Review of Current Crime Prediction Techniques

Grover, Vikas; Adderley, Richard; Bramer, Max

doi:10.1007/978-1-84628-666-7_19

Cited by 20 publications

(5 citation statements)

References 16 publications

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“…Current crime prediction techniques include statistical methods, geographical information systems (GIS) methods and miscellaneous data mining techniques. For a review on current crime prediction techniques refer to [9] and [20].…”

Section: Crime Prediction Methods and Challengesmentioning

confidence: 99%

“…With the ability to foresee where future crimes are more likely to take place, the effectiveness of crime prevention strategies can be improved [8]. Despite the advances in crime prediction tools, their application in policing operations is still limited due to practical reasons [9] and their accuracy can still be improved to further reduce crime.…”

Section: Introductionmentioning

confidence: 99%

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Design of an Agent-Based Model to Predict Crime (WIP)

Brüngger

Kadar

Cvijikj

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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In modern societies where fighting crime has a long history, establishing effective methods for crime prevention is of high significance. For this purpose, police departments worldwide undertake efforts to analyze past crime data. They aim to detect the most prolific crime areas and predict their development, in order to direct their prevention efforts. In parallel, scholars investigate possibilities to build crime prediction models, by applying various techniques, from simple regression to data mining. Acknowledging the latest advances in this field, which suggest that Agent-Based Modeling (ABM) is a promising method, in this paper we present the design of an ABM capable of predicting where and when future crimes will most probably happen. We extend the previous work by accounting for offender behavior and integrating a realistic representation of the environment. In contrast to existing models, past crime data will be included to achieve automatic calibration. Furthermore, we will assess how crime data can be used to model agent's behavior and we will include environmental data stepwise, in order to achieve the optimal balance between prediction accuracy and complexity. The resulting ABM will be developed as a crime prediction tool, and as an experimental environment to test prevention strategies.

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Section: Crime Prediction Methods and Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of an Agent-Based Model to Predict Crime (WIP)

Brüngger

Kadar

Cvijikj

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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“…(Vikas. Grover et al 2009) Examined the current techniques that are used to predict crime and criminality. They were able to narrow down their research of possible techniques to three main categories:…”

Section: Radial Basis Functionsmentioning

confidence: 99%

Automated Crime Patterns Analysis Framework for Predictive Policing using Data Mining Techniques

Nyale¹,

Kangethe²

2020

IJCATR

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The aim of this research is to study and develop an automatable technological framework that can be used to identify contributing attributes, patterns and trends from reported cases using data mining techniques. A combination of classification and association rules based data mining approach has been proposed for this study due to its effectiveness in bringing out patterns and trends that are interlinked, related and near each other.

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“…The accessibility of modern technology has permissible implementation to gather detailed data on crime [2,3]. With today's increasing crime rates, crime analysis is required, including strategies and procedures to reduce the chance of crime [4]. The fundamental component of the sustainable development of a country is security.…”

Section: Introductionmentioning

confidence: 99%

Leveraging transfer learning with deep learning for crime prediction

Butt,

Letchmunan,

Hassan

et al. 2024

PLoS ONE

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Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

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Review of Current Crime Prediction Techniques

Cited by 20 publications

References 16 publications

Design of an Agent-Based Model to Predict Crime (WIP)

Design of an Agent-Based Model to Predict Crime (WIP)

Automated Crime Patterns Analysis Framework for Predictive Policing using Data Mining Techniques

Leveraging transfer learning with deep learning for crime prediction

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