Pemodelan Indeks Pembangunan Manusia Nusa Tenggara Barat Menggunakan Geographically Weighted Regression

Faiqotul Mala, Muhamad Fariq Hidayat
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Abstract

The Human Development Index (HDI) is an indicator for measuring the level of social and economic development of a country or region. The reality is that a local-based model of autonomy is often needed because of the spatial heterogeneity that can occur due to the territory's geographical, social, cultural, or other conditions. This research aims to find spatial effects affecting HDI in West Nusa Tenggara Province. A method that can be used to accommodate is Geographically Weighter Regression (GWR). GWR analysis is the development of multiple linear regression analysis that can address territorial diversity/spatial heterogeneity so as to produce different models and predictions of parameters for each observation region. The modeling was carried out using the Gaussian Kernel Adaptive spatial weigher with an optimal bandwidth value of 27,1227 and a minimum CV value of 5,2927. The GWR model modeling resulted in 10 models for each observation location and showed that life expectancy variables, school expectance, per capita income, and average school-age significantly influenced the IPM in the West Southeast Nusa Province in 2022 with an R2 of 99.92% and a minimum AIC value of -10,0281.
利用地理加权回归建立西努沙登加拉人类发展指数模型
人类发展指数(HDI)是衡量一个国家或地区社会和经济发展水平的指标。现实情况是,由于领土的地理、社会、文化或其他条件可能导致空间异质性,因此往往需要一种基于地方的自治模式。本研究旨在发现影响西努沙登加拉省人类发展指数的空间效应。地理权重回归(GWR)是一种可用于解决这一问题的方法。GWR 分析是多元线性回归分析的发展,可以解决地域多样性/空间异质性问题,从而为每个观测区域生成不同的模型和参数预测。建模采用高斯核自适应空间权重器,最佳带宽值为 271227,最小 CV 值为 52927。通过 GWR 建模,为每个观测点建立了 10 个模型,结果表明,预期寿命变量、预期入学率、人均收入和平均学龄对 2022 年西东南努萨省的 IPM 有显著影响,R2 为 99.92%,最小 AIC 值为-10,0281。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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