Rural Settlement Turnover Across Cross-Administrative Geographical Contexts in a Plateau–Mountain Region: Evidence from Yunnan, China

Administrative boundaries and net area change can obscure rural settlement dynamics in geographically heterogeneous mountain regions. We examined these two sources of information loss in Yunnan, China, using a state–balance–process–interface framework. Settlement persistence, gain, and loss were mapped from 30 m land use data for 1990, 2000, 2010, and 2020. Five cross-administrative geographical context units (GCUs) were delineated on a 1 km grid, and turnover occurrence and conditional positive intensity were compared on a 10 km grid. Settlement area increased by 384.29 km2 over 1990–2020, whereas cumulative turnover reached 1690.36 km2, or 4.40 times the net increase. Conservative filtering of immediate reversals reduced turnover to 1117.16 km2, or 2.91 times the net increase. Both estimates retained the ordering 2010–2020 > 2000–2010 > 1990–2000. However, filtering retained only 9.46% of early-period turnover, which made its absolute magnitude particularly sensitive to temporal consistency. In 2010–2020, a near-zero net change (−20.09 km2) coexisted with In 2010–2020, near-zero net change (−20.09 km2) coexisted with substantial turnover under both baseline and conservative accounting (916.28 and 699.56 km2, respectively). GCU rankings differed across settlement state, net balance, and turnover. Lake–Basin had the highest density and turnover intensity, Hill–Mountain Transition contributed the largest absolute net gain, and Alpine Canyon had almost no turnover. Cropland supplied 71.88% of cumulative settlement gains, while other built-up land became an increasingly prominent destination of settlement losses. Independent geographical references and spatially blocked administrative comparisons supported the GCUs’ geographical relevance and modest additional information. The framework identifies contrasting land transitions for regional monitoring and planning assessment. Its wider application requires locally defined contexts and independent testing; it does not establish policy effects or ecosystem condition.