TY - JOUR
T1 - Space-time interaction of residential burglaries in Wuhan, China
AU - Ye, Xinyue
AU - Xu, Xiao
AU - Lee, Jay
AU - Zhu, Xinyan
AU - Wu, Ling
N1 - Funding Information:
This work has been supported by Basic Research Funds of National Higher Education Institutions of China (No. 2722013JC030 ), National Science & Technology Pillar Program : 2012BAH35B03 , LIESMARS (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University) Special Research Funding, Research and development of key technologies in police geographic information system from Wuhan Municipal Public Security Bureau.
Publisher Copyright:
© 2014 Elsevier Ltd.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Borrowing methods from epidemiology, studies of spatiotemporal regularities of crime have been booming in various industrialized countries. However, few such attempts are empirical studies using crime data in developing countries due to a lack of data availability. Utilizing a recent burglary dataset in Wuhan, the fourth largest city in China, current research applied the sequential kernel density estimation and the space-time K-function methods to analyze the spatiotemporal changes of hotspots of residential burglaries. The results show that, both spatial and spatiotemporal clustering exists. The hotspots were relatively stable over time. The space-time clustering, however, shows significant concentrations both in space and over time. In addition, analytic results show significant effects of distance decay in terms of occurrences of burglary incidents along the spatial and temporal dimensions. Moreover, findings from the research provide critical information on the space-time rhythm of crime, and therefore can be utilized in crime prevention practice. Finally, the implications of the findings and limitations are discussed.
AB - Borrowing methods from epidemiology, studies of spatiotemporal regularities of crime have been booming in various industrialized countries. However, few such attempts are empirical studies using crime data in developing countries due to a lack of data availability. Utilizing a recent burglary dataset in Wuhan, the fourth largest city in China, current research applied the sequential kernel density estimation and the space-time K-function methods to analyze the spatiotemporal changes of hotspots of residential burglaries. The results show that, both spatial and spatiotemporal clustering exists. The hotspots were relatively stable over time. The space-time clustering, however, shows significant concentrations both in space and over time. In addition, analytic results show significant effects of distance decay in terms of occurrences of burglary incidents along the spatial and temporal dimensions. Moreover, findings from the research provide critical information on the space-time rhythm of crime, and therefore can be utilized in crime prevention practice. Finally, the implications of the findings and limitations are discussed.
KW - Burglaries
KW - Kernel density estimation
KW - Point pattern analysis
KW - Space-time clustering
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U2 - 10.1016/j.apgeog.2014.11.022
DO - 10.1016/j.apgeog.2014.11.022
M3 - Article
AN - SCOPUS:84939995704
SN - 0143-6228
VL - 60
SP - 210
EP - 216
JO - Applied Geography
JF - Applied Geography
ER -