But new research, using cellphone location data, shows that visits to restaurants that identify as Black-owned, compared to those without a label, dropped off after some initial spikes and were inconsistent across 20 U.S cities.

Authors of the study, led by the 91̽��, say the findings raise questions about the effectiveness of such a labelling campaign, and how tech companies and even local governments could better support communities of color, whether through such programs or other means.

“Big tech plays an increasingly influential role in almost every aspect of our everyday life, especially in today’s economic recovery, and the Black-owned labelling campaign appears to be well-intended,” said , an associate professor of geography at the 91̽��who led the study through his . “But what have been the consequences? As allyship to minorities has become a core value of our time, how can big tech become a better and more inclusive ally? This research provides a timely case study.”

and have documented how the pandemic has exacerbated social and health disparities. And while the city- and statewide lockdowns of the pandemic’s early months had significant economic impacts, , less is known about specific businesses, in specific communities, over time.

The , published online Aug. 25 in the Annals of the American Association of Geographers, is among the first to use cellphone location data to estimate restaurant visits, and to use that data to compare impacts.

Several companies initiated a “Black-owned” labelling campaign in spring 2020. The program generally allows an owner to identify their business as “Black-owned,” and, through online reviews, customers can comment on ownership or other aspects of the business.

With Yelp’s “Black-owned” label as a guide, researchers then turned to visitation records from SafeGraph, which collects points-of-interest data from the GPS systems of 45 million mobile devices in the U.S. Researchers selected 20 major cities as their sample, to represent a geographic and demographic range, including Chicago, Detroit, New York and Seattle.

Overall, the researchers found there were statistically significant differences between Black-owned and “ownership-unreported” restaurants throughout the 20 cities, primarily measured by relative declines in visits. New Orleans and Detroit showed the greatest disparities, while New York showed the least. Looking at just the first few months after the labelling began, cities such as Baltimore, Denver, and Charlotte, North Carolina, were among those that showed higher spikes in visits to Black-owned restaurants than those whose ownership was unreported.

Due to the number of cities involved, and the fluctuations in the number of visitors at various stages of the pandemic, broader explanations are harder to draw. Early in the pandemic, visits to Black-owned businesses outpaced those to ownership-unreported businesses, peaking in June and July, but eventually declines led to larger disparities between businesses with and without the label.

Further research could explore the different outcomes within and among cities, Zhao said, and, through ethnographic fieldwork and interviews, how the labelling program affected individual businesses. The study cites reports of fake reviews at a Black-owned antique store in Brooklyn to underscore the possibility of harassment and other negative impacts from an online label.

First author , an assistant professor of geosciences at the University of Arkansas, said the study’s findings illuminate another example of how communities of color have suffered more negative impacts over the past two years.

“The voices of the minority, the vulnerable, and others who have been disproportionately affected by the COVID-19 pandemic need to be heard,” Huang said.

The results reveal how a label alone doesn’t necessarily generate sustained positive results, Zhao said. Tech companies have a responsibility, in launching such a program, to think through consequences, listen to business owners and provide supports, including a way for businesses to opt out. But currently, a complete opt-out appears impossible. While a restaurant can terminate the use of the label on a particular platform, previous online reviews, for example, could turn it up.

“It is easy for tech companies to set up an opt-out mechanism, but it is extremely difficult to stop the proliferation of the ownership information if it has been reposted to other outlets or platforms. So, the tech company should inform the business owner of the risk of that information remaining available even after the opt-out,” Zhao said.

Local government agencies, too, can learn from how Black-owned restaurants experienced the pandemic differently, and use the cellphone location data to inform place-based relief strategies in the future, Zhao said.

The study was funded by the Department of Geography Research Fund at the 91̽��. Additional co-authors were , a graduate student in the 91̽��Department of Geography; Shaozeng Zhang of Oregon State University; and at the University of South Carolina.

For more information, contact Zhao at zhaobo@uw.edu.

This release includes material from the University of Arkansas. 

A fire in Central Park seems to appear as in a satellite image. Colorful lights on Diwali night in India, , seem to show widespread fireworks activity.

Both images exemplify what a new 91̽��-led study calls “location spoofing.” The photos — created by different people, for different purposes — are fake but look like genuine images of real places. And with the more sophisticated AI technologies available today, researchers warn that such “deepfake geography” could become a growing problem.

So, using satellite photos of three cities and drawing upon methods used to manipulate video and audio files, a team of researchers set out to identify new ways of detecting fake satellite photos, warn of the dangers of falsified geospatial data and call for a system of geographic fact-checking.

“This isn’t just Photoshopping things. It’s making data look uncannily realistic,” said , assistant professor of geography at the 91̽��and lead author of the , which published April 21 in the journal Cartography and Geographic Information Science. “The techniques are already there. We’re just trying to expose the possibility of using the same techniques, and of the need to develop a coping strategy for it.”

