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Spatial Integration of Post-Disaster Data for Physical Impact Estimation

Graduate Researcher(s): 
Sabine C. Loos
Faculty Advisor/PI: 
Jack Baker
Earth Observatory of Singapore (EOS), Nanyang Technological University (lead); Kathmandu Living Labs (KLL); Stanford Urban Resilience Initiative (SURI); Humanitarian OpenStreetMap Team (HOT); World Bank Global Facility for Disaster Risk Reduction (GFDRR) and World Bank Big Data Program; and NASA Jet Propulsion Lab and Advanced Rapid Imaging and Analysis Center (NASA-JPL/ARIA)
Project Sponsor: 
Innovations Fund (Global Partnership for Sustainable Development Data and The World Bank)

After an earthquake, the government of the affected country (along with international agencies and nongovernmental organizations) must coalesce an increasing amount of rapidly available data sources to make key post-disaster decisions on emergency response, regional impact assessments, and contingent recovery policies. This project aims to address this issue by integrating multiple data sources that are typically available in the weeks after an earthquake into a spatially exhaustive estimate of regional building impact. Considering building damage specifically, information can range from predictive models based on shaking intensity and pre-existing building exposure to crowdsourced visual interpretation of satellite imagery by online “digital humanitarians”.

We are developing a spatial statistics framework to integrate damage between limited engineering field surveys of individual household damage with 1) a modeled estimate and 2) a remote-sensing proxy of damage obtained from InSAR-based coherence difference, along with 3) auxiliary data on shaking distribution. It, therefore, translates prediction-based models and observation-based numerical pixels in remote-sensing proxies to tangible measures of building damage. This framework, however, is meant to be a data agnostic methodology, general enough to incorporate bias, variance and spatial cross-correlations in the data and account for uncertainties in the prediction. Thus, it is adaptable to new sources and future improvements in current post-earthquake technologies.

By developing a methodology to integrate spatial building damage data together, this framework can be used to reduce the voluminous amounts available post-earthquake information together into a unified map of regional building damage with increased transparency for decision-makers. The results of the framework make for more informed estimates for seminal post-earthquake decisions, such as humanitarian response logistics and recovery-oriented impact assessments.


"Informatics for Equitable Recovery", Nepal 2015 Earthquake

While damage to buildings causes severe devastation to an affected region after a disaster, it is only one component of impact at the household and community level. The framework for spatial integration makes up the physical impact portion of a larger project on "Informatics for Equitable Recovery" aimed at defining a new metric that incorporates the physical, social, and economic dimensions of impact using empirical data collected after the 2015 earthquake in Gorkha, Nepal.