Spatial distribution, clustering and locational correlates of aquaculture enterprises in Nyeri County, Kenya

Spatially explicit information is necessary for aquaculture support, management and environmental planning. This descriptive GIS-based spatial study examined the distribution of registered aquaculture farmers and production units, assessed spatial patterns, and examined locational correlates of aquaculture enterprises across the eight sub-counties of Nyeri County, Kenya. Available dated aquaculture farmers administrative workbook records span 9 November 2021 to 7 January 2024; 1 of 1,151 aquaculture farmers records contained a malformed date entry, while undated records from the broader county register were also retained where relevant. We analysed 1,203 registered farmer records, including 1,176 valid georeferenced locations for point-pattern analysis and a previously prepared GIS water-proximity classification containing 1,191 records. Distribution was assessed using farmer counts, pond counts and pond area; spatial concentration was evaluated using kernel density and nearest-neighbour analysis; and farm locations were overlaid with agro-ecological zones, rivers, dams and roads. Registered farmer counts showed limited inequality among the eight administrative units (Gini = 0.047), whereas pond counts (Gini = 0.110) and pond area (Gini = 0.301) were more unequal; these administrative summaries do not demonstrate equitable programme reach. Kieni East and Mathira East each had the largest number of registered farmers (167), Kieni East had the most ponds (250), and Mathira West had the largest pond area (73,520 m²). Farmer count was positively but not significantly correlated with pond count ( r = 0.668, p = 0.070) and was not significantly correlated with pond area ( r = -0.472, p = 0.238). Analysis of the 1,176 valid GPS points in WGS 84 / UTM zone 37 S showed strong county-wide clustering: the observed mean nearest-neighbour distance was 0.353 km compared with an expected distance of 0.708 km, giving an index of 0.498 (z = -32.921, p < 0.001). Existing GIS output classified 846 records (71.03%) within 1 km of a mapped river or dam; because no land-availability baseline was available, this percentage is interpreted descriptively rather than as evidence of preference for water corridors. In the complete-case logistic analysis ( n = 1,138; 752 events), Mathira West and Tetu had higher odds of the short nearest-neighbour distance category and log-transformed pond area had lower odds; Kieni West, pond count and stocking status were not significant. Residual spatial autocorrelation remained, and a 5-km spatial-block sensitivity model retained Mathira West and log pond area as the most robust associations. The findings show that registered participation, spatial concentration and production-unit capacity have distinct geographies. An updated georeferenced registry would support differentiated extension, water management, pond rehabilitation, biosecurity, input delivery and market aggregation.