此项目基于 Weiss 等人于 2018 年发表的先前研究成果 (doi:10.1038/nature25181)。
Weiss 等人 (2018) 使用了道路（首次在全球范围内使用 Open Street Map 和 Google 道路数据集）、铁路、河流、湖泊、海洋、地形条件（坡度和海拔）、土地覆盖类型和国界方面的数据集。这些数据集均分配了出行速度（以穿越每种像素类型所需的时间表示）。然后，将这些数据集组合起来，生成“摩擦表面”：一张地图，其中每个像素都根据该像素内出现的类型分配了一个名义上的总体出行速度。对于当前项目，我们创建了更新的摩擦系数表面，以纳入 OSM 道路数据中的最新改进。
最少费用路径算法（在 Google Earth Engine 中运行，对于高纬度地区，则在 R 中运行）与此摩擦表面结合使用，用于计算从所有位置到最近（按时间计算）医疗机构的出行时间。医疗机构数据集使用了两个最大的全球数据库中的位置数据：(1) 在 www.healthsites.io 上整理并可供下载的 OSM 数据；(2) 从 Google 地图中提取的数据。全球数据集已扩充，纳入了最近针对非洲和澳大利亚发布的大陆级设施位置数据。为了便于比较不同数据源，我们仅使用了定义为医院和诊所的医疗机构。系统会合并同一像素内的多个点，以匹配分析的分辨率（由所选的地球表面网格化表示形式定义）。因此，可访问性地图中的每个像素都表示从相应位置到医院或诊所的最短时间（以分钟为单位）。
D.J. Weiss、A. Nelson, C.A. Vargas-Ruiz, K. Gligorić, S. Bavadekar、E. Gabrilovich, A. Bertozzi-Villa, J. Rozier, H.S. Gibson, T. Shekel,
C. Kamath, A. Lieber, K. Schulman, Y. Shao、V. Qarkaxhija、A.K. Nandi、S.H. Keddie、S. Rumisha, E. Cameron, K.E. Battle, S. Bhatt, P.W. Gething.
前往医疗机构的出行时间全球地图。Nature Medicine（2020 年）。
[null,null,[],[[["\u003cp\u003eThis dataset provides a global map of travel time to the nearest hospital or clinic, including both overall and walking-only travel times.\u003c/p\u003e\n"],["\u003cp\u003eThe data covers areas between 85 degrees north and 60 degrees south for the year 2019, with a resolution of 927.67 meters.\u003c/p\u003e\n"],["\u003cp\u003eThe map was created using a friction surface model and least-cost-path algorithms, incorporating data from OpenStreetMap, Google Maps, and other sources.\u003c/p\u003e\n"],["\u003cp\u003eHealthcare facility locations were sourced from healthsites.io, Google Maps, and other continental-scale datasets, focusing on hospitals and clinics.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is licensed under a Creative Commons Attribution 4.0 International License.\u003c/p\u003e\n"]]],[],null,["# Accessibility to Healthcare 2019\n\nDataset Availability\n: 2019-01-01T00:00:00Z--2020-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Malaria Atlas Project](https://malariaatlas.org/research-project/accessibility-to-cities/)\n\nTags\n:\n[accessibility](/earth-engine/datasets/tags/accessibility) [jrc](/earth-engine/datasets/tags/jrc) [map](/earth-engine/datasets/tags/map) [oxford](/earth-engine/datasets/tags/oxford) [population](/earth-engine/datasets/tags/population) [twente](/earth-engine/datasets/tags/twente) \n\n#### Description\n\nThis global accessibility map enumerates land-based travel time (in\nminutes) to the nearest hospital or clinic for all areas between 85\ndegrees north and 60 degrees south for a nominal year 2019. It also\nincludes \"walking-only\" travel time, using non-motorized means of\ntransportation only.\n\nMajor data collection efforts underway by OpenStreetMap, Google Maps, and\nacademic researchers have been harnessed to compile the most complete\ncollection of healthcare facility locations to date. This map was\nproduced through a collaboration between MAP (University of Oxford),\nTelethon Kids Institute (Perth, Australia), Google, and the University\nof Twente, Netherlands.\n\nThis project builds on previous work published by Weiss et al 2018\n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)).\nWeiss et al (2018) utilised datasets for roads\n(comprising the first ever global-scale use of Open Street Map and Google\nroads datasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders. These\ndatasets were each allocated a speed or speeds of travel in terms of time\nto cross each pixel of that type. The datasets were then combined to\nproduce a \"friction surface\": a map where every pixel is allocated a\nnominal overall speed of travel based on the types occurring within that\npixel. For the current project, an updated friction surface was created to\nincorporate recent improvements within OSM roads data.