Geographia Technica, Vol 20, Issue 2, 2025, pp. 229-252
GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MODEL FOR ANALYZING FACTORS OF LAND SUBSIDENCE IN JAKARTA PROVINCE
Cahyadi SETIAWAN 
, Fauzi Ramadhoan A’RACHMAN , Ode Sofyan HARDI , Ika Muti RAHMAH , Muhammad DEFFRY , Zidan FURQON, Nabilah Firdha KHAIRUNNISA, Muhammad Wahyu WARDANA
ABSTRACT: Land subsidence was a serious issue affecting infrastructure, water resources and human safety on various countries in the world, including Indonesia. Jakarta as one of the Capital cities in Indonesia is also experiencing land subsidence at varying rates which has many impacts. This study aims to analyze the influence of built-up areas, population density, groundwater, elevation, and distance from the coastline against land subsidence in Jakarta. Data was obtained using the InSAR method, Landsat 8 imagery, Statistic Central Bureau, Indonesian Geospatial Bureau, and Water Resources Agency. The analysis was conducted using Geographically Weighted Regression (GWR). The results indicate that land subsidence in Jakarta is influenced by several parameters, including groundwater levels, population density, built-up areas, elevation, and distance from the coast. The overall R² value of the Geographically Weighted Regression (GWR) model was 0.566, suggesting a moderate explanatory power. Among the variables, groundwater exhibited the strongest correlation with land subsidence (R² = 0.829), whereas elevation (R² = 0.255) and distance from the coast (R² = 0.249) showed the weakest correlations. These findings suggest that anthropogenic factors, particularly related to human activities, have a more significant impact on land subsidence than natural topographic features. High GWR values were primarily concentrated in several districts, including Penjaringan, Pademangan, Cilincing, Tanjung Priok, and Kalideres.
Keywords: Geographically Weighted Regression, Jakarta, Land Subsidence, InSAR Method, Landsat 8 Imagery
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