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Mechanisms influencing spatiotemporal differentiation of tourist towns based on geographic detector: A case study of Fujian Province. | LitMetric

AI Article Synopsis

  • The study focuses on the development of tourist towns in Fujian Province, analyzing 155 towns to understand their spatial and temporal differences using various geographical indices.
  • It identifies six key factors influencing these differences: traffic networks, urbanization levels, population distribution, industrial structure, socioeconomic conditions, and policy guidance, with interactions among these factors playing a significant role.
  • The findings can help guide the development of similar tourist towns and improve the overall planning of urban and rural areas in related regions.

Article Abstract

The construction of tourist towns is an important aspect of new-type urbanization construction. In this study, 155 tourist towns in Fujian Province were selected as samples to analyze spatiotemporal differentiation using the geographical concentration index, nearest neighbor index, and local correlation index. Then, a geographic detector model was used to detect the factors that influence the spatiotemporal differentiation of tourist towns and to analyze the explanatory power and interaction of these detection factors. Finally, the mechanisms underlying the detection factors were discussed. Factors affecting the spatiotemporal differentiation of tourist towns in Fujian Province were core factors of traffic network, level of urbanization and population distribution; important factors of industrial structure and socioeconomic basis; and a fundamental factor of policy guidance. These six factors interacted to jointly affect the spatiotemporal differentiation of tourist towns in Fujian Province. The results of this study can provide a basis for the development of tourist towns in other similar regions and have reference value for better optimizing the pattern of urban and town systems and coordinating the synergistic development of urban and rural areas.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10994384PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298078PLOS

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