Peta aksesibilitas global ini mencantumkan waktu perjalanan berbasis darat (dalam menit) ke rumah sakit atau klinik terdekat untuk semua area antara 85 derajat utara dan 60 derajat selatan untuk tahun nominal 2019. Waktu ini juga
mencakup waktu perjalanan "khusus berjalan kaki", hanya menggunakan alat transportasi
non-bermotor.
Upaya pengumpulan data besar yang dilakukan oleh OpenStreetMap, Google Maps, dan peneliti akademis telah dimanfaatkan untuk mengumpulkan koleksi lokasi fasilitas kesehatan terlengkap hingga saat ini. Peta ini dibuat melalui kolaborasi antara MAP (University of Oxford), Telethon Kids Institute (Perth, Australia), Google, dan University of Twente, Belanda.
Project ini dibangun berdasarkan pekerjaan sebelumnya yang dipublikasikan oleh Weiss et al 2018
(doi:10.1038/nature25181).
Weiss et al (2018) menggunakan set data untuk jalan (yang terdiri dari penggunaan set data jalan Google dan Open Street Map skala global pertama), jalur kereta api, sungai, danau, lautan, kondisi topografi (kemiringan dan elevasi), jenis penutup lahan, dan batas negara. Setiap set data ini dialokasikan kecepatan perjalanan dalam hal waktu untuk melintasi setiap piksel jenis tersebut. Kemudian, set data digabungkan untuk menghasilkan "permukaan gesekan": peta yang setiap pikselnya dialokasikan kecepatan perjalanan keseluruhan nominal berdasarkan jenis yang terjadi dalam piksel tersebut. Untuk project saat ini, permukaan gesekan yang diperbarui dibuat untuk
menggabungkan peningkatan terbaru dalam data jalan OSM.
Algoritma jalur biaya terendah (dijalankan di Google Earth Engine dan, untuk area lintang tinggi, di R) digunakan bersama dengan permukaan gesekan ini untuk menghitung waktu perjalanan dari semua lokasi ke fasilitas kesehatan terdekat (dalam waktu). Set data fasilitas kesehatan menggunakan data lokasi dari dua database global terbesar: (1) data OSM yang dikumpulkan dan tersedia untuk didownload di www.healthsites.io; dan (2) data yang diekstrak dari Google Maps. Kumpulan data global dilengkapi dengan lokasi fasilitas skala benua yang baru-baru ini dipublikasikan untuk Afrika dan Australia. Untuk memfasilitasi perbandingan antara sumber data, hanya fasilitas yang ditentukan sebagai rumah sakit dan klinik yang digunakan. Beberapa titik
yang ditemukan dalam piksel yang sama digabungkan agar sesuai dengan resolusi
analisis sebagaimana ditentukan oleh representasi berpetak yang dipilih dari
permukaan Bumi. Oleh karena itu, setiap piksel dalam peta aksesibilitas yang dihasilkan merepresentasikan waktu tersingkat yang dimodelkan (dalam menit) dari lokasi tersebut ke rumah sakit atau klinik.
Kredit set data sumber adalah seperti yang dijelaskan dalam makalah yang menyertainya.
Band
Ukuran Piksel 927,67 meter
Band
Nama
Unit
Min
Maks
Ukuran Piksel
Deskripsi
accessibility
mnt
0
41504.1
meter
Waktu tempuh ke rumah sakit atau klinik terdekat.
accessibility_walking_only
mnt
0
138893
meter
Waktu tempuh ke rumah sakit atau klinik terdekat menggunakan transportasi non-bermotor.
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.
Peta global waktu perjalanan menuju fasilitas layanan kesehatan. Nature Medicine (2020).
Peta aksesibilitas global ini mencantumkan waktu perjalanan berbasis darat (dalam menit) ke rumah sakit atau klinik terdekat untuk semua area antara 85 derajat utara dan 60 derajat selatan untuk tahun nominal 2019. Waktu tempuh "khusus jalan kaki" juga disertakan, hanya menggunakan transportasi non-bermotor. Upaya pengumpulan data besar-besaran sedang dilakukan oleh …
[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)"]]
