Title
A Spatial Co-Location Mining Algorithm That Includes Adaptive Proximity Improvements And Distant Instance References
Abstract
Spatial co-location pattern mining is employed to identify a group of spatial types whose instances are frequently located in spatial proximity. Current co-location mining methods have two limitations: (1) it is difficult to set an appropriate proximity threshold to identify close instances in an unknown region, and (2) such methods neglect the effects of the distance values between instances and long-distance instance effects on pattern significance. This paper proposes a novel maximal co-location algorithm to address these problems. To remove the first constraint, the algorithm uses Voronoi diagrams to extract the most related instance pairs of different types and their normalized distances, from which two distance-separating parameters are adaptively extracted using a statistical method. To remove the second constraint, the algorithm employs a reward-based verification based on distance-separating parameters to identify the prevalent patterns. Our experiments with both synthetic data and real data from Beijing, China, demonstrate that the algorithm can identify many interesting patterns that are neglected by traditional co-location methods.
Year
DOI
Venue
2018
10.1080/13658816.2018.1431839
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
Field
DocType
Spatial data mining, co-location pattern mining, reward value, Voronoi diagram, generalized extreme value distribution
Data mining,Normalization (statistics),Generalized extreme value distribution,Computer science,Spatial data mining,Synthetic data,Voronoi diagram,Data mining algorithm
Journal
Volume
Issue
ISSN
32
5
1365-8816
Citations 
PageRank 
References 
2
0.40
17
Authors
8
Name
Order
Citations
PageRank
Xiaojing Yao121.08
Liujia Chen2716.24
Congcong Wen320.40
Ling Peng422.77
Liang Yang521316.53
Tianhe Chi66016.60
Xiaomeng Wang731.08
Wenhao Yu810313.91