An Automatic Method for Discovering Significant Regional Spatial Colocation Patterns

XU Feng; CAI Jiannan; LIU Qiliang; HE Zhanjun; DENG Min

doi:10.13203/j.whugis20170008

Volume 43 Issue 10

Oct. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(10): 1538-1545. > DOI: 10.13203/j.whugis20170008

XU Feng, CAI Jiannan, LIU Qiliang, HE Zhanjun, DENG Min. An Automatic Method for Discovering Significant Regional Spatial Colocation Patterns[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1538-1545. DOI: 10.13203/j.whugis20170008

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An Automatic Method for Discovering Significant Regional Spatial Colocation Patterns

1.
Department of GeoInformatics, Central South University, Changsha 410083, China

Funds:

The National Natural Science Foundation of China 41730105

The National Natural Science Foundation of China 41601410

the Science and Technology Foundation of Hunan Province 2015SK2078

Open Research Fund of the State Key Laboratory of Resources and Environmental Information System

the Postgraduate Research and Innovation Foundation of Central South University 2017zzts174

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Author Bio:
XU Feng, PhD candidate, specializes in the methods and applications of spatial data mining. E-mail:xufengcsu@163.com
Corresponding author:
CAI Jiannan, PhD candidate. E-mail:jncai@outlook.com
Received Date: November 25, 2017
Published Date: October 04, 2018

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Abstract

Abstract

Discovery of regional spatial colocation patterns facilities understanding of the spatial dependency of different spatial features at the regional scale. However, two challenges remain:①appropriate thresholds for prevalence measures are difficult to specify without prior knowledge; and ②natural localities of regional spatial colocation patterns with different densities and shapes can hardly be automatically detected. On that account, an automatic method for discovering significant regional spatial colocation patterns is proposed in this paper. First, a nonparametric statistical model is developed to test for significance of spatial colocation patterns. Then, an adaptive spatial clustering method is modified to detect hot spots of each candidate regional spatial colocation pattern that is not identified as a statistically significant spatial colocation pattern at the global scale. At last, all hot spots are iteratively expanded until no larger statistically significant localities can be detected. Comparison between this automatic method and an existing method is carried out with both simulated and ecological datasets. Experiments show that the regional spatial colocation patterns can be effectively detected with less subjectivity and prior knowledge by this automatic method.
- spatial heterogeneity,
- regional spatial colocation patterns,
- nonparametric test,
- pattern reconstruction,
- adaptive spatial clustering

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