Spatial Scan Statistic Method for Discovering Regional Network Co-location Patterns

GUO Yiwen; CAI Jiannan; CHEN Yuanfang; DENG Min; ZHAO Bin

doi:10.13203/j.whugis20200177

Volume 47 Issue 9

Sep. 2022

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2022 > 47(9): 1383-1389. > DOI: 10.13203/j.whugis20200177

GUO Yiwen, CAI Jiannan, CHEN Yuanfang, DENG Min, ZHAO Bin. Spatial Scan Statistic Method for Discovering Regional Network Co-location Patterns[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1383-1389. DOI: 10.13203/j.whugis20200177

Citation:

PDF (4620 KB)

Spatial Scan Statistic Method for Discovering Regional Network Co-location Patterns

1.
Department of Geo-Informatics, Central South University, Changsha 410083, China

Funds:

The National Key Research and Development of China 2018YFB1004603

The National Key Research and Development of China 2016YFB0502303

the Postgraduate Research and Innovation Foundation of Central South University 2020zzts174

More Information

Author Bio:
GUO Yiwen, PhD candidate, specializes in spatial-temporal association patterns mining and applications. E-mail: yiwen.guo@csu.edu.cn
Corresponding author:
CAI Jiannan, PhD. E-mail: jncai@outlook.com
Received Date: October 02, 2020
Available Online: September 19, 2022
Published Date: September 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives Currently, most methods for mining regional co-location patterns focus on the planar geospatial space which can hardly support the analysis on the network space such as urban roads. Therefore, a regional network co-location pattern mining method is proposed based on spatial scan statistics.
Methods A network-constrained path expansion method is developed to detect the candidate paths where co-location patterns could occur. These candidates are further validated using significance tests, where the null model is constructed using a network-constrained bivariate Poisson distribution.
Results Experiment results of simulated data and taxi datasets show that the proposed method is more effective for discovering regional co-location patterns on the network space than a baseline method.Compared with traditional methods for discovering regional co-location patterns, our proposed method fully considers the network constraint properties of events, in both phases of the candidate path detection and the significance testing.
Conclusions Our method can effectively assist the resource allocation of taxis within the urban road network in Beijing by analyzing the taxi supply-demand patterns in different urban districts.
- spatial data mining,
- regional co-location patterns,
- network-constrained,
- spatial scan statistics,
- taxi supply-demand patterns

FullText(HTML)

References (25)

