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No part of this publication may be reproduced, stored in retrieval system, or transmitted in any form by any means electronic, mechanical, photocopying, recording or otherwise without permission of copy right holder. 0
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Copyright © 2022 Chad Champion All rights reserved.
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The unilateral claiming of private human experience as free raw material for translation into behavioral data.
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Table of Conten Birth of Surveillance Capitalism 10 /The Dotcom Bust 14 /Googles Success
American Industrial Revolution 18 /Three Commodities 20 /Real Estate 22 /Human Labor 24 /Assembly Line
New Digital Age 30 /Facebook 32 /Cambridge Analytica 36 /Pokemon Go 38 /The New Marketplace
/
0
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/
0
nts
Bir thfo u Sr e vi l ac n C a p i tl s m
Market Cap
Year
8
/
Searches
2
0
0
4
2
3
2
0
0
0
9
5
6
M
1
9
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5
0
M
/
B
8
/
6
1
4
2
9
2
3
8
1
2
7
7
1000
7
0
0
0
0
0
B
Googles Success/ 14
2
0
0
0
0
0
B
The Dotcom Bust/ 10
5
0
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0
B
9
The Birth of Surveillance Capitalism/The Dotcom Bust
The Dotcom Bust
The pre-bubble period of the Dotcom bubble went from 1995 to 1997, and the bubble burst in March 2000.
2001, in the teeth of the dot-com bust, Google’s leaders found their breakthrough in a series of inventions that would transform advertising. Their team learned how to combine massive data flows of personal information with advanced computational analyses to predict where an ad should be placed for maximum “click through.” Predictions were computed initially by analyzing data trails that users unknowingly left behind in the company’s servers as they searched and browsed Google’s pages. Google’s scientists learned how to extract predictive metadata from this “data exhaust” and use it to analyze likely patterns of future behavior. Prediction was the first imperative that determined the second imperative: extraction. Lucrative predictions required flows of human data at unimaginable scale. Users did not suspect that their data was secretly hunted and captured from every corner of the internet and, later, from apps, smartphones, devices, cameras and sensors. User ignorance was understood as crucial to success. Each new product was a means to more “engagement,” a euphemism used to conceal illicit extraction operations. When asked “What is Google?” the co-founder Larry Page laid it out in 2001, according to a detailed account by Douglas Edwards, Google’s first brand manager, in his book “I’m Feeling Lucky”: “Storage is cheap. Cameras are cheap. People will generate enormous amounts of data,” Mr. Page said. “Everything you’ve ever heard or seen or experienced will become searchable. Your whole life will be searchable.”risky start-ups, pricked the dot-com balloon with monetary tightening in the spring of 2000. It was the dot-coms’ misfortune to be first the beneficiaries and then the victims of these larger economic forces.
10
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Never in the history of humanity has there been this volume and variety of behavioural data, and this kind of computational power to monitor and predict human behaviour from individuals to populations. Instead of selling search to users, Google survived by turning its search engine into a sophisticated surveillance medium for seizing human data. Company executives worked to keep these economic operations secret, hidden from users, lawmakers, and competitors. Mr. Page opposed anything that might “stir the privacy pot and endanger our ability to gather data,” Mr. Edwards wrote.
Massive-scale extraction operations were the keystone to the new economic edifice and superseded other considerations, beginning with the quality of information, because in the logic of surveillance capitalism, information integrity is not correlated with revenue.Prediction was the first imperative that determined the second imperative: extraction. Lucrative predictions required flows of human data at unimaginable scale. Users did not suspect that their data was secretly hunted and captured from every corner of the internet and, later, from apps, smartphones, devices, cameras and sensors. User ignorance was understood as crucial to success. Each new product was a means to more “engagement,” a euphemism used to conceal illicit extraction operations.
