Building a Data Infrastructure for the Bioeconomy


Article by Gopal P. Sarma and Melissa Haendel: “While the development of vaccines for COVID-19 has been widely lauded, other successful components of the national response to the pandemic have not received as much attention. The National COVID Cohort Collaborative (N3C), for example, flew under the public’s radar, even though it aggregated crucial US public health data about the new disease through cross-institutional collaborations among government, private, and nonprofit health and research organizations. These data, which were made available to researchers via cutting-edge software tools, have helped in myriad ways: they led to identification of the clinical characteristics of acute COVID-19 for risk prediction, assisted in providing clinical care for immunocompromised adults, revealed how COVID infection affects children, and documented that vaccines appear to reduce the risk of developing long COVID.

N3C has created the largest national, publicly available patient-level dataset in US history. Through a unique public-private partnership, over 300 participating organizations quickly overcame privacy concerns and data silos to include 13 million patient records in the project. More than 3,000 participating scientists are now working to overcome the particular challenge faced in the United States—the lack of a national healthcare data infrastructure available in many other countries—to support public health and medical responses. N3C shows great promise for unraveling answers to questions related to COVID, but it could easily be expanded for many areas of public health, including pandemic preparedness and monitoring disease status across the population.

As public servants dedicated to improving public health and equity, we believe that to unite the nation’s fragmented public health system, the United States should establish a standing capacity to collect, harmonize, and sustain a wide range of data types and sources. The public health data collected by N3C would ultimately be but one component of a rich landscape of interoperable data systems that can guide public policy in an era of rapid environmental change, sophisticated biological threats, and an economy enabled by biotechnology. Such an effort will require new thinking about data collection, infrastructure, and regulation, but its benefits could be enormous—enabling policymakers to make decisions in an increasingly complex world. And as the interconnections between society, industry, and government continue to intensify, decisionmaking of all types and scales will be more efficient and responsive if it can rely on significantly expanded data collection and analysis capabilities…(More)”.

Prisms of the People


Book by Hahrie Han, Elizabeth McKenna, and Michelle Oyakawa: “Grassroots organizing and collective action have always been fundamental to American democracy but have been burgeoning since the 2016 election, as people struggle to make their voices heard in this moment of societal upheaval. Unfortunately much of that action has not had the kind of impact participants might want, especially among movements representing the poor and marginalized who often have the most at stake when it comes to rights and equality. Yet, some instances of collective action have succeeded. What’s the difference between a movement that wins victories for its constituents, and one that fails? What are the factors that make collective action powerful?

Prisms of the People addresses those questions and more. Using data from six movement organizations—including a coalition that organized a 104-day protest in Phoenix in 2010 and another that helped restore voting rights to the formerly incarcerated in Virginia—Hahrie Han, Elizabeth McKenna, and Michelle Oyakawa show that the power of successful movements most often is rooted in their ability to act as  “prisms of the people,” turning participation into political power just as prisms transform white light into rainbows. Understanding the organizational design choices that shape the people, their leaders, and their strategies can help us understand how grassroots groups achieve their goals.

Linking strong scholarship to a deep understanding of the needs and outlook of activists, Prisms of the People is the perfect book for our moment—for understanding what’s happening and propelling it forward….(More)”.

Morocco finds a new source of policy expertise — its own citizens


Participo: “This spring saw the release of a long-awaited report by the Commission Spéciale sur le modèle de developpement (CSMD), created in 2019 by His Majesty King Mohammed VI….

“Blue ribbon” commissions to tackle thorny issues are nothing new. But the methods employed by Morocco’s CSMD, and the proposals which resulted from them, point the way toward an entirely new approach to governance in the Middle East and North Africa (MENA) region.

Morocco’s new model of development was created through methods of collective intelligence, an emerging science that explores how groups can outperform individuals in learning, decision making, and problem-solving.

It is an ability that has long defined our species, from coordinated bands of hunters on the savannah to the networks of scientists that develop coronavirus vaccines. A complex environment has conditioned humans to pool their knowledge to survive. But collective intelligence doesn’t just happen; for the “wisdom of crowds” to emerge, a group must be organized in the right way, with the right methods and tools….

Beginning in January 2020, the CSMD launched a broad national consultation open to all Moroccan citizens, aimed at harnessing a wide variety of expertise from local communities, government, NGOs, and the private sector.

