This dataset contains data of a second national water point mapping in Liberia (2015). The first national water point inventory has been executed in 2013, and in 2017 all water points were mapped again using Akvo Flow: https://akvo.org/products/akvoflow/#overview.
This dataset contains a 2012 national water point mapping in Sierra Leone.
It also contains 2016 sanitation and hygiene data.
This data was collected with the use of Akvo Flow https://akvo.org/products/akvoflow/#overview
The data relates to French development aid for projects carried out under sovereignty and in progress since 2015. This data may be published once the agreement of the counterparty has been obtained.The French Development Agency will aim to a quarterly update of the publication of these data, in particular to take into account the new development projects financed by the Agency. These data comply with the IATI (International Initiative for Aid Transparency) standard.
This map illustrates satellite-detected landslide and flood water extent in Peshghor village and surrounding areas in Khenj District, Panjshir Province, Afghanistan as seen on Sentinel-2 satellite imagery, 10 m resolution, collected on 13 July 2018, one day after the disaster happened. The landslides and floodwaters hit villages downstream because of the break-up of the natural banks of the dam. As a result, Peshghor and surrounding villages have been cut off and damages have been reported on structures and buildings. Within the current map extent Saricha primary road is potentially affected by the landslides and the overflow of Panjshir River. Around 198 buildings are located within areas affected by the landslide and 9 within areas affected by the floods. Due to the resolution of the satellite imagery, the extent of landslide and floodwaters may be underestimated and as a consequence, the number of buildings potentially affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
The subnational INFORM model for Caucasus and Central Asia was initiated by the Regional Inter-Agency Standing Committee (IASC) Task Force for Caucasus and Central Asia and is managed by OCHA. The INFORM model is being used to support coordinated preparedness actions. Partners hope to use the model to improve cooperation between humanitarian and development actors in managing risk and building resilience across the region.
INFORM identifies areas at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM model is based on risk concepts published in scientific literature and envisages three dimensions of risk: Hazards & Exposure, Vulnerability and Lack of Coping Capacity. The INFORM model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk.
The regional subnational INFORM model for Caucasus and Central Asia is developed at the first administrative level (corresponding to the provinces/oblasts/regions and few independent cities) of the eight countries in South Caucasus and Central Asia.
The INFORM index supports a proactive crisis management framework. It will be helpful for an objective allocation of resources for disaster management as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies.
This dataset shows the number of teachers per county in kenya. It has been extracted from the Ministry of Educations' - Basic Education Statistical Booklet which captures national statistics for the Education Sector in totality.
This map illustrates shelters in the area of the Rukban border crossing on the Syrian-Jordanian border. Using satellite images collected by the WorldView-03 satellite on 23 June 2018 and the GaoFen-2 satellite on 24 June 2018, UNOSAT located 11,702 probable shelters along the Jordanian side of the border, 25 kilometers southwest of the Al Waleed crossing. This is a 12 percent increase in apparent shelters visible compared to the previous UNOSAT analysis done using an image collected 16 January 2018. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. 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 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.
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.
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.
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.
Este mapa ilustra una estimación del número de viviendas y estructuras potencialmente afectadas por el flujo piroclástico detectado por Copernicus EMS, usando una imagen satélite Sentinel-2 colectada el 4 de junio 2018. UNITAR-UNOSAT ha estimado un total de 411 viviendas / estructuras que se encuentran dentro del área afectada por el flujo piroclástico; de las cuales 260 pertenecen a la aldea de San Miguel de Los Lotes. Complejos industriales y hoteleros como el de La Reunión se encuentran también dentro de la extensión del flujo piroclástico. Este análisis es preliminar y no ha sido validado en terreno. Por favor, envíen sus comentarios a UNITAR-UNOSAT.
This map illustrates an estimation of the number of buildings potentially affected by pyroclastic flow detected by Copernicus EMS using a Sentinel-2 satellite image collected on 4 June 2018. UNITAR-UNOSAT estimates 411 buildings / structures within the pyroclastic flow, ~ 260 of which are located inside the community of San Miguel de Los Lotes. Industrial sites and the resort of La Reunion are also included in the extent of this pyroclastic flow. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates potentially affected population by flooding in the eastern sub counties of Kenya. The analysis was conducted by analyzing Sentinel-1 imagery acquired on the 4 May 2018. Within the analysis extent, more than 200,000 people are potentially affected by the floods. Magarini sub county in Kilifi County, is the one with more than 40,000 people living inside flood affected areas, followed by Dadaab, Wajir South, Garsen and Malindi sub counties. Note that some sub counties have been partially analyzed depending on the area covered by satellite imagery. 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.
