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A global map of travel time to cities to assess inequalities in accessibility in 2015

机译:2015年前往城市的全球旅行时间地图,以评估可及性方面的不平等

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摘要

The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved livelihoods and overall development. Advancing accessibility worldwide underpins the equity agenda of 'leaving no one behind' established by the Sustainable Development Goals of the United Nations(1). This has renewed international efforts to accurately measure accessibility and generate a metric that can inform the design and implementation of development policies. The only previous attempt to reliably map accessibility worldwide, which was published nearly a decade ago(2), predated the baseline for the Sustainable Development Goals and excluded the recent expansion in infrastructure networks, particularly in lower-resource settings. In parallel, new data sources provided by Open Street Map and Google now capture transportation networks with unprecedented detail and precision. Here we develop and validate a map that quantifies travel time to cities for 2015 at a spatial resolution of approximately one by one kilometre by integrating ten global-scale surfaces that characterize factors affecting human movement rates and 13,840 high-density urban centres within an established geospatial-modelling framework. Our results highlight disparities in accessibility relative to wealth as 50.9% of individuals living in low-income settings (concentrated in sub-Saharan Africa) reside within an hour of a city compared to 90.7% of individuals in high-income settings. By further triangulating this map against socioeconomic datasets, we demonstrate how access to urban centres stratifies the economic, educational, and health status of humanity.
机译:维持人类福祉的经济和人为资源在世界范围内分布不均,而是高度集中在城市中。城市中心提供的机会和服务的获取不畅(距离,交通基础设施和城市空间分布的函数)是改善生计和总体发展的主要障碍。在世界范围内促进无障碍获取是联合国可持续发展目标(1)确立的“不让任何人落后”的公平议程的基础。这已经重新进行了国际上的努力,以准确衡量可及性并生成可为发展政策的设计和实施提供信息的度量。近十年前发布的唯一一次可靠地映射全球可访问性的尝试(2),早于可持续发展目标的基准,并且排除了最近基础架构网络的扩展,特别是在资源较少的环境中。同时,Open Street Map和Google提供的新数据源现在以前所未有的细节和精度捕获了交通网络。在这里,我们开发并验证了一张地图,该地图通过整合十个表征影响人类移动速度的因素的全球尺度表面和已建立的地理空间内的13,840个高密度城市中心,以大约1公里的空间分辨率量化了2015年前往城市的时间建模框架。我们的研究结果突出表明,相对于财富而言,可及性方面存在差异,因为低收入地区(集中在撒哈拉以南非洲地区)的个人中有50.9%居住在城市的一个小时内,而高收入地区的这一比例为90.7%。通过进一步将此地图与社会经济数据集进行三角剖分,我们证明了进入城市中心如何将人类的经济,教育和健康状况分层。

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  • 来源
    《Nature》 |2018年第7688期|333-336|共4页
  • 作者单位

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Twente, Dept Nat Resources, Fac Geoinformat Sci & Earth Observat ITC, POB 217, NL-7500 AE Enschede, Netherlands;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    European Commiss, Joint Res Ctr, Unit Knowledge Sustainable Dev & Food Secur D6, Via Fermi 2749, I-21027 Ispra, Varese, Italy;

    European Commiss, Joint Res Ctr, Unit Knowledge Sustainable Dev & Food Secur D6, Via Fermi 2749, I-21027 Ispra, Varese, Italy;

    Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA;

    Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA;

    Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA;

    Vizzuality, Off D, Gwydir St, Cambridge CB1 2LJ, England;

    Univ Washington, Inst Hlth Metr & Evaluat, 2301 5th Ave,Suite 600, Seattle, WA 98121 USA;

    Univ Queensland, Ctr Biodivers & Conservat Sci, Sch Biol Sci, St Lucia, Qld 4072, Australia;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

    Imperial Coll London, Dept Infect Dis Epidemiol, London W2 1PG, England;

    Univ Oxford, Malaria Atlas Project, Big Data Inst, Nuffield Dept Med, Roosevelt Dr, Oxford OX3 7FY, England;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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