Suchergebnisse

While catastrophe (CAT) modeling of property damage is well developed, modeling of business interruption (BI) lags far behind. One reason is the crude nature of functional relationships in CAT models that translate property damage into BI. Another is that estimating BI losses is more complicated because it depends greatly on public and private decisions during recovery with respect to resilience tactics that dampen losses by using remaining resources more efficiently to maintain business function and to recover more quickly. This article proposes a framework for improving hazard loss estimation for BI insurance needs. Improved data collection that allows for analysis at the level of individual facilities within a company can improve matching the facilities with the effectiveness of individual forms of resilience, such as accessing inventories, relocating operations, and accelerating repair, and can therefore improve estimation accuracy. We then illustrate the difference this can make in a case study example of losses from a hurricane.

This paper describes the evolution of recent work on using crowdsourced analysis of remote sensing imagery, particularly high-resolution aerial imagery, to provide rapid, reliable assessments of damage caused by earthquakes and potentially other disasters. The initial effort examined online imagery taken after the 2008 Wenchuan, China, earthquake. A more recent response to the 2010 Haiti earthquake led to the formation of an international consortium: the Global Earth Observation Catastrophe Assessment Network (GEO-CAN). The success of GEO-CAN in contributing to the official damage assessments made by the Government of Haiti, the United Nations, and the World Bank led to further development of a web-based interface. A current initiative in Christchurch, New Zealand, is underway where remote sensing experts are analyzing satellite imagery, geotechnical engineers are marking liquefaction areas, and structural engineers are identifying building damage. The current site includes online training to improve the accuracy of the assessments and make it possible for even novice users to contribute to the crowdsourced solution. The paper discusses lessons learned from these initiatives and presents a way forward for using crowdsourced remote sensing as a tool for rapid assessment of damage caused by natural disasters around the world.

Abstract. In this study, we developed an enhanced approach for
large-scale flood damage and risk assessments that uses characteristics of
buildings and the built environment as object-based information to represent
exposure and vulnerability to flooding. Most current large-scale assessments
use an aggregated land-use category to represent the exposure, treating all
exposed elements the same. For large areas where previously only coarse
information existed such as in Africa, more detailed exposure data are
becoming available. For our approach, a direct relation between the
construction type and building material of the exposed elements is used to
develop vulnerability curves. We further present a method to differentiate
flood risk in urban and rural areas based on characteristics of the built
environment. We applied the model to Ethiopia and found that rural flood
risk accounts for about 22 % of simulated damage; rural damage is
generally neglected in the typical land-use-based damage models, particularly at this scale. Our approach is particularly interesting for studies in areas
where there is a large variation in construction types in the building
stock, such as developing countries.

Purpose
Using risk-related data often require a significant amount of upfront work to collect, extract and transform data. In addition, the lack of a consistent data structure hinders the development of tools that can be used with more than one set of data. The purpose of this paper is to report on an effort to solve these problems through the development of extensible, internally consistent schemas for risk-related data.


Design/methodology/approach
The consortia coordinated their efforts so the hazard, exposure and vulnerability schemas are compatible. Hazard data can be provided as either event footprints or stochastic catalogs. Exposure classes include buildings, infrastructure, agriculture, livestock, forestry and socio-economic data. The vulnerability component includes fragility and vulnerability functions and indicators for physical and social vulnerability. The schemas also provide the ability to define uncertainties and allow the scoring of vulnerability data for relevance and quality.


Findings
As a proof of concept, the schemas were populated with data for Tanzania and with exposure data for several other countries.


Research limitations/implications
The data schema and data exploration tool are open source and, if widely accepted, could become widely used by practitioners.


Practical implications
A single set of hazard, exposure and vulnerability schemas will not fit all purposes. Tools will be needed to transform the data into other formats.


Originality/value
This paper describes extensible, internally consistent, multi-hazard, exposure and vulnerability schemas that can be used to store disaster risk-related data and a data exploration tool that promotes data discovery and use.