Location-Allocation Modeling for Emergency Evacuation Planning in a Smart City Context: The Case of Earthquake in Mytilini, Lesvos, Greece

Location-Allocation Modeling for Emergency Evacuation Planning in a Smart City Context: The Case of Earthquake in Mytilini, Lesvos, Greece

Marios Batsaris (Department of Geography, University of the Aegean, Mytilene, Greece), Dimitris Kavroudakis (Department of Geography, University of the Aegean, Mytilene, Greece), Nikolaos A. Soulakellis (Department of Geography, University of the Aegean, Mytilene, Greece) and Themistoklis Kontos (Department of Environment, University of the Aegean, Mytilene, Greece)
Copyright: © 2019 |Pages: 16
DOI: 10.4018/IJAGR.2019100103

Abstract

The transition of a city to a smart city depends on the preservation of open spaces because they can ensure both a safe and a quality living. In a smart city context, it is important for planners to pre-allocate resources during planning phase in order to satisfy the demand during catastrophic events. Geo-computation approaches can contribute towards the spatial-optimization of urban open-spaces for evacuation purposes in cases of catastrophic events. This work will use a location allocation spatial model to facilitate the planing of urban evacuation actions in Mytilini, Lesvos, Greece. Spatial analysis techniques have evolved during the last decades, mainly due to increased computation resources and other complementary technological advances. In this article, the authors attempt to show the contribution of spatial analysis in emergency management planning towards the smart city vision.
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Introduction

A city development model can rely on a sustainable and technology driven economic growth in modern societies. The smart approach seems to be a promising development model by utilizing Information and Communication Technologies (ICT) to achieve a sustainable economic growth and a high quality of life (Sta, 2017). One of the main goals of Smart cities is civil protection especially before/during and after imminent disasters.

While it is difficult to predict catastrophic incidents with a sudden onset such as an earthquake city planners and relevant authorities aim to reduce the impact of such an incident by planning pre/post-disaster protocols and actions which are summarized in the disaster management cycle and discussed in detail by Naghdi et al. (2008), Miller et al. (2006) and Altay and Green (2006). Emergency evacuation as part of the disaster management process is a critical aspect of public safety in case of a catastrophic earthquake incident and are considered as rapid population displacement from dangerous (usually indoor) places to safer places (Coutinho-Rodrigues, Tralhão and Alçada-Almeida, 2012; Ye et al., 2012; Gan et al., 2016). Emergency evacuation is either considered as post-disaster (Coutinho-Rodrigues, Tralhão and Alçada-Almeida, 2012; Ye et al., 2012; Gan et al., 2016) or as a pre-disaster action (Anhorn and Khazai, 2015).

Facility location-allocation decisions are fundamental in the emergency evacuation planning process because they can provide protection and basic life-support services immediately after or during the occurrence of a catastrophic natural disaster such as an earthquake or tsunami. It is important for city planners to pre-allocate available resources considering the city’s ability to satisfy efficiently the generated population demand.

This paper examines the use of location-allocation spatial modeling to find the optimal locations of emergency evacuation shelters and simultaneously allocate the population to shelters considering network impedance parameters and shelters capacity constraints. A case study of location-allocation modeling in case of earthquake is applied for the city of Mytilini, Lesvos, Greece and two evacuation scenarios were evaluated. The first scenario considers capacity and 250m of acceptable walking distance constraints and the second considers only capacity restrictions. The main objective of this work is the presentation of location-allocation spatial modeling as a decision-making support system for Smart policy making and planning by utilizing fine scaled population distribution datasets in a smart city context.

The remainder of this paper is organized as follows. The next section provides background information about smart cities, population spatial -distribution estimation methods and disaster related literature of location-allocation spatial modeling. The third section presents the methodological approach and addresses the case study of Mytilini. The fourth section discuss the results of this paper and finally the paper is concluded with a short summary and the areas of future research.

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