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  • 500+ Downloads
    Updated December 4, 2020 | Dataset date: Oct 15, 2020
    This dataset updates: Every week
    This bucket contains FAIR COVID-19 US county level forecast data
  • Updated December 4, 2020 | Dataset date: Oct 22, 2019
    This data is by request only
    Afghanistan administrative level 0 (country), 1 (province), and 2 (district), and UNAMA region boundary polygon, line, and point shapefiles, KMZ files, geodatabase and gazetteer. REFERENCE YEAR: 2019
  • 80+ Downloads
    Updated December 4, 2020 | Dataset date: Sep 6, 2019
    This dataset updates: Every year
    Moldova administrative level 0 (country) and 1 (district, municipality or autonomous region) boundaries REFERENCE YEAR: 2019
  • 3500+ Downloads
    Updated Live | Dataset date: Dec 1, 2019-Dec 4, 2020
    This dataset updates: Live
    Data Overview This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019") You may not use this data for commercial purposes. If there is a need for commercial use of the data, please contact Metabiota at info@metabiota.com to obtain a commercial use license. The incidence data are in a CSV file format. One row in an incidence file contains a piece of epidemiological data extracted from the specified source. The file contains data from multiple sources at multiple spatial resolutions in cumulative and non-cumulative formats by confirmation status. To select a single time series of case or death data, filter the incidence dataset by source, spatial resolution, location, confirmation status, and cumulative flag. Data are collected, structured, and validated by Metabiota’s digital surveillance experts. The data structuring process is designed to produce the most reliable estimates of reported cases and deaths over space and time. The data are cleaned and provided in a uniform format such that information can be compared across multiple sources. Data are collected at the time of publication in the highest geographic and temporal resolutions available in the original report. This repository is intended to provide a single access point for data from a wide range of data sources. Data will be updated periodically with the latest epidemiological data. Metabiota maintains a database of epidemiological information for over two thousand high-priority infectious disease events. Please contact us (info@metabiota.com) if you are interested in licensing the complete dataset. Cumulative vs. Non-Cumulative Incidence Reporting sources provide either cumulative incidence, non-cumulative incidence, or both. If the source only provides a non-cumulative incidence value, the cumulative values are inferred using prior reports from the same source. Use the CUMULATIVE FLAG variable to subset the data to cumulative (TRUE) or non-cumulative (FALSE) values. Case Confirmation Status The incidence datasets include the confirmation status of cases and deaths when this information is provided by the reporting source. Subset the data by the CONFIRMATION_STATUS variable to either TOTAL, CONFIRMED, SUSPECTED, or PROBABLE to obtain the data of your choice. Total incidence values include confirmed, suspected, and probable incidence values. If a source only provides suspected, probable, or confirmed incidence, the total incidence is inferred to be the sum of the provided values. If the report does not specify confirmation status, the value is included in the "total" confirmation status value. The data provided under the "Metabiota Composite Source" often does not include suspected incidence due to inconsistencies in reporting cases and deaths with this confirmation status. Outcome - Cases vs. Deaths The incidence datasets include cases and deaths. Subset the data to either CASE or DEATH using the OUTCOME variable. It should be noted that deaths are included in case counts. Spatial Resolution Data are provided at multiple spatial resolutions. Data should be subset to a single spatial resolution of interest using the SPATIAL_RESOLUTION variable. Information is included at the finest spatial resolution provided to the original epidemic report. We also aggregate incidence to coarser geographic resolutions. For example, if a source only provides data at the province-level, then province-level data are included in the dataset as well as country-level totals. Users should avoid summing all cases or deaths in a given country for a given date without specifying the SPATIAL_RESOLUTION value. For example, subset the data to SPATIAL_RESOLUTION equal to “AL0” in order to view only the aggregated country level data. There are differences in administrative division naming practices by country. Administrative levels in this dataset are defined using the Google Geolocation API (https://developers.google.com/maps/documentation/geolocation/). For example, the data for the 2019-nCoV from one source provides information for the city of Beijing, which Google Geolocations indicates is a “locality.” Beijing is also the name of the municipality where the city Beijing is located. Thus, the 2019-nCoV dataset includes rows of data for both the city Beijing, as well as the municipality of the same name. If additional cities in the Beijing municipality reported data, those data would be aggregated with the city Beijing data to form the municipality Beijing data. Sources Data sources in this repository were selected to provide comprehensive spatiotemporal data for each outbreak. Data from a specific source can be selected using the SOURCE variable. In addition to the original reporting sources, Metabiota compiles multiple sources to generate the most comprehensive view of an outbreak. This compilation is stored in the database under the source name “Metabiota Composite Source.” The purpose of generating this new view of the outbreak is to provide the most accurate and precise spatiotemporal data for the outbreak. At this time, Metabiota does not incorporate unofficial - including media - sources into the “Metabiota Composite Source” dataset. Quality Assurance Data are collected by a team of digital surveillance experts and undergo many quality assurance tests. After data are collected, they are independently verified by at least one additional analyst. The data also pass an automated validation program to ensure data consistency and integrity. NonCommercial Use License Creative Commons License Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) This is a human-readable summary of the Legal Code. You are free: to Share — to copy, distribute and transmit the work to Remix — to adapt the work Under the following conditions: Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial — You may not use this work for commercial purposes. Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. With the understanding that: Waiver — Any of the above conditions can be waived if you get permission from the copyright holder. Public Domain — Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license. Other Rights — In no way are any of the following rights affected by the license: Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; The author's moral rights; Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. Notice — For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to this web page. For details and the full license text, see http://creativecommons.org/licenses/by-nc-sa/3.0/ Liability Metabiota shall in no event be liable for any decision taken by the user based on the data made available. Under no circumstances, shall Metabiota be liable for any damages (whatsoever) arising out of the use or inability to use the database. The entire risk arising out of the use of the database remains with the user.
