Geospatial-Temporal Analysis andClassification of Criminal Data in Manila

被引：0
|
作者：
Baculo, Maria Jeseca C. [1 ]
Marzan, Charlie S. [1 ]
de Dios Bulos, Remedios [1 ]
Ruiz, Conrado [1 ]
机构：
[1] De La Salle Univ, Manila, Philippines
来源：
2017 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA) | 2017年
关键词：
classifiers; crime analysis; predictive methods;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatio-temporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Naive Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities.
引用
收藏
页码：6 / 11
页数：6
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