Modeling Poverty Data in Indonesia with Spatial Hierarchy Structure Using HLM, GWR, and HGWR Methods

Authors

  • Cintia Septemberini  Department of Statistics, IPB University, Bogor, West Java, Indonesia
  • Muhammad Nur Aidi  Department of Statistics, IPB University, Bogor, West Java, Indonesia
  • Anang Kurnia  Department of Statistics, IPB University, Bogor, West Java, Indonesia

DOI:

https://doi.org/10.32628/IJSRSET2411126

Keywords:

Poverty, Spatial Heterogeneity, Hierarchical Structure, GWR, HLM, HGWR

Abstract

Poverty causes the majority of the Indonesian population to face challenges in fulfilling basic needs such as clothing, food, and shelter. The factors that play a role in determining the poverty rate in Indonesia tend to vary in each province; this is due to the diverse conditions resulting from spatial heterogeneity. However, poverty in Indonesia is not only influenced by factors from various regions but also by the conditions of the districts/cities within them. Districts/cities within a province form a spatial hierarchy structure. Therefore, in this study, the Hierarchical Linear Model (HLM), Geographically Weighted Regression (GWR), and Hierarchical and Geographically Weighted Regression (HGWR) methods were applied to determine the best model among the three methods in analyzing the factors affecting the poverty rate in Indonesia with a spatial hierarchy structure. The results of the analysis show that the HGWR method is the best model compared to HLM and GWR, as evidenced by the higher R-squared value of 0.8004 compared to HLM and GWR. Based on the HGWR model, most of the local estimators for population dependency ratio (G1), adjusted per capita expenditure (G2), and economic growth rate (G3) showed significance in provinces located in eastern Indonesia. In addition, the fixed effects and random effects estimators, namely the percentage of households without access to electricity (X1), the ratio of per capita normative consumption to net product (X2), and the percentage of households without access to clean water (X3), also have a significant influence on the poverty rate in Indonesia.

References

  1. A. Adji, T. Hidayat, H. Tuhiman, S. Kurniawan, and A. Maulana, “Measurement of Poverty Line in Indonesia: Theoretical Review and Proposed Improvements,” TNP2K Work. Pap., no. January, pp. 48-e, 2020, [Online]. Available: http://tnp2k.go.id/download/88787WP 48_Measurement of Poverty Line in Indonesia-Theoretical Review and Proposed Improvements.pdf
  2. C. S. Agency, “Indonesia Poverty Profile in September 2022,” no. 07, 2023.
  3. A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression the Analysis of Spatially Varying Relationships. Chichester, England: Wiley, 2002.
  4. M. N. Aidi, F. Ernawati, and Siswanto, “Analysis of Factors Affecting the Number of Poor People in Indonesia Using Geographycally Weighted Regression,” no. 4, pp. 1538–1553, 2018.
  5. M. Amiati, A. Adnan, and R. Yendra, “Poverty Data Analysis In Riau Province Using Geographically Weighted Regression Model With Exponential And Tricube Adaptive Kernels,” Int. J. Adv. Res. Publ. , vol. 4, no. 1, pp. 1–4, 2020, [Online]. Available: www.ijarp.org
  6. S. W. Raudenbush and A. S. Bryk, Hierarchical Linear Models. Thousand Oaks, CA SAGE Publications, Inc, 2002.
  7. H. Goldstein, Multilevel Statistical Models. West Sussex: John Wiley and Sons, Ltd., 2011.
  8. J. Hox, Multilevel Analysis: Techniques and Applications. New Jersey: Lawrence Erlbaum Associates Publishers, 2002.
  9. Y. Hu, B. Lu, Y. Ge, and G. Dong, “Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression,” Environ. Plan. B Urban Anal. City Sci., vol. 49, no. 6, pp. 1715–1740, 2022, doi: 10.1177/23998083211063885.
  10. S. Nakagawa, P. C. D. Johnson, and H. Schielzeth, “The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded,” J. R. Soc. Interface, vol. 14, no. 134, 2017, doi: 10.1098/rsif.2017.0213.
  11. B. Lu, P. Harris, M. Charlton, and C. Brunsdon, “The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models,” Geo-Spatial Inf. Sci., vol. 17, no. 2, pp. 85–101, 2014, doi: 10.1080/10095020.2014.917453.
  12. D. Octaviani, H. Pau, M. Fatima, and D. Putri, “Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province,” vol. 3, no. 2, pp. 189–201, 2023.
  13. W. Pribadi and F. Kartiasih, “Enviromental Quality and Poverty in Indonesia,” J. Pengelolaan Sumberd. Alam dan Lingkung., vol. 10, no. 1, pp. 89–97, 2020, doi: 10.29244/jpsl.10.1.89-97.
  14. A. Choirul Huda, A. Az-Zahra, F. Putri Yasmin, I. Wahyu Kusuma Ningrum, and W. Surya Putra, “Spatial Regression Analysis of Poverty Percentage on Eastern Indonesia in 2022,” pp. 747–756, 2023.
  15. J. Garza-Rodriguez, “Poverty and economic growth in Mexico,” Soc. Sci., vol. 7, no. 10, 2018, doi: 10.3390/socsci7100183.
  16. A. R. da Silva and A. S. Fotheringham, “The Multiple Testing Issue in Geographically Weighted Regression,” Geogr. Anal., vol. 48, no. 3, pp. 233–247, 2016, doi: 10.1111/gean.12084.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2024-02-29

Issue

Volume 11, Issue 1, January-February-2024

Section

Research Articles

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How to Cite

[1]
Cintia Septemberini, Muhammad Nur Aidi, Anang Kurnia "Modeling Poverty Data in Indonesia with Spatial Hierarchy Structure Using HLM, GWR, and HGWR Methods" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 11, Issue 1, pp.260-271, January-February-2024. Available at doi : https://doi.org/10.32628/IJSRSET2411126