Burglary crime susceptibility assessment using bivariate statistics approach of information value model

• Main Authors: Azmy, S. N., Asmadi, M. A., Abdul Rahman, Muhammad Zulkarnain, Amerudin, S., Zainon, O.
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: TH434-437 Quantity surveying
• Online Access: http://eprints.utm.my/id/eprint/93392/1/MuhammadZulkarnainAbdul2020_BurglaryCrimeSusceptibilityAssessment.pdf; http://eprints.utm.my/id/eprint/93392/; http://dx.doi.org/10.1088/1755-1315/540/1/012043

Geospatial technology advancement has boost the ability of crime assessment in terms of the accuracy of crime location and prediction. Aforetime, the crime assessment tend to focus on the development of sanction and law, as well as behaviour studies of why certain people are prone to be a victim of crime and why certain people are prone in committing crime, but none of them incorporating the idea of place of crime until 1971 (Jeffery, 1971). With technology advancement, the crime assessment of place has evolved from pin map to large scale digital mapping, effective inventory method, and adept crime analysis as well as crime prediction. The residential area of Damansara-Penchala, Kuala Lumpur and its vicinity are chosen as study area for its urban location and vastness of socioeconomic status. According to the data in Safe City Monitoring System (Sistem Pemantauan Bandar Selamat, SPBS), the monetary loss due to burglary crime activities in the study area for 2016 are sum up to RM 5,640,087 (RM 5.6 million) within 172 burglary incidence, with the mean loss of RM 32,791.00 with every offend of burglary. Apart from monetary loss, burglary also affecting the social values of the society and in terms of the perception of safe living. Instead of providing an analysis of area with high density of burglary, this paper embarks on finding the correlated social and environmental factor that leaning towards being the target of burglary crime. Utilizing the method of information value modelling, a bi-variate statistical method in the layout of raster data analysis, the vulnerability of each premise are calculated based on its association with the identified burglary indicators. The results finds that 17 significant indicators out of 18 indicators are identified as index contributing to burglary susceptibility. The burglary susceptibility mapping are acquired to contribute in predicting the premise's potential risk for the sake of future crime prevention.