ABSTRACT
Using a new approach that integrates big data and GIS into the Social Accounting Matrix (SAM) framework of economic impact analysis, in this study we explore the equity implications of extreme weather events. Focusing on the uneven distribution of impact across income groups, we show how data sources with different resolutions can be stitched together to identify sources of social vulnerability. We first employ GIS to map areas that were flooded in the aftermath of major hurricanes. The next step makes use of big data to identify production activities that were directly affected by virtue of their location in flooded areas. The direct impact estimates are then combined with a SAM model built to estimate the ripple effects. The final step decomposes SAM multipliers to determine the transmission paths through which the initial shock percolates and ultimately render households vulnerable. We then demonstrate the application of the framework to address equity planning challenges brought about by Hurricane Sandy. The results suggest that while wealthy New Yorkers may suffer the most from Sandy’s destruction, the distributional impact is on the whole egalitarian. The integration of big data into a geospatial framework thus reveals the critical role of spatial and economic structure in shaping the distributional impact of extreme weather events.
