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Using Google location history data to quantify fine-scale human mobility

Using Google location history data to quantify fine-scale human mobility
Using Google location history data to quantify fine-scale human mobility

Background: Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods: Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results: We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions: GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions.

GPS tracker data, Human mobility, Mobile phone data
1476-072X
Ruktanonchai, Nick Warren
fe68cb8d-3760-4955-99fa-47d43f86580a
Ruktanonchai, Corrine Warren
44e6fcd0-246b-480e-8940-9557dbb7c0cc
Floyd, Jessica Rhona
8a7cfe57-fda6-4fcf-9b0b-caee1a4abc15
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Ruktanonchai, Nick Warren
fe68cb8d-3760-4955-99fa-47d43f86580a
Ruktanonchai, Corrine Warren
44e6fcd0-246b-480e-8940-9557dbb7c0cc
Floyd, Jessica Rhona
8a7cfe57-fda6-4fcf-9b0b-caee1a4abc15
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Ruktanonchai, Nick Warren, Ruktanonchai, Corrine Warren, Floyd, Jessica Rhona and Tatem, Andrew J. (2018) Using Google location history data to quantify fine-scale human mobility. International Journal of Health Geographics, 17 (1). (doi:10.1186/s12942-018-0150-z).

Record type: Article

Abstract

Background: Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods: Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results: We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions: GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions.

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Accepted/In Press date: 18 July 2018
e-pub ahead of print date: 27 July 2018
Keywords: GPS tracker data, Human mobility, Mobile phone data

Identifiers

Local EPrints ID: 424743
URI: https://eprints.soton.ac.uk/id/eprint/424743
ISSN: 1476-072X
PURE UUID: a2c73d9e-31cb-4a14-b549-e209e1aa8f3e
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 05 Oct 2018 11:42
Last modified: 14 Mar 2019 01:35

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Contributors

Author: Nick Warren Ruktanonchai
Author: Corrine Warren Ruktanonchai
Author: Jessica Rhona Floyd
Author: Andrew J. Tatem ORCID iD

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