Crime Hotspots: An Evaluation of the KDE Spatial Mapping Technique

Johansson, Erik; Gahlin, Christoffer; Borg, Anton

doi:10.1109/eisic.2015.22

Cited by 21 publications

(15 citation statements)

References 4 publications

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“…Hot spot policing has been proven to be an effective means of reducing crime (Braga et al 2012;Sherman & Weisburd 1995;Rosenfeld et al 2014), and is in fact one of the policing methods that exhibits the strongest evidence for crime reduction through police work (Abt & Winship 2016). While several Swedish studies have shown that crime is highly concentrated (Marklund 2011;Uittenbogard & Ceccato 2017;Johansson et al 2015;Gerell & Kronkvist 2016;Sturup et al 2017), the evidence for hot spot policing in a Nordic context remains rather limited. The most ambitious Nordic study to date is a randomized control trial from Denmark, in which the Danish Ministry of Justice and the Danish police piloted hot spot policing in three police districts (Atterman 2017).…”

Section: Geographically Targeted Policingmentioning

confidence: 99%

Quantifying the Geographical (Un)reliabilityof Police Data

Gerell

2018

Nordisk politiforskning

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Place-based policing has attracted a substantial amount of attention, not least in relation to hot spot policing. Such policing efforts depend on geographical analysis of where crime takes place. However, while it is well known that police crime data suffer from many limitations, less is known about the extent to which the geographical reliability of these data constitutes a problem. The present study attempts to quantify the extent of this problem by exploiting the fact that in Sweden there is an alternative, and more reliable, source of geographical data for incidents of arson. The study compares the locations for car arson incidents as recorded by the police and the rescue services, respectively. The resulting quantification of differences shows that the median error for the police data is 83 meters. This presents a potential pitfall for geographical analysis, both for researchers using police data and for the police themselves in their operational and strategic analysis of crime.

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Section: Geographically Targeted Policingmentioning

confidence: 99%

Quantifying the Geographical (Un)reliabilityof Police Data

Gerell

2018

Nordisk politiforskning

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“…Most of these studies include 70% training (current) for 30% testing (future) sets. Johansson et al (2015) use a combined approach, including rolling horizon, which is producing ten times the size of a sample for the KDE algorithm, containing 70% of the original crime dataset (keeping the 70/30 ratio). The final result is calculated as the mean of the ten measurements.…”

Section: Algorithms and Validation Strategiesmentioning

confidence: 99%

“…This observation is included in their prediction model "Dynamic Covariance Kernel Density Estimation method (DSKDE)" and compared with the "Seasonal Trend decomposition based on Loess (STL)" baseline model. Hart and Zandbergen (2014) and Johansson et al (2015) use a kernel-based KDE approach without comparing it with a baseline method, both considering the PAI as one of the evaluation metrics. Only two of the kernel-based studies consider ancillary data Rosser et al 2017), yet both use different validation strategies (rollinghorizon and train-test split, respectively) and evaluation metrics (MAE, MSE, MAPE in the first publication and Hit Rate in the second publication).…”

Section: Algorithms and Validation Strategiesmentioning

confidence: 99%

A systematic review on spatial crime forecasting

et al. 2020

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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.

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“…Berdasarkan permasalahan yang terjadi, telah dilakukan penelitian [4][5][6][7][8][9][10][11][12][13][14][15][16][17] tentang aplikasi pemetaan criminal, akan tetapi aplikasi tersebut masih memiliki kekurangan seperti belum menggunakan pendekatan atau metode yang dikenal dalam proses perancangan perangkat lunak. Selain itu, proses perancangan tidak melibatkan user sehingga tidak diketahui kesesuaian antara aplikasi dengan kebutuhan user sehingga dibutuhkan penelitian lanjutan untuk merancang aplikasi yang berdasarkan pada kebutuhan user.…”

Section: Pendahuluanunclassified

Perancangan Aplikasi Peristiwa Kriminal Dengan Pendekatan Contextual Design

Adriman

Syafie

2019

InfoTekJar

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KATA KUNCI A B S T R A KKriminal, kebutuhan user, contextual design Pemetaan lokasi kriminal sangat berguna bagi pihak kepolisian dan masyarakat. Pada proses kerja selama ini, Kepolisian Republik Indonesia (POLRI) masih menggunakan pemetaan dalam bentuk konvensional seperti gambar peta pada kertas yang ditempelkan pada kantor kepolisian. Hingga saat ini, jumlah personel kepolisian pada suatu wilayah belum mencapai tahap ideal, yaitu 1 (satu) orang polisi untuk 100 orang masyarakat, sehingga pengawasan yang dilakukan akan menjadi kurang efektif. Meskipun demikian, pihak kepolisian masih menggunakan data dan informasi yang disimpan pada arsip serta peta konvensional yang memiliki banyak kelemahan seperti terbatasnya informasi yang ditampilkan, dan daya tahan peta serta arsip yang mudah rusak. Berdasarkan permasalahan yang terjadi, telah dilakukan penelitian tentang aplikasi pemetaan criminal, akan tetapi aplikasi tersebut masih memiliki kekurangan seperti belum menggunakan pendekatan atau metode yang dikenal dalam proses perancangan perangkat lunak untuk mengetahui kebutuhan user. Dari beberapa permasalahan yang ditemukan pada peta kriminal yang terdapat di kantor kepolisian, maka ditawarkan solusi dalam proses perancangan aplikasi peristiwa kriminal menggunakan pendekatan contextual design yang mampu mendapatkan kebutuhan user di lapangan. Sementara itu, dalam proses perancangan aplikasi, terdapat 6 (enam) langkah yang dilakukan, mulai dari wawancara kontekstual, interpretasi, konsolidasi data, visioning, storyboarding, user environment design, dan prototyping. Dengan adanya perancangan aplikasi peristiwa kriminal menggunakan pendekatan Contextual Design, maka diharapkan dapat memaksimalkan pengalaman user dalam menggunakan aplikasi sehingga menjadi lebih menyenangkan, menangkap kebutuhan user dengan benar, serta mendapatkan keluaran yang sesuai dengan kebutuhan mereka. Hal ini bertujuan untuk membantu pihak kepolisian sehingga dapat mempermudah kinerja mereka dalam proses mengelola data criminal.

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Crime Hotspots: An Evaluation of the KDE Spatial Mapping Technique

Cited by 21 publications

References 4 publications

Quantifying the Geographical (Un)reliabilityof Police Data

Quantifying the Geographical (Un)reliabilityof Police Data

A systematic review on spatial crime forecasting

Perancangan Aplikasi Peristiwa Kriminal Dengan Pendekatan Contextual Design

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