Next, these computational prediction products are sold into a new kind of marketplace: business customers who want to know what consumers will do in the future. Just as we have markets that trade in pork belly futures or oil futures, these new markets trade in “human futures.” Such markets have very specific competitive dynamics. They compete on the basis of who has the best predictions — in other words, who can do the best job selling certainty. The competition to sell certainty reveals the key economic imperatives of this new logic.
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The simple addition of .com contributed to those companies outperforming their competitors by 63%
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The Birth of Surveillance Capitalism/The Dotcom Bust/Googles Success
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13
The Birth of Surveillance Capitalism/The Dotcom Bust/Googles Success
If bubbles popping were extinctionlevel events, then companies like Apple, Google, and Amazon were the crocodiles of the tech ecosystem. Googles Success
inventors announce. Despite the enormous quantity of demographic data available to advertisers, the scientists note that much of an ad budget “is simply wasted…it is very difficult to identify and eliminate such waste.” Advertising had always been a guessing game: art, relationships, conventional wisdom, standard practice, but never “science.” The idea of being able to deliver a particular message to a particular person at just the moment when it might have a high probability of actually influencing his or her behavior was, and had always been, the holy grail of advertising. The inventors point out that online ad systems had also failed to achieve this elusive goal. The then-predominant approaches used by Google’s competitors, in which ads were targeted to keywords or content, were unable to identify relevant ads “for a particular user.” Now the inventors offered a scientific solution that exceeded the most-ambitious dreams of any advertising executive: In other words, Google would no longer mine behavioral data strictly to improve service for users but rather to read users’ minds for the purposes of matching ads to their interests, as those interests are deduced from the collateral traces of online behavior. With Google’s unique access to behavioral data, it would now be possible to know what a particular individual in a particular time and place was thinking, feeling, and doing. That this no longer seems astonishing to us, or perhaps even worthy of note, is evidence of the profound psychic numbing that has inured us to a bold and unprecedented shift in capitalist. The techniques described in the patent meant that each time a user queries Google’s search engine, the system simultaneously presents a specific configuration of a particular ad, all in the fraction of a moment that it takes to fulfill the search query.
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By 2001, in the teeth of the dot-com bust, Google’s leaders found their breakthrough in a series of inventions that would transform advertising. Their team learned how to combine massive data flows of personal information with advanced computational analyses to predict where an ad should be placed for maximum “click through.” Predictions were computed initially by analyzing data trails that users unknowingly left behind in the company’s servers as they searched and browsed Google’s pages. Google’s scientists learned how to extract predictive meta data from this “data exhaust” and use it to analyze likely patterns of future behavior.
Prediction was the first imperative that determined the second imperative: extraction. Lucrative predictions required flows of human data at unimaginable scale. Users did not suspect that their data was secretly hunted and captured from every corner of the internet and, later, from apps, smartphones, devices, cameras and sensors. User ignorance was understood as crucial to success. Each new product was a means to more “engagement,” a euphemism used to conceal illicit extraction operations.
Behavioral data that were once discarded or ignored were rediscovered as what I call behavioral surplus
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15
ericanAm
trialsduIn tionluveR
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Assembly Line/ 24
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Human Labor/ 22
1
9
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Real Estate/ 20
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Three Commodities/ 18
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American Industrial Revolution/Three Commodities
“A commodity is, in the first place, an object outside of us, a thing that by its properties satisfies human wants of some sort or another. ” —Karl Marx
Three Commodities
More simply put, a worker produces an object (i.e. fabric, shoes, plastic, houses, etc.) that, despite the investment of their personal labor, remains as the boss’s property. This simple, yet crucial fact turns the object into merchandise, or a commodity. The boss who possesses wealth and commodities, is, for Marx, the embodiment of the bourgeois; and the worker thus becomes the embodiment of the proletariat. More important, however, is that the bourgeois, in possessing the capital, maintains control over the use and exchange of those commodities. With this in mind, Marx continues his discussion of commodity by defining use-value and exchange-value. According to Marx, “every useful thing, as iron, and paper, may be looked at from the two points of view of quality and quantity”. The diversity of production necessarily yields diverse modes of use, and it is therefore the “work of history” to identify the various modes of use as well as the social standards by which those uses are assessed. What is especially important to extract from this preliminary definition of use-value is the claim that “use-values become a reality only by use or consumption.” More simply put, the utility, or use-value, of a commodity cannot be fully realized or assessed until the object itself has entered into a system of exchange. Use-value is thus intrinsically related and dependent upon exchange-value. Furthermore, use-value, and subsequently exchange-value, cannot be neatly defined into either quality or quantity, but instead resides within the realms of both quality and quantity.
