TY - JOUR
T1 - Bring Your Own Location Data
T2 - Use of Google Smartphone Location History Data for Environmental Health Research
AU - Hystad, Perry
AU - Amram, Ofer
AU - Oje, Funso
AU - Larkin, Andrew
AU - Boakye, Kwadwo
AU - Avery, Ally
AU - Gebremedhin, Assefaw
AU - Duncan, Glen
N1 - Publisher Copyright:
© 2022, Public Health Services, US Dept of Health and Human Services. All rights reserved.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - BACKGROUND: Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time–activity data. OBJECTIVES: We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time–activity data for environmental health research. METHODS: We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution. RESULTS: Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have histori-cal GLH data available. The provided GLH data spanned 2010–2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data ( ~ 1:8 million points), 95% of GPS time–activity points were within 100 m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment. DISCUSSION: We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time–activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture histori-cal time–activity patterns of participants, employing a “bring-your-own-location-data” citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.
AB - BACKGROUND: Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time–activity data. OBJECTIVES: We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time–activity data for environmental health research. METHODS: We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution. RESULTS: Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have histori-cal GLH data available. The provided GLH data spanned 2010–2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data ( ~ 1:8 million points), 95% of GPS time–activity points were within 100 m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment. DISCUSSION: We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time–activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture histori-cal time–activity patterns of participants, employing a “bring-your-own-location-data” citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.
UR - https://www.scopus.com/pages/publications/85141610822
U2 - 10.1289/EHP10829
DO - 10.1289/EHP10829
M3 - Article
C2 - 36356208
AN - SCOPUS:85141610822
SN - 0091-6765
VL - 130
JO - Environmental Health Perspectives
JF - Environmental Health Perspectives
IS - 11
M1 - 117005
ER -