Crime is an inevitable societal problem and is a major problem of the nation. Effective crime control requires accurate prediction for decision making in crime control planning. This research analyses the factors affecting crime risk, especially, murder cases in the Bangkok Metropolitan Area, Thailand by using a Bayesian Network. The results from the analysis are expected to be used for crime control planning.
The BayesNetCrime System
The Bayesian Network model was developed by expert
elicitation and crime theory and it learned using Hugin Researcher
6.3 machine learning software. The factors considered in this study
are classified into five main groups: variables describing
population, variables describing crime location, variables
describing types of crimes, variables describing traffic, and
variables describing the environment. Due to the uncertainty and
incomplete nature of the variables, Bayesian Network theory is used
to analyse the data since it is well suited to dealing with noisy
and incomplete crime data.
The data were collected from the National
Statistical Office of Thailand, the Royal Thai Police, the Bangkok
Metropolitan Administration and the Ministry of Transportation. In
this research, data from January 2000 to December 2003 was used.
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A Bayesian Network model for analysis of the factors
affecting crime risk
The result from this analysis can be used to
help in crime control planning and environmental design to prevent
crime. Based on data from the study, the environmental factor, the
number of drug-sale areas in a district, had the most powerful
influence on the expected murder rate. By concentrating on the
elimination of the drug trade the government could greatly reduce
the murder rate. An empirical study on the predictive accuracy
performance of the model is included. Receiver Operating
Characteristic analysis was used to test the model. The results of
the study show the model performed well.
Hugin Software
During the development of the BayesNetCrime
System, the Hugin Software package provided a number of benefits as
described below.
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Friendly User Interface. Hugin provides an easily
understood user interface environment. This has made the develop
work a lot easier and saved a huge amount of time in calculating
the probabilities.
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A Variety of Functionalities. Hugin provides a variety of
functionalities that we have found very useful in building the
Bayesian network model.
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The Fastest Decision Engine. The Hugin Decision Engine
proved to be the fastest, most efficient, and most reliable
inference engine.