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American Industrial Revolution/Three Commodities/Real Estate
Real Estate
The profession of real estate broker began around 1900 in the United States. Since then, the profession of real estate brokers has flourished in the country. The initial home sale records began in the United States in 1890. It was an attempt to create the first real estate associated in the United States. However, the attempt failed. But it helped set a base for the process which led to the creation of the National Association of Real Estate Exchanges in 1908. Curb stoners and the History of real estate, There were no set rules about who can start working as a real estate broker until 1919. Hence, there were no licenses or professional certifications to become a registered real estate broker. It led to dubious practices of home brokerage. The home brokers of that era were known as curb stoners. They used to place several placards at the front side of the homes. The curbstones left placards with a hope that the leaving homeowners will pick one of the curb stoners. The dubious practice of curb stoning was lost in history. With the fall of curb stoning, a professional standard of real estate sales arose from its ashes. The professional real estate agents gained listings from home sellers through earning their trust. Home sellers happily agreed to sell their homes through the new breed of real estate agents. It was because the professional real estate agents asked for the permission of the homeowners to gain listings. In many major cities such as Baltimore, St. Louis, and Chicago, many single real estate agents gained exclusive contracts. It made real estate agents popular among both the home buyers and the sellers. The practice of open houses and walk through soon started becoming the norm.
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Sustainability can no longer be considered a niche. In fact, consumers demand and expect it from most aspects of their lives. Even professional sports leagues, like the NFL, now have formal sustainability initiatives in place. According to a recent survey by Nielsen, 81% of consumers around the globe believe it is extremely or very important for companies to have environmental improvement as an objective.
of homes, which they could show to the buyers later on. The soldiers returning home after the end of WWII bought new homes to raise a family. It resulted in a real estate sales boom. During this time, the term e R‘ altor‘ became popular. Women, too, started working as a Realtor during this era. e R al estate has been estimated to represent approximately one-half of the world’s total economic wealth. 1 In addition, it is often viewed as an important symbol of strength, stability, and independence. Consider, for example, the symbolic importance of structures such as Saint Peter’s Basilica in o R me to the o R man Catholic Church or the buildings of the Forbidden City in Beijing to the Chinese people (see also Industry Issues 1-1 ). It is not surprising that real estate has been at the center of many regional disputes. It has been, and continues to be, a vital resource.
younger generations are expected to enter the home buying market, this socially conscious group is looking out for green features and sustainability that is built in. The World Commission on Environment and Development has defined environmental sustainability as “a process of change in which the exploitation of resources, the direction of investments, the orientation of technological development and institutional change are all in harmony and enhance both current and future potential to meet human needs and aspirations.” As fairly large consumers, the building and construction industries must be part of this “process of change.” Calls to curb resource usage and environmental impacts will continue to rise, especially as Gen Z hits adult hood in earnest. The real estate space will not be immune to their demands.