  • 5400+ Downloads
    Updated December 4, 2020 | Dataset date: Mar 10, 2020-Dec 3, 2020
    This dataset updates: Every day
    This data has been collected from various sources and is displayed in this online dashboard: http://arcg.is/uHyuO Mobile version: http://arcg.is/0q8Xfj The data is divided in two datasets: COVID-19 restrictions by country: This dataset shows current travel restrictions. Information is collected from various sources: IATA, media, national sources, WFP internal or any other. COVID-19 airline restrictions information: This dataset shows restrictions taken by individual airlines or country. Information is collected again from various sources including WFP internal and public sources. The data displayed is a collaborative effort and anybody with more accurate/updated information is highly encouraged to contact WFP GIS unit for Emergencies at the following email address: hq.gis@wfp.org
  • 40+ Downloads
    Updated December 4, 2020 | Dataset date: Apr 29, 2016
    This dataset updates: As needed
    No abstract provided
  • 80+ Downloads
    Updated December 4, 2020 | Dataset date: Jan 1, 2013-Dec 31, 2013
    This dataset updates: As needed
    Flood extent in 2013 Original dataset title: Cambodia: Flood Extent in 2013
  • 10+ Downloads
    Updated December 2, 2020 | Dataset date: Dec 2, 2020
    This dataset updates: As needed
    This geodatabase provides a set of settlement points and their names to spatially locate and identify settlement features in the Democratic Republic of the Congo. This work has been undertaken as part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) initiative in Democratic Republic of the Congo. GRID3 works with countries to generate, validate and use geospatial data on population, settlements, infrastructure, and subnational boundaries. For more information, see https://grid3.org/ Suggested Data Set Citation: Bureau Central du Recensement (BCR), Democratic Republic of the Congo, and Center for International Earth Science Information Network (CIESIN), Columbia University. 2020. GRID3 Democratic Republic of the Congo Settlement Point, Version 01 Beta. Palisades NY: Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). https://data.humdata.org/dataset/grid3-democratic-republic-of-the-congo-settlements-points-version-01-beta. Accessed DAY MONTH YEAR
  • 800+ Downloads
    Updated December 1, 2020 | Dataset date: Nov 30, 2015
    This dataset updates: Every year
    Admin 0 boundary refers to country boundary - Polygon, Admin 1 refers to Governorates - (Wilaya- province) -Polygon. These data were extracted from the GADM database (www.gadm.org), version 2.8, November 2015. They can be used for non-commercial purposes only.
  • 8700+ Downloads
    Updated December 1, 2020 | Dataset date: Aug 14, 2017
    This dataset updates: Every year
    Egypt administrative boundary level 0 (national), 1 (governorate), 2 (region), and 3 polygons, and major and minor populated places provided as a geodatabase, shapefiles, KMZ file. REFERENCE YEAR: 2017 Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
  • 1900+ Downloads
    Updated December 1, 2020 | Dataset date: Dec 27, 2017
    This dataset updates: Every year
    Cabo Verde - administrative level 0 (country), 1 (municipality / concelho), 2 (parishe / reguesia), boundary and big island polygons, and gazetteer REFERENCE YEAR: 2017 The administrative level 0, 1, and 2 shapefiles are suitable for database or GIS linkage to the Cabo Verde - Subnational Population Statistics.
  • 600+ Downloads
    Updated December 1, 2020 | Dataset date: Sep 7, 2017
    This dataset updates: Every year
    (Source to be clarified.) REFERENCE YEAR: 2017 REVISION HISTORY 18 July 2019: Topology corrections 27 September 2017: Initial upload
  • 5200+ Downloads
    Updated December 1, 2020 | Dataset date: Aug 6, 2018
    This dataset updates: Every year
    Côte d'Ivoire administrative level 0 (country), 1 (district / autonomous city), 2 (region), and 3 (department) boundary polygons, lines, and points shapefiles, geodatbase, KMZ files, and live services, and gazetteer The update of Côte d'Ivoire dataset is based on the 2012 presidential decree which reorganized the national territory into 2 Autonomous Districts and 31 administrative regions making up the 33 admin 1 units of Côte d'Ivoire. REFERENCE YEAR: 2018 Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID. These boundaries are suitable for database or GIS linkage to the Cote d'Ivoire administrative level 0-3 population statistics tables.
