Statistical and density-based clustering of geographical flows for crowd movement patterns recognition-Reference-Cited by-同舟云学术

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Statistical and density-based clustering of geographical flows for crowd movement patterns recognition

Published:2024-09 Issue: Volume:163 Page:111912
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Tang Jianbo^ORCID,Zhao Yuxin,Yang Xuexi^ORCID,Deng Min,Liu Huimin,Ding Chen^ORCID,Peng Ju^ORCID,Mei Xiaoming

Publisher

Elsevier BV

Reference40 articles.

1. Estimating spatiotemporal aggregation scales by revisiting the spatiotemporal L-function;Yan;Trans. GIS,2023

2. Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data;Andrienko;IEEE Trans. Vis. Comput. Graph.,2016

3. Classification of spatio-temporal trajectories from volunteer geographic information through fuzzy rules;Cuenca-Jara;Appl. Soft Comput.,2020

4. CLUSTMOSA: Clustering for GPS trajectory data based on multi-objective simulated annealing to develop mobility application;Dutta;Appl. Soft Comput.,2022

5. Mapping large spatial flow data with hierarchical clustering;Zhu;Trans. GIS,2014

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