Kenya – Mobility Tracking and Multi-sectoral Location Assessment in Garissa Round # 2 (01May23)

Mobility Tracking is a DTM methodology which aims to quantify the presence of population categories as well as the populations’ reasons for displacement, length of displacement and needs. Mobility tracking relies on key informant interviews (KIIs) to estimate the size, priorities and mobility dynamics of a given population. For more information on the DTM methodology, see the DTM Methodological Framework.

The second round of data collection was deployed in 2023 to understand changes in the mobility dynamics induced by prolonged drought and the recovery phase, as well as updates on mobility trends and the most urgent sectoral needs of the target mobile population groups and host communities, to assess changes since the 2022 data collection.

Key findings included:

• Displacement reportedly increased by an approximate 200% in the past 4 years:
  • 12 per cent of arrivals arrived in their location of displacement in 2020, 61 per cent arrived between 2020 and 2022 and 27 per cent arrived in 2023.
  • In nearly all the assessed sub-locations (99%) informants reported the presence of pastoralist dropouts. Across all the sub-locations, 88 per cent of pastoral dropouts happened before 2023, and dropout rates increased most drastically during the 2020-2022 drought period.
  • All arrival households (100% or 15,299 households) arrived at sub-locations that already struggled with the severe effects of drought, resource-based conflict, and ethnic clashes.[1]
• Between September 2022 and May 2023, the reported primary driver of forced displacement was drought. As of May 2024, the primary driver of displacement was floods with 23, 511 households displaced across Kenya.
• 81 per cent of returnees temporarily resided in Kenya, and 16 per cent temporarily resided in Somalia. Of those who temporarily resided in Kenya, 57 per cent were temporarily located in a location outside their immediate area of origin in Garissa County, suggesting prevalent internal migration within the county and cross-border movement dynamics.
• 10,158 child-headed households were identified in Garissa. Of these, 2,954 (29%) had no relatives or community members living near them and were separated from their legal or customary guardians and 8,181 children (81%) were reported as without permanent sources of support.
• In 33 per cent of Garissa sub-locations, shelters were reportedly not stable enough to withstand environmental hazards or security threats.
• Key informants reported 17,568 student dropouts (22% of the estimated number of students), despite concurrent reports that educational institutions were active in 96 per cent of sub-locations. The discrepancy between these figures’ warrants updated, additional investigation.
• Open defecation was reported in 66 sub-locations (44%).  [DI1] The most reported drivers for people to practice open defecation was the non-functionality of latrines (40%), difficulty in accessing the latrines (39%), lack of privacy as there was no reported partition for male and female cubicles (35%), and insecurity when accessing the latrines (11%).
  • Insecurity-related latrine issues were reported by key informants in Balambala (9 sub-locations), Dadaab (3 sub-locations), Lagdera (3 sub-locations) and Hulugho (1 sub-locations).
• Key informants reported that only 52% of the sublocations host a health facility. Furthermore, in 95% of the sub locations with a health facility, there was a reported absence of medicine and commodities
• Most respondents in sub-locations reported that the top three sources of drinking water were: motorized borehole (16%), rainwater (15%) and river water (10%).

[1] Household reportedly arrived in their new sublocation at the following time periods: 12 per cent before 2020, 61 per cent between 2020 and 2022 and 27 per cent in 2023.

 [DI1]Figures are correct, however in the dataset I can see Bush instead of open defecation, which has 0.

In the analysis shared I see that it is Bush (open defecation) under the WASH tab. However in the data table I cannot see such category.

When uploading the data to the website let’s make sure that categories have the same labels in the reporting and in the dataset