Network-constrained bivariate clustering method for detecting urban black holes and volcanoes-Reference-Cited by-同舟云学术

Network-constrained bivariate clustering method for detecting urban black holes and volcanoes

Published:2020-02-27 Issue:10 Volume:34 Page:1903-1929
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Liu Qiliang¹²,Wu Zhihui²,Deng Min²,Liu Wenkai²,Liu Yaolin³

Affiliation:

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

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

3. School of Resource and Environmental Science, Wuhan University, Wuhan, Hubei, P.R. China

Funder

The Innovation-Driven Project of Central South University

National Key Research and Development Foundation of China

National Science Foundation of China

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2020.1720027

Reference46 articles.

1. The control of the false discovery rate in multiple testing under dependency

2. An Assessment of the Effectiveness of Multiple Hypothesis Testing for Geographical Anomaly Detection

3. Controlling the False Discovery Rate: A New Application to Account for Multiple and Dependent Tests in Local Statistics of Spatial Association

4. From taxi GPS traces to social and community dynamics

Cited by 11 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. Geographical flows: a fresh perspective on quantifying urban function;Urban Informatics;2024-05-30

2. Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes;Land;2023-10-09

3. Sensing Travel Source–Sink Spatiotemporal Ranges Using Dockless Bicycle Trajectory via Density-Based Adaptive Clustering;Remote Sensing;2023-08-04

4. Spatial hotspot detection in the presence of global spatial autocorrelation;International Journal of Geographical Information Science;2023-06-01

5. Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data;International Journal of Geographical Information Science;2023-05-08