Geo-spatial crime density attribution using optimized machine learning algorithms
- Title
- Geo-spatial crime density attribution using optimized machine learning algorithms
- Creator
- Prathap B.R.
- Description
- Law enforcement agencies use various crime analysis tools. A large amount of crime data has enabled crime analysis. In this paper, the proposed research methodology uses Kernel Density Estimation (KDE) in a Geographical Information System (GIS) to analyze crime-type data. Bangalore and India newsfeeds are considered for experimental purposes. The paper introduces an optimized KDE machine learning algorithm that detects hotspots, estimates a locations crime rate, and identifies point pattern lows and highs. As a result of the experiment, the proposed methodology identified that the bandwidth of the Geographical information system influences the visualization of crime density. The paper also aids in visually determining the appropriate bandwidth for the problem using an optimized KDE algorithm. We had identified a significant correlation between Newsfeed data and Official Government data, both overall Crime and by crime type. The proposed KDE model achieved a predictive performance of 77.49%. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
- Source
- International Journal of Information Technology (Singapore), Vol-15, No. 2, pp. 1167-1178.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media B.V.
- Subject
- Crime analysis; Crime density; Kernel density estimation; Newsfeeds
- Coverage
- Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), #325(45), Kengeri-Campus, Mysore Road, Karnataka, Bangalore, 560074, India
- Rights
- Restricted Access
- Relation
- ISSN: 25112104
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Prathap B.R., “Geo-spatial crime density attribution using optimized machine learning algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14425.