Neighborhood street activity and greenspace usage uniquely contribute to predicting crime

Neighborhood street activity and greenspace usage uniquely contribute to predicting crime
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邻里街道活动和绿地使用对预测犯罪有独特贡献

DOI:
10.1038/s42949-020-00005-7
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发表时间:
2021
期刊:
npj Urban Sustainability
影响因子:
--
通讯作者:
Berman, Marc G.
Berman, Marc G.
中科院分区:
--
文献类型:
--
作者:
Schertz, Kathryn E.;Saxon, James;Cardenas-Iniguez, Carlos;Bettencourt, Luís M.;Ding, Yi;Hoffmann, Henry;Berman, Marc G.

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犯罪是一个代价高昂的社会问题。虽然影响城市犯罪的因素很多,但一个研究较少但可能很重要的因素是社区绿地。研究表明,绿地往往与犯罪负相关。衡量居民对绿地的使用,而不仅仅是物理存在,对于理解这种联系至关重要。在这里,我们使用手机移动数据来量化芝加哥和纽约市的当地街道活动和公园参观情况。我们发现,在控制社会人口因素的同时,这两个因素都与犯罪负相关。每个因素都解释了独特的差异,表明街道活动和绿地对犯罪的影响有多种途径。物理树冠与犯罪的关联较小,在芝加哥只是一个重要的预测因子。探索性的有向无环图模型进一步支持了这些发现,该模型发现了公园参观和街头活动与犯罪的不同直接路径。
Crime is a costly societal issue. While many factors influence urban crime, one less-studied but potentially important factor is neighborhood greenspace. Research has shown that greenspace is often negatively associated with crime. Measuring residents’ use of greenspace, as opposed to mere physical presence, is critical to understanding this association. Here, we used cell phone mobility data to quantify local street activity and park visits in Chicago and New York City. We found that both factors were negatively associated with crime, while controlling for socio-demographic factors. Each factor explained unique variance, suggesting multiple pathways for the influence of street activity and greenspace on crime. Physical tree canopy had a smaller association with crime, and was only a significant predictor in Chicago. These findings were further supported by exploratory directed acyclic graph modeling, which found separate direct paths for both park visits and street activity to crime.
DOI: --
发表时间: 2019
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