GIS-based interaction of location allocation models with areal interpolation techniques
thesisposted on 21.02.2014, 10:13 by Ibrahim Obaid A. Alshwesh
This research aims to explore the interactions between a selection of four location allocation models, and a selection of three interpolation techniques in the environment of Geographic Information Systems (GIS), in order to support decisions made about optimal facility locations across three case study areas in the UK and the Kingdom of Saudi Arabia. The relationship between location-allocation models and areal interpolation techniques means that in some cases, for example in the absence or unavailability of census data for smaller areas units, a researcher may be forced to use one areal interpolation technique to estimate the census data to smaller areas units or to represent the distribution of demand. The results of interactions between location allocation models and interpolation techniques were used to explore how the spatial characteristics of a problem could potentially be more or less well suited to particular areal interpolation methods (and the demand surfaces they generate) based on their assumptions and were used to examine the impacts of using those surfaces with different location-allocation models. Each location-allocation model was applied across three demand surfaces created from different areal interpolation methods. In this way, the results of this study illustrate how the inherent assumptions associated with areal interpolation techniques influence the outputs of location-allocation models and their impacts on optimal facility locations. The study demonstrated that the spatial characteristics of the case study, in terms of population densities the size of the source zones and built up areas have also played an important role in creating differences between population estimation results for each of the target areas and the three demand surfaces for each case study. The differences in demand weights for each surface, which are based on the assumptions underpinning each method, were found to be the main factors driving variations in optimal facilities selection.