Exposure matters: a synthesis framework for high-resolution building inventory development

Mia Lochhead, Ádám Zsarnóczay, Gregory Deierlein

Regional simulations of the impact of natural hazards on the built environment can be used to support disaster risk management and guide mitigation priorities. These assessments are underpinned by exposure data, often in the form of building inventories. Historically, building inventory development has primarily been driven by insurance companies and government agencies, typically targeting aggregate risk and impact measures. The increasing feasibility of modeling impacts beyond aggregate loss and the growing interest in higher-fidelity regional risk studies from a broad range of stakeholders are creating a need for detailed footprint-level building inventories. Current research studies often use varied data sources to describe the building inventory or use variable single-use methods to synthesize multiple data sources; however, few have assessed the impact of these inventory development decisions or the variability of the resulting inventory. 

Thus, this study presents (1) a systematic framework for creating footprint-level building inventories through the synthesis of multiple data sources, including the use of standardized terminology for classifying and synthesizing inventory data (2) specific implementation methods for various data types, and (3) a quantitative evaluation of how inventory development decisions impact the resulting inventory makeup and quality for a case study city. Results show that the choice of input data sources and synthesis methods can lead to substantial differences in the resulting inventory and measured risk in a community. Furthermore, these differences are geospatially clustered and concentrate in certain types of buildings, which can lead to significant biases in the results. This study aims to both motivate and address the need for more systematic and standardized approaches to building inventory development for regional natural hazard risk assessments.

Figure: The above figure shows building-level mean loss ratio for a scenario M7 earthquake on the Hayward Fault across three alternative building inventories, all representing Hayward, CA circa 2023. The results reveal notable differences in both overall loss estimates and the spatial distribution of losses across the city. Specifically, the seismic risk measured using an inventory generated by synthesizing multiple national data sources (middle panel) closely approximates that of the best-estimate inventory (right panel), which incorporates both national and high-quality local data. In contrast, results from the raw National Structure Inventory (left panel) differ significantly. This figure demonstrates that inventory development decisions can introduce substantial biases in both the amount and spatial distribution of estimated seismic risk, and that data synthesis and standardized methods can improve inventory quality.