An overview on crime prediction methods

Shamsuddin, Nurul Hazwani Mohd.; Ali, Nor Azizah; Alwee, Razana

doi:10.1109/ict-ispc.2017.8075335

Cited by 39 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ada banyak algoritma yang dapat digunakan pada metode klasifikasi, namun permaslahnnya adalah bagaimana kita dapat menentukan algoritma terbaik untuk profiling tersangka penyalahgunaan narkoba? Karena memang tidak ada standar algoritma yang baku [5] untuk penerapannya, semuanya tergantung data yang akan dijadikan penelitian. Maka dari itu, tujuan dari penelitian ini adalah penerapan metode komparasi terhadap performa algoritma dari ID3, C4.5, dan CHAID untuk menentukan algoritma yang tepat dalam menganalisis profiling pelaku tindak kejahatan penyalahgunaan narkoba.…”

Section: Pendahuluanunclassified

Untitled

2021

View full text Add to dashboard Cite

Strategi dalam pengendalian kejahatan narkoba tidak bisa didasari melalui intervensi berupa larangan saja, akan tetapi memahami pola kejahatan narkoba akan menjadi lebih baik dalam pengendaliannya, terutama untuk pencegahan dan kewaspadaan. Dengan menggunakan teknik data mining, insigth terhadap kejahatan narkoba dapat didefinisikan dengan baik, salah satunya dengan algoritma klasifikasi decision tree. Ada beberapa algoritma dengan basis decision tree, seperti ID3, C4.5, dan CHAID. Dengan banyaknya pilihan algoritma, maka metode perbandingan algoritma dipilih oleh penulis dalam penelitian ini, tujuannya untuk menentukan algoritma yang paling tepat dalam menganalisis data. Performa akurasi menjadi tolok ukur dalam perbandingannya, dan hasil dari perbandingan yang dilakukan, penulis memilih algoritma CHAID sebagai pemodelannya dengan nilai akurasi sebesar 73.89%, sedangkan yang lainnya masingmasing memiliki nilai akurasi sebesar 70.14% untuk ID3 dan 72.44% untuk C4.5.

show abstract

Section: Pendahuluanunclassified

Untitled

2021

View full text Add to dashboard Cite

show abstract

“…This theory starts with the proposition that places vary in terms of risk due to the spatial influence of criminogenic factors. Last, a recent review study summarizes past crime forecasting studies of four methods, namely support vector machines, artificial neural networks, fuzzy theory, and multivariate time series (Shamsuddin et al 2017). The authors suggest that researchers propose hybrid methods to produce better results.…”

Section: Related Workmentioning

confidence: 99%

A systematic review on spatial crime forecasting

et al. 2020

View full text Add to dashboard Cite

Background: Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects. Methods: We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics. Results: The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon. Limitations: Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems. Conclusions: There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction. Implications: Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.

show abstract

“…Some papers have emphasized on the significance of spatial and temporal information to find irregularities in crime [16], [17]. Shamsuddin et al [18] presented the first comprehensive overview of prediction methods used for crime prediction. Notably, they focus on the clustering, time series analysis, and deep learning approaches.…”

Section: Figure 1: Frequency Distribution Of Different Criminal Eventmentioning

confidence: 99%

“…Predictive mining is one of the most commonly used systematic approaches for predicting such as crime, criminal behaviour, and intrusion detection. Nine studies found that used data mining techniques for crime prediction such as classification, association role mining, ensemble approaches, and classic machine learning techniques [18], [19], [22], [34]- [36], [49]. Wang et al [37] explored the deep learning models such as Recurrent and Convolutional Neural Network for crime prediction due to their promising performance in other fields.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

et al. 2020

View full text Add to dashboard Cite

The primary objective of this study is to accumulate, summarize, and evaluate the state-ofthe-art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author's knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.

show abstract

An overview on crime prediction methods

Cited by 39 publications

References 29 publications

Untitled

Untitled

A systematic review on spatial crime forecasting

Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

Contact Info

Product

Resources

About