Post-wildfire neighborhood change: Evidence from the 2018 camp fire

IF 7.9 1区 环境科学与生态学 Q1 ECOLOGY Landscape and Urban Planning Pub Date : 2024-03-26 DOI:10.1016/j.landurbplan.2023.104997
Kathryn McConnell , Christian V. Braneon
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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.

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野火后的邻里变化:来自 2018 年营火的证据
随着破坏性极强的野火数量不断增加,了解受火灾影响地区的长期变化变得越来越重要。结合社会科学和生物物理科学的方法,我们记录了 2018 年美国营地大火后两种形式的邻里变化,研究了大火影响范围内的 17000 多座住宅建筑。我们发现,活动房屋或汽车房、价值较低的住宅以及业主不在的住宅被毁坏的概率明显较高,这为住房存量的筛选提供了证据,从而促进了物理损害的社会分层模式。虽然建筑密度和与附近道路的距离可以解释建筑价值与破坏概率之间的关系,但建筑类型仍然是结构损失的一个独立预测因素,我们无法通过在模型中添加环境协变量来完全解释这种关系。利用地理空间机器学习技术,我们确定了火灾发生 20 个月后在烧毁范围内重建的建筑物。我们发现,重建的建筑物更有可能在火灾前为业主自住,而且火灾前的平均房产价值更高,这表明成本负担的绅士化模式正在形成。我们的研究结果表明,将建筑环境作为社会不均衡灾害影响的驱动因素进行研究非常重要。我们需要为移动和机动房屋居民、租房者、低收入居民和密集社区制定野火减灾策略。
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来源期刊
Landscape and Urban Planning
Landscape and Urban Planning 环境科学-生态学
CiteScore
15.20
自引率
6.60%
发文量
232
审稿时长
6 months
期刊介绍: Landscape and Urban Planning is an international journal that aims to enhance our understanding of landscapes and promote sustainable solutions for landscape change. The journal focuses on landscapes as complex social-ecological systems that encompass various spatial and temporal dimensions. These landscapes possess aesthetic, natural, and cultural qualities that are valued by individuals in different ways, leading to actions that alter the landscape. With increasing urbanization and the need for ecological and cultural sensitivity at various scales, a multidisciplinary approach is necessary to comprehend and align social and ecological values for landscape sustainability. The journal believes that combining landscape science with planning and design can yield positive outcomes for both people and nature.
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