Elsevier

Journal of Transport Geography

Volume 19, Issue 6, November 2011, Pages 1443-1455
Journal of Transport Geography

A network-based analysis of the impact of structural damage on urban accessibility following a disaster: the case of the seismically damaged Port Au Prince and Carrefour urban road networks

https://doi.org/10.1016/j.jtrangeo.2011.08.002Get rights and content

Abstract

The magnitude 7.0 earthquake that struck Haiti in January 2010 led to an unprecedented effort in collecting and providing geographical information in support of humanitarian aid. Although most of the compiled datasets and generated maps were able to provide specific and detailed information regarding the location of damaged buildings and road interruptions, none or little information was available to describe the accessibility—or otherwise—of the urban space. Here we try to offer an alternative method to define the urban accessibility landscape in the aftermath of earthquake damage, by combining simple graph theory concepts and GIS-based spatial analysis to assess how the urban space accessibility decreases when the road network is damaged.

Highlights

► We combine GIS data processing with network analysis. ► We analyze road disruptions induced by natural hazards. ► The urban space connectivity after an earthquake is assessed. ► We quantify the increased isolation of affected areas from road network damage. ► A reduced accessibility index map is drawn for the affected area.

Introduction

When Haiti was hit by a magnitude 7.0 earthquake on January 12, 2010, very few geographic information system (GIS) datasets existed concerning this Caribbean island. In spite of this, and thanks to the voluntary effort of many individuals, governmental and non-governmental international agencies, it was possible to assemble a wide range of data sets that, even if not of use in the immediate aftermath of the disaster, could still be used in forthcoming reconstruction and aid programmes.

With a view to achieve a more specific information beyond the usual classification of closed roads compiled after the event, in this study we investigate how the interruption of road networks, caused by falling debris onto the roads, reduced the accessibility in the Port Au Prince and Carrefour areas. The low Gross Domestic Product of Haiti had, as expected, a dramatic role in the severity of the seismic consequences (Gutiérrez et al., 2005) because poorly built housing stock and the lack of anti-seismic design led to the collapse of many buildings. The low development index of Haiti presumably also contributed negatively to the lack of GIS information concerning housing and infrastructure. It is therefore worth considering how in more developed countries (where spatial information is usually collected in support of everyday governmental decision-making) the much vaster wealth of knowledge available on critical infrastructure networks could be used in similar circumstances, and how the physical failure of structural items in those networks could limit certain key functionalities.

However, in spite of the low development index of Haiti, a worldwide effort from the Internet community was soon to be seen on the World Wide Web days after the seismic event, all of which contributed to supporting humanitarian organizations, particularly by supplying geographical information that was to become crucial for emergency logistics. In particular, the open-source project Open Street Map (OSM) rapidly became the vital collecting point of the most up-to-date Haitian road network data. In tandem to all of the above, humanitarian organizations, such as the UN, started to compile damage assessments of the road infrastructures. Although this generated a wealth of information regarding the damage to the affected urban landscape, we believe that there is sometimes a need to condense such information in a manner that can be made even more applicable, such as, for example, the accessibility of the urban landscape to vehicle-equipped emergency relief agencies.

Just like when travelling within a maze, the traditional blocked roads survey is not always capable of fully capturing the impact of the disruptions on the urban blocks at a city-wide scale. Hence, starting from the publicly available data on the earthquake aftermath, this work combines graph theory and GIS spatial analysis to evaluate the reduced accessibility of the complete urban space. We do this in order to capture two aspects of the disruptions: the impossibility to freely travel along the urban road network, and the isolation of dwelling blocks that may not be easily reached by the emergency services.

Here we first show how the street vector data are converted into a graph network and how this is analyzed to identify roads isolated by the disruptions on the adjacent streets.

Secondly, through the use of raster-based techniques, we compute the accessibility of the urban space for the two cases of damaged and undamaged road networks.

Finally, we combine the accessibility measures of the two networks—pre and post the hazard, in order to identify urban areas that can pose barriers to the emergency operations.

The case study focuses on the damaged Port Au Prince and Carrefour urban road network in order to analyze the effects of the disruptions of the 2010 seismic event on urban space accessibility.

Section snippets

Research issues and literature overview

Graph theory has been successfully applied to many different systems (Albert and Barabási, 2002): from biological processes (Jeong et al., 2000) to infrastructures (Crucitti et al., 2006, Jenelius et al., 2006, Albert et al., 2004), to communications and social relationships (Barabasi et al., 2002); thus, an object, an ensemble of items, a process, relational matrices, etc, can all be represented in terms of nodes and links called graphs or networks.

Network analysis was introduced into GIS

Methods and data preparation

A graph or network is a collection of nodes or vertices V(G) and edges E(G) that make up the graph G (V|E). Graphs can be undirected, if the links can be used in both directions, or directed (i.e., one-way streets in urban streets networks). Directed links are commonly referred to as arcs.

In this analysis we choose to generate an undirected road network for two reasons: firstly because the number of one-way streets in our study area (Port Au Prince and Carrefour in Haiti) is a small percentage

Application to the 2010 Haiti earthquake

Haiti was severely affected by the earthquake of January 12, 2010. The capital (and largest city), Port Au Prince, has a metropolitan population estimated to be between 2.5 and 3 million people, and is located 25 km east-northeast of the epicentre. The earthquake caused extensive damage to buildings throughout the Port Au Prince region, including the Presidential Palace, the main seaport and the UN mission. Infrastructures were also severely affected, either directly or by the collapse of

Results

The total number of segments composing the damaged network is larger than in the original network because splitting a line at the intersections with reported disruptions generates two distinct separate lines. When considering the main component only, in the damaged and undamaged network it can be observed that the total number of streets—apparently—is increased. Therefore, in order to compare the level of connectivity, the total length of the street network must be compared (see Table 5).

It can

Conclusions

The difficulty in deploying resources into an effective supply chain was made abundantly clear to a near real-time watching world during the Haiti earthquake of 2010; however immediate, such images could only provide an overview of an extremely complex supply chain running into difficulties due to the lack of basic infrastructure information. Indeed, one of the key points concerns the changing accessibility of urban areas brought about by the closure of roads due to debris (de la Torre et al.,

Data sources and tools

Acknowledgements

This work was supported by the MANMADE project (Contract No 043363) funded by the European Commission, DG RTD, under the New and Emerging Science and Technology (NEST PATHFINDER initiative), Thematic Priority: Tackling Complexity in Science.

References (30)

  • T. Steenberghen et al.

    Spatial clustering of events on a network

    Journal of Transport Geography

    (2010)
  • G. Vandenbulcke et al.

    Mapping accessibility in Belgium: a tool for land-use and transport planning?

    Journal of Transport Geography

    (2009)
  • R. Albert et al.

    Statistical mechanics of complex networks

    Reviews of Modern Physics

    (2002)
  • R. Albert et al.

    Structural vulnerability of the North American power grid

    Physical Review E

    (2004)
  • ARUP, AA.VV, 2010. Haiti Earthquake Response – ARUP Assignment Report, Oxfam GB, 240...
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