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  • 400+ Downloads
    Updated November 27, 2020 | Dataset date: Jul 10, 2019
    This dataset updates: As needed
    Based on Republic Act 8425, otherwise known as Social Reform and Poverty Alleviation Act, dated 11 December 1997, the poor refers to individuals and families whose income fall below the poverty threshold as defined by the government and/or those that cannot afford in a sustained manner to provide their basic needs of food, health, education, housing and other amenities of life. It may be estimated in terms of percentages (poverty incidence) and total number of poor families (magnitude of poor families). Also, this dataset has been generated by combining Philippine Standard Geographic Codes (PSGC) and poverty estimates from Philippine Statistics Authority (PSA). For more details, please refer to the following documents: https://psa.gov.ph/poverty-press-releases/references https://psa.gov.ph/poverty-press-releases/technotes https://psa.gov.ph/poverty-press-releases/glossary https://psa.gov.ph/sites/default/files/Technical%20Notes%20on%202015%20SAE.pdf
  • 100+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 26, 2020
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by Governorate due to the Coronavirus pandemic in Palestine.
  • 2300+ Downloads
    Updated November 27, 2020 | Dataset date: May 20, 2020-Nov 25, 2020
    This dataset updates: Every week
    This dataset contains the number of confirmed cases, deaths and recoveries by province due to the Coronavirus pandemic in Mozambique.
  • 5900+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 26, 2020
    This dataset updates: Every day
    The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak. We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak. The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository. United States Data Data on cumulative coronavirus cases and deaths can be found in two files for states and counties. Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data. State-Level Data State-level data can be found in the us-states.csv file. date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ... County-Level Data County-level data can be found in the us-counties.csv file. date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ... In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these. Github Repository This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data
  • 400+ Downloads
    Updated November 27, 2020 | Dataset date: Jan 1, 2019-Nov 27, 2020
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by province due to the Coronavirus pandemic in Afghanistan.
  • 200+ Downloads
    Updated November 27, 2020 | Dataset date: Jan 1, 1990-Nov 27, 2020
    This dataset updates: Every day
    This dataset contains Food Prices data for all countries where the data is available. Food prices data comes from the World Food Programme (WFP) and covers foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
  • 400+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 27, 2020
    This dataset updates: Every day
    Covid-19 Impact on Humanitarian Operations Data Viz inputs
  • 5400+ Downloads
    Updated November 27, 2020 | Dataset date: Mar 10, 2020-Nov 26, 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
  • 6800+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 27, 2020
    This dataset updates: Every day
    FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
  • 40+ Downloads
    Updated November 27, 2020 | Dataset date: Apr 29, 2016
    This dataset updates: As needed
    No abstract provided
  • 3600+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 26, 2020
    This dataset updates: Every day
    This dataset contains key figures (topline numbers) on the world's most pressing humanitarian crises. The data, curated by ReliefWeb's editorial team based on its relevance to the humanitarian community, is updated regularly. The description of the files and columns can be found in the additional metadata spreadsheet file.
  • 100+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 25, 2020
    This dataset updates: Every week
    This dataset lists all contributions made by donors to the Central Emergency Response Fund (CERF). CERF receives broad support from United Nations Member States, observers, regional governments and international organizations, and the private sector, including corporations, non-governmental organizations and individuals.
  • 100+ Downloads
    Updated November 27, 2020 | Dataset date: Nov 26, 2020
    This dataset updates: Every week
    This dataset lists project funding allocations from OCHA's Central Emergency Response Fund (CERF). CERF allocations are made to ensure a rapid response to sudden-onset emergencies or to rapidly deteriorating conditions in an existing emergency and to support humanitarian response activities within an underfunded emergency.
  • 400+ Downloads
    Updated November 27, 2020 | Dataset date: Apr 13, 2020-May 28, 2020
    This dataset updates: Every week
    West and Central Africa Coronavirus covid-19 situation
  • 8100+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 1, 2020-Aug 31, 2020
    This dataset updates: Every day
    These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day. Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/
  • 300+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 13, 2020-Nov 25, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Mauritania - Infected (new cases), Deceased, Recovered. Please note that the gender data is not available yet, our teams are working on it. Thank you for your understanding.
  • 600+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 25, 2020-Nov 25, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Mali - Infected (new cases, gender if available), Deceased, Recovered.
  • 400+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 9, 2020-Nov 23, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Burkina Faso - Infected (new cases, gender), Deceased, Recovered.
  • 400+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 12, 2020-Nov 22, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Ghana - Infected (new cases, gender), Deceased, Recovered.
  • 1200+ Downloads
    Updated November 26, 2020 | Dataset date: Feb 27, 2020-Nov 25, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Nigeria per day - Infected (new cases), Deceased, Recovered.
  • 6000+ Downloads
    Updated November 26, 2020 | Dataset date: Aug 31, 2018
    This dataset updates: Every year
    Zimbabwe administrative level 0 (country), 1 (province), 2 (district) and 3 (ward) boundary polygon, line, and point shapefiles, KMZ files, geodatabase, and live services, and gazetteer. REFERENCE YEAR 2018 Please note that administrative level 3 (ward) features are identified numerically. Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID. These shapefiles are suitable for linkage by P-code to the Zimbabwe administrative levels 0 - 2 population statistics CSV population statistics tables.
  • 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.
  • 600+ Downloads
    Updated November 26, 2020 | Dataset date: Mar 19, 2020-Nov 24, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Niger - Infected (new cases, gender), Deceased, Recovered.
  • 2000+ Downloads
    Updated November 26, 2020 | Dataset date: Nov 26, 2020
    This dataset updates: Every year
    Zambia administrative level 0-2 boundaries REFERENCE YEAR: 2020 Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
  • 3400+ Downloads
    Updated Live | Dataset date: Dec 1, 2019-Nov 26, 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.