Rural sustainability planning requires spatially explicit methods that integrate agricultural resource bases, ecological condition, low-carbon feasibility, community implementation support, and cultural landscape values. Although the Circular and Ecological Sphere (CES) concept offers an integrative framework for rural transition, empirical CES studies remain largely qualitative or policy-oriented. This study develops a FarmMap-integrated Python-GIS workflow for proxy-based CES-oriented spatial prioritization in Yangpyeong-gun, a peri-rural county on the eastern fringe of the Seoul metropolitan region in Korea. Public spatial and administrative datasets were integrated into thirteen indicators grouped under five CES-relevant axes. The model does not measure realized circular material flows, governance quality, resident participation, or carbon emission reduction directly; instead, it identifies where CES-relevant spatial potentials co-occur. An axis-balanced entropy model assigned equal total weight to each axis while applying entropy weighting within axes. Robustness was tested through equal-weight, axis-emphasis, raw entropy diagnostic, Monte Carlo perturbation, and spatial-scale sensitivity analyses using 100 m diagnostic, 500 m, and eup/myeon supports. The final 250 m priority surface identified the top fifth of analyzed Yangpyeong-gun area as very-high relative priority and remained stable across weighting and spatial-support diagnostics. Rural-experience villages and village enterprises had significantly higher CES scores than random background locations. The results demonstrate a reproducible first-stage spatial screening workflow for CES-oriented rural planning while clarifying the limits of proxy-based circularity, governance, and low-carbon indicators.
E Y Park (Mon,) studied this question.