The impact of MTUP to explore online trajectories for human mobility studies

Abstract

Social media data which capture long-term personal travel activities as a set of space-time points (time series) become widely used for human mobility study. The space-time points representing individual activities are massive and need aggregation upon time dimension (besides space dimension) to show temporal mobility patterns. During the temporal aggregations, time series are sliced into different temporal layers, and the aggregation results could be impacted by four parameters, including layer size (time interval of each tempoal layer), start placement (the start time of the first layer), amount of overlap between two consecutive layers, and time series extent (temporal scope of the datasets for aggregation). Different parameterizations result in different mobility patterns, known as the "Modifiable Temporal Unit Problem" (MTUP; on the analogy of the "Modifiable Areal Unit Problem" or MAUP). While the general effects of MTUP are well examined in previous studies, MTUP is often ignored in trajectory reconstructions using sparse social media data. To fill this research gap, this paper will explore the impact of different temporal aggregation schemas (parameterizations) on the discovery of human mobility patterns using geo-tagged tweets within a 3D geospatial analytical system. The case study reveals that MTUP is significant during the process of detecting an individual's daily representative (regular) trajectories based on sparse online footprints. Comprehensive analysis on multiple aggregation results with different parameters could improve understanding of an individual's regular daily travel patterns. The interactive analytical system and visualization methods proposed by this study could minimize MTUP impact and help avoid false arguments.