PARAMETER ESTIMATION OF SPATIAL REGRESSION MODEL WITH GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD

Itsnaini Munjiyatul Fadlilah; Sugiman Sugiman; Sunarmi Sunarmi

doi:10.15294/ujm.v8i2.23796

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Affiliations

Itsnaini Munjiyatul Fadlilah
Universitas Negeri Semarang

Sugiman Sugiman
Affiliation not stated

Sunarmi Sunarmi
Affiliation not stated

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PARAMETER ESTIMATION OF SPATIAL REGRESSION MODEL WITH GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD

ESTIMASI PARAMETER MODEL REGRESI SPASIAL DENGAN METODE GEOGRAPHICALL WEIGHTED POISSON REGRESSION

Itsnaini Munjiyatul Fadlilah ,
Sugiman Sugiman ,
Sunarmi Sunarmi

Vol 8 No 2 (2019)

DOI 10.15294/ujm.v8i2.23796

Submitted: Jul 17, 2018

Published: Aug 17, 2019

Abstract

Poisson Regression is one of the non-linear regression analysis whose Poisson distributed response variable.Geographically Weighted Poisson Regression (GWPR) is one of the statistical methods to analyze spatial data with point approach. The purpose of this research is to form GWPR model with fixed bisquare and adaptive bisquare kernel function, and compare best model of GWPR with kernel fixed bisquare and adaptive bisquare function. The data of this research is the percentage of poor people in Central Java Province. In this study there are seven (7) variables related to the percentage factor of the poverty population. The test obtained 2 significant variables are population life expectancy and income per-capita population has been adjusted .Based on the result of research, it is found that GWPR model is more suitable than Poisson regression. Provided Geographically Weighted Poisson Regression model with fixed bisquare fixed function and adaptive bisquare globally in Province of Central Java . The advantage of the model can be seen from the value of AIC. The AIC value obtained in the fixed bisquare kernel is 178,7446. Whereas, The AIC value obtained in adaptive kernel bisquare is 183.2349. The GWPR model with the fixed Bisquare kernel is better than GWPR adaptive bisquare.