Year: 2026
Type: article
Abstract: This study examines structural relations between nationalities and crime types in Türkiye using the Bimax biclustering algorithm. Official judicial records from 2019–2023 (with varying nationality and crime type structures by year) were converted to binary format and analyzed in the R environment. Bimax identified compact biclusters that uncover recurring co-occurrence patterns across nationalities over the years—for example, the joint appearance of assault, qualified theft, and drug-related offenses—and highlight overlaps between groups rather than single-variable frequencies. The workflow comprises data binarization, biclustering with minimum row/column constraints, and heat-map visualization to interpret membership. Methodologically, the paper demonstrates the utility of Bimax for sparse, high-dimensional administrative data, offering an interpretable alternative to province-level analyses. Substantively, the findings provide policy-relevant signals for targeted prevention and integration strategies, indicating that crime profiles among foreign nationals are shaped by migration-related and socio-economic factors. Because the approach can produce consistent results under different threshold values and algorithm settings, it can be easily adapted to similar administrative datasets and different contextual analyses.
Source: Journal of Polytechnic
Institutions: Erzurum Technical University, Ministry of Justice, Gazi University
Language: Turkish
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7164045740