Year: 2024
Type: article
Abstract: Geographic flow clustering analysis can effectively reveal human behavioral patterns in movement.Traditional methods for studying human movement patterns are mostly based on first-order quantity analyses of point data, such as hotspots, density or clustering.Currently, relatively few second-order spatial analysis methods based on geographic flows exist.Thus, we developed a new geographic flow method based on spectral clustering and applied it to trajectory data analysis.This article uses the bike-sharing trajectories data in Shanghai in August 2016, spectral clustering analysis was conducted on the group flow patterns before, during and after rainfall, on weekdays and weekends and in the morning and evening peak.Spectral clustering was verified to exhibit better clustering effect by comparing the clustering indices of different clustering methods.This study enriches the analysis method of geographical flows, and the human mobility patterns revealed by its analysis can provide references for formulating urban green travel policies.
Source: Geocarto International
Authors: Wenwen Xing, Youjun Tu, Yuxing Gao, Zongyi He, Junli Li
Institutions: Anhui Agricultural University, Wuhan University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,390
Awards: G2321018413, G5037457697
OpenAlex ID: W4392912296