Data for Mobility Changes in Response to COVID-19

[ U.S. mobility Data (ndjson) | U.S. mobility Data (csv) | U.S. m50_index Data (alternate csv) ]

Descartes Labs is releasing mobility statistics (representing the distance a typical member of a given population moves in a day) at the US admin1 (state) and admin2 (county) level. A technical report describing the motivation behind this work with methodology and definitions is available at arxiv.org/pdf/2003.14228.pdf. We intend to update the data in this repository regularly.

Note: Data for 2020-04-20, 2020-05-29, 2020-10-08, 2020-12-11 through 2020-12-18, 2021-01-08 through 2021-01-14, 2021-04-07, 2021-04-12 and 2021-04-21 to present did not meet quality control standards, and was not released.

Mobility Data

NDJSON format data can be found in the DL-us-mobility.ndjson file.

{"cc": "US", "admin_level": 2, 
"admin1": "New Mexico", "admin2": "Santa Fe County", "fips": "35049", 
"date": ["2020-03-24", "2020-03-25", "2020-03-26"], 
"samples": [1337, 1292, 1331], 
"m50": [0.155, 0.278, 0.095], 
"m50_index": [2, 4, 1]}
...

The same data is available in CSV format in the DL-us-mobility-daterow.csv file.

date,country_code,admin_level,admin1,admin2,fips,samples,m50,m50_index
2020-03-26,US,2,"New Mexico","Santa Fe County","35049",1331,0.095,1
...

An alternate arrangement of the same data in CSV format with dates in the header, which may be preferable for some users, is in the DL-us-m50.csv, DL-us-m50_index.csv and DL-us-samples.csv files.

country_code,admin_level,admin1,admin2,fips,2020-03-24,2020-03-25,2020-03-26
US,2,"New Mexico","Santa Fe County","35049",2,4,1
...

Field description

• country_code: ISO 3166-1 alpha-2 code.
• admin_level: 0 for country, 1 for admin1, 2 for admin2 granularity.
• admin1: GeoNames ADM1 feature name for the first-order administrative division, such as a state in the United States.
• admin2: GeoNames ADM2 feature name for the second-order administrative division, such as a county or borough in the United States.
• fips: FIPS code, a standard geographic identifier, to make it easier to combine this data with other data sets.
• samples: The number of samples observed in the specified region.
• m50: The median of the max-distance mobility for all samples in the specified region.
• m50_index: The percent of normal m50 in the region, with normal m50 defined during 2020-02-17 to 2020-03-07.

Visualization and Analysis

• GeoDS Lab @ UW-Madison - geods.geography.wisc.edu/covid19/physical-distancing/

• Descartes Labs - www.descarteslabs.com/mobility/#overview

• Nik Haldimann - Lockdown Hack #2: Towards proving that social distancing works

• Ballad Health - COVID-19 Physical Distancing Scorecard

• The Institute for Health Metrics and Evaluation (IHME) - COVID-19: What’s New for April 17, 2020

• DataHaven (Regional Data Cooperative) - COVID-19 in Connecticut

• Mathematical Humanity Lab, Dartmouth College - When and how will the COVID-19 pandemic end in the United States?

• Beyond Limits - Beyond Limits Coronavirus Dynamic Predictive Model

• City of Los Angeles and RMDS - 2020 COVID-19 Computational Challenge

• Climate Central - Travel and Air Pollution During COVID-19

• Russ Gerber - Communicating Disease - The Startup - Medium

• Hawaii Data Collaborative - Tracking COVID-19 In Hawaii

• Center for Data Innovation - Visualizing How Far People Are Traveling in the United States

• Google Cloud AI and Harvard Global Health Institute - COVID-19 Public Forecasts

License and Attribution

This data is licensed under a Creative Commons Attribution 4.0 International License, which requires attribution to "Descartes Labs." Scientific publications may cite,

Warren, Michael S. & Skillman, Samuel W. "Mobility Changes in Response to COVID-19". arXiv:2003.14228 [cs.SI], Mar. 2020. arxiv.org/abs/2003.14228

For online use, please additionally link to our page at descarteslabs.com/mobility/.

