Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model

Abstract

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases that tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73% and 77% when predicting property crimes and violent crimes, respectively.

A Survey on Societal Event Forecasting with Deep Learning

“…For traditional machine learning models, linear regression [85,107,131,156], ridge and lasso regression [14,87], support vector regression (SVR) [4,14,156] have been proposed to analyze the crime dynamics in regression problems. Classification models have been extensively studied in crime hotspot prediction, including Support Vector Machine (SVM) [68,69], Logistic Regression [106], Naive Bayes [57,144], shallow neural network [106,144,153], k-nearest neighbors algorithm [155], clustering based model [26,56,152], decision tree [1,110], and ensemble models [3,10,15,51,57,75,106,108,144,153]. Some researchers studied more advanced approaches.…”

An Integrated Graph Model for Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism

“…In some of the police districts, one police station may serve multiple communities, while it is also possible that one community is covered by more than one police district. As the smallest cell of a city, the community is usually selected as the spatial unit to predict the spatial-temporal patterns of crime in previous studies [6,[20][21][22][23]. However, in this study, spatial areas were divided by police districts.…”

“…In recent years, spatial-temporal crime prediction technology has been rapidly developed. With deriving data that include the crime incidents number, population density, weather variables, etc., spatial-temporal patterns of assault, robbery, theft, or other types of crimes can be predicted with the help of machine learning (especially deep learning) and other methods [1][2][3][4][5][6][7][8]. It provides references and predictions about when and where the crime hotspot would be to the police in advance, so it contributes to crime prevention as well as better police resources allocations.…”

Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern Analysis

“…In [6], Edoka used different classification models, such as Logistic Regression, K Nearest Neighbors and XGBoost to classify the types of crimes in dataset of crimes from Chicago crime porter. In [7], the authors build an ensemble learning model to predict spatial occurrences of crimes of different types. Cichosz [8] used point of interestbased data (such as bus stops, cinema halls, etc.)…”