The Footprint of Tourism on Leisure Pattern: A Comparative Analysis of Tourism and Non-Tourism Villages Using Spatial Statistical Algorithms in GIS

Document Type : Research Paper

Authors

1 Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

2 Department of Tourism Management, Faculty of Cultural Heritage, Handicrafts and Tourism, University of Mazandaran, Babolsar, Iran

3 Department of Geography and Tourism Planning, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

10.22034/gp.2026.71656.3520

Abstract

Objective: The purpose of this study is to compare and analyze the spatial pattern of leisure in tourism and non-tourism villages in order to determine the impact of tourism on the spatial distribution and concentration of leisure indicators.

Methods: The research method is descriptive–analytical, and both spatial and statistical data were collected through a questionnaire and spatial datasets. The statistical population consisted of rural residents of the Dezpart region. Using a convenience sampling method, 246 individuals were determined as the sample size. In the ArcGIS environment, spatial statistical algorithms including Moran’s I, Hot and Cold Spot Analysis, and Spatial Clustering Analysis were used to identify clusters and spatial distribution patterns across the rural areas.

Results: The Results revealed a significant difference (p = 0.000) between tourist villages, with a mean rank of 217.5, and non-tourist villages, with a mean rank of 86.5, in terms of the overall leisure time indicators. Furthermore, the results indicated that both active and passive leisure time in tourist villages were significantly better and qualitatively different compared to non-tourist villages. In this regard, the results showed that indicators such as recreational travel, sports activities, family tourism, internet use, and scientific study were also considerably more favorable in tourist villages. Spatial analysis using the hot spot–cold spot algorithm and Moran's I index demonstrated that the observed differences were mainly influenced by the tourism status of the villages, and the spatial pattern was random.

Conclusions: These findings underscore that rural tourism is an effective factor in enhancing the quality, diversity, and participation in leisure time, as well as improving the well-being of residents.

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