Small-Area Estimations from Survey Data for High-Resolution Maps of Urban Flood Risk Perception and Evacuation Behavior

Small-Area Estimations from Survey Data for High-Resolution Maps of Urban Flood Risk Perception and Evacuation Behavior
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根据城市洪水风险感知和疏散行为的高分辨率地图的调查数据进行小区域估计

DOI:
10.1080/24694452.2022.2105685
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发表时间:
2023
影响因子:
3.9
通讯作者:
Howe, Peter D.
Howe, Peter D.
中科院分区:
法学2区
文献类型:
--
作者:
Rufat, Samuel;Howe, Peter D.

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由于收集与决策相关的准确信息存在挑战,“行为盲”风险评估、绘图和政策无法考虑个人对风险的反应。社会调查数据中存在有用的空间信息,尽管存在潜在偏差,但有时仍会分析这些数据的空间模式。本文探讨了是否可以通过专门设计的调查从人口普查和危险暴露数据中推断风险认知和适应行为。一个根本问题是调查在绘制结果之前应采取哪些预防措施。我们发现混合多级回归和(综合)后分层(MRP-MRSP)模型可以促进从个体调查数据到不同尺度(包括 200 米网格单元)的小区域估计的过渡。我们使用在法国巴黎地区收集的市级调查数据来演示该模型。我们发现,只要存在强大的空间预测因素(例如危险暴露),模型的准确性在更精细的尺度上不会降低。我们的研究结果表明,可以通过这种降尺度技术来估计各种洪水风险感知和疏散行为。尽管这种类型的模型尚未在地理学家中普遍使用,但我们的研究表明,它可以改进调查结果的绘图,特别是可以为风险评估和政策提供空间明确的行为信息。
“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.
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