GIS-based spatial autocorrelation analysis and use of aerial photos metrics for land price per plot: a case study of Tembalang sub-district

Abstract

Abstract Tembalang sub-district is included in the suburban area of Semarang which is growing very rapidly in the housing market growth sector. Research on housing policies and existing land market value mechanisms was carried out by empirical analysis using traditional econometric models with multiple regression analysis and GIS-based spatial autocorrelation models. This research was conducted by modeling land prices based on land parcels from the interpolation of land price samples from the field survey results as well as analyzing the level of data closeness to the Tax Object Selling Value (NJOP). Land parcel data was created using the 2018 metric aerial photograph (10 cm spatial resolution). The dependent variable in the regression analysis model used data on the average value of land prices for each zone in 2018. The variables used in the first modeling, namely the geographic location, transportation accessibility, trade center and service intensity were used as independent variables. The apply Radial Basis Function (RBF), spatial autocorrelation model, in integration and comparative analysis of models with a focus on the analysis of factors affecting land prices, especially heterogeneity of spatial characters.