Spatial pattern analysis of built-up land use and zoning conformity using polygon-based statistics: a case study of Tegal city, Indonesia

Rapid urban expansion in coastal cities increasingly challenges the effectiveness of spatial planning enforcement. Tegal city, located on Java’s northern coast, is experiencing intensive built-up land development accompanied by growing spatial clustering. This study analyses polygon-level built-up land patterns using Ripley’s K-function to identify multi-scale clustering, Global Moran’s I to assess spatial autocorrelation, and Local Moran’s I (LISA) to detect local clusters. Built-up Land Use/Land Cover (LULC) polygons derived from OpenStreetMap (2024) were compared with the official Spatial Plan (RTRW 2021–2031). The objectives are to identify the clustering behaviour of built-up areas and assess conformity between actual land development and planned zoning. The results reveal a concentric-clustered spatial structure and localized zones of spatial non-conformity with the official spatial plan. Transitional areas exhibit significant land-use mismatches, indicating weaknesses in spatial-use control. A Global Moran’s I value of 0.145 (p < 0.05) indicates weak but statistically significant positive spatial autocorrelation. These findings highlight the importance of strengthening zoning enforcement and updating detailed spatial plans (RDTR). The study demonstrates how integrated polygon-based spatial statistics can support routine spatial planning evaluation and zoning enforcement in rapidly developing coastal cities.