TY - JOUR
T1 - Modeling total crime and the affecting factors in Central Java using geographically weighted regression
AU - Runadi, T.
AU - Widyaningsih, Y.
AU - Lestari, D.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/1/29
Y1 - 2020/1/29
N2 - 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.
AB - 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.
KW - bisquare
KW - crime
KW - Gaussian
KW - geographically weighted regression (GWR)
UR - http://www.scopus.com/inward/record.url?scp=85079693193&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1442/1/012026
DO - 10.1088/1742-6596/1442/1/012026
M3 - Conference article
AN - SCOPUS:85079693193
SN - 1742-6588
VL - 1442
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012026
T2 - Basic and Applied Sciences Interdisciplinary Conference 2017, BASIC 2017
Y2 - 18 August 2017 through 19 August 2017
ER -