支持环境正义的地球观测数据:将未经许可的家禽业务与社会脆弱性指数联系起来
Mirela G Tulbure1, Júlio Caineta1, Brooke Cox1
1Center for Geospatial Analytics North Carolina State University Raleigh NC USA.
GeoHealth
|December 19, 2024
概括
这项研究使用人工智能和地球观测数据准确地绘制了家禽集中动物养操作 (CAFO). 调查结果显示,家禽CAFO经常聚集在脆弱的社区,突出了潜在的环境正义问题.
科学领域:
- 环境科学 环境科学
- 环境正义 环境正义
- 地理空间分析是什么
背景情况:
- 集中的动物养作业 (CAFO) 产生大量的废物,对环境健康构成风险,特别是对弱势社区.
- 现有的CAFO影响研究经常使用不完整的公共记录,低估了操作的范围.
- 无联邦许可经营并产生干废的家禽CAFO在很大程度上没有记录环境正义 (EJ) 影响.
研究的目的:
- 使用地球观测 (EO) 数据和深度学习准确地绘制家禽CAFO位置,解决未经许可的设施中的数据缺口.
- 使用社会脆弱性指数 (SVI) 评估家禽CAFO密度和社区脆弱性之间的空间关系.
- 提高对与家禽CAFO相关的环境正义影响的理解.
主要方法:
- 精制家禽CAFO位置使用文学衍生的启发式结合EO数据和深度学习算法.
- 应用空间分析技术,包括局部空间协会指标 (LISA),以识别聚类模式.
- 通过测量错误分类特征的减少来量化数据准确性的改进.
主要成果:
- 将家禽CAFO密度的高估值降低了54%,并删除了美国各地超过50%的错误分类特征.
- 在有高社会脆弱性指数 (SVI) 评分的人口普查区中确定了家禽CAFO的显著集群.
- 发现北卡罗来纳州高密度家禽CAFO片区的三分之一也表现出高SVI,特别是在东部农村地区.
结论:
- 使用人工智能准确地绘制未经许可的家禽CAFO,对于了解它们对环境正义的影响至关重要.
- 禽类CAFO不成比例地位于脆弱的社区,这表明了潜在的环境正义问题.
- 需要进一步的研究和全面的数据来充分评估和解决家禽CAFO对环境和社会的影响.
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