Spatio-temporal analysis of urban crime leveraging multisource crowdsensed data

被引：16
作者：
Zhou B. [1 ]
Chen L. [2 ]
Zhao S. [1 ]
Zhou F. [1 ]
Li S. [1 ]
Pan G. [1 ]
机构：
[1] Zhejiang University, Hangzhou
[2] Xiamen University, Xiamen
来源：
Personal and Ubiquitous Computing | 2023年 / 27卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Crowd sensed data; Crowd sensing; Public safety; Spatio-temporal correlation; Urban crime;
D O I：
10.1007/s00779-020-01456-6
中图分类号：
学科分类号：
摘要：
Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions. © 2021, Springer-Verlag London Ltd., part of Springer Nature.
引用
收藏
页码：599 / 612
页数：13
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