Movement Range Maps

Movement Range Maps inform researchers and public health experts about how populations are responding to physical distancing measures. These datasets have two different metrics: Change in Movement and Stay Put. The Change in Movement metric looks at how much people are moving around and compares it to a baseline period that predates most social distancing measures. The Stay Put metric looks at the fraction of the population that appears to stay within a small area surrounding their home for an entire day.

Features

Differential Privacy

Differential privacy minimizes risk of re-identification of individual data with the help of possible additional information — even information we cannot anticipate now. Applying a DP framework takes into account the sensitivity of the data set and adds noise proportionally to ensure that with high probability no one can re-identify users.

Timely

This data is updated daily. Each metric in this data set is produced for a given administrative region once per day.

Publicly Available

We are now releasing Facebook Movement Range Maps publicly. The Facebook Movement Range data is available in visualized format for 14 countries here and in csv format for a longer list of countries here.

“Besides measuring the effect that social distancing measurements have on people’s mobility -- which is presented once a week in National TV -- having access to this dataset has enabled us to detect regions that are interconnected which has given us a lot of context regarding the dynamics of the pandemic. This information has been key for us to inform the decision making process in such important times.””

Paola Villarreal Rodríguez, Coordinator of Research, Prospective and Repositories, National Council of Science and Technology, Government of Mexico

Who Uses Movement Range Maps

Researchers

Researchers from around the world at schools like the Harvard School of Public Health, National TsingHua University, or the University of Venice use Movement Range data to understand how populations are responding to physical distancing measures.

Public health experts

Public health experts can use Movement Range data to assess non-pharmaceutical interventions and other policies meant to reduce the rate of coronavirus transmission.

Nonprofits and NGOS

Nonprofits like Direct Relief analyze change in movement treadlines that have become part of daily situational reports for policymakers.

Methodology

People who use Facebook on a mobile device have the option of providing their precise location in order to enable products like Nearby Friends and Find Wi-Fi, and get local content and ads. Movement Range Trends are produced by aggregating and de-identifying this data. Only people who opt in to Location History and background location collection are included. People with very few location pings in a day are not informative for these trends, and, therefore, we only include people whose location is observed for a meaningful period of the day. You can find a tutorial here, which will walk you through the creation of a map and dataset which identifies potential problem areas for COVID-19 vulnerable populations using two publicly available datasets produced by the Facebook Data for Good team.

Case Studies

This study evaluates the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. It shows that in... This study evaluates the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. It shows that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, the study developed a simple agent-based spatial model. The model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread.

Movement Range Maps

Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

This study evaluates the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. It shows that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, the study developed a simple agent-based spatial model. The model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread.

Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is... Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. We confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

Movement Range Maps

Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. We confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

Social Progress Imperative & INCAE Business School used Movement Range Map data to make closing and reopening recommendations to Costa Rican government agencies. After observing Facebook data over the... Social Progress Imperative & INCAE Business School used Movement Range Map data to make closing and reopening recommendations to Costa Rican government agencies. After observing Facebook data over the Christmas holidays which showed high levels of mobility, protocols and restrictions were introduced, such as closing beaches in the afternoon. The reduction in mobility was very strong, decreasing from an average positive mobility index of 0.0123 to a decrease in mobility of -0.0627.

Movement Range Maps

Data-Informed Response to COVID-19: Costa Rica Case Study

Social Progress Imperative & INCAE Business School used Movement Range Map data to make closing and reopening recommendations to Costa Rican government agencies. After observing Facebook data over the Christmas holidays which showed high levels of mobility, protocols and restrictions were introduced, such as closing beaches in the afternoon. The reduction in mobility was very strong, decreasing from an average positive mobility index of 0.0123 to a decrease in mobility of -0.0627.

We propose a trade-off between both research directions and present a multimodal learning approach that combines the advantages of statistical regression and machine learning models for predicting... We propose a trade-off between both research directions and present a multimodal learning approach that combines the advantages of statistical regression and machine learning models for predicting local COVID19 cases in Germany. This novel approach enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest MSE scores throughout our observational period in our benchmark study.

Movement Range Maps

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

We propose a trade-off between both research directions and present a multimodal learning approach that combines the advantages of statistical regression and machine learning models for predicting local COVID19 cases in Germany. This novel approach enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest MSE scores throughout our observational period in our benchmark study.

In the News

Governor Gavin Newsom References Facebook Movement Range data in press conference Facebook Op-Ed: If states don’t coordinate lockdown procedures immediately, our future is grim Los Angeles Times

Download this Data

To download this data, go here. To work with a visualization of this data, go here.