Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017

Article History

Submited : September 22, 2020
Published : July 23, 2021

The level of poverty in a Regency/city in South Sulawesi in 2017 is different. The grouping of poverty status can be done based on the value of the HeadCount Index (HCI) of South Sulawesi. Factors affecting poverty will differ for each area being observed. The statistical modeling method developed for data analysis by taking into account the location factor is semiparametric Geographical Weighted Logistic Regression (GWLR). The GWLR semiparametric Model consists of parameters that are affected by the location and not affected by the location. The parameter estimator of the GWLR semiparametric model used in this research was obtained using the maximum method likelihood estimation. The result of a semiparametric model of GWLR each district/city in South Sulawesi in 2017 has the value Estimator parameter for global parameters is the same value for each location, namely, a3 = 0.1724, a4 = 0.0204, and a6 = 0.0261 whereas the parameter estimator for local parameters has different values so that GWLR semiparametric model of each district/city.


  1. Badan Pusat Statistik. Data dan Informasi Kemiskinan Sulawesi Selatan Tahun 2018. Makassar: BPS, 2018.
  2. Nakaya, T, Fotheringham A.S. & Brudson C. Semiparametric geographically weighted Generalised Linier Model In GWR 4.0. Japan : Ritsumeikan University, 2005.
  3. Cressie, N.A.C. Statistics For Spatial Data. New York: John Wiley and Sons, Inc., 1993.
  4. Ribeiro, M.C., Sousa, A.J. & Pereira, M.J. A Coregionalization Model Can Assist Spesification of Geographically Weighted Poisson Regression: Application to an Ecological study. Spatial and Spatio-temporal Epidemiology. 17(2016): 1-13, 2016.
  5. Isaaks, E.H. & Srivastava, R.M. An Introduction to Applied Geostatistics. New York: Oxford University Press, 1989.
  6. Goulard, M. & Voltz, M. Linier Coregionalization Model: Tools for Estimation and Choice of Cross-Variogram Matrix. Mathematical Geology. 24(3): 269-286, 1992.
  7. Anselin, L. Spatial Econometric : Methods and Models. Dordrecht: Kluwer Academic Publisher, 1998.
  8. Rosa, A.A. Penggunaan pembobot fixed kernel dan fixed bisquare kernel pada model Geographically Weighted Regression. Makassar, 2015.
  9. Chasco, C., Garcia, I., & Vicens, J. Modeling spatial variations in household disposable income with Geographically Weighted Regression. Munich Personal RePEc Archive Paper No. 1682, 2007.


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