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  • 7100+ Downloads
    Updated May 16, 2019 | Dataset date: Jan 8, 2019
    This dataset updates: Every month
    The dataset provides the estimated Rohingya refugee population statistics by location (lat/lon), settlement type in Cox's Bazar district, Bangladesh. The dataset is updated regularly. The source is the RRRC-UNHCR Family Counting Exercise and IOM Needs and Population Monitoring (NPM) in Cox's Bazar
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Jul 28, 2016
    This dataset updates: Never
    This map illustrates satellite-detected water extent and evolution in the eastern part of Bangladesh as imaged by the SENTINEL-1 satellite on 30 June 2016 and 24 July 2016. The analysis shows an expansion of waters of 75% between the two dates within the entire analyzed zone. Heavy rains at the onset of the Monsoon season have caused flooding. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Jul 29, 2016
    This dataset updates: Never
    Preliminary Satellite Detected Waters Evolution in Central Bangladesh Report (28 July 2016)
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Jul 27, 2016
    This dataset updates: Never
    This map illustrates satellite-detected water extent and evolution in Dhaka and Rajshahi divisions of the Central Bangladesh as imaged by the SENTINEL-1 satellite on 30 June 2016 and 24 July 2016. The analysis shows an expansion of waters of ~75% between the two dates. Heavy rains at the onset of the Monsoon season have caused flooding. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 6, 2017
    This dataset updates: Never
    This map illustrates satellite-detected potentially affected shelters in Leda refugee camp in Nhilla Union, Cox s Bazar District, and Chittagong Division of Bangladesh. About 18,350 people are registered in this camp. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired 05 June 2017 and could identify 215 possible damaged shelter structures within the extent of the camp. Kindly note that the number of damaged shelters could have been underestimated as some groups of shelter structures might be identified as one structure. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 6, 2017
    This dataset updates: Never
    This map illustrates satellite-detected potentially affected shelters in Nayapara refugee camp in Teknaf Union, Cox s Bazar District, and Chittagong Division of Bangladesh. About 12,500 people are registred in this camp. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired the 05th of June 2017 and could identify 560 possible damaged shelter structures within the extent of the camp. Kindly note that the number of damaged shelters could have been underestimated as some groups of shelter structures might be identified as one structure.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 50+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 7, 2017
    This dataset updates: Never
    This map illustrates satellite-detected possible damaged shelter structures in Kutupalong Refugee Camp, Palong Khali Union and Cox Bazar District, Chittagong Division, Bangladesh. The UNITAR-UNOSAT analysis used Pleiades satellite imagery acquired the 6th and the 7th June 2017 as post-images. The UNITAR-UNOSAT analysis identified 1,105 possible damaged shelter structures within the extent of the camp. Kindly note that the number of possible shelter damaged structures could have been under or overestimated in some areas due to the cloud cover and the delimitation of the camp extent. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR – UNOSAT.
  • This map illustrates satellite-detected surface water extent in the northern part of Bangladesh using a TerraSAR-X satellite image acquired on the 16 August 2017. In the analysed area; 221,915 ha of lands are likely affected. The population exposure analysis using WorldPop data shows that 2,143,586 people are potentially affected by floods in this analysed zone: ~1,800,000 are located in Rangpur Division and ~280,000 in Dhaka Division. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Aug 23, 2017
    This dataset updates: Live
    This map illustrates satellite-detected surface water extent in the central and southern parts of Bangladesh using a Sentinel-1 satellite image acquired on the 22 August 2017. In the analysed area; 608,747 ha of lands are likely affected. These lands are mainly cropland irrigated and rainfed areas and estimated to 475,000 ha. The population exposure analysis using WorldPop data shows that ~6,400,000 people are potentially affected by floods in the analysed zone: ~2,300,000 are located in Chittagong Division and ~2,170,000 in Dhaka Division. Please note that for visualization purposesanalyzed area is wider than the extent of this map. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Aug 23, 2017
    This dataset updates: Live
    This map illustrates satellite-detected surface water extent in the central and northern parts of Bangladesh using a Sentinel-1 satellite image acquired on the 22 August 2017. The total analysed area is about 4,284,431 ha. In this analysed area; 1,099,369 ha (39%) of lands are likely affected. These lands are mainly cropland irrigated and rainfed areas and estimated to 1,039,350 ha. The population exposure analysis using WorldPop data shows that ~10,000,000 people are potentially affected by floods in the analysed zone: ~5,400,000 are located in Dhaka Division and ~2,750,000 in Rajshahi Division. Please note that for visualization purposes the analyzed area is wider than the extent of this map.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 40+ Downloads