Green building really kicked off in the early 1990s, reaching more widespread awareness about a decade later. It was long heralded as a special feature, something that was above and beyond the norm. Now, it has gone mainstream, and many expect a certain amount of sustainability in their homes and commercial buildings. Some governing bodies are even requiring resource-efficient measures to be in place. According to the National Association of Homebuilders, home buyers want — and will pay more for — sustainable features like energy-efficient appliances, windows and the like, alongside features that ensure better air quality. As
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American Industrial Revolution/Three Commodities/Real Estate/Assembly Line/Human Labor
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Human Labor The 1915 annual average civilian labor force participation rate is estimated at 56.3 percent. This percentage isn’t strictly comparable to the 2015 annual average of 62.7 percent, because of differences in survey coverage and definitions. However, despite the similarity in overall labor force participation, the participation rates of men and women were very different from each other 100 years ago. The 1920 census shows that, among people ages 14 and older, the proportion of the population that was in the total labor force was 85 percent for men and 23 percent for women in January of that year. (Civilian labor force data by gender are not available for 1915.) In contrast, the Current Population Survey shows a 2015 annual average civilian labor force participation rate for people ages 16 and older of about 69 percent for men and nearly 57 percent for women. Young boys were much more likely to be in the labor force in 1920 than now. Not surprisingly, women of all ages are much more likely to be in the labor force now than in 1920. Half of all boys ages 14 to 19 were in the labor force in 1920; nowadays, about one-third of boys age 16 to 19 are in the labor force. Labor force participation among girls those ages hasn’t shown as much change.
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Children worked in large numbers in mines, glass factories, the textile industry, agriculture, and canneries.
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The number of children employed in factories rose steadily In the early history of child labor in America all but the over the last three decades of the nineteenth century. By privileged few were expected to work. Child labor was more 190 roughly .1 7 million children under the age of sixteen manageable and cheaper than adult labor and children worked in factories; less than half that many childrenwere less likely to strike. Children were employed in the had been employed thirty years before. Large numberswhole range of American industries and their working of children labored in textile mills, mines, glass factories, conditions varied according to the jobs they were expected and canneries. Factory managers preferred to hire children to undertake. Children had no choice in the type of jobs because they worked for the lowest wages. Other urbanthey did, it depended on what was available in the location youths worked as newsboys, messengers, bootblacks, and they were raised in. What kind of jobs did Children have in peddlers. In rural settings they were likely to work on farms. America in the 180s for child labor? The following table describes the types of jobs and work that employed Child Many young lives were lost in industrial labor. Working children were often exposed to toxic substances. Some were poisoned when their bodies absorbed dyes in textile mills or phosphorus used in making matches. Others inhaled varnish used in furniture manufacturing or fumes from rubber making. Many suffered from lung diseases like bronchitis and tuberculosis due to poor ventilation, or air circulation. Some children were shipped from state to state, following seasonal work in agriculture or canning. In southern cotton mills, children who operated looms c ( loth-weaving devices) throughout the night had cold water thrown in their faces to keep them awake. Long working hours for children also meant that accidents were more likely to occur. Even in the best of conditions, working children were denied their right to an education. If they made it to adulthood, they had little alternative but to continue working in unskilled industrial work.
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American Industrial Revolution/Three Commodities/Real Estate/Assembly Line
Assembly Line
The last chapter of capitalism unfolded in the early 20th century and was epitomized by Henry Ford and his Model T. At first, the Model T was simply regarded as the affordable car that finally made the Ford Motor Company profitable. But it turned out to be much more. The Model T embodied a mutation we now call mass production. It solved the premium puzzle of its time, reducing the price of an automobile by 60 percent or more, and thrived in the emerging environment of mass consumption. Ford’s Model T not only changed the entire framework of production but also set the stage for another automotive pioneer, Alfred Sloan, to establish the modern, professionally managed, multidivisional company as the basis for wealth creation in the 20th century. In the end, the Model T’s power had nothing to do with cars per se. Mass production could be applied to anything—and it was. It provided the gateway to a new era because it revealed a parallel universe of economic value hidden in mass-market consumers and accessible to companies that could create affordable versions of previously unattainable goods such as cars. That potential for wealth creation remained invisible to those who clung to the 19th-century framework of small-factory.
