Examining village characteristics for forest management using self- and geographic self-organizing maps: A case from the Baekdudaegan mountain range network in Korea

•Villages were classified using unsupervised machine learning algorithms.•Optimal SOM size was fixed using topological quantization and topographic errors.•South Korean villages were characterized using 18 socio-ecological indicators.•Clustering villages with forest networks may support nature conservation. Understanding the village characteristics linked to forest networks is essential for the scientific management of forest resources. Forests are complex socio-ecological systems. This study classifies the resources and characteristics of forest networks and neighboring villages using unsupervised learning algorithms: self-organizing maps (SOM) and geographic-self-organizing maps (Geo-SOMs). Considering ecological, economic, and sociocultural indicators, 18 covariates of 379 villages in two forest networks of the Baekdudaegan Mountain Range in South Korea were analyzed. The data visualizing map size was fixed based on changes in quantization and topographic errors of the same grid maps, and the number of clusters was determined by comparing K-means and hierarchical clustering techniques. An optimal map size of 17 × 12 grids and six clusters was used for further classification of the input data for both SOM and Geo-SOM analyses. The common characteristics of villages were identified using SOM classification, whereas geographically bounded characteristics were identified using Geo-SOM. The approach introduced in this study can be applied to socio-ecological classification and the design of sustainable forest management policies that link the remote sensing and geographic information systems.