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

Khan, Muzammil; Ali, Azmat; Alharbi, Yasser

doi:10.1155/2022/4830411

Cited by 26 publications

(4 citation statements)

References 18 publications

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“…Among all four models, the Neural Network has the best outcome. (Khan et al, 2022) presented a crime prediction model by analyzing and comparing Naive Bayes, Random Forest, and Gradient Boosting Decision Tree algorithms.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Divyasri¹,

Saranya

P.Kathiravan

2023

Preprint

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Crimes are a social issue that affects not only an individual but also humanity. Crime classification techniques for crime forecasting are an emerging research area. generally, Crime data are centrally organized with regular maintenance of the criminal registers that can aid officers in sharing observations and improve early alert approaches to keep the citizens secure within their towns. Hence, the aim of this study is to compare the performance of the state-of-the-art Dynamic Ensemble Selection of Classifier algorithms for predicting crime. We used five different benchmark crime datasets (Chicago, San Francisco, Pheonix, Boston, and Vancouver) for this experimental research work. The performance of the state-of-the-art dynamic ensemble selection of classifiers algorithms was evaluated and compared using various performance evaluation metrics such as accuracy, F1-score, precision, and recall. The KNORA Dynamic ensemble algorithms, which select the subset of ensemble members before the forecasting, outperformed the typical machine learning algorithms, and also the traditional ensemble algorithm techniques in terms of accuracy showed that the dynamic ensemble algorithms are more powerful. This ability to predict crimes within urban societies can help citizens, and law enforcement makes precise informed conclusions and preserves the neighborhoods more unassailably to improve the quality of life for humans.

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Section: Related Workmentioning

confidence: 99%

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Divyasri¹,

Saranya

P.Kathiravan

2023

Preprint

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show abstract

“…Law enforcement agencies and policymakers strive to develop effective strategies to combat crime and allocate resources efficiently. In recent years, there has been a growing interest in utilizing mathematical and statistical methods to predict crime occurrences and assist in proactive law enforcement efforts (Kang and Kang 2017;Khan et al 2022). These methods leverage the power of data analysis, modelling, and predictive analytics to identify patterns, understand the underlying factors, and forecast crime rates and hotspots.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the context of crime prediction, this assumption may not hold true due to evolving criminal behaviours, changes in social and economic factors, and shifts in law enforcement strategies. If the relationships between crime variables change, the predictive accuracy of the Bayesian network may decrease (Khan et al 2022). Additionally, Bayesian networks may struggle with rare or low-frequency events.…”

Section: Bayesian Networkmentioning

confidence: 99%

Exploring the usefulness of the INLA model in predicting levels of crimes in the City of Johannesburg, South Africa

Coleman,

Mokilane,

Holloway

et al. 2023

Preprint

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Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to optimize resource allocation and personnel management for crime prevention. Traditionally, the Poisson model has been the widely used model for crime prediction. However, recent statistical advancements introduce Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focusses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction, secondly developing a model using no demographic information other than clustering attributes as an exogenous variable, thirdly quantifying prediction uncertainty, and finally addressing data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model accurately ranked areas by crime levels, achieving a 29% Mean Absolute Percentage Error (MAPE) and 0.8 R-Squared value for crime predictions.

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“…Also, in [13], an online advertising network was constructed and analyzed using Laplacian SVM to detect human trafcking advertisements. Khan et al [14] proposed a crime prediction model by comparing three known algorithms, Naïve Bayes, random forest, and gradient boosting decision tree, and classifed the top ten crimes from the San Francisco crime data. A combined framework of graph representation learning and machine learning methods is introduced to predict the amount of money exchanged among criminal agents and to recover the missing criminal partnerships [15].…”

Section: Introductionmentioning

confidence: 99%

Fast Prediction for Criminal Suspects through Neighbor Mutual Information‐Based Latent Network

Jhee,

Kim,

Park

et al. 2023

International Journal of Intelligent Systems

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One of the interesting characteristics of crime data is that criminal cases are often interrelated. Criminal acts may be similar, and similar incidents may occur consecutively by the same offender or by the same criminal group. Among many machine learning algorithms, network-based approaches are well-suited to reflect these associative characteristics. Applying machine learning to criminal networks composed of cases and their associates can predict potential suspects. This narrows the scope of an investigation, saving time and cost. However, inference from criminal networks is not straightforward as it requires being able to process complex information entangled with case-to-case, person-to-person, and case-to-person connections. Besides, being useful at a crime scene requires urgency. However, predictions from network-based machine learning algorithms are generally slow when the data is large and complex in structure. These limitations are an immediate barrier to any practical use of the criminal network geared by machine learning. In this study, we propose a criminal network-based suspect prediction framework. The network we designed has a unique structure, such as a sandwich panel, in which one side is a network of crime cases and the other side is a network of people such as victims, criminals, and witnesses. The two networks are connected by relationships between the case and the persons involved in the case. The proposed method is then further developed into a fast inference algorithm for large-scale criminal networks. Experiments on benchmark data showed that the fast inference algorithm significantly reduced execution time while still being competitive in performance comparisons of the original algorithm and other existing approaches. Based on actual crime data provided by the Korean National Police, several examples of how the proposed method is applied are shown.

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

Cited by 26 publications

References 18 publications

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Exploring the usefulness of the INLA model in predicting levels of crimes in the City of Johannesburg, South Africa

Fast Prediction for Criminal Suspects through Neighbor Mutual Information‐Based Latent Network

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