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The New Marketplace/ 38
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New Digital Age/Facebook
Facebook is not just any corporation. It reached trillion-dollar status in a single decade by applying the logic of what I call surveillance capitalism — an economic system built on the secret extraction and manipulation of human data — to its vision of connecting the entire world. Facebook and other leading surveillance capitalist corporations now control information flows and communication infrastructures across the world. Facebook as we now know it was fashioned from Google’s rib. Mark Zuckerberg’s start-up did not invent surveillance capitalism. Google did that. In 2000, when only 25 percent of the world’s information was stored digitally, Google was a tiny start-up with a great search product but little revenue. Facebook as we now know it was fashioned from Google’s rib. Mark Zuckerberg’s start-up did not invent surveillance capitalism. Google did that. In 2000, when only 25 percent of the world’s information was stored digitally, Google was a tiny start-up with a great search product but little revenue.
He turned to Google for answers.
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New Digital Age/Facebook/Cambridge Analytica
What Cambridge Analytica did was use complex corporate setups to launder your data Cambridge Analytica
It can be hard to remember from down here, beneath the avalanche of words and promises and apologies and blog posts and manifestos that Facebook has unleashed upon us over the course of the past year, but when the Cambridge Analytica story broke one year ago, Mark Zuckerberg’s initial response was a long and deafening silence. It took five full days for the founder and CEO of Facebook, the man with total control over the world’s largest communications platform – to emerge from his Menlo Park cloisters and address the public. When he finally did, he did so with gusto, taking a new set of talking points (“We have a responsibility to protect your data, and if we can’t then we don’t deserve to serve you”) on a seemingly unending roadshow, from his own Facebook page to the mainstream press to Congress and on to an oddly earnest discussion series he’s planning to subject us to at irregular intervals for the rest of 2019. The culmination of all that verbosity came earlier this month, when Zuck unloaded a 3,000-word treatise on Facebook’s “privacy-focused” future (a phrase that somehow demands both regular quotation marks and ironic scare quotes), a missive that was perhaps best described by the Guardian’s Emily Bell as “the nightmarish college application essay of an accomplished sociopath”. The so-called pivot to privacy is in many ways the logical conclusion to the earth-shaking (and market-moving) response to the Cambridge Analytica story, which plunged Facebook into the greatest crisis in its then 14-year history. After nearly a year of its critics demanding that it respect users’ privacy, here was Facebook saying: “Fine, privacy you shall have.” (More on whether what’s being offered is actually privacy later.)
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Google’s ideal society is a population of distant users, not a citizenry. It idealizes people who are informed, but only in the ways that the corporation chooses. It means for us to be docile, harmonious, and, above all, grateful.
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35
Pokemon Go
2001, in the teeth of the dot-com bust, Google’s leaders found their breakthrough in a series of inventions that would transform advertising. Their team learned how to combine massive data flows of personal information with advanced computational analyses to predict where an ad should be placed for maximum “click through.” Predictions were computed initially by analyzing data trails that users unknowingly left behind in the company’s servers as they searched and browsed Google’s pages. Google’s scientists learned how to extract predictive metadata from this “data exhaust” and use it to analyze likely patterns of future behavior. Prediction was the first imperative that determined the second imperative: extraction. Lucrative predictions required flows of human data at unimaginable scale. Users did not suspect that their data was secretly hunted and captured from every corner of the internet and, later, from apps, smartphones, devices, cameras and sensors. User ignorance was understood as crucial to success. Each new product was a means to more “engagement,” a euphemism used to conceal illicit extraction operations.
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The number of daily active users in the US alone and via iPhone is 827,205.
When asked “What is Google?” the co-founder Larry Page laid it out in 2001, according to a detailed account by Douglas Edwards, Google’s first brand manager, in his book “I’m Feeling Lucky”: “Storage is cheap. Cameras are cheap. People will generate enormous amounts of data,” Mr. Page said. “Everything you’ve ever heard or seen or experienced will become searchable. Your whole life will be searchable.”risky start-ups, pricked the dot-com balloon with monetary tightening in the spring of 2000. It was the dot-coms’ misfortune to be first the beneficiaries and then the victims of these larger economic forces. 36
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New Digital Age/Facebook/Cambridge Analytica/Pokemon Go
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Instead of selling search to users, Google survived by turning its search engine into a sophisticated surveillance medium for seizing human data. Company executives worked to keep these economic operations secret, hidden from users, lawmakers, and competitors. Mr. Page opposed anything that might “stir the privacy pot and endanger our ability to gather data,” Mr. Edwards wrote.
