@article{ref1,
title="Detecting hotspots of urban residents' behaviours based on spatio-temporal clustering techniques",
journal="GeoJournal",
year="2017",
author="Zhang, Pengdong and Deng, Min and Shi, Yan and Zhao, Ling",
volume="82",
number="5",
pages="923-935",
abstract="Hotspots are regions where the number of spatial objects is obviously high within the time intervals. As the behaviours of urban residents are considered as a typical kind of spatio-temporal pattern, the detection of hotspots of urban residents' behaviours appears necessary, since important information might be discovered in hotspots. In this paper, we propose an approach to detect the spatio-temporal hotspots of urban residents' behaviours. This approach is validated based on the GPS data of floating cars of a city in southern China. The approach consists of four main steps: first, the effective spatio-temporal trajectories and the important characteristic points contained in each trajectory are extracted from the GPS data; second, the spatio-temporal clusters are generated by clustering the characteristic points based on a kernel density estimation algorithm; third, the spatio-temporal hotspots are detected by filtering the spatio-temporal clusters with high densities; last, the detected hotspots are analysed and interpreted. The results show that the proposed approach is effective and useful in detecting hotspots of urban residents' behaviours.<p /> <p>Language: en</p>",
language="en",
issn="0343-2521",
doi="10.1007/s10708-016-9720-4",
url="http://dx.doi.org/10.1007/s10708-016-9720-4"
}