References

[1]	Deng M, He Z J, Liu Q L, et al. Multi-scale Approach to Mining Significant Spatial Co-location Patterns[J]. Transactions in GIS, 2017, 21(5): 1023-1039 doi: 10.1111/tgis.12261
[2]	Shekhar S, Huang Y. Discovering Spatial Colocation Patterns: A Summary of Results[C]//International Symposium on Spatial and Temporal Databases, Redondo Beach, USA, 2001
[3]	边馥苓, 万幼. k-邻近空间关系下的空间同位模式挖掘算法[J]. 武汉大学学报·信息科学版, 2009, 34(3): 331-334 http://ch.whu.edu.cn/article/id/1196 Bian Fuling, Wan You. A Novel Spatial Co-location Pattern Mining Algorithm Based on k-Nearest Feature Relationship[J]. Geomatics and Information Science of Wuhan University, 2009, 34 (3): 331-334 http://ch.whu.edu.cn/article/id/1196
[4]	钱烽. 同位模式挖掘研究[D]. 杭州: 浙江大学, 2012 Qian Feng. Research on Mining of Co-location Patterns[D]. Hangzhou: Zhejiang University, 2012
[5]	陈袁芳, 蔡建南, 刘启亮, 等. 城市犯罪时空同现模式的非参数检验方法[J]. 武汉大学学报·信息科学版, 2019, 44(12): 1883-1892 doi: 10.13203/j.whugis20180112 Chen Yuanfang, Cai Jiannan, Liu Qiliang, et al. A Nonparametric Test-Based Approach for Mining Spatio-Temporal Co-occurrence Patterns of Urban Crimes[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12): 1883-1892 doi: 10.13203/j.whugis20180112
[6]	Kolasa J, Pickett S T A. Ecological Heterogeneity [M]. New York, USA : Springer, 1991
[7]	Goodchild M F. The Fundamental Laws of GIScience[C]// Invited Talk at University Consortium for Geographic Information Science, Santa Barbara, USA, 2003
[8]	沙宗尧, 李晓雷. 异质环境下的空间关联规则挖掘[J]. 武汉大学学报·信息科学版, 2009, 34(12): 1480-1484 http://ch.whu.edu.cn/article/id/1471 Sha Zongyao, Li Xiaolei. Algorithm of Mining Spatial Association Data Under Spatially Heterogeneous Environment[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12): 1480-1484 http://ch.whu.edu.cn/article/id/1471
[9]	Huang Y, Shekhar S, Xiong H. Discovering Colocation Patterns from Spatial Data Sets: A General Approach[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(12): 1472-1485 doi: 10.1109/TKDE.2004.90
[10]	Yoo J S, Shekhar S, Smith J, et al. A Partial Join Approach for Mining Co-location Patterns[C]// The 12th Annual ACM International Workshop on Geographic Information Systems, Washington, USA, 2004
[11]	Yoo J S, Shekhar S. A Joinless Approach for Mining Spatial Colocation Patterns[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18 (10): 1323-1337 doi: 10.1109/TKDE.2006.150
[12]	Barua S, Sander J. Mining Statistically Significant Co-location and Segregation Patterns[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(5): 1185-1199 doi: 10.1109/TKDE.2013.88
[13]	Celik M, Kang J M, Shekhar S. Zonal Co-location Pattern Discovery with Dynamic Parameters[C]// The 7th IEEE International Conference on Data Mining, Omaha, USA, 2007
[14]	Eick C F, Parmar R, Ding W, et al. Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets[C]//The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Irvine, USA, 2008
[15]	Qian F, Chiew K, He Q M, et al. Mining Regional Co-location Patterns with kNNG[J]. Journal of Intelligent Information Systems, 2014, 42(3): 485-505 doi: 10.1007/s10844-013-0280-5
[16]	蔡建南, 刘启亮, 徐枫, 等. 多层次空间同位模式自适应挖掘方法[J]. 测绘学报, 2016, 45(4): 475-485 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201604014.htm Cai Jiannan, Liu Qiliang, Xu Feng, et al. An Adaptive Method for Mining Hierarchical Spatial Co-location Patterns[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 475-485 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201604014.htm
[17]	Wang S, Huang Y, Wang X S. Regional Co - locations of Arbitrary Shapes[M]//Advances in Spatial and Temporal Databases, Berlin, Heidelberg: Springer, 2013
[18]	Xie Z X, Yan J. Kernel Density Estimation of Traffic Accidents in a Network Space[J]. Computers, Environment and Urban Systems, 2008, 32(5): 396-406 doi: 10.1016/j.compenvurbsys.2008.05.001
[19]	田晶, 王一恒, 颜芬, 等. 一种网络空间现象同位模式挖掘的新方法[J]. 武汉大学学报·信息科学版, 2015, 40(5): 652-660 doi: 10.13203/j.whugis20130448 Tian Jing, Wang Yiheng, Yan Fen, et al. A New Method for Mining Co-location Patterns Between Network Spatial Phenomena[J]. Geomatics and Information Science of Wuhan University, 2015, 40 (5): 652-660 doi: 10.13203/j.whugis20130448
[20]	Yu W H. Spatial Co-location Pattern Mining for Location-Based Services in Road Networks[J]. Expert Systems with Applications, 2016, 46: 324-335 doi: 10.1016/j.eswa.2015.10.010
[21]	Cai J N, Deng M, Liu Q L, et al. Nonparametric Significance Test for Discovery of Network - Constrained Spatial Co-location Patterns[J]. Geographical Analysis, 2019, 51(1): 3-22 doi: 10.1111/gean.12155
[22]	Paloheimo J E. A Spatial Bivariate Poisson Distribution[J]. Biometrika, 1972, 59(2): 489-492 doi: 10.1093/biomet/59.2.489
[23]	Babaei A, Jabbari B. Distance Distribution of Bivariate Poisson Network Nodes[J]. IEEE Communications Letters, 2010, 14(9): 848-850 doi: 10.1109/LCOMM.2010.080610.100668
[24]	Karlis D. An EM Algorithm for Multivariate Poisson Distribution and Related Models[J]. Journal of Applied Statistics, 2003, 30(1): 63-77 doi: 10.1080/0266476022000018510
[25]	冯晓梅. 供需平衡状态下的出租车发展规模研究[D]. 成都: 西南交通大学, 2010 Feng Xiaomei. Study on the Development Scale of Taxi Under the Balance of Supply and Demand[D]. Chengdu: Southwest Jiaotong University, 2010