    Updated May 15, 2019 | Dataset date: Aug 15, 2017
    This dataset updates: Never
    This map illustrates satellite-detected surface water extent in the central part of Bangladesh using a Sentinel-1 satellite image acquired on the 12 August 2017 with a total surface of 4,280,650 ha. In this analyzed area; 1,644,983 ha (38%) of lands are likely affected. These lands are are mainly cropland irrigated and rainfed areas and estimated to 1,576,351 ha. The population exposure analysis using WorldPop data shows that ~17,000,000 people are potentially affected by floods in the analysed zone: ~8,400,000 are located in Dhaka Division and ~5,750,000 in Rajshahi Division. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: May 31, 2017
    This dataset updates: Never
    This map illustrates the satellite-detected water extent in the District of Chittagong, Chittagong Division, in the southeastern part of Bangladesh after the tropical cyclone Mora-17. The UNITAR-UNOSAT analysis used a Sentinel-1 satellite image acquired on the 30 May 2017 and detected several areas with standing waters. In the district of Chittagong ~4,790 ha are likely flooded and almost 12 km of the local roads seem to be affected. The population exposure analysis using WorldPop data shows that ~70,000 people are potentially affected by floods within map extent. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 30+ Downloads
    Updated May 15, 2019 | Dataset date: May 31, 2017
    This dataset updates: Never
    This map illustrates satellite-detected surface water extent in the District of Cox's Bazar, Chittagong Division, located in the southeastern part of Bangladesh after the tropical cyclone Mora-17 using a Sentinel-1 satellite image acquired on the 30 May 2017. A total of 23,058 ha surface waters were observed. In many zones, the affected lands are mainly agricultural. The analysis of this image reveals also that about 234 km of roads mainly tertiary roads seem to be potentially affected. The population exposure analysis using WorldPop data shows that ~267,000 people are potentially affected by floods within map extent. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 2, 2017
    This dataset updates: Never
    This map illustrates satellite-detected surface water extent around Maheshkhali, Chakaria and Cox'S Bazar Sadar Upazilas in the District of Cox's Bazar, Chittagong Division in the south eastern part of Bangladesh as detected by a TerraSAR-X image acquired on the 1st June 2017. A total of ~20,000 ha of lands seem to be affected with flood waters. Some of the affected lands are mainly agricultural fields. The analysis of this satellite image reveals also that about 100 km of roads with mainly tertiary roads seem to be also potentially affected. We can also estimate, within this map extent and using the WorldPop data, about 265,000 people living in flood affected zones. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 14, 2018
    This dataset updates: Live
    This map illustrates satellite-detected surface water extent in Teknaf Upazila, District of Cox's Bazar, Chittagong Division, located in the southeastern part of Bangladesh as detected using a Sentinel-1 satellite image acquired on the 13 June 2018 compared to a Sentinel-1 satelllite image acquired on 22 May 2018. The total analysed area is about 5,000 ha, and about 1,600 ha of surface waters could be observed on 13th of June 2018 whereas 1,050 ha were observed on 22 May 2018. The increase of observed surface waters in this area is about 50 %. Within the camps' extents, 52 ha of water were detected and the most affected seems to be camp 25. Please note that in many zones, the affected lands are mainly agricultural and open areas. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 19, 2018
    This dataset updates: Live
    This map illustrates satellite-detected surface water extent in the northeastern part of Bangladesh using a Sentinel-1 satellite image acquired on the 15 June 2018. In the analysed area; about 500,000 ha of lands are likely affected. The population exposure analysis using WorldPop data shows that 3,500,000 people are potentially affected by floods in this analysed zone: ~1,500,000 are located in Sylhet Division and ~1,000,000 in Sumanganj Division and about 35% of the population is leaving within or close to inundated areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Jun 20, 2018
    This dataset updates: Live
    This map illustrates the evolution of satellite detected waters and the related wet conditions in the Cox's Bazar Myanmar nationals refugee camps located in Ukhia Upazilla, as deduced from the analysis of two Radarsat-2 Spotlight images with 0.5m resolution acquired on 16 June 2018 & 23 May 2018. The evolution of surface waters was classified into three classes of change: low, moderate and high. This analysis shows that some camps experienced a lower increase of wet conditions/surface waters, as camp 1W and camp 2E. Whereas some have moderately changed, as camp 2W, camp 6 and camp 14, others have greatly changed, as camp 17, camp 8W and camp 20 and its extension. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.
