TITLE:
Optimal Service Planning in a Temporary City
AUTHORS:
Muteb Alotaibi, Graham Clarke, Nick Malleson
KEYWORDS:
Location-Allocation Modelling, The Hajj, Service Analysis, Temporary City
JOURNAL NAME:
Journal of Service Science and Management,
Vol.13 No.5,
October
12,
2020
ABSTRACT: Temporary cities provide unique challenges in terms of urban planning and management. In particular, there are problems in terms of providing adequate service provision and ensuring an equitable access to those services. In addition, when temporary cities also involve a mass gathering for an event, there is a need for a strategic plan for managing the movement of this crowd over time. The Hajj is an annual religious event which hosts about 3 million pilgrims each year. It is the second largest annual gathering of Muslims in the world. The pilgrims reside for a number of days in the “tent city” of Mina. The organisers of the Hajj must plan the location of services to support this huge temporary population and also provide a plan to facilitate the performing of the religious rituals in an easy and safe manner (in particular to avoid overcrowding). In this paper, we estimate and map the distribution of populations across Mina during the Hajj and explore the location of existing services to investigate how well served the population is within various parts of the City. For illustrative purposes, we explore access to health centres and civil defence (mainly fire services). After analysing the current provision of these services across the City (through building suitable accessibility measures) we use a location allocation model to compare the current provision with a set of optimal, model-based locations. The model is also used to operationalise a series of what-if scenarios including reducing the number of facilities in line with increased Government concerns over escalating costs and changing demand in line with pilgrim movements throughout the day. The results of the location-allocation modelling could also help revamp staffing rotas—not only can the model provide optimal locations they can estimate the workloads associated with each facility location (based on the volume of local demand), meaning the thousands of health workers could be also located more optimally.