A comparative assessment of the statistical methods based on urban population density estimation

Population density is important spatial information for addressing the use and access to land resources in cities under the Sustainable Development Goals. This is because the spatial data support appropriate spatial policies at the spatial scale and predicts how much land will be consumed in the future. The study aims to compare and evaluate the regression tools in the context of estimating the population density difference. The three analysis tools used are Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The sampling area covers cities around Turkiye. Comparative results showed that the two most important descriptive variables in the Random Forest-Based Classification model are the density difference of the new developed area and the connectivity. The three main explanatory variables of the Multiple Linear Regression model are centrality, vehicle ownership, and accessibility. The results of the Multiple Linear Regression model (a non-spatial model) and the Geographically Weighted Regression model (a spatial model), were found to be quite similar. The importance of accessibility and connectivity is more evident in the Multiple Linear Regression model when the Random Forest-Based Classification model highlights the density values in the new development areas.

Automated summary

Population density is essential for addressing land use and accessibility issues in cities under the Sustainable Development Goals. The study compares and evaluates regression tools to estimate population density differences. The analysis tools used include Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The results show the importance of descriptive variables such as density difference and connectivity in the Random Forest-Based Classification model, and explanatory variables like centrality, vehicle ownership, and accessibility in the Multiple Linear Regression model. The results of the non-spatial Multiple Linear Regression model and the spatial Geographically Weighted Regression model are found to be similar.