TY - GEN
T1 - Integrating near repeat and social network approaches to analyze crime patterns
AU - Hu, Tao
AU - Ye, Xinyue
AU - Duan, Lian
AU - Zhu, Xinyan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/30
Y1 - 2017/10/30
N2 - Crime does not occur randomly or uniformly across time, space, or social groups. The near-repeat phenomenon states that the risk of repeat victimization would increase at the nearby locations within a certain time period. The empirical evidence of near-repeat patterns has been widely reported in many different crime types across the world. Meanwhile, the growing ability to connect anywhere and anytime has led to the increasing use of social network analytics. In particular, social network analytics have been applied to the studies of criminal networks. This paper integrates near repeat and social network approaches on exploring spatiotemporal crime patterns. The spatiotemporal burglary data in five boroughs (the Bronx, Brooklyn, Staten Island, Manhattan and Queen) of NYC were analyzed and compared based on average clustering coefficient, degree centrality, closeness centrality, and closeness centrality. Furthermore, the implications and limitations of the findings are presented.
AB - Crime does not occur randomly or uniformly across time, space, or social groups. The near-repeat phenomenon states that the risk of repeat victimization would increase at the nearby locations within a certain time period. The empirical evidence of near-repeat patterns has been widely reported in many different crime types across the world. Meanwhile, the growing ability to connect anywhere and anytime has led to the increasing use of social network analytics. In particular, social network analytics have been applied to the studies of criminal networks. This paper integrates near repeat and social network approaches on exploring spatiotemporal crime patterns. The spatiotemporal burglary data in five boroughs (the Bronx, Brooklyn, Staten Island, Manhattan and Queen) of NYC were analyzed and compared based on average clustering coefficient, degree centrality, closeness centrality, and closeness centrality. Furthermore, the implications and limitations of the findings are presented.
KW - buglary
KW - crime analysis
KW - near repeat
KW - social network
UR - http://www.scopus.com/inward/record.url?scp=85040984067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040984067&partnerID=8YFLogxK
U2 - 10.1109/GEOINFORMATICS.2017.8090949
DO - 10.1109/GEOINFORMATICS.2017.8090949
M3 - Conference contribution
AN - SCOPUS:85040984067
T3 - International Conference on Geoinformatics
BT - 2017 25th International Conference on Geoinformatics, GeoInformatics 2017
PB - IEEE Computer Society
T2 - 25th International Conference on Geoinformatics, GeoInformatics 2017
Y2 - 2 August 2017 through 4 August 2017
ER -