The October 2022 issue of IEEE Spectrum is here!

Close bar

How Facebook and Google Track Public’s Movement in Effort to Fight COVID-19

Location data provide rich resource for decision makers, scientists, and the public

4 min read
A man uses his smart phone in New York's Central Park, Tuesday, March 31, 2020. A woman sits behind him, also on a phone.
Photo: Mary Altaffer/AP

How we move about in our communities—where we go and how often—greatly affects the spread of COVID-19. And few know our whereabouts better than Facebook and Google.

So, in an effort to help researchers combat the pandemic, the two companies say they are now making their troves of GPS-based mobility data available. The data comes from users who opt in to location services on the companies’ platforms and is provided for public health use in an aggregated, anonymized way.

Such data is vital to public health researchers’ efforts to understand trends in population movement and predict the spread of the disease, which is caused by the novel coronavirus SARS-CoV-2. Local government officials can use the data to make informed decisions on travel and social distancing interventions.

Data for Good

Both Facebook and Google are providing information about where people are going, but the companies differ in the way they are releasing the information.

Facebook, through its Data for Good program, provides mobility datasets and maps directly to researchers upon request. Facebook generates the data in file formats that support epidemiological models and case data.

“We’re sharing the data in a way that public health researchers can use,” says Laura McGorman, policy lead for Facebook’s Data for Good program. “Once a researcher signs a license agreement, they can request data through our mapping portal and get it the next day,” she says.

The mobility datasets let researchers look at population movement between two points, movement patterns such as commutes, and whether people are staying close to home or visiting many parts of town. Facebook’s is the “only source of mobility data in machine-readable format” that is global and free of charge, says McGorman.

Data for Good started three years ago as an initiative to help track evacuations and displacement after natural disasters. It has since expanded to address disease and, most recently, COVID-19. The company gathers its information from people using Facebook on their mobile phones with the location history feature enabled. Data is aggregated to protect individual privacy.

Scientists have used Facebook’s data in several ways over the last few weeks to study the pandemic. For example, scientists at the Institute for Disease Modeling in Bellevue, Washington used Facebook’s mobility data to study how social distancing measures and a stay-at-home orders have affected movement near Seattle. They found that population movement indeed declined, which led to reduced transmission of the virus.

Separately, researchers in Italy used Facebook’s mobility data to analyze how lockdown orders affect economic conditions and create an economic segregation effect. A report from the National Tsing Hua University in Taiwan used Facebook’s data to show that travel restrictions reduced the spread of the virus.

Facebook’s program is also supplying the bulk of the data for the COVID-19 Mobility Data Network. The recently-formed group, composed of a network of epidemiologists, uses mobility data to generate daily situation reports for decision makers who are implementing social distancing interventions.

Google’s mobility tracking tool

Separately, Google on April 3 announced that it had launched a mobility tracking tool called COVID-19 Community Mobility Reports. The web-based tool is available freely to the public and provides insights on how communities have reduced or increased their visits to certain types of places.

The public can go to the website and choose a region, such as a state or country. The tool then generates graphs on a downloadable PDF displaying the percentage change in visits over the last few weeks to places such as retail stories, pharmacies, parks, places of work and public transportation hubs in that region.

In the county where Indianapolis is located, for example, people have reduced their visits to grocery and pharmacy stories by 17% and to other retail locations by 45%, since February 23. Visits to parks, however, have increased 54%.

In a blog post highlighting the resource, Google executives wrote that they believe the mobility reports could help shape business hours, inform delivery service offerings, or indicate a need to add additional buses or trains to a particular public transportation hub.

The company pulls the data from Google users who have opted in to location tracking services. The information is aggregated and anonymized, and does not provide real-time data in an effort to protect privacy.

Mobility data similar to that from Facebook and Google have already informed decisions of government officials. Tennessee Governor Bill Lee on April 2 issued a statewide order for residents to stay at home after he reviewed mobility data released by tech startup Unacast. The information, gleaned from mobile phone location data, showed that people in some regions, such as Nashville, had significantly reduced their daily travel, but people in many other Tennessee counties had not. This convinced Lee that a statewide order was necessary.

Beyond mobility

Both Facebook and Google are releasing other kinds of data to coronavirus researchers and the public. Data for Good offers population density maps as well as social connectedness indices. The latter relies on aggregated, anonymous friendship connections on Facebook to measure the general connectedness of two geographic regions.

That type of information can help predict the spread of the virus and where to put resources. Researchers at NYU used the social connectedness data to show[PDF] that geographic regions with strong social ties to two early COVID-19 hotspots—in New York and Italy—had higher cases of the illness. Separately, an organization funded by the World Bank used Facebook’s population density data to help determine where coronavirus testing facilities and extra beds should be located.

Google and Apple last week announced an ambitious effort to provide the technological support for digital contact tracing. The strategy would allow people with certain Bluetooth-enabled apps to find out if they have been in the vicinity of people who have tested positive for the novel coronavirus.

Digital contact tracing has been touted by public health specialists as a strategy to help reopen the economy in a safe way, but privacy and ethical considerations have been hotly debated.

The Conversation (0)

Metamaterials Could Solve One of 6G’s Big Problems

There’s plenty of bandwidth available if we use reconfigurable intelligent surfaces

12 min read
An illustration depicting cellphone users at street level in a city, with wireless signals reaching them via reflecting surfaces.

Ground level in a typical urban canyon, shielded by tall buildings, will be inaccessible to some 6G frequencies. Deft placement of reconfigurable intelligent surfaces [yellow] will enable the signals to pervade these areas.

Chris Philpot

For all the tumultuous revolution in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other is the move to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads: After years of designing superefficient transmitters and receivers, and of compensating for the signal losses at the end points of a radio channel, they’re beginning to realize that they are approaching the practical limits of transmitter and receiver efficiency. From now on, to get high performance as we go to higher frequencies, we will need to engineer the wireless channel itself. But how can we possibly engineer and control a wireless environment, which is determined by a host of factors, many of them random and therefore unpredictable?

Perhaps the most promising solution, right now, is to use reconfigurable intelligent surfaces. These are planar structures typically ranging in size from about 100 square centimeters to about 5 square meters or more, depending on the frequency and other factors. These surfaces use advanced substances called metamaterials to reflect and refract electromagnetic waves. Thin two-dimensional metamaterials, known as metasurfaces, can be designed to sense the local electromagnetic environment and tune the wave’s key properties, such as its amplitude, phase, and polarization, as the wave is reflected or refracted by the surface. So as the waves fall on such a surface, it can alter the incident waves’ direction so as to strengthen the channel. In fact, these metasurfaces can be programmed to make these changes dynamically, reconfiguring the signal in real time in response to changes in the wireless channel. Think of reconfigurable intelligent surfaces as the next evolution of the repeater concept.

Keep Reading ↓Show less