TY - JOUR
T1 - Forecasting, clustering and patrolling criminal activities
AU - Calvo, Hiram
AU - Godoy-Calderón, Salvador
AU - Moreno-Armendáriz, Marco A.
AU - Martínez-Hernández, Víctor M.
N1 - Publisher Copyright:
© 2017 IOS Press and the authors. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Tools that perform pattern recognition analysis of crimes, comprising at the same time forecasting, clustering, and recommendations on real data such as patrolling routes, are not fully integrated; modules are developed separately, and thus, a single workflow providing all the steps necessary to perform this analysis has not been reported. In this paper, we propose forecasting criminal activity in a particular region by using supervised classification; then, to use this information to automatically cluster and find important hot spots; and finally, to optimize patrolling routes for personnel working in public security. The proposed forecasting model (CR- +) is based on the family of Kora- Logical-Combinatorial algorithms operating on large data volumes from several heterogeneous sources using an inductive learning process. We perform two analyses: punctual prediction and tendency analysis, which show that it is possible to punctually predict one out of four crimes to be perpetrated (crime family, in a specific space and time), and two out of three times the place of crime, despite of the noise of the dataset. The forecasted crimes are then clustered using a density-based clustering algorithm, and finally route patrolling routes were crafted using an ant-colony optimization algorithm. For three different patrolling requirements, we were always able to find optimal routes in shorter time compared to commonly used random walk algorithms. We present a case study based on real crime data from the municipality of Cuautitlán Izcalli, in Mexico.
AB - Tools that perform pattern recognition analysis of crimes, comprising at the same time forecasting, clustering, and recommendations on real data such as patrolling routes, are not fully integrated; modules are developed separately, and thus, a single workflow providing all the steps necessary to perform this analysis has not been reported. In this paper, we propose forecasting criminal activity in a particular region by using supervised classification; then, to use this information to automatically cluster and find important hot spots; and finally, to optimize patrolling routes for personnel working in public security. The proposed forecasting model (CR- +) is based on the family of Kora- Logical-Combinatorial algorithms operating on large data volumes from several heterogeneous sources using an inductive learning process. We perform two analyses: punctual prediction and tendency analysis, which show that it is possible to punctually predict one out of four crimes to be perpetrated (crime family, in a specific space and time), and two out of three times the place of crime, despite of the noise of the dataset. The forecasted crimes are then clustered using a density-based clustering algorithm, and finally route patrolling routes were crafted using an ant-colony optimization algorithm. For three different patrolling requirements, we were always able to find optimal routes in shorter time compared to commonly used random walk algorithms. We present a case study based on real crime data from the municipality of Cuautitlán Izcalli, in Mexico.
KW - Forecasting models for crime analysis
KW - Spatio-temporal similarity function
KW - ant-colony systems
KW - clustering
KW - patrolling routes optimization
KW - pattern recognition
KW - public security
KW - supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85021801697&partnerID=8YFLogxK
U2 - 10.3233/IDA-170883
DO - 10.3233/IDA-170883
M3 - Artículo
AN - SCOPUS:85021801697
SN - 1088-467X
VL - 21
SP - 697
EP - 720
JO - Intelligent Data Analysis
JF - Intelligent Data Analysis
IS - 3
ER -