As Zhao and his co-authors point out, fake locations and other inaccuracies have been part of mapmaking since ancient times. That’s due in part to the very nature of translating real-life locations to map form, as no map can capture a place exactly as it is. But some inaccuracies in maps are spoofs created by the mapmakers. The term “paper towns” describes discreetly placed fake cities, mountains, rivers or other features on a map to prevent copyright infringement. On the more lighthearted end of the spectrum, an official Michigan Department of Transportation highway map in the 1970s included the fictional cities of a play on “Beat OSU” and “Go Blue,” because the then-head of the department wanted to give a shoutout to his alma mater while protecting the copyright of the map.

But with the prevalence of geographic information systems, Google Earth and other satellite imaging systems, location spoofing involves far greater sophistication, researchers say, and carries with it more risks. In 2019, the director of the National Geospatial Intelligence Agency, the organization charged with supplying maps and analyzing satellite images for the U.S. Department of Defense, .

To study how satellite images can be faked, Zhao and his team turned to an AI framework that has been used in manipulating other types of digital files. When applied to the field of mapping, the algorithm essentially learns the characteristics of satellite images from an urban area, then generates a deepfake image by feeding the characteristics of the learned satellite image characteristics onto a different base map — similar to how popular image filters can map the features of a human face onto a cat.

Next, the researchers combined maps and satellite images from three cities — Tacoma, Seattle and Beijing — to compare features and create new images of one city, drawn from the characteristics of the other two. They designated Tacoma their “base map” city and then explored how geographic features and urban structures of Seattle (similar in topography and land use) and Beijing (different in both) could be incorporated to produce deepfake images of Tacoma.

In the example below, a Tacoma neighborhood is shown in mapping software (top left) and in a satellite image (top right). The subsequent deep fake satellite images of the same neighborhood reflect the visual patterns of Seattle and Beijing. Low-rise buildings and greenery mark the “Seattle-ized” version of Tacoma on the bottom left, while Beijing’s taller buildings, which AI matched to the building structures in the Tacoma image, cast shadows — hence the dark appearance of the structures in the image on the bottom right. Yet in both, the road networks and building locations are similar.

The untrained eye may have difficulty detecting the differences between real and fake, the researchers point out. A casual viewer might attribute the colors and shadows simply to poor image quality. To try to identify a “fake,” researchers homed in on more technical aspects of image processing, such as color histograms and frequency and spatial domains.

Some simulated satellite imagery can serve a purpose, Zhao said, especially when representing geographic areas over periods of time to, say, understand urban sprawl or climate change. There may be a location for which there are no images for a certain period of time in the past, or in forecasting the future, so creating new images based on existing ones — and clearly identifying them as simulations — could fill in the gaps and help provide perspective.

The study’s goal was not to show that geospatial data can be falsified, Zhao said. Rather, the authors hope to learn how to detect fake images so that geographers can begin to develop the data literacy tools, similar to today’s fact-checking services, for public benefit.

“As technology continues to evolve, this study aims to encourage more holistic understanding of geographic data and information, so that we can demystify the question of absolute reliability of satellite images or other geospatial data,” Zhao said. “We also want to develop more future-oriented thinking in order to take countermeasures such as fact-checking when necessary,” he said.

Co-authors on the study were Yifan Sun, a graduate student in the 91̽��Department of Geography; Shaozeng Zhang and Chunxue Xu of Oregon State University; and Chengbin Deng of Binghamton University.

For more information, contact Zhao at zhaobo@uw.edu.

A new interactive map from 91̽�� geographer aims to answer those questions in real time.

Zhao produced this of the coronavirus, which updates every few hours with data from the Centers for Disease Control and Prevention, the World Health Organization, the People’s Republic of China, and other government agencies, including those in Hong Kong, Macau and Taiwan. By zooming in on various countries – such as China – users can see the numbers of cases, recoveries and deaths, as well as trends over time.

“Mapping is a powerful tool to tell social, cultural and political phenomena,” said Zhao, an assistant professor of geography who specializes in the social implications of maps and using innovative methods in mapping. “As a geographer, and in what people call the ‘post-truth era,’ it’s important to weigh in with data sources to show people how things are happening.”

The outbreak of the coronavirus, which is believed to have originated in Wuhan, China in December, has been declared a public health emergency by the WHO. Tens of thousands have been infected, and more than 1,000 have died.

Zhao recently produced an online atlas to illustrate the . The coronavirus, he said, is another societal issue that people can gain perspective on by seeing it on a map.

While the Chinese government has been criticized for its response to the crisis, and concerns have arisen about its transparency, Zhao said China’s National Health Commission data is the most accurate available for the country.

But the outbreak is not just affecting China or the city of Wuhan, he added. It is a global issue, and that’s the perspective users can gain from the map. The numbers on display are important, too, he said, because there are more recoveries than deaths.

“That can give people encouragement,” he said.

As more detailed data become available, Zhao plans to add county-level totals from China, and state and provincial totals from the United States and Canada, respectively.

Note: is best viewed on a larger screen. 

For more information, including links to download Zhao’s data, contact Zhao at zhaobo@uw.edu or 206-685-3846.