Exposure matters
A synthesis framework for high-resolution building inventory development
Bibliographic Data
| ID | 22028635 |
|---|---|
| Authors | Meredith Lochhead (0009-0006-7460-7456, Stanford University, corresponding author), Adam Zsarnóczay (0000-0001-6895-1417, Stanford University), Gregory G Deierlein (0000-0002-0067-3817, Stanford University) |
| Year | 2026 |
| Volume | 139 |
| Pages | 106148 |
| Publication date | 2026-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2026.106148 |
| OpenAlex | W7154448319 |
| Language | EN |
| Citations received | 1 |
| References cited | 41 |
Regional simulations of the impact of natural hazards on the built environment can be used to support disaster risk management and guide mitigation priorities, and these assessments are underpinned by 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. This study presents (1) a systematic framework for creating footprint-level building inventories through the synthesis of multiple data sources, (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 paper aims to both motivate and address the need for more systematic and standardized approaches to building inventory development for regional natural hazard risk assessments
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |