Mapping Spatiotemporal Patterns of Disabled People: The Case of the St. Jude’s Storm Emergency

Abstract

Emergency management can greatly benefit from an understanding of the spatiotemporal distribution of individual population groups because it optimizes the allocation of resources and personnel needed in case of an emergency caused by a disaster. In practice, however, vulnerable population groups, such as people with disability, tend to be overlooked by emergency officials. Tasks such as identifying people who are vulnerable in an emergency generally are approached statically using census data, without taking into account the spatiotemporal dynamics of disabled people’s concentrations as observed in large metropolitan areas such as London, United Kingdom. Transport data gathered by automatic fare collection methods combined with accessibility covariates have the potential of being a good source for describing the distribution of this concentration. As a case study, data from the peak of the St. Jude’s Day storm in London on October 28, 2013, were used to model the within-day fluctuation of disabled people, employing discrete spatiotemporal variation methods. Specifically, Poisson spatiotemporal generalized linear models were built within a hierarchical framework, ranging from simple to more complex ones, taking into account spatiotemporal interactions that emerge between space-time units. The performance of the resulting models in terms of their ability to explain the effects of the covariates, as well as predict future disabled peoples counts, were compared relative to each other using the deviance information criterion and posterior predictive check criterion. Analysis of results revealed a distinct spatiotemporal pattern of disabled transport users that potentially could be used by emergency planners to inform their decisions.