Proceedings ArticleDOI
Geospatial-temporal analysis and classification of criminal data in Manila
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TLDR
Geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatio-temporal hotspots in Manila, the most densely populated city in the Philippines and compared the performance measures of the BayesNet, Naïve Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities.Abstract:
The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatio-temporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Naive Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities.read more
Citations
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Journal ArticleDOI
Spatio-temporal crime HotSpot detection and prediction: a systematic literature review
Umair Muneer Butt,Sukumar Letchmunan,Fadratul Hafinaz Hassan,Mubashir Ali,Anees Baqir,Hafiz Husnain Raza Sherazi +5 more
TL;DR: This study unfolds the following major aspects: the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; the utility of time series analysis techniques and deep learning techniques in Crime trend prediction; and the potential challenges faced by the state-of-the-art techniques and the future research directions.
Crime hotspots: An evaluation of the KDE spatial mapping technique : Spatial analysis
TL;DR: The results suggest that the usage of the KDE algorithm to predict residential burglaries performs well overall when having access to enough crimes, but is capable with small data sets as well.
Journal ArticleDOI
Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities
Umair Muneer Butt,Sukumar Letchmunan,Fadratul Hafinaz Hassan,Mubashir Ali,Anees Baqir,Tieng Wei Koh,Hafiz Husnain Raza Sherazi +6 more
TL;DR: In this paper, the authors presented a novel approach by empowering the authorities to better visualize the threats, by identifying and predicting the highly-reported crime zones in the smart city, and investigated the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crime occurrence.
Journal ArticleDOI
Applied Intelligent Data Analysis to Government Data Related to Criminal Incident: A Systematic Review
Kleber Henrique de Jesus Prado,Kleber Henrique de Jesus Prado,Layse Santos Souza,Israel Dias de Jesus Junior,Methanias Colaço Júnior +4 more
TL;DR: To identify, characterize and meta-analyze the approaches and intelligent algorithms used to discover patterns on criminal incident data, observing the Decision Tree, Logistic Regression, Naive Bayes, Random Forest, and Support Vector Machine algorithms.
Proceedings ArticleDOI
Evaluating the Performance of Hierarchical Clustering algorithms to Detect Spatio-Temporal Crime Hot-Spots
TL;DR: Hierarchical Density-based spatial clustering of applications with noise is used to find spatio-temporal crime hot-spots by clustering and the results shows that this technique outperforms others.
References
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Proceedings ArticleDOI
Crime analysis and prediction using data mining
TL;DR: This work has an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster and is focusing mainly on crime factors of each day.
Journal ArticleDOI
Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots
TL;DR: An analysis study is introduced by combining the findings of Denver crimes dataset with its demographics information in order to capture the factors that might affect the safety of neighborhoods and to help agencies to predict future crimes in a specific location within a particular time.
Journal ArticleDOI
A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA
TL;DR: Crime and accident datasets from Denver City, USA during 2011 to 2015 consisting of 372,392 instances of crime are analyzed by using a number of Classification Algorithms to assess trends and patterns that are assessed by BayesNet, NaiveBayes, J48, JRip, OneR and Decision Table.
Proceedings ArticleDOI
Improved method of classification algorithms for crime prediction
TL;DR: The result from the analysis demonstrated that Naïve Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for groups 2, which clearly indicates thatNaíve Bayesesian calculation is supportive for prediction in diverse states in USA.
Proceedings ArticleDOI
A web-based geographical information system for crime mapping and decision support
TL;DR: Results from the prototype development demonstrate that for a Web-based crime hotspot mapping system, rich Internet application technology in combination with open source software is an effective solution.