[1]	HU Deyong, QIAO Kun, WANG Xingling, ZHAO Limin, JI Guohua. Comparison of Three Single-window Algorithms for Retrieving Land-Surface Temperature with Landsat 8 TIRS Data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 869-876. DOI: 10.13203/j.whugis20150164
[2]	FENG Qi, CHENG Xuejun, SHEN Xin, XIAO Xiao, WANG Lihui, ZHANG Wen. Inland Riverine Turbidity Estimation for Hanjiang River with Landsat 8 OLI Imager[J]. Geomatics and Information Science of Wuhan University, 2017, 42(5): 643-647. DOI: 10.13203/j.whugis20141002
[3]	WANG Yuzhuo, LIU Xiuguo, ZHANG Wei. Raster River Networks Extraction Based on Parallel Multiple Flow Direction Algorithms[J]. Geomatics and Information Science of Wuhan University, 2015, 40(12): 1646-1652,1682. DOI: 10.13203/j.whugis20140645
[4]	LI Yuguang, LI Qingquan. A Fast Algorithm for Huge Volume Floating Car Data Map-Matching:A Vector to Raster Map Conversion Approach[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 724-728. DOI: 10.13203/j.whugis20140071
[5]	DONG Jian, PENG Rencan, CHEN Yi, LI Ning. An Algorithm for Centre Line Generation Based on Model of Approaching Intersection of Buffering Borderline from Reciprocal Direction[J]. Geomatics and Information Science of Wuhan University, 2011, 36(9): 1120-1123.
[6]	ZHANG Junfeng, FEI Lifan, HUANG Lina, LIU Yining. Real-Time Dynamic Rendering Algorithm of Terrain Using 3D_DP Method and Quad_TIN Model[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3): 346-350.
[7]	LAN Qiuping, FEI Lifan, LIU Yining. An Approach on Calculating Firn Volume Change from Multi-temporal DEMs[J]. Geomatics and Information Science of Wuhan University, 2010, 35(10): 1222-1225.
[8]	HUANG Lina, FEI Lifan. Experimental Investigation on the Three Dimension Generalization of Contour Lines using 3D D-P Algorithm[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 55-58.
[9]	YAN Huiwu, ZHU Guorui, XU Zhiyong, GAO Shan. Volume Rendering and 3D Modeling of Hydrogeologic Layer Based on Kriging Algorithm[J]. Geomatics and Information Science of Wuhan University, 2004, 29(7): 611-614.
[10]	CHENG Penggen, GONG Jianya, SHI Wenzhong, LIU Shaohua. Geological Object Modeling Based on Quasi Tri-prism Volume and Its Application[J]. Geomatics and Information Science of Wuhan University, 2004, 29(7): 602-307.

