Abstract
Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence (AI) techniques as an analytical tool. Among them, Gradient Tree Boosting (GTB) is a newly emerged AI technique for forecasting especially in crime analysis. GTB possesses a unique feature among other AI techniques which is its robustness towards any data representation and distribution. Subsequently, this study would like to adopt GTB in modelling crime rates based on 8 defined crime types. Similar to other AI techniques, GTB’s overall performance is heavily influenced by its input parameter configuration. To assess such a challenge, this study would like to propose a hybrid DA-GTB crime forecasting model that is equipped with a metaheuristic optimization algorithm called Dragonfly Algorithm (DA) in optimizing GTB’s three main parameters namely number of trees, size of individual trees and learning rate. From the experimental result obtained, the application of DA for parameter optimization yielded a positive impact in enhancing GTB forecasting performance as it produced the smallest error compared to non-optimized GTB. This indicates that the proposed model is able to perform well using time series data with a limited and small sample size.
Highlights
Crime forecasting is an analysis technique used to predict and forecast crime patterns as accurate as possible so that it forms significant insights into possible future crime trends based on past crime data
The main objective of this study is to propose an improved crime forecasting model that is able to predict crime rates efficiently by properly tuning the required parameters of an Artificial Intelligence (AI) technique using a metaheuristic algorithm
It is found that researchers have shifted their interest towards the application of artificial intelligence techniques in crime forecasting due to their capability to produce high forecasting performance accuracy
Summary
Crime forecasting is an analysis technique used to predict and forecast crime patterns as accurate as possible so that it forms significant insights into possible future crime trends based on past crime data. The application of artificial intelligence techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM), fuzzy logic and genetic programming in crime forecasting has been extensively studied by researchers due to their capability to produce high forecasting performance accuracy. This is because artificial intelligence techniques possess some nonlinear functions which are able to detect nonlinear patterns in data (Rather et al, 2017). A poorly input parameter configuration leads to poor forecasting performance (Amroune et al, 2018)
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