Evidence map›Paper›PMID 38828213›Full record

ArticleLandscape and urban planning2024

Post-wildfire neighborhood change: evidence from the 2018 Camp Fire.

Kathryn McConnell, Christian V Braneon

Abstract read
In one paragraph

Article in Landscape and urban planning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kathryn McConnellPopulation Studies and Training Center, Brown University.ORCID 0000-0003-4395-5483
Christian V BraneonColumbia Climate School, CUNY Institute for Demographic Research (CIDR), Carbon Direct, USA.

Funding

Science/Technical CoreP2CHD041020 · NICHD · BROWN UNIVERSITY · PI Margot Jackson · 2016 to 2026
$7.6M
Spatial Analysis CoreR24HD041020 · NICHD · BROWN UNIVERSITY · PI FOSTER, ANDREW D. · 2001 to 2016
$7.4M
NICHD NIH HHS P2C HD041020NICHD NIH HHS R24 HD041020
6 · The paper itself

Abstract

As the number of highly destructive wildfires grows, it is increasingly important to understand the long-term changes that occur to fire-affected places. Integrating approaches from social and biophysical science, we document two forms of neighborhood change following the 2018 Camp Fire in the United States, examining the more than 17,000 residential structures within the burn footprint. We found that mobile or motor homes, lower-value residences, and absentee owner residences had a significantly higher probability of being destroyed, providing evidence that housing stock filtering facilitated socially stratified patterns of physical damage. While the relationship between building value and destruction probability could be explained by measures of building density and distance to nearby roads, building type remained an independent predictor of structure loss that we could not fully explain by adding environmental covariates to our models. Using a geospatial machine learning technique, we then identified buildings that had been reconstructed within the burn footprint 20 months after the fire. We found that reconstructed buildings were more likely to have been owner-occupied prior to the fire and had higher average pre-fire property value, suggesting an emerging pattern of cost-burden gentrification. Our findings illustrate the importance of examining the built environment as a driver of socially uneven disaster impacts. Wildfire mitigation strategies are needed for mobile and motor home residents, renters, low-income residents, and dense neighborhoods.

Indexed as

built environmentdisastergentrificationlandscape changeneighborhood changeWildfire

Identifiers

PMID38828213
PMCPMC11139020

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.