Its multi-channel approach was designed to reflect four indicators that studies suggest are critical to producing collective intelligence: a diversity of participants and information sources; a critical mass of contributions; a sufficiently rich exchange of information at each “touch point”; and an effective process to synthesize contributions into a coherent whole.

The CSMD created an online platform with opportunities to give quick feedback (“What is one thing you want to change about Morocco?”), as well as more detailed proposals on themes like health care and territorial inequality. A social media campaign reached an estimated 3.2 million citizens, with dozens of “participatory workshops” live-streamed on Facebook and YouTube.

To seek out the knowledge of those least connected to these channels, the CSMD conducted 30 field visits to struggling urban districts, universities, and remote villages in the High Atlas mountains. These field visits featured learning sessions with social innovators and rencontres citoyennes (“citizen encounters”) where groups of 20 to 30 local residents, balanced by age and gender, shared stories and aspirations….(More)”.

Tracking Economic Activity in Response to the COVID-19 using nighttime Lights


Paper by Mark Roberts: “Over the last decade, nighttime lights – artificial lighting at night that is associated with human activity and can be detected by satellite sensors – have become a proxy for monitoring economic activity. To examine how the COVID-19 crisis has affected economic activity in Morocco, we calculated monthly lights estimates for both the country overall and at a sub-national level. By examining the intensity of Morocco’s lights in comparison with the quarterly GDP data at the national level, we are also able to confirm that nighttime lights are able to track movements in real economic activity for Morocco….(More)”.

Essential Requirements for Establishing and Operating Data Trusts


Paper by P Alison Paprica et al: “Increasingly, the label “data trust” is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe and equitable way. However, there is a gap in terms of practical guidance about how to establish and operate a data trust.

In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify essential requirements for the establishment and operation of data trusts. Preliminary findings were presented during the meeting then refined as participants and co-authors identified relevant literature and contributed to this manuscript.

Twelve (12) minimum specification requirements (“min specs”) for data trusts were identified. The foundational min spec is that data trusts must meet all legal requirements, including legal authority to collect, hold or share data. In addition, there was agreement that data trusts must have (i) an accountable governing body which ensures the data trust advances its stated purpose and is transparent, (ii) comprehensive data management including responsible parties and clear processes for the collection, storage, access, disclosure and use of data, (iii) training and accountability requirements for all data users and (iv) ongoing public and stakeholder engagement.

Based on a review of the literature and advice from participants from 15 Canadian organizations/initiatives, practical guidance in the form of twelve min specs for data trusts were agreed on. Public engagement and continued exchange of insights and experience is recommended on this evolving topic…(More)”.

Data Protection in the Humanitarian Sector – A Blockchain Approach


Report by Andrej Verity and Irene Solaiman: “Data collection and storage are becoming increasingly digital. In the humanitarian sector, data motivates action, informing organizations who then determine priorities and resource allocation in crises.

“Humanitarians are dependent on technology and on the Internet. When life-saving aid isn’t delivered on time and to the right beneficiaries, people can die.” -Brookings

In the age of information and cyber warfare, humanitarian organizations must take measures to protect civilians, especially those in critical and vulnerable positions.

“Data privacy and ensuring protection from harm, including the provision of data security, are therefore fundamentally linked—and neither can be realized without the other.” -The Signal Code

Information in the wrong hands can risk lives or even force aid organizations to shut down. For example, in 2009, Sudan expelled over a dozen international nongovernmental organizations (NGOs) that were deemed key to maintaining a lifeline to 4.7 million people in western Darfur. The expulsion occurred after the Sudanese Government collected Internet-accessible information that made leadership fear international criminal charges. Responsible data protection is a crucial component of cybersecurity. As technology develops, so do threats and data vulnerabilities. Emerging technologies such as blockchain provide further security to sensitive information and overall data storage. Still, with new technologies come considerations for implementation…(More)”.

Geolocation Data for Pattern of Life Analysis in Lower-Income Countries


Report by Eduardo Laguna-Muggenburg, Shreyan Sen and Eric Lewandowski: “Urbanization processes in the developing world are often associated with the creation of informal settlements. These areas frequently have few or no public services exacerbating inequality even in the context of substantial economic growth.

