The GIS based Criminal Hotspot Analysis using DBSCAN Technique

Abstract

Spatially Data mining used efficiently to extract any potential patterns and associations to detect hidden information from multiple sources data. In this paper, data mining Density-based spatial clustering of applications with noise DBSCAN algorithm is emphasised. The importance in this work was using a prototype software to process the giving data into an understandable outcome throw clustering technique, it is a powerful method for criminal activities detection and pattern recognition to get useful information that can help police to reduce crimes. Spatial data mining is practical with geographical crimes data set and processing a large amount of crimes data. Police conventional way was manual and time-consuming using a pin on the wall. Therefore, it has to be developed and merged with advanced techniques. In this study, data mining clustering method was used to examine Baltimore, Maryland’s crimes information. The processed criminal data from the state of Maryland, Baltimore City was 340,924 cases and 16 attributes to reflect the cases between 2012-2018. DBSCAN algorithm is utilized to cluster crimes incidents focused on certain predefined events and the outcome of these clusters employed to find hotspots. The clustering findings are visualized by the GIS to make crimes distribution on the map at real-time for the law enforcement to understand and interact

Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities

“…The other three algorithms are all general and can be used in many research fields. Especially, DBSCAN is such a classical algorithm, and both itself and its variants have been applied in various situations [4,23,24]. As introduced in Section 3.3, DPC has two parameters, but it provides an easy and useful method for parameter selection, but it still needs manual parameter setting for each dataset.…”

“…After the processing of activity areas, many further analyses or applications can proceed. Mohammed clustered criminal data based on DBSCAN to find criminal hotspots [24]. This work took place in Baltimore, Maryland, and obtained hotspots of different kinds of criminal incidents.…”

“…To verify the effectiveness of the proposed framework, ELV, three existing methods were also tested, namely, density peak clustering (DPC) [25], density-based spatial clustering of applications with noise (DBSCAN) [4,23,24], and Hierarchical DBSCAN (HDB-SCAN) [5,[27][28][29]. As introduced in Sections 1 and 2, DPC is a simple and effective clustering algorithm that was proposed recently and has been applied in many fields.…”

“…Some studies proposed methods to select an appropriate number manually [21] or automatically [22], but this is hard for large-scale spatial data with varying densities. Density-based clustering, such as the density-based spatial clustering of applications with noise (DBSCAN) [4,23,24] and density peak clustering (DPC) [25], can effectively alleviate the problem of noise. The uses of DBSCAN and DPC are limited by the selection of the parameters related to density, which have a great impact on the clustering results.…”

Speed-Related Traffic Accident Analysis Using Gis-Based Dbscan and NNH Clustering

“…NNH algorithm, coded in the free CrimeStat software, performs point-based clusters directly and presents convex hulls of cluster zones (Levine, 2013;Levine, 2014;Kundakci, 2014;Kundakci and Tuydes-Yaman, 2014; Türe-Kibar and Tuydes-Yaman, 2020; Keskin et al, 2011;Drawve, 2016). DBSCAN method also focused on grouping geocoded traffic accidents (Mohammed and Baiee, 2020;Agrawal et al, 2018;Zhang et al, 2018;Wang et al, 2019;Szénási et al, 2018;Szénási et al, 2020;Islam et al, 2021), with slightly different processing of the point data and produces list of nodes with appointed cluster numbers or noise ones, which are not clustered.…”