Practical geospatial and sociodemographic predictors of human mobility

Corrine Ruktanonchai, Virginia Tech
Shengjie Lai, University of Southampton
Chigozie E Utazi, School of Geography and Environmental Science, University of Southampton
Adam Sadilek, Google
Andrew J. Tatem, University of Southampton
Alessandro Sorichetta, University of Southampton

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. This 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 from 2018 - 2019. We used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting. We quantified seasonal changes in mobility patterns across years and undertook a Bayesian spatiotemporal analysis to identify relevant geospatial and socioeconomic covariates important to predicting human movement patterns, while accounting for spatial and temporal autocorrelations. We found that school holidays were a key predictor of mobility, plus socioeconomic and geospatial variables including urbanicity, poverty, female education, accessibility to major population centres and temperature. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through low and middle-income settings.

Keywords: Bayesian methods / estimation, Big data / Social media, International migration, Internal migration

See paper.

  Presented in Session 170. Understanding Migration: Applying New Data and Methods