In the past, the high costs of gathering data through traditional surveying methods made it challenging to study how these under-served areas evolve through time and in relation to the metropolitan area to which they belong. However, the advent of mobile phones and smartphones in particular presents an opportunity to generate new insights on these old questions.

In June 2019, Orbital Insight and the United Nations Development Programme (UNDP) Arab States Human Development Report team launched a collaborative pilot program assessing the feasibility of using geolocation data to understand patterns of life among the urban poor in Cairo, Egypt.

The objectives of this collaboration were to assess feasibility (and conditionally pursue preliminary analysis) of geolocation data to create near-real time population density maps, understand where residents of informal settlements tend to work during the day, and to classify universities by percentage of students living in informal settlements.

The report is organized as follows. In Section 2 we describe the data and its limitations. In Section 3 we briefly explain the methodological background. Section 4 summarizes the insights derived from the data for the Egyptian context. Section 5 concludes….(More)”.

Handbook of Research on Politics in the Computer Age


Book edited by Ashu M. G. Solo: “Technology and particularly the Internet have caused many changes in the realm of politics. Aspects of engineering, computer science, mathematics, or natural science can be applied to politics. Politicians and candidates use their own websites and social network profiles to get their message out. Revolutions in many countries in the Middle East and North Africa have started in large part due to social networking websites such as Facebook and Twitter. Social networking has also played a role in protests and riots in numerous countries. The mainstream media no longer has a monopoly on political commentary as anybody can set up a blog or post a video online. Now, political activists can network together online.

The Handbook of Research on Politics in the Computer Age is a pivotal reference source that serves to increase the understanding of methods for politics in the computer age, the effectiveness of these methods, and tools for analyzing these methods. The book includes research chapters on different aspects of politics with information technology, engineering, computer science, or math, from 27 researchers at 20 universities and research organizations in Belgium, Brazil, Cape Verde, Egypt, Finland, France, Hungary, Italy, Mexico, Nigeria, Norway, Portugal, and the United States of America. Highlighting topics such as online campaigning and fake news, the prospective audience includes, but is not limited to, researchers, political and public policy analysts, political scientists, engineers, computer scientists, political campaign managers and staff, politicians and their staff, political operatives, professors, students, and individuals working in the fields of politics, e-politics, e-government, new media and communication studies, and Internet marketing….(More)”.

Index: Secondary Uses of Personal Data


By Alexandra Shaw, Andrew Zahuranec, Andrew Young, Stefaan Verhulst

The Living Library Index–inspired by the Harper’s Index–provides important statistics and highlights global trends in governance innovation. This installment focuses on public perceptions regarding secondary uses of personal data (or the re-use of data initially collected for a different purpose). It provides a summary of societal perspectives toward personal data usage, sharing, and control. It is not meant to be comprehensive–rather, it intends to illustrate conflicting, and often confusing, attitudes toward the re-use of personal data. 

Please share any additional, illustrative statistics on data, or other issues at the nexus of technology and governance, with us at info@thelivinglib.org

Data ownership and control 

  • Percentage of Americans who say it is “very important” they control information collected about them: 74% – 2016
  • Americans who think that today’s privacy laws are not good enough at protecting people’s privacy online: 68% – 2016
  • Americans who say they have “a lot” of control over how companies collect and use their information: 9% – 2015
  • In a survey of 507 online shoppers, the number of respondents who indicated they don’t want brands tracking their location: 62% – 2015
  • In a survey of 507 online shoppers, the amount who “prefer offers that are targeted to where they are and what they are doing:” 60% – 2015 
  • Number of surveyed American consumers willing to provide data to corporations under the following conditions: 
    • “Data about my social concerns to better connect me with non-profit organizations that advance those causes:” 19% – 2018
    • “Data about my DNA to help me uncover any hereditary illnesses:” 21% – 2018
    • “Data about my interests and hobbies to receive relevant information and offers from online sellers:” 32% – 2018
    • “Data about my location to help me find the fastest route to my destination:” 40% – 2018
    • “My email address to receive exclusive offers from my favorite brands:”  56% – 2018  

