Crime in Proportions: Applying Compositional Data Analysis to European Crime Trends for 2022
Onur Bat{\i}n Do\u{g}an, Fatma Sevin\c{c} Kurnaz

TL;DR
This paper applies compositional data analysis to European crime data from 2022, revealing regional crime patterns and relationships between different crime types through clustering and principal component analysis.
Contribution
It introduces the use of CoDA for analyzing crime data, providing new insights into regional crime profiles and interdependencies among crime types in Europe.
Findings
Identified three main crime clusters aligned with geographical regions.
Revealed regional similarities and divergences in crime patterns.
Linked specific crime types to particular countries and regions.
Abstract
This article investigates crime patterns across European countries in 2022 using Compositional Data Analysis (CoDA) to address limitations of traditional statistical approaches in dealing with the relative nature of crime data. Recognizing crime types as components of a whole, we employ CoDA to explore relationships between different crime categories while respecting their inherent interdependencies. The study utilizes k-means clustering to group countries based on their crime profiles, identifying three distinct clusters largely aligning with geographical locations. This clustering is visualized through t-SNE and geographic mapping, revealing regional similarities. Further analysis using Robust Principal Component Analysis on identified crime clusters reveals insightful relationships between specific crime types, such as homicide, smuggling, and financial crimes, and how their…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGeochemistry and Geologic Mapping
