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Spatial Cluster Detection in Spatial Flow Data
Author(s) -
Tao Ran,
Thill JeanClaude
Publication year - 2016
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12100
Subject(s) - computer science , cluster analysis , data mining , spatial analysis , robustness (evolution) , cluster (spacecraft) , flow (mathematics) , information flow , spatial ecology , artificial intelligence , geography , remote sensing , mathematics , programming language , ecology , biochemistry , chemistry , linguistics , geometry , philosophy , biology , gene
As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single‐location spatial events or fail to preserve the integrity of spatial flow events. In this research we introduce a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K‐function, while maintaining the integrity of flow data. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgrades the classical hot spot detection method to the stage of “hot flow” detection. Several specific aspects of the method are discussed to provide evidence of its robustness and expandability, such as the multiscale issue and relative importance control, using a real data set of vehicle theft and recovery location pairs in Charlotte, NC.

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