Cited By

Cited by

Periodical cited type(17)

1.	冉烽均，龚川. 基于OpenStreetMap数据的土地利用制图. 北京测绘. 2024(02): 238-244 .
2.	樊潇. 以建立草原公园为抓手，推动牧区草原转型升级. 中国草食动物科学. 2022(01): 61-64 .
3.	李霞，潘冬荣，孙斌，姜佳昌，俞慧云，王红霞，杜笑村，吴丹丹. 甘肃省草地退化概况分析——基于甘肃省第一、二次草原普查数据. 草业科学. 2022(03): 485-494 .
4.	刘志刚，关文昊，何国兴，蒲小鹏，纪童，杨军银，李强，柳小妮. 黄河源5种高寒植物光谱特征分析及识别. 草原与草坪. 2022(04): 23-30 .
5.	申紫雁，刘昌义，胡夏嵩，周林虎，许桐，李希来，李国荣. 黄河源区高寒草地不同深度土壤理化性质与抗剪强度关系研究. 干旱区研究. 2021(02): 392-401 .
6.	王俊奇，王广军，梁四海，杜海波，彭红明. 1996—2015年黄河源区植被覆盖度提取和时空变化分析. 冰川冻土. 2021(02): 662-674 .
7.	朱宁，王浩，宁晓刚，刘娅菲. 草地退化遥感监测研究进展. 测绘科学. 2021(05): 66-76 .
8.	沈贝贝，侯路路，丁蕾，范蓓蕾，毛平平，徐大伟，闫瑞瑞，辛晓平，陈金强. 数字牧场研究进展浅析. 中国农业信息. 2021(05): 1-11 .
9.	刘炜，孙海霞，杨晓波. 基于高光谱图像的协同分层波谱识别——以兰州、榆林地区为例. 红外与毫米波学报. 2020(01): 99-110 .
10.	韩万强，靳瑰丽，岳永寰，王惠宁，宫珂，吴雪儿，吾鲁帕·阿得尔卡里. 伊犁绢蒿荒漠草地3种主要植物光谱及植被指数改进. 新疆农业科学. 2020(05): 950-957 .
11.	刘炜，孙海霞，杨晓波，董建民. 对数变换、导数变换的高寒草地反射光谱特征分析与识别——以那曲地区HJ-1A/HSI图像为例. 光谱学与光谱分析. 2020(07): 2200-2207 .
12.	董元，董梦，单莹. 基于高光谱遥感的树种识别. 华北理工大学学报(自然科学版). 2020(04): 11-16 .
13.	付晶莹，彭婷，江东，林刚，边鹏，韩昊. 草地资源立体观测研究进展与理论框架. 资源科学. 2020(10): 1932-1943 .
14.	苏玥. 基于遥感的草地退化研究综述. 内蒙古科技与经济. 2019(06): 53-54+56 .
15.	查向浩，王玉洁，李有文，王超，莫治新. 草地土壤碳密度研究进展. 北方园艺. 2019(09): 159-163 .
16.	王云艳，罗冷坤，周志刚. 改进型DeepLab的极化SAR果园分类. 中国图象图形学报. 2019(11): 2035-2044 .
17.	张良培，刘蓉，杜博. 使用量子优化算法进行高光谱遥感影像处理综述. 武汉大学学报(信息科学版). 2018(12): 1811-1818 .

Other cited types(9)

Get Citation

PDF

XML

Article views (1134) PDF downloads (100) Cited by(26)

Spatial Scan Statistic Method for Discovering Regional Network Co-location Patterns

Abstract

References

Related Articles

Cited by

Periodical cited type(17)

Other cited types(9)

Catalog

Related

Spatial Scan Statistic Method for Discovering Regional Network Co-location Patterns

Abstract

References

Related Articles

Cited by

Periodical cited type(17)

Other cited types(9)

Catalog

Related

Export File

Citation

Format

Content