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

Authors

  • Tri Yuliyanti Mathematics Department, UIN Walisongo, Indonesia
  • Emy Siswanah Mathematics Department, UIN Walisongo, Indonesia
  • Lulu Choirun Nisa Mathematics Education Department, UIN Walisongo, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v6i2p213-227

Keywords:

mixed geographically weighted regression, wls, 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.

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References

Anselin, L. (1999) Spatial Econometrics. Dallas: University of Texas.

Apriyani, N. F., Yuniarti, D. and Hayati, M. N. (2018) ‘Pemodelan Mixed Geographically Weighted Regression (MGWR) (Studi Kasus: Jumlah Penderita Diare di Provinsi Kalimantan Timur Tahun 2015)’, Jurnal Eksponensial, 9(1), pp. 59–66.

BAPPEDA (2020) Basis Data Terpadu Program Penanganan Fakir Miskin (BDT PPFM) Provinsi Jawa Tengah tahun 2019, BAPPEDA. Available at: http://bappeda.jatengprov.go.id.

BPS (2013) Pengembangan Model Sosial Ekonomi: Penggunaan Metode Geographically Weighted Regression (GWR) untuk Analisis Data Sosial dan Ekonomi. Jakarta, Indonesia: Badan Pusat Statistik.

Caraka, R. E. and Yasin, H. (2017) Geographically Weighted Regression (GWR) Sebuah Pendekatan Regresi Geografis. Yogyakarta: Mobius.

Chasco, C., García, I. and Vicéns, J. (2007) Modeling Spastial Variations in Household Disposible Income with Geographically Weighted Regression. 1682.

Darsyah, M. Y., Wasono, R. and Agustina, M. F. (2015) ‘Pemodelan MGWR Pada Tingkat Kemiskinan di Provinsi Jawa Tengah’, Value Added: Majalah Ekonomi dan Bisnis, 11(1), pp. 67–71.

Draper, N. R. and Smith, H. (1998) Applied regression analysis. John Wiley & Sons.

Huda, N. (2017) Ekonomi Pembangunan Islam. Jakarta, Indonesia: Kencana.

Kusnandar, D., Debataraja, N. N. and Fitriani, S. (2021) ‘Pemodelan Sebaran Total Dissolved Solid Menggunakan Metode Mixed Geographically Weighted Regression’, Jurnal Aplikasi Statistika & Komputasi Statistik, 13(1), pp. 9–16.

Leung, Y., Mei, C. L. and Zhang, W. X. (2000) ‘Statistical tests for spatial nonstationarity based on the geographically weighted regression model’, Environment and Planning A, 32(1), pp. 9–32.

Nasikun (2008) Sistem Sosial Indonesia. Bandung, Indonesia: PT. RajaGrafindo Persada.

Safitri, R. N., Suyitno and Hayati, M. N. (2020) ‘Penerapan Model Mixed Geographically Weighted Regression dengan Fungsi Pembobot Adaptive Tricube pada IPM 30 Kabupaten/Kota di Propinsi Kalimantan Timur, Kalimantan Tengah, dan Kalimantan Selatan Tahun 2016’, Jurnal Eksponensial, 11(2), pp. 107–116.

Wuryanti, I. F., Purnami, S. W. and Purhadi (2013) ‘Pemodelan Mixed Geographically Weighted Regression (MGWR) pada Angka Kematian Balita di Kabupaten Bojonegoro Tahun 2011’, Jurnal Sains dan Semi Pomits, 2(1), pp. 66–71.

Yasin, H., Warsito, B. and Hakim, A. R. (2018) ‘Pemodelan Pertumbuhan Ekonomi Di Provinsi Banten Menggunakan Mixed Geographically Weighted Regression’, Media Statistika, 11(1), pp. 53–64.

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Published

2022-08-31

How to Cite

Yuliyanti, T., Siswanah, E., & Nisa, L. C. (2022). 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. Indonesian Journal of Statistics and Its Applications, 6(2), 213–227. https://doi.org/10.29244/ijsa.v6i2p213-227

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