Maps of the population of Dhi Qar Governorate using spatial interpolation techniques in Geographic Information Systems (GIS)

Abstract

The research aims to predict the population maps of Dhi Qar Governorate by comparing the spatial interpolation methods Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF), The research also seeks to harness spatial interpolation techniques to produce maps with a high level of perceptual accuracy in representing the population data of the study area. This is achieved after exploring the statistical and spatial nature of the used databases, analyzing them, and determining their distribution using a variety of spatial data exploration tools available within the GIS (Geographic Information Systems) environment. These tools contribute to evaluating the characteristics, distribution, and analysis of data, including testing data distribution, identifying its direction, and uncovering its spatial correlations. This highlights the importance of the study and understanding the distribution pattern of the population data in the study area, thereby facilitating the preparation of future plans that serve spatial organization by building spatial models for the distribution of the total population, urban population, and rural population at the level of administrative units of the study area. This is done using Geostatistical Analysis methods within the concept of spatial interpolation in GIS. The research also seeks to predict the population maps for Dhi Qar Governorate by comparing spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF). The study concluded, after verifying the accuracy of the results using the Cross-Validation curve, that the Radial Basis Function (RBF) method is the most accurate model in spatial prediction of population data for representing the numerical distribution data of the rural population in the study area, as it had the lowest Root Mean Square Error (RMSE) of 24731.19. It was followed by the Inverse Distance Weighting (IDW) method for representing the data of the elderly population with an RMSE of 25236.635. This indicates that the use of statistical criteria improves the accuracy of spatial interpolation and that the spatial prediction of the population data for the study area using the Radial Basis Function (RBF) method was more accurate than the Inverse Distance Weighting (IDW) method based on statistical indicators.