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

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.

There was a strong impact of the national lockdown on India’s electricity consumption. It dropped on average 28.5 percent in the week after the implementation of the lockdown and was on average... There was a strong impact of the national lockdown on India’s electricity consumption. It dropped on average 28.5 percent in the week after the implementation of the lockdown and was on average still 25.8 percent below normal throughout April. When some restrictions were eased in May, electricity consumption recovered, but it remained 14 percent below normal levels. Monthly averages remained below normal afterwards, suggesting a lingering drag on the economy. Not all Indian states and Union territories have been affected equally.

Movement Range Maps

Examining the Economic Impact of COVID-19 in India through Daily Electricity Consumption and Nighttime Light Intensity

There was a strong impact of the national lockdown on India’s electricity consumption. It dropped on average 28.5 percent in the week after the implementation of the lockdown and was on average still 25.8 percent below normal throughout April. When some restrictions were eased in May, electricity consumption recovered, but it remained 14 percent below normal levels. Monthly averages remained below normal afterwards, suggesting a lingering drag on the economy. Not all Indian states and Union territories have been affected equally.

The COVID-19 pandemic has disrupted economic activity in India. When COVID-19 started to spread in India through domestic contagion, the Indian authorities enacted a series of measures to combat the... The COVID-19 pandemic has disrupted economic activity in India. When COVID-19 started to spread in India through domestic contagion, the Indian authorities enacted a series of measures to combat the pandemic, including a strict national lockdown from March 25 to May 4. Subsequently, the authorities classified all districts into three zones with varying containment measures. This study estimates the impact of this differential relaxation of containment measures (zone classification) on aggregate economic activity in India.

Movement Range Maps

Examining the Economic Impact of India’s Zone Classification to contain COVID-19

The COVID-19 pandemic has disrupted economic activity in India. When COVID-19 started to spread in India through domestic contagion, the Indian authorities enacted a series of measures to combat the pandemic, including a strict national lockdown from March 25 to May 4. Subsequently, the authorities classified all districts into three zones with varying containment measures. This study estimates the impact of this differential relaxation of containment measures (zone classification) on aggregate economic activity in India.

We explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting because of its large and fast pandemic... We explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting because of its large and fast pandemic peak, and for its implementation of a sustained, generalized lockdown. We find private mobility sources to be both correlated and complementary. Using them, we evaluate the efficiency of implemented policies, and provide a insights into what new normal means in Spain.

Disaster Maps

Private Sources of Mobility Data Under COVID-19

We explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting because of its large and fast pandemic peak, and for its implementation of a sustained, generalized lockdown. We find private mobility sources to be both correlated and complementary. Using them, we evaluate the efficiency of implemented policies, and provide a insights into what new normal means in Spain.

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.