Geographically Weighted Poisson Regression Model  with Adaptive Bisquare Weighting Function (Case study: data on number of leprosy cases  in Indonesia 2020)

Ineu  Sintia; Suyitno Suyitno; Memi Nor  Hayati

doi:10.20956/j.v19i1.21879

Authors

Ineu Sintia Universitas Mulawarman
Suyitno Suyitno Universitas Mulawarman
Memi Nor Hayati Universitas Mulawarman

DOI:

https://doi.org/10.20956/j.v19i1.21879

Keywords:

Adaptive Bisquare, GCV, GWPR, Overdispersi, Kusta

Abstract

Abstract Geographically Weighted Poisson Regression (GWPR) is a Poisson regression model which is applied on spatial data. The parameter estimation of GWPR is done in each observation location through spatial weighting. This study aims to determine the GWPR model of the number of leprosy cases in each province of Indonesia 2020 and to find the influencing factors. The research uses secondary data collected from Indonesian Ministry of Health and Central Statistics Agency. The spatial weighting is calculated by using the adaptive bisquare function, while the optimum bandwidth is determined by using Generalized Cross-Validation criteria (GCV). The parameter estimation of GWPR uses Maximum Likelihood Estimation (MLE) method. The result of research show that the closed form of Maximum Likelihood (ML) estimator can not be found analytically and that the approximation of ML estimator is found by using Newton-Raphson iterative method. Based on the parameter significance test of the GWPR model, the factors that influenced the number of leprosy cases locally are the percentage of households that have access to proper sanitation, population density, the percentage of people who experience health complaints and outpatient, the number of health workers, the percentage of poor people, the percentage of districts/cities that carry out healthy living community movement (GERMAS) and the percentage of habitable houses. While the factors that globally affected the number of leprosy cases are the percentage of households that have access to proper sanitation, population density, the percentage of people who experience health complaints and outpatient, the number of health workers, the percentage of poor people, the percentage of districts/cities that carry out GERMAS.

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