Crime Prediction Model Research Articles

Crime Prediction Through Collaborative Analysis of Proximate Police Stations Data

Crime prediction is a crucial aspect of law enforcement strategies and crime prevention efforts. Machine learning has emerged as a valuable tool in crime prediction, allowing for more accurate and data-driven forecasting. In this study, we focus on forecasting the number of crimes at Pathumwan Police Station in Thailand. Utilizing criminal records from various police stations across Thailand, we employ the K-Means clustering algorithm to group police stations exhibiting similar crime patterns to the Pathumwan Police Station. The clustering results reveal that Wang Thonglang, Nang Loeng, Dusit, Bang Sue, Thung Maha Mek, Samre, Sutthisan, Pak Khlong San, Bangkok Yai, Bangkok Noi, Makkasan, Bang Yi Ruea, and Talat Phlu Police Station are clustered together with Pathumwan Police Station. Subsequently, we apply the Long Short-Term Memory (LSTM) model to forecast crimes at Pathumwan Police Station. The training dataset comprises paired data from police stations within the same cluster as Pathumwan Police Station. Our findings indicate that combining data from Wang Thonglang, Nang Loeng, Dusit, Bang Sue, Thung Maha Mek, Samre, Sutthisan, Pak Khlong San, Bangkok Yai, Bangkok Noi, Makkasan, Bang Yi Ruea, and Talat Phlu Police Station with Pathumwan Police Station results in lower errors in Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) compared to using only Pathumwan Police Station data. This collaborative approach enhances the accuracy of crime prediction models and contributes to more effective law enforcement strategies.

Machine Learning-Based Prediction Methods for Home Burglary Crimes

In today’s rapidly evolving society, as technology continues to advance, various new forms and methods of crime emerge incessantly. It becomes particularly crucial to accurately predict future criminal behaviors. This paper delves into the study of forecasting home burglary crimes in the realm of property-related offenses. Utilizing a dataset of criminal cases, relevant variables with high correlation to crime prediction are selected as features. Through employing diverse machine learning algorithms, the likelihood of the occurrence of home burglary crimes is forecasted. Consequently, a crime prediction model specifically tailored for home burglary cases is constructed, and the accuracy of the model is evaluated. By using the accuracy of the model as the benchmark, the optimal crime prediction model is chosen, and a system is implemented for building and evaulating the model. Experimental results demonstrate that the developed crime prediction model is capable of effectively foreseeing home burglary crimes, thereby providing valuable support and scientific evidence for the prevention and handling of such criminal cases.

Predicting and analysing initiator crime environments based on machine learning for improving urban safety

PurposeInitiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.Design/methodology/approachWe compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.FindingsBy analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.Originality/valueOur findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Research on the method of predicting the trend of criminal activities based on time series analysis from the perspective of criminal procedure law

Abstract Criminal activity has always been a problem that seriously affects people’s sense of security and well-being in life, and it is also the focus of public security departments in terms of security prevention and control. This study aims to use scientific methods to analyze the time series of existing historical data related to criminal activities and to advance inference or determination of the future trend of criminal activities. In this paper, after mining the spatiotemporal pattern of criminal activities using the centered moving average method, the spatiotemporal cycle characteristics and time distribution characteristics of criminal activities are analyzed. Based on the conclusions drawn, an improved LSTNet algorithm is proposed by combining deep learning spatio-temporal network, convolutional neural network CNN, recurrent neural network RNN, autoregressive AR, and specially designed Skip-RNN, and a GRU-gated recurrent unit structure is used to realize the optimization of the prediction accuracy of the criminal activities, to solve the problems related to the spatio-temporal dependencies, and to establish an improved ST- Res Net crime prediction model. Finally, the prediction performance of the model is examined. The RMSE, MSE, and MAPE values of this paper’s prediction model are 0.88, 5.12, and 5.12%, respectively, which are better than other models. The experiments demonstrate that the prediction model presented in this paper can enhance the accuracy of criminal activity prediction to a certain extent and can be employed in the prevention and management of criminal activities.

Unsupervised Domain Adaptation for Crime Risk Prediction Across Cities

Crime risk prediction is crucial for city safety and residents’ life quality. However, without labeled data, it is challenging to predict crime risk in cities. Due to municipal regulations and maintenance costs, it is not trivial for many cities to collect high-quality labeled crime data. In particular, some cities have lots of labeled data while others may have few. It has been possible to develop a crime prediction model for a city without labeled crime data by learning knowledge from a city with abundant data. Nevertheless, the inconsistency of relevant context data between cities exacerbates the difficulty of this prediction task. To this end, this article proposes an effective unsupervised domain adaptation model (UDAC) for crime risk prediction across cities while addressing the contexts’ inconsistency issue. More specifically, we first identify several similar source city grids for each target city grid. Based on these source city grids, we then construct auxiliary contexts for the target city, to make contexts consistent between the two cities. A dense convolutional network with unsupervised domain adaptation is designed to learn high-level representations for accurate crime risk prediction and simultaneously learn domain-invariant features for domain adaptation. The effectiveness of our model is verified through extensive experiments using three real-world datasets.

AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction

Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear and dynamic spatial dependency and temporal patterns of a specific crime category, while keeping the underlying structure of the model interpretable. In this article, we develop AIST, an A ttention-based I nterpretable S patio T emporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest information) and recurring trends of crime. Extensive experiments show that AIST outperforms the state-of-the-art techniques in terms of accuracy (e.g., AIST shows a decrease of 4.1% on mean average error and 7.45% on mean square error for the Chicago 2019 crime dataset) and interpretability. 1

Auditing the fairness of place-based crime prediction models implemented with deep learning approaches

Place-based crime prediction models implemented with deep learning leverage the spatio-temporal patterns of historical crimes, together with built-environment factors, to predict aggregate volumes of crime incidents at specific locations. Nevertheless, related work has shown that crime incident data can suffer from under-reporting bias whereby certain social groups report more crimes than others and whereby certain crimes are recorded more often by the police. In addition, under the umbrella of crime opportunity theories that suggest that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based crime models implemented with deep learning. However, prior work has shown that human mobility data collected from cell phones can suffer from sampling bias because certain population groups are not equally represented due to lack of cell phone ownership. Despite these well reported data bias concerns, no related work has yet looked into the fairness of place-based crime prediction models implemented with deep learning; nor into the effect that adding human mobility data from cell phones as a predictor might have on their fairness. In this paper, we use publicly available fine-grained crime incident and human mobility data from the US to conduct a comprehensive fairness audit across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we explore potential root causes of the models' fairness metrics by looking into crime data bias, mobility data bias and algorithmic bias. Our results show that unfair predictions are pervasive across place-based crime prediction models implemented with deep-learning due to the inherent data bias in the crime incident data; that adding mobility features extracted from cell phone data decreases fairness under certain circumstances; and that part of the fairness loss can be explained by bias in the crime data, and in the predictive algorithms that might be exacerbating the data bias, with the mobility data bias having an insignificant effect on fairness loss.

Crime Prediction Methods Based on Machine Learning: A Survey

The objective of crime prediction, one of the most important technologies in social computing, is to extract useful information from many existing criminal records to predict the next process-related crime. It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas. With the rapid growth of big data, the Internet of Things, and other technologies, as well as the increasing use of artificial intelligence in forecasting models, crime prediction models based on deep learning techniques are accelerating. Therefore, it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms. Existing crime prediction methods can be roughly divided into two categories: those based on conventional machine learning and those based on contemporary deep learning. This survey analyses the fundamental theories and procedures. The most frequently used data sets are then enumerated, and the fundamental procedures of various algorithms are also analyzed in this paper. In light of the insufficient scale of existing data in this field, the ambiguity of data types used to predict crimes, and the absence of public data sets that have a significant impact on the research of algorithm models, this survey proposes the construction of a machine learning-based big data research model to address these issues. Future researchers who will enter this field are provided with a guide to the direction of future research development.

Enhancing short-term crime prediction with human mobility flows and deep learning architectures

Place-based short-term crime prediction models leverage the spatio-temporal patterns of historical crimes to predict aggregate volumes of crime incidents at specific locations over time. Under the umbrella of the crime opportunity theory, that suggests that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based short-term crime models. Researchers have used call detail records (CDR), data from location-based services such as Foursquare or from social media to characterize human mobility; and have shown that mobility metrics, together with historical crime data, can improve short-term crime prediction accuracy. In this paper, we propose to use a publicly available fine-grained human mobility dataset from a location intelligence company to explore the effects of human mobility features on short-term crime prediction. For that purpose, we conduct a comprehensive evaluation across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we show that adding human mobility flow features to historical crimes can improve the F1 scores for a variety of neural crime prediction models across cities and types of crimes, with improvements ranging from 2% to 7%. Our analysis also shows that some neural architectures can slightly improve the crime prediction performance when compared to non-neural regression models by at most 2%.

