Crime in Proportions: Applying Compositional Data Analysis to European Crime Trends for 2022
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910832926916608 |
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| author | Doğan, Onur Batın Kurnaz, Fatma Sevinç |
| author_facet | Doğan, Onur Batın Kurnaz, Fatma Sevinç |
| contents | 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 prevalence varies across countries. The findings reveals distinct crime patterns across Europe, highlighting regional commonalities while also highlighting divergences like Norway and Latvia that deviate from their expected geographical classifications. Moreover, the study identifies specific crime groups; for example, it pairs countries high in corruption and smuggling, such as Austria, with those countries that exhibit a higher relevance to homicide and smuggling, such as Luxembourg. It also points to the presence of financial crimes like fraud in countries such as Romania and Estonia. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Crime in Proportions: Applying Compositional Data Analysis to European Crime Trends for 2022 Doğan, Onur Batın Kurnaz, Fatma Sevinç Applications 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 prevalence varies across countries. The findings reveals distinct crime patterns across Europe, highlighting regional commonalities while also highlighting divergences like Norway and Latvia that deviate from their expected geographical classifications. Moreover, the study identifies specific crime groups; for example, it pairs countries high in corruption and smuggling, such as Austria, with those countries that exhibit a higher relevance to homicide and smuggling, such as Luxembourg. It also points to the presence of financial crimes like fraud in countries such as Romania and Estonia. |
| title | Crime in Proportions: Applying Compositional Data Analysis to European Crime Trends for 2022 |
| topic | Applications |
| url | https://arxiv.org/abs/2502.12099 |