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Article

Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events

by 1,2, 3, 2,4 and 2,4,*
1
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
2
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
3
Department of Geography, Kent State University, Kent, OH 44242-0001, USA
4
National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(7), 440; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070440
Received: 26 May 2020 / Revised: 5 July 2020 / Accepted: 15 July 2020 / Published: 16 July 2020
Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different properties. This article suggests a method for assessing the level of spatiotemporal clustering or spatiotemporal autocorrelation that may exist in a set of human activity events when they are associated with different categorical attributes. This method extends the Voronoi structure from 2D to 3D and integrates a sliding-window model as an approach to spatiotemporal tessellations of a space-time volume defined by a study area and time period. Furthermore, an index was developed to evaluate the partial spatiotemporal clustering level of one of the two event categories against the other category. The proposed method was applied to simulated data and a real-world dataset as a case study. Experimental results show that the method effectively measures the level of spatiotemporal clustering patterns among human activity events of multiple categories. The method can be applied to the analysis of large volumes of human activity events because of its computational efficiency. View Full-Text
Keywords: partial spatiotemporal autocorrelation; levels of spatiotemporal clustering; spatiotemporal Delaunay triangulation; sliding-window model; geographical events partial spatiotemporal autocorrelation; levels of spatiotemporal clustering; spatiotemporal Delaunay triangulation; sliding-window model; geographical events
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MDPI and ACS Style

Gong, J.; Lee, J.; Zhou, S.; Li, S. Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events. ISPRS Int. J. Geo-Inf. 2020, 9, 440. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070440

AMA Style

Gong J, Lee J, Zhou S, Li S. Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events. ISPRS International Journal of Geo-Information. 2020; 9(7):440. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070440

Chicago/Turabian Style

Gong, Junfang, Jay Lee, Shunping Zhou, and Shengwen Li. 2020. "Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events" ISPRS International Journal of Geo-Information 9, no. 7: 440. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070440

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