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邻里环境与平均人口步行的相关性:利用聚合的、匿名的手机数据来确定人们在哪里行走。

Neighbourhood correlates of average population walking: using aggregated, anonymised mobile phone data to identify where people walk.

机构信息

School of Social Sciences, The University of Queensland, Brisbane, 4072, Australia.

School of Earth and Environmental Sciences, The University of Queensland, Queensland, 4072, Australia.

出版信息

Health Place. 2022 Sep;77:102892. doi: 10.1016/j.healthplace.2022.102892. Epub 2022 Aug 13.

Abstract

Understanding and monitoring socio-spatial patterns of population walking mobility can inform urban planning and geographically targeted health promotion strategies aimed at increasing population levels of physical activity. In this study we use aggregated, anonymous mobile phone mobility data to examine the association between neighbourhood physical and social characteristics and residents' weekly walking behaviour across 313 neighbourhoods in a large metropolitan region of Queensland, Australia. We find that residents in neighbourhoods that are highly fragmented by streets with speed limits above 50 kmph, residents in neighbourhoods with high retail density and those living is economically disadvantaged neighbourhoods walk fewer kilometres and minutes on average per week than their counterparts. These findings can inform urban planning policy on the minimum specifications required in newly developing neighbourhoods and provide targets for retro-fitting features into existing neighbourhoods.

摘要

了解和监测人口步行流动性的社会空间模式,可以为城市规划和有针对性的地理健康促进策略提供信息,以提高人口的身体活动水平。在这项研究中,我们使用聚合的、匿名的移动电话流动数据,来研究澳大利亚昆士兰州一个大城市地区 313 个街区的邻里物理和社会特征与居民每周步行行为之间的关系。我们发现,与那些限速在 50 公里/小时以上的街道高度分割的街区、零售密度高的街区以及经济条件差的街区的居民相比,他们每周平均步行的公里数和分钟数更少。这些发现可以为新开发街区的最低规格要求提供城市规划政策信息,并为现有街区的功能改造提供目标。

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