Measuring Disaster Preparedness of UK Cities from Open Spatial Databases

Bharat Kunwar () and Anders Johansson ()
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Bharat Kunwar: University of Bristol
Anders Johansson: University of Bristol

A chapter in Traffic and Granular Flow '13, 2015, pp 265-271 from Springer

Abstract: Abstract In recent years, we have seen a surge in the number of natural disasters (Munich, Loss events worldwide 2013, 2013). Rapid urbanisation and population growth are contributing factors. However, the planning tools available are usually specific to a region and incompatible in new areas. Therefore, aim of the overall project is to utilise growing wealth of crowd-sourced open spatial databases like OpenStreetMap (OSM) (Haklay and Weber, Pervasive Comput IEEE 7(4):12–18, 2008), computational mobility and behavioural models to achieve rapid simulation of large-scale evacuation effort in response to major crises. As part of an initial effort to gain insight into disaster resilience of various UK cities, 7 amenities across 11 cities have been studied. Correlations between population count (GPWv3) (Center for International Earth Science Information Network (CIESIN)/Columbia University and Centro Internacional de Agricultura Tropical (CIAT), Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, 2005) and number of critical amenities that have the potential to suffer increase in demand during a crisis have been looked at. Similarly, correlations between pairs of potentially interdependent population weighted amenities have also been investigated by working with the assumption that if they are spatially well correlated, they can work better. As the work is ongoing, a worldwide geographically specific ‘Evacuation-Friendliness Index’ is envisioned at the end of this project. As the research focus expands take suitability of road networks for emergency evacuation and dynamic effects using agents based models, the outcome is expected to have implication on emergency planning in the short term by testing multiple strategies in the run up to a disaster and influence policy makers in the long term by identifying weakest links and bottlenecks in a city system.

Keywords: Road Network; Agent Base Modelling; Disaster Preparedness; Disaster Resilience; Gridded Population (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-10629-8_32

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