Modeling total crime and the affecting factors in Central Java using geographically weighted regression

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

Analysing the relationship between number of crime cases and affecting factors became an interesting research topic over the last ten years. The total number of crime in Indonesia did not show a consistent decrease. In order to upgrade people safeness quality, the government need to know the factors influence people committing crime acts. Rather than using classical regression analysis, geographically weighted regression (GWR) was preferable since it gave a better representative model by effectively resolve spatial non-stationary problem which is generally exist in spatial data of social phenomenon. Spatial non-stationary is a situation when the relationship between variables are significantly different in each location of observation point, so that classic regression analysis will result a misleading interpretation in some location. GWR handled the spatial non-stationary problem by generating a single model in each observation point which allow different relationship to exist at different point in space. This study used number of crime cases (y) as the dependent variable and the factors which affect the number of crime cases as independent variables that consist of the number of illiterates (X1), the number of unemployed (X2), the number of poor population (X3), population density (X4), the number of victims of drug (X5). This study used secondary data collected by POLRI, BPS and Indonesian Ministry of Social Affairs in Central Java during 2015. GWR generated model for 35 city/regency in Central Java.

Original languageEnglish
Article number012026
JournalJournal of Physics: Conference Series
Volume1442
Issue number1
DOIs
Publication statusPublished - 29 Jan 2020
EventBasic and Applied Sciences Interdisciplinary Conference 2017, BASIC 2017 - , Indonesia
Duration: 18 Aug 201719 Aug 2017

Keywords

  • bisquare
  • crime
  • Gaussian
  • geographically weighted regression (GWR)

Fingerprint

Dive into the research topics of 'Modeling total crime and the affecting factors in Central Java using geographically weighted regression'. Together they form a unique fingerprint.

Cite this