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

Walczak, Steven

doi:10.3389/fpsyg.2021.587943

Cited by 15 publications

(6 citation statements)

References 71 publications

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“…Recent studies have demonstrated the predictive power of Artificial Neural Networks (ANN) associated with its ability to approximate any continuous function. ANN are utilized in a wide of variety of applications in engineering, technology and time series forecasting in fields such as biology, finance, medicine and economics social science (Dilek et al, 2015 ; Fu et al, 2018 ; Palmer et al, 2008 ; Walczak, 2021 ). ANN are robust in their application, which include tasks related with pattern classification, the estimate of continuous variables and time series forecasting (Kaastra & Boyd, 1996).…”

Section: Analysesmentioning

confidence: 99%

An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and Tobago

Smith¹,

Haines²

2022

Int Criminol

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The study assessed the changes in murder counts and patterns under COVID-19 conditions in The Republic of Trinidad and Tobago. Initial research indicates that crime rates and patterns have changed under the COVID-19 pandemic possibly because of government implemented restrictions. The specific impact of these responses on murder has not been examined. To fill this gap in the literature the study utilized several interrupted time-series analyses to assess the change(s) in murder trends and their possible relationship to restrictions in movement associated with COVID-19. Overall, murder decreased while under COVID-19 conditions. However, the change was not consistent across different classifications of murder, with some increasing while others continued a downward trend that started before COVID-19 restrictions. The findings suggest that the change in murder trends under COVID-19 was not based on a linear relationship with restrictions in movement. Rather the results suggest that the data contains nonlinear components, which are associated with an initial but inconsistent change in murder counts among different classifications of murder. Which was followed by a phase of diminishing return on the effect of restriction on movement on the murder count. Further, in support of the findings the forecasting of murder was found to be better achieved by an algorithm which supports continuous non-linear functions (Artificial Neural Networks) when compared to a linear approach (Bayesian Structural Time-Series). Supplementary Information The online version contains supplementary material available at 10.1007/s43576-022-00075-w.

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Section: Analysesmentioning

confidence: 99%

An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and Tobago

Smith¹,

Haines²

2022

Int Criminol

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“…Given these benefits, ML procedures have been gaining visibility and strength in Applied Social and Health Sciences. Some works have focused on predicting different types of crimes, and even the location of where they occur, using neural networks and Multilayer Perceptron (MLP) [21]. But there has also been a proliferation of works that aim to predict the type of IPV with Random Forest and SVM classifiers [22] or use neural networks to determine accurate predictors of this type of violence [23].…”

Section: Introductionmentioning

confidence: 99%

Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain

et al. 2023

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Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedings, subsequently, withdraw charges for different reasons. Research in this field has been focusing on identifying the factors underlying women victims’ decision to disengage from the legal process to enable intervention before this occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, none have used machine learning models to predict disengagement from legal proceedings in IPVW cases. This could represent a more accurate way of detecting these events. This study applied machine learning (ML) techniques to predict the decision of IPVW victims to withdraw from prosecution. Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. Once the best models had been obtained, explainable artificial intelligence (xAI) techniques were applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results were compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters was combined with the variables of the previous study, showing that ML-based models had a better predictive accuracy in all cases and that by adding one new variable to the previous work’s predictive model, the accuracy to detect withdrawal improved by 7.5%.

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“…Over the years, crime rate has been on the increase within public universities. As the population of a country increases, crime rates within that country increases (Walczak 2021). This also impacts the accurate prediction of crime (Pratibha et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria

Akinwumi

2023

AIMS Research Publication

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The prediction of crime rates has been a challenging problem for law enforcement agencies and policymakers. The main challenge in developing a predictive model for crime rate is the unavailability and quality of data. Crime data is often incomplete, inconsistent, and biased, which has always affected the accuracy of the scholars’ designed model. For example, the use of traditional methods such as manual crime analysis and statistical analysis are often limited by the volume and complexity of the data. The need for a more accurate and efficient predictive model that can handle large and complex datasets is increasing. This research developed a predictive model using the KNN algorithm that is into accurate prediction of crime rates and helps policymakers to make better decisions. The KNN algorithm was implemented using Python programming language with scikit-learn libraries. The primary dataset was collected from security units of Adekunle Ajasin University, Akungba Akoko and Akungba police station. The Algorithm was implemented on a Laboratory Local Area Network at the Information and Communication Technology Application Centre (ICTAC), Adekunle Ajasin University, Akungba Akoko. The performance evaluation was based on precision, recall, and F1 score. The results show that the KNN algorithm can accurately predict crime rates with an average accuracy of 88%. The model demonstrates that the KNN algorithm can be a useful tool for predicting crime rates. In addition, the model is hoped to enhance crime prevention strategies, reduce crime rates, and ultimately improve the quality of life in university communities. Keywords: Crime, Crime Rate, Prediction, K-Nearest Neighbour, Public Universities. Akinwumi, D.A. (2023): The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria. Journal of Advances in Mathematical & Computational Science. Vol. 11, No. 2. Pp 11-24. dx.doi.org/10.22624/AIMS/MATHS/V11N2P1. Available online at www.isteams.net/mathematics-computationaljournal.

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

Cited by 15 publications

References 71 publications

An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and Tobago

An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and Tobago

Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain

The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria

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