Spatial Analysis Of Forest Deforestation In Indonesia Using Geographically Weighted Panel Regression Method

Soraya Maghrisa Yasmine, Lilis Siti Badriah

Abstract


Deforestation refers to the process of removing or significantly reducing the area of forest that results in vegetation cover and forest ecosystems. Deforestation has become a global issue that attracts the attention of many countries around the world, including Indonesia. The purpose of this study is to model Geographically Weighted Panel Regression (GWPR) in the case of forest deforestation in each province in Indonesia. The method used is quantitative with Geographically Weighted Regression (GWR). The data used is secondary data from the BPS Indonesia covering 33 provinces and an annual period from 2014-2021. The result of this study is the GTWR Fixed Gaussian kernel model: Y gaussian = 35495 + 0.07156x1 − 0.43973x2 + 0.71015x3 + 0.23306x4 − 0.59132x5. GTWR Fixed Bisquare kernel model: Y bisquare = −8.43550 − 0.85441x1 − 1.33293x2 + 2.60602x3 + 0.81375x4 − 1.80997x5. The GTWR Fixed Gaussian model is better than the Fixed Bisquare model in modeling Deforestation data in Indonesia in 2014-2021. This research is expected to provide information related to the condition of forest deforestation in Indonesia and what factors affect forest deforestation cases in Indonesia through the GWPR model.

Keywords: [Forest, Indonesia, Geographically Weighted Panel Regression (GWPR)].


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References


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