Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78715
Title: Understanding the functionality of human activity hotspots from their scaling pattern using trajectory data
Authors: Jia, T 
Ji, Z
Keywords: Trajectory data
Human activity hotspots
Scaling
Urban functionality
Bayesian inference model
Issue Date: 2017
Publisher: Molecular Diversity Preservation International (MDPI)
Source: ISPRS international journal of geo-information, Nov. 2017, v. 6, no. 11, 341 How to cite?
Journal: ISPRS international journal of geo-information 
Abstract: Human activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of human activity hotspots from their scaling pattern in a reliable way. Specifically, a large number of stopping locations are extracted from trajectory data, which are then aggregated into activity hotspots. Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land use. Importantly, this scaling pattern remains stable over time. This finding inspires us to devise an allometric ruler to identify the activity hotspots, whose functionality could be reliably estimated using the stopping locations. Thereafter, a novel Bayesian inference model is proposed to infer their urban functionality, which examines the spatial and temporal information of stopping locations covering 75 days. Experimental results suggest that the functionality of identified activity hotspots are reliably inferred by stopping locations, such as the railway station.
URI: http://hdl.handle.net/10397/78715
EISSN: 2220-9964
DOI: 10.3390/ijgi6110341
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