and Applications, 3rd ed. Boston: Artech House, 2017.

Kemp, Karen K., ed. Encyclopedia of Geographic Information Science. Newbury Park, CA: Sage, 2007.

Longley, Paul A., Michael F. Goodchild, David J. Maguire, and David W. Rhind. Geographic Information Science and Systems. 4th ed. Hoboken, NJ: Wiley, 2015.

Marshall, Tim. Prisoners of Geography: Ten Maps That Explain Everything about the World. New York: Scribner, 2015.

McNamara, Joel. GPS for Dummies. Hoboken, NJ: Wiley, 2004.

National Academies of Science, Engineering, and Medicine. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. Washington, DC: National Academies Press, 2019.

Riffenburgh, Beau. Mapping the World: The Story of Cartography. London: Andre Deutsch, 2015.

Shekhar, Shashi, and Sanjay Chawla. Spatial Databases: A Tour. London: Pearson, 2003.

Shekhar, Shashi, Hui Xiong, and Xun Zhou, eds. Encyclopedia of GIS. 2nd ed. New York: Springer, 2017.

Shekhar, Shashi, Steven Feiner, and Walid Aref. “Spatial Computing.” Communications of the ACM 59, no. 1 (2016): 72–81.

University Consortium for Geographic Information Science. A UCGIS Call to Action: Bringing the Geospatial Perspective to Data Science Degrees and Curricula. Summer 2018. https://www.ucgis.org/assets/docs/UCGIS-Statement-on-Data-Science-Summer2018.pdf.

Xie, Yiqun, Emre Eftelioglu, Reem Ali, Xun Tang, Yan Li, Ruhi Doshi, and S. Shekhar. “Transdisciplinary Foundations of Geospatial Data Science.” ISPRS International Journal of Geo-Information 6, no. 12 (2017): 395.

Index

Algorithms, 17, 20–21

plane sweep, 144–146

ArcInfo, 99

Augmented reality, 13

“Autonomous Real-Time Ground Ubiquitous Surveillance Imaging System (ARGUS-IS),” 83

