PDF(Pubmed)
Abstract：
OBJECTIVE: Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC).
METHODS: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level.
RESULTS: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC.
CONCLUSIONS: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.
METHODS: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level.
RESULTS: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC.
CONCLUSIONS: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.
摘要：
目标：交通相关暴露，比如空气污染和噪音，对人类健康有不利影响，尤其是在城市地区。然而,在理解社区遣散的影响方面仍然存在重要的研究和知识差距，道路基础设施和机动道路交通所施加的物理隔离措施，限制对货物的访问，服务,或者社会关系，破坏社会结构，并可能对健康产生不利影响。我们旨在有力地量化城市环境中的社区遣散指标，例如其在纽约市（NYC）的表征。
方法：我们使用地理空间位置数据和降维技术来捕获纽约市社区遣散变化。我们采用了主成分追求，模式识别算法,结合因子分析作为一种新的方法来估计社区戒断指数。我们在道路基础设施的人口普查区块组（CBG）级别使用了2019年的公共数据，道路交通活动,和行人基础设施。作为社区遣散指数的示范性应用，我们调查了社区遣散和交通碰撞之间的关联，作为道路安全的代表，2019年在纽约市处于CBG水平。
结果：我们的数据揭示了一个与社区遣散相关的多维因素，解释了74％的数据变化。在调整后的分析中，一般的交通碰撞，特别是那些涉及行人或骑自行车的人，与纽约市社区遣散指数的增加呈非线性相关。
结论：我们使用全国可用的数据为纽约市开发了高空间分辨率的社区遣散指数，使其在美国其他城市的复制变得可行。我们的发现表明，整个CBG的社区遣散指数的增加可能与纽约市交通碰撞的增加有关。社区遣散费指数，这提供了一个新颖的交通相关的曝光，可用于为减轻健康风险和增进福祉的公平城市政策提供信息。
方法：我们使用地理空间位置数据和降维技术来捕获纽约市社区遣散变化。我们采用了主成分追求，模式识别算法,结合因子分析作为一种新的方法来估计社区戒断指数。我们在道路基础设施的人口普查区块组（CBG）级别使用了2019年的公共数据，道路交通活动,和行人基础设施。作为社区遣散指数的示范性应用，我们调查了社区遣散和交通碰撞之间的关联，作为道路安全的代表，2019年在纽约市处于CBG水平。
结果：我们的数据揭示了一个与社区遣散相关的多维因素，解释了74％的数据变化。在调整后的分析中，一般的交通碰撞，特别是那些涉及行人或骑自行车的人，与纽约市社区遣散指数的增加呈非线性相关。
结论：我们使用全国可用的数据为纽约市开发了高空间分辨率的社区遣散指数，使其在美国其他城市的复制变得可行。我们的发现表明，整个CBG的社区遣散指数的增加可能与纽约市交通碰撞的增加有关。社区遣散费指数，这提供了一个新颖的交通相关的曝光，可用于为减轻健康风险和增进福祉的公平城市政策提供信息。
