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  • Time Period of the Dataset [?]: August 07, 2023-August 07, 2023 ... More
    Modified [?]: 7 August 2023
    Dataset Added on HDX [?]: 8 August 2023
    This dataset updates: Never
    UNOSAT code LS20230804GEO, GDACS Id: 1102154 This map illustrates satellite-detected landslides/mudflow in Oni District, Racha-Lechkhumi and Kvemo Svaneti Region, Georgia as observed from a Pléiades imagery acquired on 06 August 2023 at 14:30 local time. Within the analyzed area of 9,600 ha, about 220 ha of landslide scars were observed. UNITAR-UNOSAT identified 30 affected structures and 6 potentially affected structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).
  • Time Period of the Dataset [?]: June 28, 2022-June 28, 2022 ... More
    Modified [?]: 2 August 2022
    Dataset Added on HDX [?]: 2 August 2022
    This dataset updates: Never
    UNOSAT code: FL20220627GEO This map illustrates satellite-detected surface waters in Samegrelo-Zemo Svaneti and Imereti Regions, Georgia as observed from a Sentinel-1 image acquired on 23 Jun 2022 at 19:11 local time. Within the analyzed area of about 2,500 km2, about 50 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 1,600 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 United Nations Satellite Centre (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.
  • 50+ Downloads
    Time Period of the Dataset [?]: January 01, 1950-December 31, 2050 ... More
    Modified [?]: 28 September 2018
    Dataset Added on HDX [?]: 28 September 2018
    This dataset updates: Never
    The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.
  • 60+ Downloads
    Time Period of the Dataset [?]: March 01, 2015-March 01, 2015 ... More
    Modified [?]: 7 December 2016
    Dataset Added on HDX [?]: 6 December 2016
    This dataset updates: Never
    According to information by the Ministry of Labour, Health and Social Affairs , 118 651 persons with disabilities are registered as recipients of state social assistance by 1 March, 2015 in Georgia that constitutes 3 percent of total population resided in Georgia. This dataset provides a breakdown of the number of persons with disabilities by first administrative level (region), and a detailed breakdown for the districts belonging to the capital city of Tbilisi. The provided information depicts the number of disabled persons receiving state social pension/allowance (beneficiaries) across the country. In light of this, state policy determines the total number of disabled persons by the sum of beneficiaries, which directly is connected to the actual number of disabled people living in Georgia. The actual number of disabled persons in Georgia is likely to be higher.
  • 30+ Downloads
    Time Period of the Dataset [?]: December 31, 2015-December 31, 2015 ... More
    Modified [?]: 29 January 2016
    Dataset Added on HDX [?]: 14 January 2016
    This dataset updates: Never
    This dataset is part of the data series [?]: UNDRR - GAR15 Global Exposure
    The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.