Effects of demand estimates on the evaluation and optimality of service centre locations

Public service systems, such as emergency health care, police or fire brigades, are critical for day-to-day functioning of the society. To design and operate these systems efficiently much data needs to be collected and properly utilised. Here, we use the OpenStreetMap (OSM) data to model the demand points (DPs), which approximate the geographical location of customers, and the road network, which is used to access or distribute services. We consider all inhabitants as customers, and therefore to estimate the demand, we use the available population grids. People are changing their location in the course of the day and thus the demand for services is changing accordingly. In this paper, we investigate how the used demand estimate affects the optimal design of a public service system. We calculate and compare efficient designs corresponding to two demand models, a night-time demand model when the majority of inhabitants rest at home and the demand model derived from the 24-hour average of the population density. We propose a simple measure to quantify the differences between population grids and we estimate how the size of differences affects the optimal structure of a public service system. Our analyses reveal that the efficiency of the service system is not only dependent on the placement strategy, but an inappropriate demand model has significant effects when designing a system as well as when evaluating its efficiency.