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ISPRS Int. J. Geo-Inf. 2017, 6(11), 341; doi:10.3390/ijgi6110341

Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data

1,2
and
1,*
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2
Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
*
Author to whom correspondence should be addressed.
Received: 2 September 2017 / Revised: 26 October 2017 / Accepted: 2 November 2017 / Published: 5 November 2017
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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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. View Full-Text
Keywords: trajectory data; human activity hotspots; scaling; urban functionality; Bayesian inference model trajectory data; human activity hotspots; scaling; urban functionality; Bayesian inference model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jia, T.; Ji, Z. Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data. ISPRS Int. J. Geo-Inf. 2017, 6, 341.

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