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Crime Risk Factors Analysis

Crime Risk Factors Analysis. Application of Bayesian Network. Introduction. Urbanization causes the crime problem to increase in magnitude and complexity To provide effective control, with limited resources, law enforcement agencies need to be proactive in their approach

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Crime Risk Factors Analysis

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  1. Crime Risk Factors Analysis Application of Bayesian Network

  2. Introduction • Urbanization causes the crime problem to increase in magnitude and complexity • To provide effective control, with limited resources, law enforcement agencies need to be proactive in their approach • Since cities and crime pattern are dynamic in nature, a system is required which can develop a model that provide accurate predictions and must also constantly updates changes various parameters over time • BN has been adopted for inference and prediction in the crime problem domain

  3. Introduction Contd. • The use of BN enable us to use the incomplete existing data to setup initial model and to continue the enhancement of the model’s predictive capabilities as new data is added

  4. Analysis of the Factors Affecting Crime Risk • Identifying Crime Pattern Characteristic • Establish the relationship between various factors • Determine the crime risk level • Recognize the crime data pattern (structure learning) • Predict crime risk factors

  5. Expert Probabilities Elicitations • Eliciting probabilities from experts involve some difficulties, therefore Keeney and von Winterfeldt’s process for eliciting expert judgment was employed • The formal process to elicit probabilities from expert consists of seven steps • Identification and selection of the issues • Identification and selection of the experts • Discussion and the refinement of the issues • Training for elicitation • Elicitation • Analysis, Aggregation and resolution of disagreement • Documentation and Communication

  6. Data Processing • The purpose is to prepare raw data for subsequent process of analysis, it involves three steps • Data consolidation • Data Selection • Data Transformation • A set of data containing 1000 records were obtained • The data contain 20 variables, which were organized in five groups of factors • Population, Crime Locations, Types of Crimes, Traffic and Environment

  7. Bayesian Network Model • The BN developed for this research project was based on crime pattern analysis carried out by Brantingham and Brantingham and the theory of crime control through environmental design • Pattern theory focuses on the environment and crime, and maintains that crime location, characteristics of such locations, movement path that brings offenders and victims together at such locations, and people’s perception of crime locations are significant objects for study

  8. Bayesian Network Model Contd. • Pattern theory synthesizes Its attempts to explain how changing spatial and temporal ecological structures influence crime trends and patterns • The model was constructed and tested using Hugin software • Conditional Probabilities Tables (CPT) • Sample of CPT • The probability of each input node was calculated using data contained in the training example for each state, the variables have five states, 0=very low, 1=low, 2=medium, 3=high and 4=very high

  9. Learning Bayesian Network • Input • Consists of prior crime data & background knowledge • Output • Revised Bayesian Network • Learning Algorithm • To determine which links to be included in the DAG • To determine the parameters A B Population statistics, Crime, Geographic Data Learning Algorithm C Revised Bayesian Network

  10. The Expectation Maximization Algorithm • The Expectation Maximization (EM) algorithm was used within the Hugin software for the data • In the study since we have the complete set of data, the EM algorithm was used to count the frequencies of probabilities, it consist of two iteration steps • Expectation step (E-step) calculates the expectation of the missing statistic • Maximization step (M-step) maximizes a certain function • The two steps are performed until convergence

  11. The Expectation Maximization Algorithm Initial network and training data Computation E-step Expected Count Updated Network Reparametrize M-step The iteration steps of the Expectation Maximization Algorithm

  12. Results • The results shows that the factors that considerably affected crime risk in Bangkok Metropolitan area were environment, types of crime, crime location, traffic and population. • Crime risk probability given murder is “yes” makes environment (drug sale and low standard housing) as the primary reason on the expected murder rate

  13. Results

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