TY - GEN
T1 - Optimization of criminal hotspots based on underlying crime controlling factors using geospatial discriminative pattern
AU - Wang, Dawei
AU - Ding, Wei
AU - Stepinski, Tomasz
AU - Salazar, Josue
AU - Lo, Henry
AU - Morabito, Melissa
PY - 2012
Y1 - 2012
N2 - Criminal activities are unevenly distributed over space. The concept of hotspots is widely used to analyze the spatial characters of crimes. But existing methods usually identify hotspots based on an arbitrary user-defined threshold with respect to the number of a target crime without considering underlying controlling factors. In this study we introduce a new data mining model - Hotspots Optimization Tool (HOT) - to identify and optimize crime hotspots. The key component of HOT, Geospatial Discriminative Patterns (GDPatterns), which capture the difference between two classes in spatial dataset, is used in crime hotspot analysis. Using a real world dataset of a northeastern city in the United States, we demonstrate that the HOT model is a useful tool in optimizing crime hotspots,and it is also capable of visualizing criminal controlling factors which will help domain scientists further understanding the underlying reasons of criminal activities.
AB - Criminal activities are unevenly distributed over space. The concept of hotspots is widely used to analyze the spatial characters of crimes. But existing methods usually identify hotspots based on an arbitrary user-defined threshold with respect to the number of a target crime without considering underlying controlling factors. In this study we introduce a new data mining model - Hotspots Optimization Tool (HOT) - to identify and optimize crime hotspots. The key component of HOT, Geospatial Discriminative Patterns (GDPatterns), which capture the difference between two classes in spatial dataset, is used in crime hotspot analysis. Using a real world dataset of a northeastern city in the United States, we demonstrate that the HOT model is a useful tool in optimizing crime hotspots,and it is also capable of visualizing criminal controlling factors which will help domain scientists further understanding the underlying reasons of criminal activities.
KW - Crime Hotspot
KW - Footprint
KW - Geospatial Discriminative Pattern
KW - Hotspots Optimization Tool
UR - https://www.scopus.com/pages/publications/84864327404
UR - https://www.scopus.com/pages/publications/84864327404#tab=citedBy
U2 - 10.1007/978-3-642-31087-4_57
DO - 10.1007/978-3-642-31087-4_57
M3 - Conference contribution
AN - SCOPUS:84864327404
SN - 9783642310867
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 553
EP - 562
BT - Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings
T2 - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012
Y2 - 9 June 2012 through 12 June 2012
ER -