Consumer Attitudes 

  • Academic study participants willing to donate personal data to research if it could lead to public good: 60% – 2014
  • Academic study participants willing to share personal data for research purposes in the interest of public good: 25% – 2014
  • Percentage who expect companies to “treat [them] like an individual, not as a member of some segment like ‘millennials’ or ‘suburban mothers:’” 74% – 2018 
    • Percentage who believe that brands should understand a “consumer’s individual situation (e.g. marital status, age, location, etc.)” when they’re being marketed to: 70% – 2018 Number who are “more annoyed” by companies now compared to 5 years ago: 40% – 2018Percentage worried their data is shared across companies without their permission: 88% – 2018Amount worried about a brand’s ability to track their behavior while on the brand’s website, app, or neither: 75% – 2018 
  • Consumers globally who expect brands to anticipate needs before they arise: 33%  – 2018 
  • Surveyed residents of the United Kingdom who identify as:
    • “Data pragmatists” willing to share personal data “under the right circumstances:” 58% – 2017
    • “Fundamentalists,” who would not share personal data for better services: 24% – 2017
    • Respondents who think data sharing is part of participating in the modern economy: 62% – 2018
    • Respondents who believe that data sharing benefits enterprises more than consumers: 75% – 2018
    • People who want more control over their data that enterprises collect: 84% – 2018
    • Percentage “unconcerned” about personal data protection: 18% – 2018
  • Percentage of Americans who think that government should do more to regulate large technology companies: 55% – 2018
  • Registered American voters who trust broadband companies with personal data “a great deal” or “a fair amount”: 43% – 2017
  • Americans who report experiencing a major data breach: 64% – 2017
  • Number of Americans who believe that their personal data is less secure than it was 5 years ago: 49% – 2019
  • Amount of surveyed American citizens who consider trust in a company an important factor for sharing data: 54% – 2018

Convenience

Microsoft’s 2015 Consumer Data Value Exchange Report attempts to understand consumer attitudes on the exchange of personal data across the global markets of Australia, Brazil, Canada, Colombia, Egypt, Germany, Kenya, Mexico, Nigeria, Spain, South Africa, United Kingdom and the United States. From their survey of 16,500 users, they find:

  • The most popular incentives for sharing data are: 
    • Cash rewards: 64% – 2015
    • Significant discounts: 49% – 2015
    • Streamlined processes: 29% – 2015
    • New ideas: 28% – 2015
  • Respondents who would prefer to see more ads to get new services: 34% – 2015
  • Respondents willing to share search terms for a service that enabled fewer steps to get things done: 70% – 2015 
  • Respondents willing to share activity data for such an improvement: 82% – 2015
  • Respondents willing to share their gender for “a service that inspires something new based on others like them:” 79% – 2015

A 2015 Pew Research Center survey presented Americans with several data-sharing scenarios related to convenience. Participants could respond: “acceptable,” “it depends,” or “not acceptable” to the following scenarios: 

  • Share health information to get access to personal health records and arrange appointments more easily:
    • Acceptable: 52% – 2015
    • It depends: 20% – 2015
    • Not acceptable: 26% – 2015
  • Share data for discounted auto insurance rates: 
    • Acceptable: 37% – 2015
    • It depends: 16% – 2015
    • Not acceptable: 45% – 2015
  • Share data for free social media services: 
    • Acceptable: 33% – 2015
    • It depends: 15% – 2015
    • Not acceptable: 51% – 2015
  • Share data on smart thermostats for cheaper energy bills: 
    • Acceptable: 33% – 2015
    • It depends: 15% – 2015
    • Not acceptable: 51% – 2015

Other Studies

  • Surveyed banking and insurance customers who would exchange personal data for:
    • Targeted auto insurance premiums: 64% – 2019
    • Better life insurance premiums for healthy lifestyle choices: 52% – 2019 
  • Surveyed banking and insurance customers willing to share data specifically related to income, location and lifestyle habits to: 
    • Secure faster loan approvals: 81.3% – 2019
    • Lower the chances of injury or loss: 79.7% – 2019 
    • Receive discounts on non-insurance products or services: 74.6% – 2019
    • Receive text alerts related to banking account activity: 59.8% – 2019 
    • Get saving advice based on spending patterns: 56.6% – 2019
  • In a survey of over 7,000 members of the public around the globe, respondents indicated:
    • They thought “smartphone and tablet apps used for navigation, chat, and news that can access your contacts, photos, and browsing history” is “creepy;” 16% – 2016
    • Emailing a friend about a trip to Paris and receiving advertisements for hotels, restaurants and excursions in Paris is “creepy:” 32% – 2016
    • A free fitness-tracking device that monitors your well-being and sends a monthly report to you and your employer is “creepy:” 45% – 2016
    • A telematics device that allows emergency services to track your vehicle is “creepy:” 78% – 2016
  • The number of British residents who do not want to work with virtual agents of any kind: 48% – 2017
  • Americans who disagree that “if companies give me a discount, it is a fair exchange for them to collect information about me without my knowing”: 91% – 2015

