Crime Hotspot Analysis and Mapping Using Geospatial Technology in Dessie City, Ethiopia

TL;DR

This study employs geospatial technology to map and analyze crime patterns in Dessie City, Ethiopia, identifying hot spots and spatial autocorrelation to inform targeted law enforcement strategies.

Contribution

It introduces a geospatial analysis approach using semivariogram and Moran's I to identify crime hot spots in Dessie, Ethiopia, providing insights for better resource allocation.

Findings

Identified key crime hot spots in Hote, Arada, and Segno neighborhoods.

Detected significant positive spatial autocorrelation in crime distribution.

Revealed a north-south crime trend with specific exceptions.

Abstract

Over the past few decades, crime and delinquency rates have increased drastically in many countries; nevertheless, it is important to note that crime trends can differ significantly by geographic region. This study's primary goal was to use geographic technology to map and analyze Dessie City's crime patterns. To investigate the geographic clustering of crime, the researchers used semivariogram modeling and spatial autocorrelation analysis with Moran'sI. The neighborhoods of Hote, Arada, and Segno in Dessie's central city were found to be crime-prone "hot spot" locations, as evidenced by statistically significant high Z-scores ranging from 0.037 to 4.608. On the other hand, low negative Z-scores ranging from -3.231 to -0.116 indicated "cold spot" concentrations of crime in the city's north-central sub-cities of Menafesha and Bounbouwha. With an index of 0.027492 and a Z-score of 3.297616 (p<0.01), the analysis overall showed a substantial positive spatial autocorrelation, suggesting a clustered pattern of crime in Dessie. The majority of crimes showed a north-south directionality, except for murder, which trended from northeast to southwest. The mean center of all crime types was found in the central Hote area. To address the complicated problem of rising crime rates in Dessie and other developing metropolitan areas, more focused and efficient enforcement techniques, and resource deployment can be informed through the knowledge acquired from the geospatial analysis.