Estimating Economic Activity Using Geospatial Big Data in East Java, Indonesia: Relative Spatial GDP Index Approach
DOI:
https://doi.org/10.34123/jurnalasks.v17i2.856Keywords:
East Java, Geospatial Big Data, GRDP, Relative Spatial GDP Index, Remote Sensing, Robust RegressionAbstract
Introduction/Main Objectives: GRDP serves as a fundamental indicator for assessing regional economic performance in Indonesia and plays a critical role in development planning. Background Problems: Conventional GRDP measurement in Indonesia relies on survey-based approaches, which are time-consuming, costly, and provide limited spatial detail. Novelty: This study introduces a Relative Spatial GDP Index (RSGI) constructed from geospatial big data such as remote sensing and point of interest (POI) to estimate GRDP more granular in East Java. This approach represents the first geospatial data driven GRDP index developed at such fine spatial resolution in Indonesia. Research Methods: Four weighting schemes were applied to generate RSGI variations, which were then evaluated through regression modeling against official GRDP. They are equal weight, pearson correlation, spearman correlation, and principal component analysis (PCA). Finding/Results: The RSGI PCA produced the best performance (RMSE = 0.73047; MAE = 0.48185; MAPE = 7.00%; R² = 0.7618). PCA weight outperformed other weight by capturing shared variance and generating objective weights that better represent spatial economic intensity. The RSGI PCA demonstrates a strong and significant correlation with GRDP at the sub-district level and provides a robust tool for fine-scale economic estimation.
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