ABSTRACT
This dataset presents perceived values and socioeconomic indicators collected in Siaya, a rural county in Kenya in 2022. The data was obtained from 300 household surveys and group interviews conducted in six sub-counties across eleven villages. Socioeconomic data were collected with a special focus on climate change vulnerability. Information on housing, health, water accessibility and usage, electricity accessibility and usage, extreme weather events, community service, and information accessibility were mapped across survey questions. The user-perceived value (UPV) game - a perception-based surveying approach - was used to elicit local communities' needs and perceptions of climate change challenges. The UPV game involves asking interviewees to select which graphically depicted items would be most necessary in different situations and probing them for the reasons behind their choices (why-probing). The data was collected in two languages (Dholuo and English) and then translated into English. These surveys and interviews were conducted to better understand the needs of rural Kenyan communities and their perceptions of climate change, with the aim to identify ways to build resilience. Kenyan policymakers can use the dataset to inform county-level energy and development plans, while researchers and development practitioners can use the dataset to better design their research and programmes to reflect local needs and values.
ABSTRACT
The 2019 Energy Act requires each of Kenya's 47 counties to independently develop energy plans. As county energy planning accelerates, it is important to understand the availability and readiness of data required to facilitate it. This article identifies, evaluates, and pre-processes openly available data to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. In this way, it provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis, and guidance to replicate this work in other counties. We classify the readiness level of each data type for county energy planning on a traffic light scale (i.e. green, amber, red) based on availability, accessibility, recency, accuracy, spatial resolution, and format (i.e. whether processing is required before use). Of the 25 core data inputs for OnSSET at the county-level, we find that 14 have a green, six have an amber, and five have a red readiness-level. Data processing requirements are documented, and the processed data for Kitui county are made available as a ready-to-use set of input parameters for OnSSET. While this data was collected for Kitui, the data sources and processing steps are largely applicable in other counties.