Next, these computational prediction products are sold into a new kind of marketplace: business customers who want to know what consumers will do in the future. Just as we have markets that trade in pork belly futures or oil futures, these new markets trade in “human futures.” Such markets have very specific competitive dynamics. They compete on the basis of who has the best predictions — in other words, who can do the best job selling certainty. The competition to sell certainty reveals the key economic imperatives of this new logic.
Massive-scale extraction operations were the keystone to the new economic edifice and superseded other considerations, beginning with the quality of information, because in the logic of surveillance capitalism, information integrity is not correlated with revenue.Prediction was the first imperative that determined the second imperative: extraction. Lucrative predictions required flows of human data at unimaginable scale. Users did not suspect that their data was secretly hunted and captured from every corner of the internet and, later, from apps, smartphones, devices, cameras and sensors. User ignorance was understood as crucial to success. Each new product was a means to more “engagement,” a euphemism used to conceal illicit extraction operations.
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New Digital Age/Facebook/Cambridge Analytica/Pokemon Go/The New Marketplace
We are learning how to write the music, and then we let the music make them dance. The New Marketplace
Google created the first insanely lucrative markets to trade in human futures, what we now know as online targeted advertising, based on their predictions of which ads users would click. Between 2000, when the new economic logic was just emerging, and 2004, when the company went public, revenues increased by 3,590 percent. This startling number represents the “surveillance dividend.” It quickly reset the bar for investors, eventually driving start-ups, apps developers and established companies to shift their business models toward surveillance capitalism. The promise of a fast track to outsized revenues from selling human futures drove this migration first to Facebook, then through the tech sector and now throughout the rest of the economy to industries as disparate as insurance, retail, finance, education, health care, real estate, entertainment and every product that begins with the word “smart” or service touted as “personalized.” Surveillance capitalism’s economic imperatives were refined in the competition to sell certainty. Early on it was clear that machine intelligence must feed on volumes of data, compelling economies of scale in data extraction. Eventually it was understood that volume is necessary but not sufficient. The best algorithms also require varieties of data — economies of scope.
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This realization helped drive the “mobile revolution” sending users into the real world armed with cameras, computers, gyroscopes and microphones packed inside their smart new phones. In the competition for scope, surveillance capitalists want your home and what you say and do within its walls. They want your car, your medical conditions, and the shows you stream; your location as well as all the streets and buildings in your path and all the behavior of all the people in your city. They want your voice and what you eat and what you buy; your children’s play time and their schooling; your brain waves and your bloodstream. Nothing is exempt
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As we move into the third decade of the 21st century, surveillance capitalism is the dominant economic institution of our time. In the absence of countervailing law, this system successfully mediates nearly every aspect of human engagement with digital information. The promise of the surveillance dividend now draws surveillance economics into the “normal” economy, from insurance, retail, banking and finance to agriculture, automobiles, education, health care and more. Today all apps and software, no matter how benign they appear, are designed to maximize data collection about you. Personal data. Historically, great concentrations of corporate power were associated with economic harms. But when human data are the raw material and predictions of human behavior are the product, then the harms are social rather than economic. The difficulty is that these novel harms are typically understood as separate, even unrelated, problems, which makes them impossible to solve. Instead, each new stage of harm creates the conditions for the next stage, which is impossible to foresee.
39
Hey Google, Can you lock the doors?
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Hey Google, Can you purchase a bassinet?
