Mixed geographically weighted regression (MGWR) model with weighted adaptive bi-square for case of dengue hemorrhagic fever (DHF) in Surakarta

International Conference on Mathematics: Education, Theory and Application

数学;教育

Astuti, H.N.^1 ; Saputro, D.R.S.^1 ; Susanti, Y.^1

Department of Mathematics, FMIPA, Universitas Sebelas Maret, Indonesia^1

关键词: adaptive bi-square;    Dengue hemorrhagic fever;    Geographically weighted regression;    Geographically weighted regression models;    Global parameters;    Linear regression models;    Local parameters;    Significant variables;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/855/1/012007/pdf
DOI  :  10.1088/1742-6596/855/1/012007

学科分类：发展心理学和教育心理学

来源: IOP

【 摘 要 】

MGWR model is combination of linear regression model and geographically weighted regression (GWR) model, therefore, MGWR model could produce parameter estimation that had global parameter estimation, and other parameter that had local parameter in accordance with its observation location. The linkage between locations of the observations expressed in specific weighting that is adaptive bi-square. In this research, we applied MGWR model with weighted adaptive bi-square for case of DHF in Surakarta based on 10 factors (variables) that is supposed to influence the number of people with DHF. The observation unit in the research is 51 urban villages and the variables are number of inhabitants, number of houses, house index, many public places, number of healthy homes, number of Posyandu, area width, level population density, welfare of the family, and high-region. Based on this research, we obtained 51 MGWR models. The MGWR model were divided into 4 groups with significant variable is house index as a global variable, an area width as a local variable and the remaining variables vary in each. Global variables are variables that significantly affect all locations, while local variables are variables that significantly affect a specific location.