Dimension Reduction of Socioeconomic Factors in Deforestation Analysis in Indonesia Using Sparse PCA
DOI:
https://doi.org/10.34123/jurnalasks.v18i1.954Keywords:
Deforestation, Dimensionality Reduction, Indonesia, Socioeconomic Indicators, Sparce Principal Component Analysis, Spatial AnalysisAbstract
Introduction/Main Objectives: Deforestation remains a major environmental challenge in Indonesia under diverse socio-economic conditions. This study applies Sparse Principal Component Analysis (SPCA) to identify the key socio-economic variables associated with deforestation patterns. Background Problems: Analyses of deforestation drivers often involve numerous correlated variables, leading to multicollinearity and making interpretation difficult. Therefore, an approach is needed to reduce data dimensionality while retaining the most relevant information. Novelty: This study employs SPCA to simultaneously perform dimensionality reduction and variable selection, producing a more interpretable framework for identifying socio-economic factors related to deforestation at the provincial level in Indonesia. Research Methods: Provincial-level socio-economic data from Statistics Indonesia were analyzed using SPCA to address multicollinearity and derive interpretable components. Spatial autocorrelation was assessed using Moran’s I. Finding/Results: SPCA reduced the variables into two interpretable components and identified six key contributing variables while excluding three with limited influence. Moran’s I values for the first (0.402) and second (0.258) sparse principal components indicated significant positive spatial clustering of provinces with similar deforestation-related characteristics. Research Limitations: The analysis is limited to provincial-level secondary data and may not fully capture local-scale variations or all determinants of deforestation.
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