Review on Automobile Crime Prediction Model

Review on Automobile Crime Prediction Model

Interpretable machine learning models for crime prediction

The relationship between crime patterns and associated variables has drawn a lot of attention. These variables play a critical role in crime prediction. While traditional regression models are capable of revealing the contribution of the variables, they are not optimal for crime prediction. In contrast, machine learning models are more effective for crime prediction, but most of them cannot estimate the contribution of each individual variable. This study aims to overcome this limitation by taking advantage of the interpretability of advanced machine learning models. Based on the routine activity theory and crime pattern theory, this study selects 17 variables for the crime prediction. The XGBoost algorithm is adopted to train the prediction model. A post-hoc interpretable method, Shapley additive explanation (SHAP), is used to discern the contribution of individual variables. A variable with a higher SHAP value has a higher contribution to the crime prediction model. In addition to the global model for the entire area, a local model is calibrated at each study unit, revealing the spatial variation of the variables' unique contributions. Among all 17 variables used in this model, the proportion of the non-local population and the ambient population aged 25–44 contribute more than other variables in predicting crime. The more the ambient population aged 25–44 in the area, the more the public thefts. Additionally, local SHAP values are mapped to demonstrate each variable's contribution to the crime prediction model across the study area. The results of the local models can help the police tackle the most important factors at each location, while the global model identifies the important factors for the entire region.

Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis (EDA) is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well.

Crime hotspot prediction based on dynamic spatial analysis

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

Predicting Crime and Other Uses of Neural Networks in Police Decision Making.

Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

A Hybrid GCN and LSTM Structure Based on Attention Mechanism for Crime Prediction

Globalization has been the major contributor to economy boom. While at the same time, it has stimulated the development of crime method as frequent cross-border communication allowed. With the improvement in big data and prediction system of policing work, it has become a new research field to establish an efficient crime prediction model, by which police departments could clamp down on criminal activities more accurately. Besides, this model will be quite beneficial for commanding and dispatching police force thus to improve work efficiency. This paper proposes a combination model, which uses Long Short-Term Memory Network (LSTM) and Graph Convolutional Network (GCN) to predict crime rate and takes advantage of the Attention mechanism to improve the experimental result. By extracting the spatio-temporal characteristics of crimes and increasing the proportion of typical feature, it can not only predict crime quantity, but also detect the degree of crime risk in each region. A rolling forecast of crime data for about three years in the Boston of the United States shows that our model has good prediction performance.

Crime Analysis and Prediction Model Using Data Mining and Machine Learning Techniques Comparative Analysis

Crime Analysis and Prediction Model Using Data Mining and Machine Learning Techniques Comparative Analysis

Criminal psychology trend prediction based on deep learning algorithm and three-dimensional convolutional neural network

In the modern era, the empirical analysis of countermeasures against crime has developed rapidly from the perspective of crime causes. The concept of punishment based on preventionism has been vigorously developed. We aimed to apply the principle of convolutional neural network (CNN) algorithm in deep learning to frame a crime prediction model in three-dimensional CNN (3D CNN) for representative driver behaviours offenses. Results indicated that the 3D CNN prediction model predicted road traffic offences through the detection of five driver offender behaviours with an accuracy rate of 96%. The prediction model we propose appears to hold great prospects for driver offender behaviour trending and to provide a reference for future studies on crime prevention.

Preventing crimes against public health with artificial intelligence and machine learning capabilities

Criminal acts that endanger public health have seriously threatened people's health and life. How to prevent such criminal acts from occurring has become the focus of attention from all walks of life. There are few studies on the prevention of crimes endangering public health, and the results are not satisfactory. With the rapid development of artificial intelligence technology, machine learning algorithms are widely used in various fields. Based on the background of the times, this paper applies machine learning algorithms to the prevention research of crimes endangering public health, aiming to improve the efficiency of crime prevention. First of all, this paper establishes a predictive criminal behavior model based on support vector machine and random forest algorithm, and uses the model to analyze its performance. This article takes a certain city in our province as a specific investigation object, collects relevant case data of criminal acts endangering public health in the city from January to October 2018, and predicts criminal behaviors, and compares them with the actual crimes data collected later. At the same time, a random questionnaire survey was conducted on the citizens of this city to analyze the factors leading to crimes that endanger public health and their enthusiasm for participating in legislation. The experimental results show that Lagrangian interpolation can make the data set more complete, with a standard deviation of 1.19; the crime prediction model based on support vector machine and random forest algorithm can basically predict the incidence of crime, and the trend of its predicted data is basically consistent with the trend of actual data; 48.32% of the people believe that imperfect laws and regulations are the main reason for the frequent occurrence of crimes endangering public health, but only 18% are willing to actively participate in relevant legislation. The above results show that the prediction model established by artificial intelligence algorithm can effectively predict criminal behaviors that endanger public health and provide reliable data for prevention.