Assessing Healthcare Accessibility Using Euclidean GIS Methods in Data-Constrained Rural Settings: Evidence from Yobe State, Nigeria
DOI:
https://doi.org/10.56919/usci.2651.047Keywords:
Spatial, Accessibility, GIS, Modelling, EuclideanAbstract
Spatial accessibility to healthcare remains a critical challenge in rural and semi-urban regions where limited or incomplete road network data constrain the application of conventional network-based GIS models. This study evaluates the effectiveness of Euclidean (straight-line) distance modelling as a contextually appropriate alternative for assessing healthcare accessibility in data-constrained environments. A cross-sectional geospatial design was employed, involving a complete census of 46 healthcare facilities and 146 settlements across Nangere Local Government Area, Yobe State, Nigeria. Facility coordinates were obtained through GPS survey and analysed using GIS techniques including Average Nearest Neighbour (ANN), buffer analysis, and desire-line mapping. Results indicate a dominance of primary healthcare facilities, with only one secondary-level facility and no tertiary facility in the study area. ANN analysis produced a nearest-neighbour ratio of 1.09 (z = 1.135; p = 0.256), suggesting a random to slightly dispersed spatial distribution. Accessibility assessment based on the WHO 5 km benchmark revealed significant service gaps, with 51 settlements located beyond 5 km, recording an average distance of 7.71 km and a maximum of 12.83 km to the nearest facility. In contrast, only 13 settlements were within 1 km, indicating highly uneven spatial coverage. Population facility analysis further revealed disparities, with wards such as Pakarau and Tikau (populations 22,297 and 19,721, respectively) having only one PHCC each, far below recommended standards. Similarly, only 5 maternity/PHC facilities exist against a projected requirement of 24, highlighting critical infrastructure deficits. The study demonstrates that although Euclidean distances tend to underestimate actual travel distances, they reliably preserve spatial patterns of accessibility and effectively identify underserved areas. It concludes that Euclidean GIS modelling provides a practical, transparent, and sufficiently robust tool for healthcare accessibility assessment in data-limited rural settings. The study recommends the integration of GIS-based planning for equitable facility siting, prioritization of settlements beyond 5 km, and investment in geospatial data systems to support future hybrid accessibility modelling.
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