The Impact of Spatial Resolution and Representation on Human Mobility Predictability

Abstract

The study of human mobility patterns is important for both understanding human behaviour, a social phenomenon and to simulate infection transmission. Factors such as geometry representation, granularity, missing data and data noise affect the reliability, validity, and credibility of human mobility data, and any models drawn from this data. This thesis discusses the impact of spatial representations of human mobility patterns through a series of analyses using entropy and trip-length distributions as evaluation criteria, Voronoi decomposition and square grid decomposition as alternative geometry representations. I further examine a spectrum of spatial granularity, from dimensions associated with social interaction, to city, and provincial scale, and toggle analysis between raw data and post-processed data to understand the impact of noisy data and missing data influence estimation. A dataset I was involved with collecting – SHED1 – featuring multi-sensor data collection over 5 weeks among 39 participants – has been used for the experiments. An analysis of the results further strengthens the findings of Song et al., and demonstrates comparability in predictability of human mobility through geometric representation between Voronoi decomposition and square grid decompositions, suggesting a scale dependence of human mobility analysis, and demonstrating the value of using missing data analysis throughout the study.