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Urban Heat Management and the Legacy of Redlining
Open accessAbstract
Problem, research strategy, and findings: Historical patterns of discrimination and disinvestment have shaped the current landscape of vulnerability to heat in U.S. cities but are not explicitly considered by heat mitigation planning efforts. Drawing upon the equity planning framework and developing a broader conceptualization of what equity means can enhance urban heat management. Here I ask whether areas in Baltimore (MD), Dallas (TX), and Kansas City (MO) targeted for disinvestment in the past through practices like redlining are now more exposed to heat. I compare estimates of land surface temperature (LST) derived from satellite imagery across the four-category rating system used to guide lending practices in cities around the United States, summarize the demographic characteristics of current residents within each of these historical designations using U.S. Census data, and discuss the connection between systematic disinvestment and exposure to heat. LST and air temperatures are not equivalent, which makes it difficult to reconcile existing research on the human health impacts of heat exposure that rely on a sparse network of air temperature monitoring stations with more granular LST data. Areas of these cities that were targeted for systematic disinvestment in the past have higher mean land surface temperatures than those that received more favorable ratings. Poor and minority residents are also overrepresented in formerly redlined areas in each of the three study cities.Takeaway for practice: By examining areas that have experienced sustained disinvestment, cities may be able to more quickly narrow the focus of heat mitigation planning efforts while furthering social equity. Efforts to mitigate the negative impacts of rising temperatures in U.S. cities must be tailored to the local climate, built environment, and sociodemographic history. Finally, geospatial data sets that document historical policies are useful for centering and redressing current inequalities when viewed through an equity planning lens.
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Metadata
- Authors
- Wilson, B.
- Affiliations
- University of Virginia (US)
- Corresponding author
- Wilson, Bev
- ORCID
- Wilson, Bev
- Venue
- Journal of the American Planning Association
- Publisher
- Informa UK Limited
- Published
- 2020-05-22
- Volume
- 86
- Issue
- 4
- Pages
- 443-457
- ISSN
- 0194-4363 (ISSN-L)
- 1939-0130
- OpenAlex
- W3028418816
- Cited by
- 285
- References
- 94
- Citations by year
- 2026: 32 · 2025: 54 · 2024: 68 · 2023: 72 · 2022: 40 · 2021: 17 · 2020: 2
- FWCI
- 43.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Planning Association (publishedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare
- Topics
- Social Sciences › Sociology and Political Science › Environmental Justice and Health Disparities
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Social Sciences › Urban Studies › Urban Planning and Governance
- Keywords
- urban heat, redlining, divestiture, land surface temperature, heat vulnerability, social equity, equity planning, racial and socioeconomic disparities, Baltimore, Dallas, Kansas City
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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