Nonparametric Significance Test for Discovery of Network-Constrained Spatial Co-Location Patterns-Reference-Cited by-同舟云学术

Nonparametric Significance Test for Discovery of Network-Constrained Spatial Co-Location Patterns

Published:2018-02-19 Issue:1 Volume:51 Page:3-22
ISSN:0016-7363
Container-title:Geographical Analysis
language:en
Short-container-title:Geogr Anal

Author:

Cai Jiannan¹²,Deng Min¹²,Liu Qiliang¹²,He Zhanjun²,Tang Jianbo²,Yang Xuexi²

Affiliation:

1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education; Changsha Hunan P. R. China

2. Department of Geo-informatics; Central South University; Changsha Hunan P. R. China

Funder

National Key Research and Development Foundation of China

National Natural Science Foundation of China

Innovation-Driven Project of Central South University

Publisher

Wiley

Subject

Earth-Surface Processes,Geography, Planning and Development

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/gean.12155/fullpdf

Reference41 articles.

1. Agrawal , R. R. Srikant 1994 Proceedings of the 20th International Conference Very Large Data Bases

2. Mining Statistically Significant Co-Location And Segregation Patterns;Barua;IEEE Transactions on Knowledge and Data Engineering,2014

3. Simple Monte Carlo Tests for Spatial Patterns;Besag;Journal of the Royal Statistical Society. Series C (Applied Statistics),1977

4. Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space;Borruso;Transactions in GIS,2008

5. Buyong , T. 2007 Spatial Data Analysis for Geographic Information Science

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Discovering spatial co‐location patterns of urban facilities and their asymmetric characteristics;Transactions in GIS;2024-06-25

2. Mining significant local spatial association rules for multi-category point data;Heliyon;2024-02

3. RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Model;Lecture Notes in Computer Science;2024

4. Inferring nonwork travel semantics and revealing the nonlinear relationships with the community built environment;Sustainable Cities and Society;2023-12

5. Discovering Prevalent Co-location Patterns Without Collecting Co-location Instances;Intelligent Information and Database Systems;2023