A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection-Reference-Cited by-同舟云学术

A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection

Published:2017-11-29 Issue:3 Volume:34 Page:293-315
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Zhao Pengxiang¹,Liu Xintao¹,Shen Jingwei²,Chen Min³⁴⁵^ORCID

Affiliation:

1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong

2. Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing, PR China

3. Key Laboratory of Virtual Geographic Environment, Ministry of Education of PRC, Nanjing Normal University, Nanjing, PR China

4. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, PR China

5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, PR China

Funder

National Natural Science Foundation of China

Area of Excellence

university startup project

A university startup project

An Area of Excellence project

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2017.1404140

Reference33 articles.

1. Local Indicators of Spatial Association-LISA

2. Context-aware taxi demand hotspots prediction

3. Community structure in social and biological networks

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