Geographically weighted panel regression using Haversine distance for mapping sustainable development goals
DOI:
https://doi.org/10.12928/bamme.v6i1.16002Keywords:
adaptive kernel, geographically weighted panel regression, GWR, Haversine distance, SDGsAbstract
The Sustainable Development Goals (SDGs) vary widely across Asian countries, indicating that the factors driving SDGs achievement may vary by location. Global models may miss these local variations, so this study used Geographically Weighted Panel Regression (GWR Panel), a method that estimates separate regression coefficients for each geographical location. The GWR Panel in this study was used to capture spatially varying SDGs determinants across 46 Asian countries from 2015 to 2024. This study also compares four Adaptive Kernel functions (Gaussian, Exponential, Bisquare, Tricube) with Haversine distances, as kernel choice directly affects which neighboring countries influence each local coefficient estimate, where applying the incorrect kernel to spatially heterogeneous data can lead to biased local estimates. The best kernel was selected using Cross-Validation (CV). The Adaptive Exponential Kernel produced the lowest CV value (81.686), compared to Adaptive Gaussian (83.128), Bisquare (84.485), and Tricube (85.095), confirming it as the most accurate kernel for this data. The results identified 16 distinct country groups, demonstrating that SDGs determinants vary across Asia. Education, gender, economic growth, infrastructure, environment, institutions, and partnerships are universally important. Meanwhile, water, health, hunger, and climate show the greatest regional variation. SDGs policies should be adjusted to local contexts.
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