Autonomous vehicles, 11–12

Batch processing, 134–135

BeiDou System, 38

Business-continuity planning, 150–151

Business transactions, 146–147

Cambridge Analytica, 154–155

Cassini map of France, 97

Chronometer, 30

Claudius Ptolemy, 94–95

COBOL, 136

Collaborative-mapping tools, 122–123

Colocation pattern, 185–190

Control Segment, 35–36

Co-occurrence pattern, 185–190

Data science, 164

Datums, 105–106

Deep learning, 88–89

Disaster recovery, 150–151

Drones, 9

Electromagnetic waves, 33

Electronic Numerical Integrator and Computer (ENIAC), 133–143

Emerging spatial patterns, 194

Extreme spatial collaboration, 14

Facebook, 154–156

General Data Protection Regulation (GDPR), 157

GEOGLAM, 87, 114–115

Gerrymandering, xii, 120

Global navigation satellite systems (GNSS), 26

Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS), 38

GPS, 25–57

environmental monitoring, 44–45

GPS III, 39

jamming, 49

modernization, 57

orbit, 31–32

propulsion, 32

satellites, 31–33

signals, 33

surveying, 43–44

Graph data, 18–19

Ground truthing, 191

Harvard Graphics Lab, 99

Hollerith, Herman, 132–133

Hollerith punch card, 133

Hollerith tabulating machine, 132

Hot spots, 162

Hyperspectral sensing, 81

Identical distribution assumption, 166–169

Impedance mismatch, 139–140

Indian Constellation (NavIC), 39

Indoor positioning, 53–55

Kyllo v. United States, 86

Landsat, 65

Latitude, 27

Lidar, 11–12, 77

Linear search, 143

Location prediction, 175–177

Location trace, xii

Longitude, 27–29

LORAN, 30

MapQuest, 100

Maps, 106–112

abstractions, 107

cartograms, 111

choropleth, 108

reference, 106

thematic, 106

topological, 112

Mercator projection, 95–96

Nadir, 72–73

Nanosatellite technologies, 88

National Flood Insurance Program (NFIP), 91–92

Navigation, historical, 26–31

NAVSTAR, 31

Nonspatial data, 17

Normalized Difference Vegetation Index (NDVI), 74

Online transaction-processing (OLTP), 150

Open Geodata Interchange Standard, 141

Outliers

global, 181–182

spatial, 181–182

Pixel, 18

Precision agriculture, 45–46

Prediction, location, 175–177

Privacy, 207

Radar, 75–76

Raster data, 18

Regulation (GDPR), 157

Remote sensing, 9–10, 59–90

Corona program, 63–65

early systems, 62–63

Revolutionary War, 97–98

Ride sharing, 4–5

Roomba, 7–8

R-tree hierarchies, 142

Semantic gap, 138

Sensors, 69–70

active, 69

passive, 69

Snow, John, 161–162

Sorted file, 138

Spatial, 6

Spatial autocorrelation, 170

Spatial computing, 2

Spatial data, 17–21

graph data, 18–19

raster data, 18

vector data, 19

Spatial databases, 127–160

Spatial data science, 161–195

colocations and co-occurrences, 185–190

emerging spatial patterns, 194

global outliers, 181–182

hot spots, 177–181

location-prediction models, 175–177

spatial outliers, 181–182

Spatial heterogeneity, 170

Spatial join, 144

Spatial resolution, 72

Spectral band, 72

Spectral signature, 71

SQL, 137

Street address, 102

Support, 186–187

Synthetic-aperture radar (SAR), 81

Temporal resolution, 73

Triangulation (multilateration or trilateration), 33–35

UAVs, 9

UNIVAC I, 134

US Census, 131–133

Vector spatial data, 19

Visibility analysis or terrain analysis, 118

Visible band, 70

Wide Area Augmentation System (WAAS), 37

Wi-fi positioning, 53–54

Wireless Emergency Alert system, 52

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AI Ethics, Mark Coeckelbergh

Auctions, Timothy P. Hubbard and Harry J. Paarsch

The Book, Amaranth Borsuk

Carbon Capture, Howard J. Herzog

Citizenship, Dimitry Kochenov

Cloud Computing, Nayan B. Ruparelia

Collaborative Society, Dariusz Jemielniak and Aleksandra Przegalinska

Computational Thinking, Peter J. Denning and Matti Tedre

Computing: A Concise History, Paul E. Ceruzzi

The Conscious Mind, Zoltan E. Torey

Contraception: A Concise History, Donna J. Drucker

Critical Thinking, Jonathan Haber

Crowdsourcing, Daren C. Brabham

Cynicism, Ansgar Allen

Data Science, John D. Kelleher and Brendan Tierney

Deep Learning, John Kelleher

Extraterrestrials, Wade Roush

Extremism, J. M. Berger

Fake Photos, Hany Farid

fMRI, Peter A. Bandettini

Food, Fabio Parasecoli

Free Will, Mark Balaguer

The Future, Nick Montfort

GPS, Paul E. Ceruzzi

Haptics, Lynette A. Jones

Information and Society, Michael Buckland

Information and the Modern Corporation, James W. Cortada

Intellectual Property Strategy, John Palfrey

The Internet of Things, Samuel Greengard

Irony and Sarcasm, Roger Kreuz

Machine Learning: The New AI, Ethem Alpaydin

Machine Translation, Thierry Poibeau

Macroeconomics, Felipe Larraín B.

Memes in Digital Culture, Limor Shifman

Metadata, Jeffrey Pomerantz

The Mind–Body Problem, Jonathan Westphal

MOOCs, Jonathan Haber

Neuroplasticity, Moheb Costandi

Nihilism, Nolen Gertz

Open Access, Peter Suber

Paradox, Margaret Cuonzo

Photo Authentication, Hany Farid

Post-Truth, Lee McIntyre

Quantum Entanglement, Jed Brody

Recycling, Finn Arne Jorgensen

Robots, John Jordan

School Choice, David R. Garcia

Self-Tracking, Gina Neff and Dawn Nafus

Sexual Consent, Milena Popova

Smart Cities, Germaine R. Halegoua

Spaceflight, Michael J. Neufeld

Spatial Computing, Shashi Shekhar and Pamela Vold

Sustainability, Kent E. Portney

Synesthesia, Richard E. Cytowic

The Technological Singularity, Murray Shanahan

3D Printing, John Jordan

Understanding Beliefs, Nils J. Nilsson

Virtual Reality, Samuel Greengard

Waves, Frederic Raichlen

SHASHI SHEKHAR is McKnight Distinguished Professor in the Department of Computer Science and Engineering at the University of Minnesota.

PAMELA VOLD is a writer and communications professional based in St. Paul, Minnesota.

Table of Contents

Cover

Contents

Series Foreword

Preface

Acknowledgments

1 What Is Spatial Computing?

2 Where Am I? Positioning, Outdoors and Indoors

3 What’s There? Remote Sensing

4 Geographic Information Systems and Cartography

5 Spatial Databases

6 Spatial Data Science

7 The Future of Spatial Computing

Glossary

Bibliography

Further Reading

Index