Data Brokers, Intermediaries, and Third Parties 

  • Americans who consider it acceptable for a grocery store to offer a free loyalty card in exchange for selling their shopping data to third parties: 47% – 2016
  • Number of people who know that “searches, site visits and purchases” are reviewed without consent:  55% – 2015
  • The number of people in 1991 who wanted companies to ask them for permission first before collecting their personal information and selling that data to intermediaries: 93% – 1991
    • Number of Americans who “would be very concerned if the company at which their data were stored sold it to another party:” 90% – 2008
    • Percentage of Americans who think it’s unacceptable for their grocery store to share their shopping data with third parties in exchange for a free loyalty card: 32% – 2016
  • Percentage of Americans who think that government needs to do more to regulate advertisers: 64% – 2016
    • Number of Americans who “want to have control over what marketers can learn about” them online: 84% – 2015
    • Percentage of Americans who think they have no power over marketers to figure out what they’re learning about them: 58% – 2015
  • Registered American voters who are “somewhat uncomfortable” or “very uncomfortable” with companies like Internet service providers or websites using personal data to recommend stories, articles, or videos:  56% – 2017
  • Registered American voters who are “somewhat uncomfortable” or “very uncomfortable” with companies like Internet service providers or websites selling their personal information to third parties for advertising purposes: 64% – 2017

Personal Health Data

The Robert Wood Johnson Foundation’s 2014 Health Data Exploration Project Report analyzes attitudes about personal health data (PHD). PHD is self-tracking data related to health that is traceable through wearable devices and sensors. The three major stakeholder groups involved in using PHD for public good are users, companies that track the users’ data, and researchers. 

  • Overall Respondents:
    • Percentage who believe anonymity is “very” or “extremely” important: 67% – 2014
    • Percentage who “probably would” or “definitely would” share their personal data with researchers: 78% – 2014
    • Percentage who believe that they own—or should own—all the data about them, even when it is indirectly collected: 54% – 2014
    • Percentage who think they share or ought to share ownership with the company: 30% – 2014
    • Percentage who think companies alone own or should own all the data about them: 4% – 2014
    • Percentage for whom data ownership “is not something I care about”: 13% – 2014
    • Percentage who indicated they wanted to own their data: 75% – 2014 
    • Percentage who would share data only if “privacy were assured:” 68% – 2014
    • People who would supply data regardless of privacy or compensation: 27% – 2014
      • Percentage of participants who mentioned privacy, anonymity, or confidentiality when asked under what conditions they would share their data:  63% – 2014
      • Percentage who would be “more” or “much more” likely to share data for compensation: 56% – 2014
      • Percentage who indicated compensation would make no difference: 38% – 2014
      • Amount opposed to commercial  or profit-making use of their data: 13% – 2014
    • Percentage of people who would only share personal health data with a guarantee of:
      • Privacy: 57% – 2014
      • Anonymization: 90% – 2014
  • Surveyed Researchers: 
    • Percentage who agree or strongly agree that self-tracking data would help provide more insights in their research: 89% – 2014
    • Percentage who say PHD could answer questions that other data sources could not: 95% – 2014
    • Percentage who have used public datasets: 57% – 2014
    • Percentage who have paid for data for research: 19% – 2014
    • Percentage who have used self-tracking data before for research purposes: 46% – 2014
    • Percentage who have worked with application, device, or social media companies: 23% – 2014
    • Percentage who “somewhat disagree” or “strongly disagree” there are barriers that cannot be overcome to using self-tracking data in their research: 82% – 2014 

SOURCES: 

“2019 Accenture Global Financial Services Consumer Study: Discover the Patterns in Personality”, Accenture, 2019. 

“Americans’ Views About Data Collection and Security”, Pew Research Center, 2015. 

