Downscalling survey data to infer risk perception and adaptive behavior from census and hazard exposure data
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Keywords: small-area estimations, geospatial, survey data, risk perception, vulnerability, adaptive behavior
Abstract Type: Paper Abstract
Authors:
Samuel Rufat, CY Cergy Paris University, France
Peter Howe, Utah State University
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Abstract
“Behavior-blind” risk assessments, mapping, and policy do not account for individual responses to risks, due to challenges in collecting accurate information at scales relevant to decision-making. There is useful spatial information in social survey data that is sometimes analyzed for spatial patterns despite potential biases. This article explores whether risk perception and adaptive behavior can be inferred from census and hazard exposure data with a specifically designed survey. An underlying question is what precautions surveys should take before mapping the results. We find that a hybrid multilevel regression and (synthetic) poststratification (MRP-MRSP) model can facilitate the transition from individual survey data to small-area estimations at different scales, including 200-m grid cells. We demonstrate this model using municipal-level survey data collected in France. We find that model accuracy is not decreased at finer scales provided there is a strong spatial predictor such as flood hazard exposure. Our findings show that a wide range of risk perception and adaptive behavior can be estimated with such downscaling techniques. Although this type of modeling is not yet commonly used among geographers, our study suggests that it can improve mapping of survey results and, in particular, can provide spatially explicit behavioral information for risk and vulnerability assessment and policy.
Downscalling survey data to infer risk perception and adaptive behavior from census and hazard exposure data
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Paper Abstract