The impact of alternative distance measures and temporal variation in demand on location-allocation decisions

Abstract

The aim of the study was to explore the impact of spatio-temporal variations in demand and alternative-based GIS measures on location–allocation. Location-allocation models are mathematical formulations that seek to optimise facility locations (supply) in relation to the spatial distribution of demand and transport networks. However, there are a number of shortcomings to many location-allocation analyses applied to spatial planning. First, in most analyses the demands that are used as inputs to the models are static. Second, location-allocation models usually fail to incorporate demand trends and most analyses only use the most recent demand estimates. Third, distances separating supply and demand locations have been modelled as 3D, by incorporating variations in elevation or as 2D by assuming that the earth's surface is a continuous plane. This thesis addresses these shortcomings by investigating the impact of these factors on location-allocation decisions making based on different case studies. The first analysis explored the impact of short-term spatio-temporal variations through a case study of EMS location planning in Leicestershire. This was achieved by comparing a residential-based location model, which assumes that demand is static with an alternative location model – the travel-to-work model that incorporates dynamic changes in demand due to journey-to-work. The results of the analysis showed clear differences in both approach, in terms of selected locations, demand allocated to selected locations, Average Weighted Distance (AWD) and demand coverage. In the second analysis, the impact of the long-term spatio-temporal variations in demand was explored through a case study of deriving optimal locations for outreach clinics in Leicester. A trend-weighted approach that incorporates the trajectories of changes in demand was compared with the Non-trend weighted model - a traditional approach that ignores the changing demand trend. The results from the comparative analysis indicated that neglecting demand trend over time, consistently underestimates AWD to selected outreach clinic. This finding suggests that current and future access to outreach clinic may be underestimated, when trends in demand are ignored. Finally, the third analysis compared 2D and 3D distances based on a case study of finding optimal locations for EMS in the hilly areas of South Yorkshire and Sheffield. The results indicated that location-allocation results derived from 3D distance measures are not significantly different from results derived from 2D distance measures. Overall, the finding from the comparative analysis using simulated elevation surfaces, demonstrate that other factors such demand weight distributions and distribution of demand and supply locations influence the outcome of the P-median model. The study concluded that 3D distance is more suited for vehicle routing and allocation problems and siting of Automatic External Defiribilators (AEDs) in building interiors and therefore may not be suitable for location planning in the outdoor environment