  • 4700+ Downloads
    Updated December 1, 2020 | Dataset date: Aug 2, 2014
    This dataset updates: Every year
    Central African Republic administrative level 0 (country), 1 (prefecture / préfecture), 2 (sub-prefecture / sous-préfecture), 3, and Bangui level 4 boundary polgyons and lines, endorsed by RO on January 2016. REFERENCE YEAR: 2014 Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
  • 400+ Downloads
    Updated December 1, 2020 | Dataset date: Sep 8, 2017
    This dataset updates: Every year
    Belize administrative level 0 (nation) and 1 (district) boundaries REFERENCE YEAR: 2017 These boundaries are suitable for database or GIS linkage to the level 0 and 1 Belize administrative level 0-2 population statistics. Version history 22 July 2019 P-coding and gazetteer according to COD-PS Version history: 22 July 2019 P-codes added to conform to COD-PS; gazetteer added 8 September 2017 Initial upload
  • 400+ Downloads
    Updated November 30, 2020 | Dataset date: Jan 1, 1998
    This dataset updates: Every year
    Yearly biomass Production and anomalies retrieved from DMP and computed by BioGenerator The data used comes from the data generated by the COPERNICUS terrestrial service, the European Commission's Earth observation programme. The research that led to the current version of the product has received funding from various research and technical development programmes of the European Commission. The product is based on data from PROBA-V (©) and SPOT-VEGETATION (©) ESA
  • 400+ Downloads
    Updated November 30, 2020 | Dataset date: Oct 5, 2008
    This dataset updates: Every year
    Ces fichiers représentent les différents chefs lieux de régions de Côte d'voire selon les Districts. Ces fichiers ont été mise à jour par OCHA-CI en collaboration avec le CNTIG et ITOS.
  • Updated November 28, 2020 | Dataset date: Nov 27, 2020
    This dataset updates: Never
    Izabal department (GT-IZ), Guatemala: AI predictions of building footprint on Bing Maps images (approximately 2016-2019), see https://github.com/rodekruis/automated-building-detection. Produced in support to DRRT Guatemala for hurricane Eta and Iota. Coordinate reference system: WGS 84 / EPSG:4326
  • 100+ Downloads
    Updated November 28, 2020 | Dataset date: Dec 12, 2014
    This dataset updates: Never
    The Ebola Treatment Units collected by UNMEER now with 3 word addresses so that partners can communicate the precise location of each unit quickly and easily.
  • 100+ Downloads
    Updated November 28, 2020 | Dataset date: Nov 3, 2014
    This dataset updates: Never
    We have provided the 3 word addresses of each health centre within the West African Region. what3words is a simple, real-time, location referencing system which solves many of the key logistical issues facing aid and humanitarian organisations, for whom street addresses, GPS co-ordinates, and other systems don't exist or are problematic. Using words means non-technical people can find any location more accurately and most importantly, communicate it more quickly, more easily and with less ambiguity than any other system. For more information, to get our API or batch encode your coordinates visit http://www.developer.what3words.com
  • 1100+ Downloads
    Updated November 28, 2020
    This dataset updates: Never
    Using OSM's extracts, we have addressed the IDP camps in Nepal to assist those on ground to communicate the location of the camps easily and quickly.
  • 2500+ Downloads
    Updated November 28, 2020 | Dataset date: Aug 31, 2015
    This dataset updates: Never
    This dataset depicts the Health Infrastructure of Nepal as points with 3 word addresses so that whoever is on ground can easily communicate the location of these centres.
  • 18000+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 17, 2020
    This dataset updates: Every week
    The #COVID19 Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The researched information available falls into five categories: Social distancing Movement restrictions Public health measures Social and economic measures Lockdowns Each category is broken down into several types of measures. ACAPS consulted government, media, United Nations, and other organisations sources. For any comments, please contact us at info@acaps.org Please note note that some measures together with non-compliance policies may not be recorded and the exact date of implementation may not be accurate in some cases, due to the different way of reporting of the primary data sources we used.
  • UNOSAT code: TC20201119GTM This map illustrates satellite-detected surface waters in Izabal department of Guatemala as observed from a Sentinel-1 image acquired on 23 November 2020 at 05:45 Local time. Within the analyzed area of about 6,000 km2, a total of about 150 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 8,600 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • UNOSAT code: TC20201116HND This map illustrates satellite-detected surface waters in Cortes, Atlantida, and Yoro departments of Honduras as observed from a Sentinel-1 image acquired on 23 November 2020 at 05:45 Local time. Within the analyzed area of about 3,500 km2, a total of about 170 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 35,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.