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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi
Debora Dwi Kurniawati
Brawijaya University
Mathematics

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Haversine-Based Geographically Weighted Panel Regression of Human Development in Gorontalo (2016–2025) Debora Dwi Kurniawati; Henny Pramoedyo; Suci Astutik; Friansyah Gani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40886

Abstract

Spatial disparities in human development indicate that socioeconomic factors may influence development outcomes differently across locations. This study aims to analyze spatially varying relationships between the Human Development Index and its key determinants in districts and cities in Gorontalo Province, Indonesia, during the period 2016--2025. The analysis uses balanced panel data and models human development as a function of mean years of schooling, life expectancy at birth, and real per capita expenditure. A geographically weighted panel regression approach is applied, with spatial relationships modeled using great-circle distances and an adaptive kernel weighting scheme, while a fixed-effects panel model serves as the global reference. The results reveal a clear spatial heterogeneity in the effects of the explanatory variables, where education consistently shows the strongest positive influence on human development in all regions, followed by health conditions. Economic expenditure exhibits a weaker and spatially varying effect and is not influential in the provincial capital. These findings underscore the importance of accounting for spatial heterogeneity in regional development analyses and support the formulation of place-based human development policies tailored to local conditions.
Spatial Heterogeneity of Poverty Determinants in Indonesia A Hierarchical Geographically Weighted Regression Approach Debora Dwi Kurniawati; Henny Pramoedyo; Suci Astutik
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.42956

Abstract

This study employs the Hierarchical Geographically Weighted Regression (HGWR) model to analyze poverty determinants in Indonesia, addressing spatial heterogeneity and hierarchical data structures simultaneously. Using data across 34 provinces and 508 regencies/cities, the HGWR model with a Gaussian kernel (bandwidth = 15) substantially outperforms the global Ordinary Least Squares (OLS) regression, increasing the R2 value from 0.502 to 0.754. The Moran's I test on the global model residuals (0.2216, p 0.001) justifies the urgency of accounting for spatial nonstationarity, while the HGWR post-estimation residuals show that spatial autocorrelation is successfully eliminated (-0.000641, p = 0.416). At the regency/city level, adjusted per capita expenditure and the poverty line significantly reduce the poverty headcount rate, whereas the average years of schooling shows no significant localized effect. At the provincial level, the Human Development Index (HDI) consistently reduces poverty (mean coefficient of -0.8082) but exhibits substantial spatial variation, where the impact is strongest in eastern Indonesia (coefficients -0.95) and weakest in Java (coefficients -0.65). Conversely, expected years of schooling exhibits a positive mean coefficient (3.4755), with its positive effects highly concentrated in Java. These findings conclude that poverty reduction strategies in Indonesia must be place-based rather than uniform, prioritizing provincial HDI improvements in eastern Indonesia where the marginal returns of development policy are highest.
Co-Authors Friansyah Gani Henny Pramoedyo Henny Pramoedyo Suci Astutik