“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 the Paris region, France. We find that model accuracy is not decreased at finer scales provided there is a strong spatial predictor such as hazard exposure. Our findings show that a wide range of flood risk perception and evacuation 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 assessment and policy.