ObjectiveThis study evaluates POI-based spatial accessibility to candidate electric-vehicle charging facilities in central Ningbo and develops a transparent screening framework for identifying urban destinations with comparatively weak road-network access.MethodsCandidate charging-facility POIs and urban functional POIs were collected through the Amap Web Service API from 20 to 30 June 2026. A directed OpenStreetMap motor-vehicle network was used to calculate nearest-facility road-network distance. Network and Euclidean distances were compared directly; fixed-rate energy use was reported only as an illustrative conversion of access distance. Non-parametric group comparisons, alternative priority-weighting schemes, and outlier-exclusion tests were used to assess robustness.ResultsThe final inventory contained 1,306 candidate charging-facility POIs and 9,426 urban functional POIs; 32 functional POIs were unreachable on the constructed network. The median network distance was 0.626 km, compared with a median Euclidean distance of 0.365 km, and the median detour ratio was 1.577. Network distance exceeded Euclidean distance for 93.59% of valid pairs (Wilcoxon p < 0.001). District and POI-category differences were significant, with Beilun and tourism/park POIs showing the greatest mean access distances. The baseline priority model classified 1,409 POIs as high priority and 940 as very high priority; alternative weighting schemes retained 76.5%–93.8% of the baseline top-quartile set.ConclusionThe proposed contribution is a reproducible, data-light diagnostic workflow that integrates network topology, destination context, direct Euclidean comparison, and uncertainty testing. The results identify areas for further operational investigation rather than verified demand shortfalls or construction sites. No user-behaviour, charging-transaction, station-capacity, queueing, or route-specific vehicle-energy data were available.