SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION

  • Sigit Nugroho Department of Mathematics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia
  • Dyah Setyo Rini Department of Mathematics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia https://orcid.org/0000-0003-3758-4292
  • Tommy Jomecho Statistics Indonesia - Bengkulu Province, Indonesia
  • Cinta Rizky Oktarina Department of Mathematics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia
  • Stevy Cahya Pratiwi Department of Mathematics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia
  • Elisabeth Evelin Karuna Department of Mathematics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia
DOI: https://doi.org/10.30598/barekengvol18iss2pp0759-0772
Keywords: Poverty, Spatial Effects, Mixed Geographically Weighted Regression

Abstract

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender  and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().

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Published
2024-05-25
How to Cite
[1]
S. Nugroho, D. Rini, T. Jomecho, C. Oktarina, S. Pratiwi, and E. Karuna, “SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION”, BAREKENG: J. Math. & App., vol. 18, no. 2, pp. 0759-0772, May 2024.
Issue
Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2024 Sigit Nugroho, Dyah Setyo Rini, Tommy Jomecho, Cinta Rizky Oktarina, Stevy Cahya Pratiwi, Elisabeth Evelin Karuna

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