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Hey Google, set a reminder for my doctors appointment
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Privacy means that we have decision rights over our experience. As Justice Louis Brandeis pointed out years ago, these rights enable us to decide what is shared and what is private. Without those decision rights, we have no protection from surveillance capitalism’s economies of action, which are gradually being institutionalized as a global means of behavioural monitoring and modification in the service of its commercial objectives. These systems are a direct assault on human agency and individual sovereignty as they challenge the most elemental right to autonomous action. Without agency there is no freedom, and without freedom there can be no democracy.
hological If you’ve hide, you
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Index A
B
C
D
E
44
Application 10, 8, 26, 21, 20 American 16,23, Apple 12,19 Amazon 26,19 Assembly 16, 21, Automotive 25, 21, Analytica 32, 33, Advertising 36, 33, 38 Behavioral 10, 11, 26, 21 Bubble 10,14,25, Bust 10, 8, 26, 21, 20 Browsed 10,5,6,12,19 Business 13, 20, 21, 26
Capitalism 10, 18, 25, 28, 30, 36, 37, 38 Commodity 18,5,6,12,19 Consumer 16, 17, 20, 21, 22, 23, 30, 36 Company 10, 11, 14, 20, 23, 36, 38 Competition 14 Data 10, 14, 26, 21, 25, 28, 29, 32, 33, 36, 38 Dotcom 10, 11, 14, 15 Designed 30, 33
Economic 10, 20, 21, 21, 20 Extraction 10, 14, 22 33, 36, 40, 44 Executive 11, 18, Ecosystem 10, 14
F G
H
I
Facebook 26, 33, Ford 25 Sample 2, 8, 26, 21, 20 Sample 2,5,6,12,19 Google 10, 12, 13, 14, 17 20, 34, 36, 42, 45 Global 29, 33, 43 Sample 2, 8, 26, 21, 20 Sample 2,5,6,12,19
Human 20, 22, 23, 40 Household 22, 23 History 11, 12, 23, 40 Humanity 11
Internet 10, 11, 12, 35 Information 10, 15, 25, 33, 40, 41, 42 Imperative 11, 13, 20, 26,
J
Justice 40, 44,
K
Karl 18, 19
L
Lucrative 11, 15, 26, 21, 20 Labor 16,22,
M
N
O
P
Q
Marx 18, 19 Model T 25, 8, 26, 21, 20 Modern 30, 35, 44 Monetary 10, 30, 35, 44
Niantic 36 National 12, 14, 22, Nation 23, 39, Name 10, 13, 25, 30, 31
Occupied 25, 26, Open 27 Obstacle 10, 14, 23, 41
Prediction 10,15,6,12,19 Personal 18, 8, 26, 21, 20 Participation 11, 6,12,19, 22, 36 Population 20, 21, 22 Pokemon 36, 37,
Quantity 10, 13, 18, 33, 36, 41, 42, 44 Quality 12, 14, 25 Quantify 10, 15, 16, 18, 22
R
S
Real Estate 20,21,6,12,19 Sample 2, 8, 26, 21, 20 Sample 2,5,6,12,19
Surveillance 10, 11, 12, 16, 35, 36, 37, 38, 39, 42, 44, 45, 46, Search 10, 11, 12, 30, 33, 42, 44
T
Targeted 2,5,6,12,19 Tagged 17, 20
U
User 10, 11, 26, 21, 20
V
Valuable 10, 40, 44 Valuable 36
45
W
Wage 37, 38 Watch 10, 30, 32, 33 Waste 18, 22, 23, 21, 20
X
Y
Z
46
Yourself 30, 40, 44, You 40, 44
Colophon
Typefaces Display and Headings / Kulturista Designed by Tomas Brousil Published by Suitcase Type Foundry Body Text / SF Pro Designed and developed at Apple Inc. Decorative & Numerals / SF Mono Designed and developed at Apple Inc.
Tools and Software Adobe Creative Cloud / InDesign, Illustrator, Photoshop Equipment / Macbook Pro Retina Display 2017
Acknowledgments Designer / Michael Vasquez Photography / Sourced from Pexels
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