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Switching exploration modes in human mobility
Lu Zhong1,2,3, Lei Dong4, Qi R Wang5
1Department of Computer Science, Rensselaer Polytechnic Institute , Troy, NY, USA.
Abstract:
Recent advances in human mobility research have revealed consistent pairwise characteristics in movement behaviour, yet existing mobility models often overlook the spatial and topological structure of mobility networks. By analysing millions of devices' anonymized cell phone trajectories, we show that human mobility networks exhibit a pronounced polycentric and modular structure, in which movements within spatial modules differ fundamentally from movements between modules. This finding challenges the common assumption of uniform mobility dynamics across spatial scales. Inspired by switching behaviours in animal movement patterns, we introduce a 'switch mechanism' to distinguish intra-module and inter-module exploration modes. Incorporating this mechanism into a generative mobility model allows us to simultaneously reproduce individual-level mobility statistics and emergent network-level structures, including high modularity, long topological distances and frequent long-range travel. Our results provide a unified mechanistic explanation for the emergence of polycentric human mobility patterns and highlight the importance of scale-dependent movement dynamics, with implications for urban planning, transportation modelling and epidemic forecasting.
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What is a Mode?
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
Selected Data About Geographic Locations
