TY - JOUR
T1 - Building asset value mapping in support of flood risk assessments
T2 - A case study of Shanghai, China
AU - Wu, Jidong
AU - Ye, Mengqi
AU - Wang, Xu
AU - Koks, Elco
PY - 2019/2/14
Y1 - 2019/2/14
N2 - Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census level). To bridge the gap between census-level BFA data and geo-coded building asset value data, this article introduces a method for building asset value mapping, using Shanghai as an example. This method consists of a census-level BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data, combined with LandScan population density grid data and a financial appraisal of building asset values. Validation with statistical data and field survey data confirms that the method can produce good results, but largely constrained by the resolution of the population density grid used. However, compared with other models with no disaggregation in flood exposure assessment that involves Shanghai, the building asset value mapping method used in this study has a comparative advantage, and it will provide a quick way to produce a building asset value map for regional flood risk assessments. We argue that a sound flood risk assessment should be based on a high-resolution-individual building-based-building assetvalue map because of the high spatial heterogeneity of flood hazards.
AB - Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census level). To bridge the gap between census-level BFA data and geo-coded building asset value data, this article introduces a method for building asset value mapping, using Shanghai as an example. This method consists of a census-level BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data, combined with LandScan population density grid data and a financial appraisal of building asset values. Validation with statistical data and field survey data confirms that the method can produce good results, but largely constrained by the resolution of the population density grid used. However, compared with other models with no disaggregation in flood exposure assessment that involves Shanghai, the building asset value mapping method used in this study has a comparative advantage, and it will provide a quick way to produce a building asset value map for regional flood risk assessments. We argue that a sound flood risk assessment should be based on a high-resolution-individual building-based-building assetvalue map because of the high spatial heterogeneity of flood hazards.
KW - Building asset value mapping
KW - Exposure
KW - Flood risk management
KW - Shanghai
UR - http://www.scopus.com/inward/record.url?scp=85061577970&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061577970&partnerID=8YFLogxK
U2 - 10.3390/su11040971
DO - 10.3390/su11040971
M3 - Article
AN - SCOPUS:85061577970
VL - 11
SP - 1
EP - 19
JO - Sustainability
JF - Sustainability
SN - 2071-1050
IS - 4
M1 - 971
ER -