See the LICENSE for the terms of use for this data.

Contact Us

If you have questions, please contact us at:

[email protected]

We also encourage you to register with us in order to receive updates about the analysis methodology or changes to this data.

Citations

Song Gao, Jinmeng Rao, Yuhao Kang, Yunlei Liang, Jake Kruse, "Mapping county-level mobility pattern changes in the United States in response to COVID-19". arXiv:2004.04544 [physics.soc-ph], Apr. 2020. arxiv.org/abs/2004.04544

Shuo Chen, Qin Li, Song Gao, Yuhao Kang, Xun Shi, "Mitigating COVID-19 outbreak via high testing capacity and strong transmission-intervention in the United States". medRxiv, Apr. 2020. www.medrxiv.org/content/10.1101/2020.04.03.20052720v1

IHME COVID-19 health service utilization forecasting team, Christopher JL Murray, "Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries". medRxiv, Apr. 2020. www.medrxiv.org/content/10.1101/2020.04.21.20074732v1

Song Gao, Jinmeng Rao, Yuhao Kang, Yunlei Liang, Jake Kruse, Doerte Doepfer, Ajay K. Sethi, Juan Francisco Mandujano Reyes, Jonathan Patz, Brian S. Yandell, "Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic". arXiv:2004.11430 [cs.SI], Apr. 2020. arxiv.org/abs/2004.11430

Sepehr Ghader, Jun Zhao, Minha Lee, Weiyi Zhou, Guangchen Zhao, Lei Zhang, "Observed mobility behavior data reveal social distancing inertia". arXiv:2004.14748 [cs.CY], Apr. 2020. arxiv.org/abs/2004.14748

Nabarun Dasgupta, Michele Jonsson Funk, Allison Lazard, Benjamin Eugene White, Stephen W. Marshall, "Quantifying the social distancing privilege gap: a longitudinal study of smartphone movement". medRxiv, May 2020. www.medrxiv.org/content/10.1101/2020.05.03.20084624v1

Donghai Liang, Liuhua Shi, Jingxuan Zhao, Pengfei Liu, Joel Schwartz, Song Gao, Jeremy A Sarnat, Yang Liu, Stefanie T Ebelt, Noah C Scovronick, Howard Chang, "Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States". medRxiv, May 2020. www.medrxiv.org/content/10.1101/2020.05.04.20090746v1

Grant McKenzie, Benjamin Adams, "A country comparison of place-based activity response to COVID-19 policies". arXiv:2005.08738 [cs.SI], May 2020. arxiv.org/abs/2005.08738

Teodoro Alamo, D. G. Reina, Pablo Millán, "Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory". arXiv:2006.01731 [q-bio.PE], June 2020. arxiv.org/abs/2006.01731

Ivan Franch-Pardo, Brian M. Napoletano, Fernando Rosete-Verges and Lawal Billa. "Spatial analysis and GIS in the study of COVID-19. A review." Science of The Total Environment, June 2020. 140033. www.sciencedirect.com/science/article/pii/S0048969720335531

Petrônio CL Silva, Paulo VC Batista, Hélder S. Lima, Marcos A. Alves, Frederico G. Guimarães, and Rodrigo CP Silva. "COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions." Chaos, Solitons & Fractals, July 2020. 110088. www.sciencedirect.com/science/article/abs/pii/S0960077920304859

Xiao Huang, Zhenlong Li, Yuqin Jiang, Xinyue Ye, Chengbin Deng, Jiajia Zhang, Xiaoming Li, "The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic". medRxiv, Aug. 2020. www.medrxiv.org/content/10.1101/2020.07.31.20143016v1

Related Press Coverage

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• Medium Coronavirus Blog - A Graph to Watch. March 30, 2020.
• ABC 6 Philadelphia - INTERACTIVE: Cell phone data shows residents staying at home during COVID-19 outbreak. March 30, 2020.
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• PBS Arizona - Why the air in metro Phoenix is fresher these days. April 1, 2020.
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