\n\nLeast-cost-path algorithms (run in Google Earth Engine and, for\nhigh-latitude areas, in R) were used in conjunction with this friction\nsurface to calculate the time of travel from all locations to the nearest\n(in time) healthcare facility. The healthcare facilities dataset utilized\nlocation data from two of the largest global databases: (1) OSM data that\nwas collated and made available for download at\n[www.healthsites.io](https://www.healthsites.io/); and (2) data\nextracted from Google Maps. The global datasets were augmented with\ncontinental-scale facility locations that were recently published for\nAfrica and Australia. To facilitate comparisons between data sources, only\nfacilities defined as hospitals and clinics were used. Multiple points\nfound within the same pixel were merged to match the resolution of the\nanalysis as defined by the selected gridded representation of the Earth's\nsurface. Each pixel in the resultant accessibility map thus represents the\nmodelled shortest time (in minutes) from that location to a hospital or\nclinic.\n\nSource dataset credits are as described in the accompanying paper.\n\n### Bands\n\n\n**Pixel Size**\n\n927.67 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|------------------------------|-------|-----|---------|------------|------------------------------------------------------------------------------|\n| `accessibility` | min | 0 | 41504.1 | meters | Travel time to the nearest hospital or clinic. |\n| `accessibility_walking_only` | min | 0 | 138893 | meters | Travel time to the nearest hospital or clinic using non-motorized transport. |\n\n### Terms of Use\n\n**Terms of Use**\n\nThis work is licensed under a [Creative Commons Attribution\n4.0 International License](https://creativecommons.org/licenses/by/4.0/).\n\n### Citations\n\nCitations:\n\n- D.J. Weiss, A. Nelson, C.A. Vargas-Ruiz, K. Gligorić, S. Bavadekar,\n E. Gabrilovich, A. Bertozzi-Villa, J. Rozier, H.S. Gibson, T. Shekel,\n C. Kamath, A. Lieber, K. Schulman, Y. Shao, V. Qarkaxhija, A.K. Nandi,\n S.H. Keddie, S. Rumisha, E. Cameron, K.E. Battle, S. Bhatt, P.W. Gething.\n Global maps of travel time to healthcare facilities. Nature Medicine (2020).\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.Image('Oxford/MAP/accessibility_to_healthcare_2019');\nvar accessibility = dataset.select('accessibility');\nvar accessibilityVis = {\n min: 0.0,\n max: 41556.0,\n gamma: 4.0,\n};\nMap.setCenter(18.98, 6.66, 2);\nMap.addLayer(accessibility, accessibilityVis, 'Accessibility');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_accessibility_to_healthcare_2019) \n[Accessibility to Healthcare 2019](/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_healthcare_2019) \nThis global accessibility map enumerates land-based travel time (in minutes) to the nearest hospital or clinic for all areas between 85 degrees north and 60 degrees south for a nominal year 2019. It also includes \"walking-only\" travel time, using non-motorized means of transportation only. Major data collection efforts underway by ... \nOxford/MAP/accessibility_to_healthcare_2019, accessibility,jrc,map,oxford,population,twente \n2019-01-01T00:00:00Z/2020-01-01T00:00:00Z \n-60 -180 85 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://malariaatlas.org/research-project/accessibility-to-cities/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_healthcare_2019)"]]
