Research on Irregularly Shaped Spatio-Temporal Abnormal Cluster Pattern Mining for Spatial Point Data Sets

WAN You; ZHOU Jiaogen; WENG Min

doi:10.13203/j.whugis20150069

Volume 42 Issue 7

Jul. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(7): 924-930. > DOI: 10.13203/j.whugis20150069

WAN You, ZHOU Jiaogen, WENG Min. Research on Irregularly Shaped Spatio-Temporal Abnormal Cluster Pattern Mining for Spatial Point Data Sets[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 924-930. DOI: 10.13203/j.whugis20150069

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Research on Irregularly Shaped Spatio-Temporal Abnormal Cluster Pattern Mining for Spatial Point Data Sets

1.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2.
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

Funds:

The National Natural Science Foundation of China 41471327

The National Natural Science Foundation of China 41001231

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Author Bio:
WAN You, PhD, lecturer, specializes in geographical big data and spatial data mining. E-mail:wanyou@whu.edu.cn
Corresponding author:
WENG Min, PhD, associate professor. E-mail:wengmin@whu.edu.cn
Received Date: August 06, 2015
Published Date: July 04, 2017

Graphical Abstract

Abstract

Abstract

Spatio-temporal abnormal cluster pattern is an important spatial point pattern. The pattern results can reflect the distribution and evolution of spatio-temporal events timely and accurately. Early researches has verified the scan statistic based clustering methods are very effective in detection spatial and spatio-temporal abnormal cluster pattern. However, due to the fixed shape of scan window, traditional scan statistic based clustering methods have limitation on obtaining exact shape and size of cluster. This paper proposed an improved irregularly shaped spatio-temporal abnormal cluster pattern mining algorithm stAntScan. The algorithm constructs the spatio-temporal neighborhood matrix by a newly defined 26 directions spatio-temporal neighbor cells. Then the algorithm improves the ant colony optimization based method to fit for spatio-temporal scanning on three-dimensional large data set. In the end, the Monte Carlo simulation method is used to test the significance of clusters. Experimental results on both simulated data and real Weibo check-in data have testified the efficiency and accuracy of stAntScan on irregularly shaped spatio-temporal abnormal cluster pattern mining. And compared with the classical SaTScan, it gets much better results in finding exact shape and size of clusters.
- spatio-temporal clustering,
- spatio-temporal abnormal,
- spatial point pattern,
- spatial data mining,
- spatio-temporal data mining

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References

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