Google Research

Practical geospatial and sociodemographic predictors of human mobility

  • Corrine W. Ruktanonchai
  • Shengjie Lai
  • Chigozie E. Utazi
  • Alex D. Cunningham
  • Patrycja Koper
  • Grant E. Rogers
  • Nick W. Ruktanonchai
  • Adam Sadilek
  • Dorothea Woods
  • Andrew J. Tatem
  • Jessica E. Steele
  • Alessandro Sorichetta
Nature Scientific Reports (2021)


Background: Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modeling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe seasonal subnational mobility in Kenya, therefore enabling better modelling of seasonal mobility across low and middle income country (LMIC) settings. Methods: To do this, we used the Google Aggregated Mobility Research Dataset -- containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates important to predicting human movement patterns, while accounting for spatial and temporal autocorrelations. Results: Mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, respectively, across both years. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly predicted mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. Conclusions: These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates and predictors of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

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