“Data Donation: Sharing Personal Data for Public Good?”, ResearchGate, 2014.

Data privacy: What the consumer really thinks,” Acxiom, 2018.

“Exclusive: Public wants Big Tech regulated”, Axios, 2018.

Consumer data value exchange,” Microsoft, 2015.

Crossing the Line: Staying on the right side of consumer privacy,” KPMG International Cooperative, 2016.

“How do you feel about the government sharing our personal data? – livechat”, The Guardian, 2017. 

“Personal data for public good: using health information in medical research”, The Academy of Medical Sciences, 2006. 

“Personal Data for the Public Good: New Opportunities to Enrich Understanding of Individual and Population Health”, Robert Wood Johnson Foundation, Health Data Exploration Project, Calit2, UC Irvine and UC San Diego, 2014. 

“Pew Internet and American Life Project: Cloud Computing Raises Privacy Concerns”, Pew Research Center, 2008. 

“Poll: Little Trust That Tech Giants Will Keep Personal Data Private”, Morning Consult & Politico, 2017. 

“Privacy and Information Sharing”, Pew Research Center, 2016. 

“Privacy, Data and the Consumer: What US Thinks About Sharing Data”, MarTech Advisor, 2018. 

“Public Opinion on Privacy”, Electronic Privacy Information Center, 2019. 

“Selligent Marketing Cloud Study Finds Consumer Expectations and Marketer Challenges are Rising in Tandem”, Selligent Marketing Cloud, 2018. 

The Data-Sharing Disconnect: The Impact of Context, Consumer Trust, and Relevance in Retail Marketing,” Boxever, 2015. 

Microsoft Research reveals understanding gap in the brand-consumer data exchange,” Microsoft Research, 2015.

“Survey: 58% will share personal data under the right circumstances”, Marketing Land: Third Door Media, 2019. 

“The state of privacy in post-Snowden America”, Pew Research Center, 2016. 

The Tradeoff Fallacy: How Marketers Are Misrepresenting American Consumers And Opening Them Up to Exploitation”, University of Pennsylvania, 2015.

Facebook’s AI team maps the whole population of Africa


Devin Coldewey at TechCrunch: “A new map of nearly all of Africa shows exactly where the continent’s 1.3 billion people live, down to the meter, which could help everyone from local governments to aid organizations. The map joins others like it from Facebook  created by running satellite imagery through a machine learning model.

It’s not exactly that there was some mystery about where people live, but the degree of precision matters. You may know that a million people live in a given region, and that about half are in the bigger city and another quarter in assorted towns. But that leaves hundreds of thousands only accounted for in the vaguest way.

Fortunately, you can always inspect satellite imagery and pick out the spots where small villages and isolated houses and communities are located. The only problem is that Africa is big. Really big. Manually labeling the satellite imagery even from a single mid-sized country like Gabon or Malawi would take a huge amount of time and effort. And for many applications of the data, such as coordinating the response to a natural disaster or distributing vaccinations, time lost is lives lost.

Better to get it all done at once then, right? That’s the idea behind Facebook’s Population Density Maps project, which had already mapped several countries over the last couple of years before the decision was made to take on the entire African continent….

“The maps from Facebook ensure we focus our volunteers’ time and resources on the places they’re most needed, improving the efficacy of our programs,” said Tyler Radford, executive director of the Humanitarian OpenStreetMap Team, one of the project’s partners.

The core idea is straightforward: Match census data (how many people live in a region) with structure data derived from satellite imagery to get a much better idea of where those people are located.

“With just the census data, the best you can do is assume that people live everywhere in the district – buildings, fields, and forests alike,” said Facebook engineer James Gill. “But once you know the building locations, you can skip the fields and forests and only allocate the population to the buildings. This gives you very detailed 30 meter by 30 meter population maps.”

That’s several times more accurate than any extant population map of this size. The analysis is done by a machine learning agent trained on OpenStreetMap data from all over the world, where people have labeled and outlined buildings and other features.

First the huge amount of Africa’s surface that obviously has no structure had to be removed from consideration, reducing the amount of space the team had to evaluate by a factor of a thousand or more. Then, using a region-specific algorithm (because things look a lot different in coastal Morocco than they do in central Chad), the model identifies patches that contain a building….(More)”.