This map illustrates satellite-detected flood water extent over Dadaab & Lagdera Sub Counties, Garissa County, Kenya. The analysis was conducted analyzing Sentinel-1 image acquired on the 4 May 2018. Within the analysis extent, ~ 43,300 ha of land appear to be inundated and around 23,000 people are living inside this flood water extent. Within the analysis extent, around 23,300 ha of inundated land are located inside Dabaad Sub County, potentially affecting 18,200 people. Several refugee camps, specially the ones located inside Dadaab Sub County, seem to be affected by the floods, being Ifo 2 Refugee Camp the most affected one. 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.
This map illustrates satellite-detected flood water extent over Wajir county, Kenya. The analysis was conducted by analyzing Sentinel-1 imagery acquired on the 4 May 2018. Within the analysis extent, around 111,800 ha of land appears to be inundated and more than 46,300 people are living inside this flood water extent.Within the analysis extent, the sub county of Wajir West presents 68,000 ha of land inundated and more than 6,100 people potentially affected while Wajir South presents ~ 26,300 ha of land inundated and ~ 19,000 people potentially affected. 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.
This map illustrates satellite-detected flood water extent along Tana River, Galole and Garsen sub counties, Tana River county, Kenya. The analysis was conducted by analyzing Sentinel-1 imagery acquired on the 4 May 2018. The analysis extent is focused on the river bed of Tana, the surrounding land between the primary road and the limit of the Tana River boundary, specifically where the population is concentrated. Within the analysis extent, around 22,700 ha of land appears to be inundated and more than 21,600 people are living inside this flood water extent. 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.
This map illustrates satellite-detected flood water extent along Tana River, Bura Sub County, Kenya. The analysis was conducted analyzing Sentinel-1 image acquired on the 4 May 2018. The analysis extent is focused on the river bed of Tana and the surrounding land between the primary road and the limit of the Tana River boundary, specifically were the population is concentrated. Within the analysis extent, more than 9,100 ha of land appear to be inundated and more than 9,800 people are living inside this flood water extent. 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.
This map illustrates satellite-detected flood water extent over Garsen, Magarini, Malindi and Lamu West Sub Counties, Kenya. The analysis was conducted analyzing Sentinel-1 image acquired on the 4 May 2018. Within the extent of the map more than 25,000 ha of land appear to be inundated and around 20,000 people are living inside this flood water extent. Within the map extent, around 13,000 ha of inundated land are located inside Garsen sub county, potentially affecting 5,000 people. 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.
El siguiente mapa ilustra el recorrido de las observaciones directas colectadas en campo por el equipo UNDAC en apoyo a la emergencia provocada por el derrame de petróleo producido el 2 de marzo de 2018 por el pozo Lizama 158 (propiedad Ecopetrol), a lo de la quebrada Lizama y Caño Muerto hasta su confluencia con el rio Sogamoso, en Barrancabermeja, Colombia. Dichas observaciones fueron tomadas durante los días 15 y 18 de abril 2018 utilizando la aplicación UN-ASIGN que permite colectar fotos georreferenciadas y su información asociada desde el terreno. UNOSAT en apoyo al equipo UNDAC, ha integrado dicha información que incluye por un lado la localización y distribución espacial de los distintos puntos de control visitados - visibles en el mapa de referencia – y a su vez las distintas fotos tomadas en terreno durante fichas visitas de control. Por favor, envíen sus comentarios a UNITAR-UNOSAT.
This map illustrates satellite-detected damage in the subdistricts of Kafr Batna and Irbin and in the eastern part of Damascus city, Syrian Arab Republic. Using satellite imagery collected 6 March 2018 and comparing with imagery acquired 23 February 2018, UNITAR - UNOSAT conducted a Rapid Damage Assessment, over a total area of 94.7 square kilometers, to provide an overview of areas of recent damage. The area analyzed was divided in cells and each cell was assessed searching for presence of new damage. Our analysis shows that 14% of the cells were affected by major new damage, with presence of buildings completely destroyed or severely damaged between 23 February 2018 and 6 March 2018. In addition 14% of the cells showed signs of minor new damage, with visible impact craters, debris or moderately damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.