A Comparative Study on the Spatial Statistical Models for the Estimation of Population Distribution

Abstract

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population's distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

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“…The exponential variogram model is chosen as the most effective for population density. It confirms the results of previous studies in this matter (Oh and Hwang, 2015;Zhang et al, 2016;Zhou et al, 2013).…”

“…On the other hand, the SSE value for the exponential model was significantly smaller. Comparing this result with the previous studies (Oh and Hwang, 2015;Zhang et al, 2016), the exponential variogram was accepted for investigation of spatial continuity of the population density.…”

“…For observing spatial variability of the logarithmized population density, empirical semivariograms (Journel, 1989;Oh and Hwang, 2015;Pebesma, 2004;Zawadzki, 2011) were considered as follows:…”

“…Variogram models applied in this paper were taken into account earlier in work by Oh and Hwang (2015), for purposes of investigation of population density spatial distribution.…”