Urban Form and Structure Explain Variability in Spatial Inequality of Property Flood Risk among US Counties
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ID: 282180
2023
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Abstract
Understanding the relationship between urban form and structure and spatial
variation of property flood risk has been a longstanding challenge in urban
planning and city flood risk management. Yet limited data-driven insights exist
regarding the extent to which variation in spatial inequality of property flood
risk in cities can be explained by heterogenous features of urban form and
structure. In this study, we explore eight key features (i.e., population
density, point of interest density, road density, minority segregation, income
segregation, urban centrality index, gross domestic product, and human mobility
index) related to urban form and structure to explain variability in spatial
inequality of property flood risk among 2567 US counties. Using rich datasets
related to property flood risk, we quantify spatial inequality in property
flood risk and delineate features of urban form and structure using
high-resolution human mobility and facility distribution data. We identify
significant variation in spatial inequality of property flood risk among US
counties with coastline and metropolitan counties having the greatest spatial
inequality of property flood risk. The results also reveal variations in
spatial inequality of property flood risk can be effectively explained based on
principal components of development density, economic activity, and centrality
and segregation. Using a classification and regression tree model, we
demonstrate how these principal components interact and form pathways that
explain levels of spatial inequality in property flood risk in US counties. The
findings offer important insights for the understanding of the complex
interplay between urban form and structure and spatial inequality of property
flood risk and have important implications for integrated urban design
strategies to address property flood risk as cities continue to expand and
develop.
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| Authors | Junwei Ma; Ali Mostafavi |
| Journal | arXiv |
| Year | 2023 |
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