Rapid urbanization and industrial restructuring reshape landscapes and may elevate landscape ecological risk (LER) with strong spatial heterogeneity and scale dependence. To support territorial spatial planning with governance-relevant units, we assessed LER for township-level units (towns and subdistricts) in the Nanjing Metropolitan Area (NMA), China, and explored its drivers using a coupled Optimal-Parameter Geodetector and multiscale geographically weighted regression (OPGD-MGWR) framework. Using GlobeLand30 land-use maps for 2000, 2010 and 2020, we constructed an LER index, examined spatial clustering (Global Moran's I and LISA), and quantified global determinants, interactions, and locally varying effects. From 2000 to 2020, cropland declined while built-up land expanded, and LER remained moderate to high, forming a persistent concentric pattern (low core–high periphery–low outer edge) with significant positive spatial autocorrelation (Moran's I > 0.43). OPGD identified highway accessibility and nighttime lights as the dominant factors, with most factor pairs showing bivariate or nonlinear enhancement. MGWR revealed clear scale heterogeneity: NDVI and distance to district/county centers had near-global negative effects, whereas nighttime lights and water area exhibited strong local non-stationarity. The proposed township-level framework links risk levels, dominant drivers, and effective scales, enabling differentiated risk governance across multi-jurisdictional metropolitan regions. • Landscape ecological risk was assessed at township level from 2000 to 2020. • Cropland loss and built-up growth reinforced persistent landscape ecological risk. • Risk showed a persistent concentric low–high–low spatial pattern. • Highway access and night lights were dominant risk drivers. • Multiscale regression revealed strong spatial heterogeneity in drivers.
Wang et al. (Mon,) studied this question.