Single-Index Multinomial Model for Analyzing Crime Data

Kwabena Gyamfi Duodu, University of Texas at El Paso


We develop a flexible single-index multinomial model for analyzing crime data. In addition to the number of crimes reported, the data also includes covariates such as location, time of day, weather, and other demographic factors. We provide an estimation algorithm and develop R code for the single-index multinomial model. Using simulations, we evaluate the performance of the proposed estimation algorithm. When applied to crime data, the single-index multinomial model provides important insights into crime trends and risk variables, assisting in the development of tailored crime prevention programs. Policymakers and law enforcement organizations can use the model’s projections to more efficiently allocate resources and design preemptive strategies to solve crime-related concerns. Finally, the single-index multinomial model demonstrates itself to be a reliable tool for assessing crime data and improving knowledge and management of crime occurrences in varied areas.

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Recommended Citation

Duodu, Kwabena Gyamfi, "Single-Index Multinomial Model for Analyzing Crime Data" (2023). ETD Collection for University of Texas, El Paso. AAI30635369.