In response to the escalating incidence of burglary incidents in rapidly urbanizing metropolitan regions, this study innovatively integrates Emerging Hot Spot Analysis (EHSA) with Space-Time Pattern Mining (STPM) to examine the spatio-temporal dynamics of burglary across Selangor, Kuala Lumpur Federal Territory (KLFT) and Putrajaya Federal Territory (PFT) between 2015 and 2020. This paper aims to delineate the intricate interplay between urban land use configurations and the evolving patterns of burglary, thereby addressing critical research gaps in crime mapping and predictive resource allocation. The research employed robust methodological framework within the ArcGIS Pro 3.1 environment, the research stratifies crime data into four distinct temporal intervals to construct space-time netCDF cubes, applies the Getis-Ord Gi* statistic with False Discovery Rate (FDR) correction to identify statistically significant clusters, and utilizes the Mann-Kendall trend test to classify hotspots into eight categories (new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating, and historical). The results reveal a nuanced spatial clustering of burglary incidents that is significantly influenced by varied land use types—ranging from residential and industrial zones to open spaces—thereby enhancing the granularity of hotspot detection and offering empirical insights into the temporal evolution of crime patterns. The study dinds that the integration of advanced geospatial analyses not only clarifies the complex dynamics between urban morphology and burglary occurrences but also provides a solid empirical basis for informed law enforcement and urban planning strategies. Moreover, these findings underscore the need for ongoing longitudinal investigations and the development of adaptive, data-driven models to refine predictive capabilities further and foster sustainable urban safety initiatives.

Analisis ruangan hot spot jenayah pecah rumah di negeri Selangor, Kuala Lumpur dan Putrajaya pada tahun 2015-2020 (Spatial analysis of crimes hot spot of housebreaking in Selangor, Putrajaya, and Kuala Lumpur in 2015-2020)

This study aims to identify the proximity of offenders' homes to the theft location in Kuching, Sarawak. The attribute data includes the total number of theft offenders in Kuching in 2018, categorized as solo and co-offenders. It also details the modus operandi used in these thefts, as well as the offenders' employment and educational backgrounds. At the same time, spatial data consists of Kuching and Samarahan land use types, Kuching police station sector boundaries, the home addresses of offenders, and the locations where thefts occurred. This study applied standard deviation ellipses, Euclidean distance analysis, and kernel density estimation. The findings indicate that young people and individuals in their late twenties (15-29 year olds) are at a higher risk of engaging in theft activities. Their target areas are often close to their homes, particularly in urban areas lacking CCTV and security guards. Offenders in urban areas, who are unemployed at the age of 15-29 years old and have low education (SPM level), tend to be thieves. Both solo offenders and co-offenders tend to commit theft near their home. The total number of solo offenders of theft is 78.5%, and co-offenders are 21.4%. The analysis reveals a high level of spatial clustering among offenders, with their homes concentrated in the urban center of Kuching, where crime is most prevalent. The findings imply that offenders' residences are localized in specific urban areas rather than being evenly distributed across the districts of Kuching, Sarawak. The offenders typically travel short distances when engaging in property crime, especially theft. The study suggests that land use planning should consider the concentration of offenders in urban areas. The study also highlights the importance of targeted patrols in high-crime urban areas, especially those lacking CCTV and security guards.

Flooding increasingly threatens socio-economic resilience in Malaysia, particularly in vulnerable districts such as Padang Terap, Kedah. Using a GIS-based framework integrating Spatial Autocorrelation (Moran’s I) and Optimized Hotspot Analysis (Getis-Ord Gi*), this study quantifies spatial clustering of flood-prone areas across four inundation levels (0.3 m–3.7 m). Results reveal intensifying positive spatial autocorrelation with rising flood depths, reflecting hydrological connectivity and topographic controls. Hotspots are consistently concentrated in Belimbing Kanan, Belimbing Kiri, and Padang Temak, emphasizing severe spatial heterogeneity in flood risk distribution. These findings demonstrate that flood hazards are not randomly dispersed but spatially structured, necessitating geographically targeted risk mitigation strategies. Incorporating hotspot insights into planning can optimize resource allocation, strengthen adaptive capacity, and inform flood-resilient urban development. This research advocates for integrating fine-scale spatial analyses into national disaster frameworks to enhance Malaysia’s climate resilience agenda. Future work should embed socio-economic vulnerability metrics and spatiotemporal models to refine flood risk governance and promote equitable, anticipatory disaster management.