  • Updated May 15, 2019 | Dataset date: Jun 25, 2018
    This dataset updates: Live
    This map illustrates satellite-detected surface water extent in the northeastern parts of Bangladesh (Sylhet distric) & India (Assam, Tipura and Mizoram states) using a Sentinel-1 satellite image acquired on the 15 June 2018. The analysis shows an increase of surface waters in Sylhet district (Bangladesh) and Assam state (India). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 5700+ Downloads
    Updated May 9, 2019 | Dataset date: Feb 5, 2019
    This dataset updates: Every month
    Consolidated 4W data from Inter Sector Coordination Group (ISCG), collected from the partners and organisations involved in the humanitarian response in Cox's Bazar, Bangladesh.
  • 40+ Downloads
    Updated May 8, 2019 | Dataset date: May 7, 2019
    This dataset updates: As needed
    This shapefile represents digitization work carried out by UNOSAT and REACH from March-May 2019. Work is ongoing and this shapefile is reflective of digitization efforts up to 7 May 2019. As future iterations and improvements to the footprints are made, additional versions will be released. The majority of digitization work was carried out using Januray 2019 NPM-IOM drone imagery. The specific images used are provided in the attribute table. The original classification scheme developed by UNOSAT was comprised of two structure classes: "Structure" and "Bridge." An area based classification was then performed in which all polygons < 5 m2 were reclassified as "< 5m2 - Likely Latrine, Tubewell, or Shower." All polygons > 5 m2 also retained the UNOSATs original classification. Additionally, attribute fields containing camp name information, and the image source information were added. Limitations: The work has not been ground truthed and is based on expert interpretation of UAV imagery. In addition to relying on imagery interpretation, the footprints are bound by the limitations present in the UAV images that were utilized. Credits: UNOSAT- REACH, 2019
  • 30+ Downloads
    Updated May 2, 2019 | Dataset date: Mar 26, 2019-Apr 18, 2019
    This dataset updates: Never
    Data collected in Bangladesh between March-April, 2019. Their analysis contributed to the Xchange Foundation's “We do not believe Myanmar!”: The Rohingya Survey 2019 (http://xchange.org/reports/TheRohingyaSurvey2019.html)
  • 400+ Downloads
    Updated April 29, 2019 | Dataset date: Jan 1, 2015-Dec 31, 2017
    This dataset updates: Every year
    This dataset contains agency- and publicly-reported data on sexual violence and abuse against aid workers between January 2015 and December 2017.
  • 2300+ Downloads
    Updated April 25, 2019 | Dataset date: Apr 17, 2018
    This dataset updates: Every three months
    The majhee block system represents an important aspect of communities within the Rohingya refugees settled in collective or camp-like settings in Cox's Bazar district. A majhee is a community leader belonging to the Rohingya refugee population, while a block is the area for which he is responsible. The NPM majhee blocks mapping exercise was first conducted during NPM Baseline 9, between 5 and 20 February 2018. As part of the majhee interview process (key informants), enumerators walked the perimeter of each majhee block with guidance from the KI. As they walked, the field team traced their path, marking up the boundary on the tablet or paper map. Upon returning to NPM office, details of these boundaries were finalized on the paper maps. The paper maps were then received by the NPM digitizing team. This team carefully digitized the Mahjee zone perimeters in GIS, using high-resolution NPM UAV imagery as an underlying reference. Boundaries were assigned the NPM Block_ID attribute, which represents a unique identifier for each Site Assessment Location. In this manner, boundaries can be uniquely linked back to a mahjee. The majhee blocks mapping exercise is now embedded in NPM regular assessment activities (baseline and site assessment), hence regularly updated with a frequency of approximately three weeks. The majhee block system is not an official form of governance. The scope of this exercise is purely descriptive and not prescriptive. Names and boundaries adopted in this exercise do not imply official endorsement or acceptance by IOM.
  • 4500+ Downloads
    Updated April 17, 2019 | Dataset date: Mar 10, 2019
    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.
  • 500+ Downloads
    Updated April 15, 2019 | Dataset date: Sep 1, 2018-Mar 31, 2019
    This dataset updates: Every month
    This page provides the data published in the Attacks on Health Care Monthly News Brief. The first dataset covers events where health workers were killed, kidnapped or arrested (KKA) and incidents where health facilities were damaged or destroyed by a perpetrator including state and non-state actors, criminals, individuals, students and other staff members. The remaining datasets include threats and incidents of violence as well as protests and other events affecting health care between September and December 2018. For a breakdown on the number of health workers killed, kidnapped or arrested (KKA), see In Harms Way dataset. All data contains incidents identified in open sources. Categorized by country.