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  • 2800+ Downloads
    Updated Live | Dataset date: Dec 1, 2019-Sep 21, 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.
  • 4500+ Downloads
    Updated September 22, 2020 | Dataset date: Mar 10, 2020-Sep 21, 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
  • 30+ Downloads
    Updated September 22, 2020 | Dataset date: Apr 29, 2016
    This dataset updates: As needed
    No abstract provided
  • 50+ Downloads
    Updated September 21, 2020 | Dataset date: Jan 1, 2011-Dec 31, 2011
    This dataset updates: As needed
    Flood extent in 2011 Original dataset title: Cambodia Flood Extent in 2011
  • 600+ Downloads
    Updated September 17, 2020 | Dataset date: Dec 31, 2006
    This dataset updates: As needed
    Provinces of Equatorial Guinea REFERENCE YEAR: 2006 The dataset represents the provinces of Equatorial Guinea with harmonized PCODE of ROWCA and Humanitarian Response pcodes
  • 100+ Downloads
    Updated September 17, 2020 | Dataset date: Jan 1, 2018
    This dataset updates: As needed
    Location of health facilities (hospital) in Indonesia.
  • 80+ Downloads
    Updated September 17, 2020 | Dataset date: Jun 1, 2015
    This dataset updates: As needed
    Indonesia - Education facilities (Elementary, Junior, Vocational and Senior High School)
  • 15000+ Downloads
    Updated September 16, 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.
  • Updated September 11, 2020 | Dataset date: Aug 1, 2019-Mar 31, 2020
    This dataset updates: Every year
    This survey was conducted by IOM DTM covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. The dataset has education facilities' names, locations and the services available there.
  • 10+ Downloads
    Updated September 11, 2020 | Dataset date: Aug 1, 2019-Mar 31, 2020
    This dataset updates: Every year
    This survey was conducted by IOM DTM covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. The dataset has health facilities' names, locations and the services available there.
  • 300+ Downloads
    Updated September 11, 2020 | Dataset date: Feb 1, 2020-Mar 31, 2020
    This dataset updates: Every year
    The dataset has IDPs households and individuals with age and gender disaggregated data at sub national level. A site assessment is a sub-component of mobility tracking. It aims to collect data on population presence, living conditions and needs in a particular displacement site or community.
  • Updated September 10, 2020 | Dataset date: Sep 9, 2020
    This dataset updates: As needed
    This work has been undertaken as part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) initiative in Senegal. 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: Center for International Earth Science Information Network (CIESIN), Columbia University and Novel-T. 2020. GRID3 Senegal Settlement Extents Version 01, Alpha. Palisades, NY: Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). Source of building Footprints “Ecopia Vector Maps Powered by Maxar Satellite Imagery”© 2020. DOI: https://doi.org/doi:10.7916/d8-x8gh-ms26 . Accessed DAY MONTH YEAR
  • Updated September 10, 2020 | Dataset date: Sep 9, 2020
    This dataset updates: As needed
    This work has been undertaken as part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) initiative in Togo. 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: Center for International Earth Science Information Network (CIESIN), Columbia University and Novel-T. 2020. GRID3 Togo Settlement Extents Version 01, Alpha. Palisades, NY: Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). Source of building Footprints “Ecopia Vector Maps Powered by Maxar Satellite Imagery”© 2020. DOI: https://doi.org/10.7916/d8-qdxc-0c73 . Accessed DAY MONTH YEAR
  • 5300+ Downloads
    Updated September 10, 2020 | Dataset date: Sep 10, 2020
    This dataset updates: Every year
    China administrative level 0 (country), 1 (province, autonomous region, municipality, or special administrative region), and 2 (prefecture-level units) boundaries and gazetteer REFERENCE YEAR: 2020
  • Updated Live
    This dataset updates: Live
    List of airports in United States Minor Outlying Islands, with latitude and longitude. Unverified community data from ourairports.com.
  • 7600+ Downloads
    Updated September 8, 2020 | Dataset date: Sep 8, 2020
    This dataset updates: As needed
    This spatial database contains the outline of the camps, settlements, and sites where Rohingya refugees are staying in Cox's Bazar, Bangladesh.
  • 100+ Downloads
    Updated September 6, 2020
    This dataset updates: Every year
    Overpass Service Query to extract from the live OpenStreetMap database the Settlement place names for Guinea, Liberia, Mali and Sierra Leone. OpenStreetMap Ebola Response These links launch the Overpass Turbo web application to extract live data. Data is downloaded automatically. Rename the file called "interpreter" for better documentation of the Query content. More information
  • 1400+ Downloads
    Updated September 5, 2020 | Dataset date: Jan 1, 2018-Jan 1, 2019
    This dataset updates: Never
    This dataset contains shapefiles for Guinea, Liberia, and Sierra Leone from the OpenStreetMap (OSM) project. Each country has its individual file. The dataset counts with contributions of hundreds of users. This dataset is updated daily. The original dataset can be downloaded from the OSM West Africa Ebola response wiki.
  • 300+ Downloads
    Updated Live | Dataset date: Sep 2, 2019
    This dataset updates: Live
    This dataset contains an active archive of flood event records from 1985 to present. Details such as the country affected, the number of people killed, the number of people displaced, the cost of damages, and a measure of the magnitude of the flood are included for each flood event. The archive is updated on an ongoing basis and new flood event are added immediately. The information presented in this Archive is derived from news, governmental, instrumental, and remote sensing sources.
  • 1000+ Downloads
    Updated September 4, 2020 | Dataset date: Dec 17, 2018
    This dataset updates: Every year
    Ecuador administrative level 0 (country), level 1 (province), 2 (canton), and 3 (parroquia) boundary polygons. REFERENCE YEAR: 2018 The administrative level 0-2 shapefiles are suitable for database or GIS linkage to the "Ecuador population statistics for administrative level 1, 2, and 3" shapefiles on HDX. Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
  • 600+ Downloads
    Updated September 4, 2020 | Dataset date: Sep 7, 2017
    This dataset updates: Every year
    Acquired from www.gadm.org. Configured and adjusted to conform to COD-PS by OCHA REFERENCE YEAR: 2017 These boundaries are suitable for database or GIS linkage to the Cuba administrative levels 0-2 population statistics tables.
  • 1800+ Downloads
    Updated September 4, 2020 | Dataset date: Jul 6, 2017
    This dataset updates: Every year
    Admin Level 1 Boundaries (Departments) and Admin Level 2 Boundaries (Districts) of Congo REFERENCE YEAR: 2017 The dataset represents the departments and districts of Congo with harmonized PCODE of ROWCA and Humanitarian Response pcodes Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
  • 1800+ Downloads
    Updated September 4, 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.
  • 1200+ Downloads
    Updated September 4, 2020 | Dataset date: Feb 1, 2019
    This dataset updates: Every year
    Acquired and P-coded by OCHA in 2019 REFERENCE YEAR: 2019 The administrative level 0 and 1 shapefiles and geodatabase feature classes are suitable for database or GIS linkage to the Bhutan administrative level 0-1 population statistics CSV tables.
  • 500+ Downloads
    Updated September 4, 2020 | Dataset date: Sep 21, 2017
    This dataset updates: Every year
    Bermuda administrative level 0 (country), 1 (parish) and 2 (municipality) boundaries REFERENCE YEAR: 2017 These boundaries are suitable for database or GIS linkage to the Bermuda administrative level 0-2 population statistics tables. See description of administrative level 2.