Spatial patterns of interprovincial mountain geographical names in China and implications for regional governance

Suffering from fragile environment, poor accessibility and infrastructure, as well as social, political and economic marginality, the interprovincial mountain geographical entities are difficult areas for the regional governance in China. By analyzing the spatial patterns and the influencing factors of the interprovincial mountain geographical names (IMGNs), the goal of this research is to clarify the geographical features of IMGNs and offer alternatives for the management of interprovincial mountain regions in China. The spatial visualization, the analysis of spatial agglomeration and the influencing factors of IMGNs were all implemented under a geographical information system. Results showed that there were 6869 IMGNs in China according to the database of “China’s Second National Survey of Geographical Names (2014–2018)”, including 4209 mountain geographical names, 1684 mountain peak geographical names and 976 the other mountain geographical names. Hunan Province had the largest number of names while Shanghai had the smallest number of names. In addition, the spatial variance of the mountain peak names and the mountain names were larger than that of the other mountain geographical names, and the IMGNs showed a significant clustering phenomenon in the southern part of China. The relative elevation and the population had an impact on the distribution of the IMGNs. The largest number of the names occurred where the relative elevation was between 1000–2000 m and where the population was between 40–50 million. Density of unnamed interprovincial mountain geographical entities declined from west to east in China. The analysis of generic names of different types of IMGNs implied that the naming of IMGNs is inconsistent. Based on these analyses, it is suggested that the government should take the IMGNs as management units, strengthen the naming of unnamed interprovincial mountain geographical entities, standardize the generic names of IMGNs and identify areas of poverty based on the survey of IMGNs.