Low Welfare Status Modeling Using Mixed Geographically Weighted Regression Method with Fixed Tricube Weighting Function

Pemodelan Status Sejahtera Rendah Menggunakan Metode Mixed Geographically Weighted Regression Dengan Fungsi Pembobot Fixed Tricube

Abstract

Mixed Geographically Weighted Regression (MGWR) is a method for analyzing spatial data in regression that produces local and global parameters. Parameter estimation using WLS with a fixed tricube weighting function. The object of research in this study is poor population (X1), female household heads (X2), the education (X3), individuals with disabilities (X4), individuals having chronic disease (X5), individuals works (X6), uninhabitable houses (X7), and low welfare status (Y). This reseach applied to the low welfare status (Y) of each district/town in Central Java in 2019, and produced local variables are X1, X3, X5 and global variables are X2, X4, X6, and X7. However, only X1, X4, and X7 have a significant effect on Y in each district/town in Central Java, and X3 has a significant effect on only a few districts/cities, the other, X2, X5, and X6 have no significant effect on the model. The predictor variable has an effect of 98.92% on the model while the remaining 1.18% affected by other factors. The MGWR method divides 2 groups based on significant variables, (a) The first, a district/town whose low welfare status affected by X1, X3, X4, X7 covering Cilacap, Purbalingga, Kendal, Batang, Brebes, Pekalongan Town, and Tegal Town, (b) The second, districts/town whose low welfare status affected by X1, X4, X7 covering Banjarnegara, Purworejo, Temanggung, Kudus, Wonosobo, Pekalongan, Pemalang, Jepara, Wonogiri, Boyolali, Tegal, Magelang, Sukoharjo, Banyumas, Grobogan,  Klaten, Karanganyar,  Kebumen, Blora,  Semarang Town, Pati, Sragen, Demak, Magelang Town, Salatiga Town, Surakarta Town, Semarang, and Rembang.