Open AccessAPPLICATION OF KERNEL DENSITY ESTIMATION TO IDENTIFY MOTORCYCLE THEFT HOT SPOTS IN KUCHING, SARAWAK
Author(s) -
Norita Jubit,
Tarmiji Masron,
Azizan Marzuki
Publication year - 2021
Publication title -
planning malaysia
Language(s) - English
Resource type - Journals
eISSN - 1675-6215
pISSN - 0128-0945
DOI - 10.21837/pm.v19i19.1067
Subject(s) - kernel density estimation , hot spot (computer programming) , boundary (topology) , location data , computer science , computer security , transport engineering , geography , business , engineering , statistics , mathematics , mathematical analysis , estimator , operating system
Motorcycle theft is the most frequently reported cases worldwide, including in Malaysia. This study aims to identify the hot spot areas for motorcycle theft in Kuching. The spatial data include police station sector boundary, road data and latitud and longitude data while attribute data consists of motorcycle theft by year, address of the incident and time. Kernel Density Estimation (KDE) helps to find the hot spot areas of motorcycle theft. Motorcycle theft in Kuching has been reported as more frequent during the day at 54.8% and at 45% during the night from the year 2015 to 2017. Hot spot locations change by year and time. The study found that most of the hot spot areas of motorcycle theft were detected within the Sentral boundary. This indicates that the city centre is an area with a high density of motorcycle theft. This study can help authorities to improve the prevention measures for motorcycle theft while the findings can help in preventing motorcycle theft by police sector boundary.