Scale-Adjusted Platform Visibility in a Historic Urban District: A Bias-Aware Spatial Screening Framework Using POI Data

Map-platform visibility in historic urban districts does not necessarily increase in proportion to facility supply. This study develops a scale-adjusted spatial screening framework to identify locations where platform visibility is higher or lower than expected from POI quantity. The Gulou area in Kaifeng, China, is examined using 1461 points of interest (POIs) and API-derived rating and image fields. Thirteen POI categories are grouped into four functional types, and an entropy-weighted platform visibility index is constructed for 141 POI-containing grids at an approximately 200 m resolution. A cross-validated linear benchmark estimates the scale-based expectation of platform visibility, while a random forest examines nonlinear associations among functional characteristics. Consumption-stay facilities account for 58.38% of all POIs, whereas public-support facilities show substantially lower platform-field coverage. After scale adjustment, 20 high-supply–below-expected grids and 21 low-supply–above-expected grids are identified as candidate locations for field verification. Repeated cross-validation showed class-specific stability, with stronger persistence of the high-supply–below-expected stratum than of the low-supply–above-expected stratum, while threshold and grid-origin tests indicated uncertainty in exact spatial boundaries. The framework therefore treats platform records as a selective observation layer and uses scale-adjusted deviations to formulate spatial hypotheses for future field verification. Its practical usefulness for field auditing remains to be independently validated.