Understanding place‐to‐place interactions using flow patterns derived from in‐app mobile phone location data

Functional roles of neighbourhoods change throughout the day, as both a cause and consequence of human mobility fluctuations. Here we review how neighbourhoods can be characterised by origin–destination flows derived from individual level GPS‐enabled in‐app data. These are used to track individual trajectories from start to end points prior to aggregation. We leverage securely held individual level in‐app mobile phone location data that preserve spatial and temporal flexibility in representing place‐to‐place interactions. The data are available at the individual level and are aggregated for reporting of origin–destination analysis at the Middle layer Super Output Area (MSOA) level to accommodate disclosure control and positional uncertainty. We show how in‐app mobile phone location data for Greater London enhance our understanding of the relationships between places, and demonstrate how these relationships may change over the course of the day. Finally, we discuss how such analysis can inform transport policy and the contribution of our approach to extending geodemographic research.