Seasonal Floods Analysis for Twic East and Duk, Jonglei State, with imagery as of 11 September 2020.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Since the beginning of the exercise in the last quarter of 2019, IOM’s Displacement Tracking Matrix (DTM) conducted Village Assessment Survey (VAS) across 7 counties covering 182 out of 219 bomas (achieving 83 per cent coverage) in Wau, Rubkona, Bor South, Torit, Magwi, Aweil Centre and Malakal counties. Additional 9 focus group discussions were conducted, due to varying perspectives on boma boundaries. To supplement the findings from the boma questionnaire, key informant interviews were conducted at 560 educational facilities and 150 health facilities. In addition to this, Facility, Infrastructure and Service mapping component geo referenced 3,227 facilities including administrative buildings, education facilities, healthcare facilities, markets, religious buildings, transport points and water points.

Seasonal Floods Analysis in Unity State for 5, 11 and 12 August 2020

Seasonal Floods Analysis in Jonglei State for12, 14 and 19 August 2020

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggregated data about COVID-19 cases in South Sudan and neighbouring countries compiled by UNICEF, with the aim of mapping population inflows at risk of COVID-19 transmission. This map shows the total number of incoming individual movements recorded across all flow monitoring points.Data reflects the situation as of 31 May. 

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggreagated data about COVID-19 cases in South Sudan and neighbouring countries compiled by UNICEF, with the aim of mapping population flows that may be vulnerable to COVID-19 transmission. Data on individuals travelling from areas with COVID-19 cases includes both those entering from neighbouring countries as well as internal movements within South Sudan.The data reflects the situation as of 31 May

This map displays the number of IDPs by county as of March 2020 (round 8 of mobility tracking). 

This map displays the number of returnees by county as of March 2020 (round 8 of mobility tracking). 

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggregated data about COVID-19 cases in neighbouring countries compiled by UNICEF, with the aim of mapping population inflows at risk of COVID-19 transmission. Data on individuals travelling from areas with COVID-19 cases includes both those entering from neighbouring countries as well as internal movements within South Sudan. The reported volume of individuals entering South Sudan from neighbouring countries has decreased considerably in April compared to March due to several factors:​a reduction of population flows due to regional COVID-19 preventive measuresa reduction of flow monitoring pointsmore precise geographic data on areas with confirmed COVID-19 casesData reflects the situation as of 30 April. 

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggreagated data about COVID-19 cases in neighbouring countries compiled by UNICEF, with the aim of mapping population inflows at risk of COVID-19 transmission. Data on individuals travelling from areas with COVID-19 cases includes both those entering from neighbouring countries as well as internal movements within South Sudan. The reported volume of individuals entering South Sudan from neighboring countries has decreased considerably in April compared to March due to several factors:a reduction of population flows due to regional COVID-19 preventative measuresa reduction of flow monitoring pointsmore precise geographic data on areas with confirmed COVID-19 casesData reflects the situation as of 30 April. 

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggregated data about COVID-19 cases in neighbouring countries compiled by UNICEF, with the aim of mapping population inflows at risk of COVID-19 transmission. The analysis is indicative of incoming movements 73 flow monitoring points at South Sudan’s borders and internal transport hubs, of which 65 recorded incoming cross-border travel in March 2020. This map shows inflows from COVID-19 affected areas in neighbouring countries in March 2020. COVID-19 cases in neighbouring countries are mapped at the first administrative level in Kenya, the Democratic Republic of Congo and the Central African Republic, and at the second administrative level in Uganda, Sudan and Ethiopia.

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggregated data about COVID-19 cases in neighbouring countries compiled by UNICEF, with the aim of mapping population inflows at risk of COVID-19 transmission. The analysis is indicative of incoming movements 73 flow monitoring points at South Sudan’s borders and internal transport hubs, of which 65 recorded incoming cross-border travel in March 2020. This map shows inflows from affected areas in neighbouring countries in March 2020. Cases are mapped at the first administrative level for Kenya, the Central African Republic and the Democratic Republic of Congo, and at the second administrative level for Ethiopia, Uganda and Sudan.

IOM DTM, UNHCR and REACH combined their flow monitoring data for South Sudan with geographically disaggregated data about COVID-19 cases in neighbouring countries compiled by UNICEF, with the aim of mapping population inflows at risk of COVID-19 transmission. The analysis is indicative of incoming movements 73 flow monitoring points at South Sudan’s borders and internal transport hubs, of which 65 recorded incoming cross-border travel in March 2020. This map shows overall inflows from neighbouring countries in March 2020.

These maps display IDP and returnee numbers by state on individual state maps (A4). 

The map displays displacement locations, their data source and flooding in Leer County, Unity State as of November 2019. 

The map displays displacement locations, their data source and flooding as of November 2019. 

This map shows the human mobility network tracked by DTM's Flow Monitoring Registry in South Sudan and between South Sudan and neighbouring countries, based on interviews carried out at 39 Flow Monitoring Points between 01/02/2020 and 25/03/2020. Figures are only indicative of existing trends among respondents at the active FMPs since DTM does not have full coverage of cross-border or internal flows. Participation in the survey is voluntary and anonymous.Flows are mapped at the second administrative level for neighbouring countries and at the county or payam level for South Sudan. Flows to and from camps are mapped using UNHCR or IOM camp coordinates where available.The map is best visualized in Adobe Acrobat or Chrome, other PDF readers may result in graphic distortions.

This map displays IDP and returnee locations in Western Equatoria for round 7 (November 2019).