Defining the related environmental risk factors for motorcycle theft crimes

Pei-fen Kuo, I. Gede Brawiswa Putra, Hafsah Fatihul Ilmy, Chui Sheng Chiu, Cheng Yen Wu

Research output: Contribution to conferencePaperpeer-review

Abstract

Unlike burglary or other fixed property theft, stealing of motor vehicles not only increases the fear of future crime but also limits the victims' mobility, which causes significant inconvenience. In addition, motorcycle theft is a growing but neglected problem around the world. Although motorcycle theft disrupts social order and increases fear of crime significantly, it is one of the least studied forms of crime. Most studies focus on the car theft and its spatial-temporal characteristics. For example, inner cities, multi-racial areas, and disadvantaged neighborhoods (e.g., low-income, high dropout rate) or crime attractors (e.g., bars, liquor stores) tend to have higher car theft rates. For the reasons stated above, this study used local data and spatial regression models to define motorcycle theft hotspots and the corresponding environmental factors. Kernel density estimation result illustrates central business area in Taipei City become the hotspot of motorcycle theft. In the other hand, regression model show that divorce rate is positively related to motorcycle theft significantly. However, other variable which is income, education, and population number are not statistically significant. It is our hope that this study will help police officers to define related factors and more accurately predict future crime rates, which will assist in the design of corresponding policies and enforcement strategies to prevent crime.

Original languageEnglish
Pages2550-2559
Number of pages10
Publication statusPublished - 2018 Jan 1
Event39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 - Kuala Lumpur, Malaysia
Duration: 2018 Oct 152018 Oct 19

Conference

Conference39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018
CountryMalaysia
CityKuala Lumpur
Period18-10-1518-10-19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Earth and Planetary Sciences(all)
  • Computer Networks and Communications

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