Deciphering Property Crime Through OLS Regression : A Demographic Study

This study explores the correlation between property crime and demographic factors in Kuala Lumpur and Putrajaya using spatial autocorrelation (SA) and ordinary least squares (OLS) regression from 2015 to 2020. The 2016 SA analysis shows a significant increase in Moran's I index (0.012905), with a positive z-score of 2.020088 and a p value of 0.043374, indicating spatial clustering of crime. The study examines how factors like total population, household areas, residential areas, male populations and female populations influence number of property crime cases, revealing varying relationships year by year. By highlighting fluctuations in R-squared and F-statistic values, this research challenges static crime models, advocating for adaptable, data-driven strategies in crime prevention. These findings emphasize the importance of continuous policy adjustments to address the evolving socioeconomic dynamics of urban areas.

Publication Details

Journal
Unimas Institutional Repository (Universiti Malaysia Sarawak)
Published
2025-02-06
Primary Topic
Crime Patterns and Interventions
Type
article
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article

Deciphering Property Crime Through OLS Regression : A Demographic Study

Unimas Institutional Repository (Universiti Malaysia Sarawak)
Crime Patterns and Interventions
article

Deciphering Property Crime Through OLS Regression : A Demographic Study

article en

Abstract

This study explores the correlation between property crime and demographic factors in Kuala Lumpur and Putrajaya using spatial autocorrelation (SA) and ordinary least squares (OLS) regression from 2015 to 2020. The 2016 SA analysis shows a significant increase in Moran's I index (0.012905), with a positive z-score of 2.020088 and a p value of 0.043374, indicating spatial clustering of crime. The study examines how factors like total population, household areas, residential areas, male populations and female populations influence number of property crime cases, revealing varying relationships year by year. By highlighting fluctuations in R-squared and F-statistic values, this research challenges static crime models, advocating for adaptable, data-driven strategies in crime prevention. These findings emphasize the importance of continuous policy adjustments to address the evolving socioeconomic dynamics of urban areas.

Unimas Institutional Repository (Universiti Malaysia Sarawak)
Sustainable cities and communities
Openalex Percentile: Top 40%
Crime Patterns and Interventions
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