The Relationship between Neighborhood Characteristics and Homicide in Karachi, Pakistan

Salma Hamza,Aqil Tariq,Muhammad Fahad Baqa,Hua Liu,Imran Khan,Linlin Lu,Syed Nawaz-Ul-Huda,Farkhunda Burke

doi:10.3390/su13105520

Abstract

The geographical concentration of criminal violence is closely associated with the social, demographic, and economic structural characteristics of neighborhoods. However, few studies have investigated homicide patterns and their relationships with neighborhoods in South Asian cities. In this study, the spatial and temporal patterns of homicide incidences in Karachi from 2009 to 2018 were analyzed using the local indicators of spatial association (LISA) method. Generalized linear modeling (GLM) and geographically weighted Poisson regression (GWPR) methods were implemented to examine the relationship between influential factors and the number of homicides during the 2009–2018 period. The results demonstrate that the homicide hotspot or clustered areas with high homicide counts expanded from 2009 to 2013 and decreased from 2013 to 2018. The number of homicides in the 2017–2018 period had a positive relationship with the percentage of the population speaking Balochi. The unplanned areas with low-density residential land use were associated with low homicide counts, and the areas patrolled by police forces had a significant negative relationship with the occurrence of homicide. The GWPR models effectively characterized the varying relationships between homicide and explanatory variables across the study area. The spatio-temporal analysis methods can be adapted to explore violent crime in other cities with a similar social context.

Highlights

The mean absolute deviation (MAD) and AICc values for geographically weighted Poisson regression (GWPR) were lower than Generalized linear modeling (GLM), while the percent deviance explained for GWPR was higher than for GLM. These results indicate that the GWPR model outperformed GLM in homicide occurrence modeling
The results of this study suggest that the area of high-high homicide clusters expanded from 2009 to 2013 and decreased from 2013 to 2018
The neighborhood determinants of homicide were consistent with routine activity theory and social disorganization theory

Summary

Introduction

The geographical distribution of crime is generally seen as a function of the supply of motivated offenders, target availability, and the presence of guardians and can be measured using indirect estimates of the three elements [19]. Due to their impact on targets and guardianship, empirical analyses using routine activity theory have found that neighborhood characteristics such as age composition, ethnic diversity, marital status, population measures and characteristics, employment status, income levels, and dwelling values are influential predictors of crime [20,21]. Some literature has found that mixed land use was associated with a decrease in homicide and aggravated assault [26]

Methods

Results

Conclusion