Analysis of Spatial Layout Influencing Factors in National Forest Tourism Villages: A Case Study of Liaoning Province

Forests, as tourism resources with ecological and aesthetic value, play a significant role in rural development. Forest villages, which rely on forest resources, are an essential component of rural construction. Studying the spatial distribution characteristics and influencing factors of national forest villages within provincial administrative areas provides valuable insights into the sustainable development of rural tourism and the achievement of rural revitalization goals. This study examines 125 national forest villages in Liaoning Province. Based on the data on the geographical locations of the villages and their related influencing factors collected during the period from May to December 2024, spatial indices such as the nearest neighbor index, Gini index, and kernel density have been analyzed using mathematical statistics and ArcGIS spatial analysis methods. Additionally, this research investigates various factors influencing the distribution of forests and rural areas, as well as the interaction mechanisms among these factors. The results indicate the following. (1) The spatial distribution of national forest villages in Liaoning Province is clustered and uneven, with a pattern of “dense in the east and west, sparse in the middle”. (2) The number of forest villages in Liaoning Province is generally positively correlated with forest coverage, temperature, rainfall, road network density, and river network density. Conversely, it is negatively correlated with economic development level, population density, total population, and altitude. (3) Geographical exploration results suggest that economic development level and forest coverage rate are the most significant factors affecting the spatial differentiation of forest and rural areas in Liaoning Province. Interaction analysis reveals that river network density and forest coverage rate have the strongest combined effect, followed by total economic output and forest coverage rate.