Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning
Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning
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DOI：
10.1136/injuryprev-2021-044412
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发表时间：
2021-12-07
影响因子：
3.7
通讯作者：
Goldstick, Jason
中科院分区：
文献类型：
--
作者：
Jay, Jonathan;de Jong, Jorrit;Goldstick, Jason
目的拆除废弃的建筑物可以减少附近的枪支暴力。然而，这些影响可能在不同的城市和不同的时间尺度上有所不同。我们的目标是使用一种结合机器学习和航空图像的新方法，确定拆毁对枪支暴力影响的潜在缓和因素。方法统计2000年至2020年纽约州罗切斯特发生的致命和非致命枪击事件的年度数量。治疗方法是2009年至2019年进行的拆迁。分析单位为152x152米网格正方形。我们使用了差异法来测试效果：(A)每次拆迁后的一年，(B)随着时间的推移拆迁累积。作为主持人，我们使用了通过使用卷积神经网络从航空图像中提取信息而生成的建筑环境类型，这是一种深度学习方法，与k-均值聚类相结合。我们根据构建环境集群对我们的主要模型进行分层，以测试其适宜性。结果1次拆除可减少14%的枪击事件(发生率比(IRR)=0.86，95%可信区间(CI)0.83~0.90，P
Purpose Demolishing abandoned buildings has been found to reduce nearby firearm violence. However, these effects might vary within cities and across time scales. We aimed to identify potential moderators of the effects of demolitions on firearm violence using a novel approach that combined machine learning and aerial imagery. Methods Outcomes were annual counts of fatal and non-fatal shootings in Rochester, New York, from 2000 to 2020. Treatment was demolitions conducted from 2009 to 2019. Units of analysis were 152x152 m grid squares. We used a difference-in-differences approach to test effects: (A) the year after each demolition and (B) as demolitions accumulated over time. As moderators, we used a built environment typology generated by extracting information from aerial imagery using convolutional neural networks, a deep learning approach, combined with k-means clustering. We stratified our main models by built environment cluster to test for moderation. Results One demolition was associated with a 14% shootings reduction (incident rate ratio (IRR)=0.86, 95% CI 0.83 to 0.90, p