GIS-based interaction of location allocation models with areal interpolation techniques

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

Evaluation of Index-Based Methods for Impervious Surface Mapping from Landsat-8 to Cities in Dry Climates; A Case Study of Buraydah City, KSA

The natural landscape is fast turning into impervious surfaces with the increase in urban density and the spatial extent of urbanized areas. Remote sensing data are crucial for mapping impervious surface area (ISA), and several methods for ISA extraction have been developed and implemented successfully. However, the heterogeneity of the ISA spectra and the high similarity of the ISA spectra to those of bare soil in dry climates were not adequately addressed. The objective of this study is to determine which spectral impervious surface index best represents impervious surfaces in arid climates using two seasonal Landsat-8 images. We attempted to compare the performance of various impervious surface spectral Index for ISA extraction in dry climates using two seasonal Landsat-8 data. Specifically, nine indices, i.e., band ratio for the built-up area (BRBA), built-up area extraction method (BAEM), visible red near infrared built-up index (VrNIR-BI), normalized ratio urban index (NRUI), enhanced normalized difference impervious surfaces index (ENDISI), dry built-up index (DBI), built-up land features extraction index (BLFEI), perpendicular impervious surface index (PISI), combinational biophysical composition index (CBCI), and two impervious surface binary methods (manual method and ISODATA unsupervised classification). According to the results, PISI and CBCI combined with the manual method had the best accuracy with 88.5% and 88.5% overall accuracy (OA) and 0.76 and 0.81 kappa coefficients, respectively, while DBI combined with the manual method had the lowest accuracy with 75.37% OA and 0.56 kappa coefficients. PISI is comparatively more stable than the other approaches in terms of seasonal sensitivity. The ability of PISI to discriminate ISA from soil and vegetation accounts for much of its good performance. In addition, spring is the ideal time of the year for mapping ISA from Landsat-8 images because the impervious surface is generally less likely to be confused with bare soil and sand at this time of year. Therefore, this study can be used to determine spectral indices for studying ISA extraction in drylands in conjunction with binary approaches and seasonal effects