National Tourism Policy and Spatial Patterns of Domestic Tourism in South Korea

Abstract

The implementation of national tourism policy is manifested in the development of destinations and in patterns of tourism activity. Based on Brenner's notions of spatial rescaling and spatial selectivity, this study assesses the changing distribution of domestic tourism in South Korea between 1989 and 2011, and relates these changes to the national tourism policies put in place during those years. Spatial statistical techniques including Moran's global I statistic and local indicators of spatial association are employed. Findings suggest that while domestic tourism activity became less concentrated at the macro level during the study period, at a finer scale it is clear that this deconcentration is nevertheless occurring in a clustered manner. Thus, while the traditional emphasis on Seoul and the southeast appears to have declined, the distribution of tourism's benefits is still uneven. Observations with regards to future tourism policy and concomitant spatial patterns of development and activity are provided.

Territory and Sustainable Tourism Development: a Space-Time Analysis on European Regions

“…Moreover, despite the close relationship between tourism and territory, only a few works applied spatial panel data models in tourism: Marrocu, Paci (2013) analysed the determinants of tourism flows between 107 Italian locations; Yang, Fik (2014) examined spatial spillovers and spatial heterogeneity in order to explain the variability in tourism growth across 342 cities in China; Kang et al (2014) analysed the territorial impacts of national tourism policies in South Korea; Ma et al (2015) focused on the spatial correlation between tourism and urban economic growth in 272 Chinese regions; Majewska (2015) applied techniques of exploratory spatial data analysis to study the inter-regional agglomeration effects in tourism activities in Poland.…”

Seasonal Spatial Activity Patterns of Visitors with a Mobile Exercise Application at Seoraksan National Park, South Korea

“…Sixth, spatial autocorrelation analyses via global Moran's I statistic were used to reveal the seasonal spatial patterns of activity areas. Global Moran's I statistic has been commonly used to measure spatial clustering [39] based on Tobler's First Law of Geography [40]. The global Moran's I statistic is measured as follows: Data analysis consisted of eight steps, which were implemented via ArcGIS (version 10.4.1., ESRI Redlands, NY, USA) and the ArcGIS Spatial Statistics Tool extension.…”

“…[39,41,42]. Seventh, Getis-Ord G * i statistic (hereafter, G * i statistic) was employed to identify where the significant hot spots of seasonal activity were located in the study area.…”

Spatial autocorrelation analysis of tourist arrivals using municipal data: A Serbian example

“…In this study a global autocorrelation analysis was carried out for each year using Spatial Autocorrelation tool in ArcGIS Spatial Statistics toolbox which implement Global Moran's I index. Global Moran's I shows a formal indication of the degree of linear association between the spatial units and their neighbour (Yu, Wei, 2008) and has been used in some recent tourism research (Zhang, et al, 2011;Yang, Wong, 2013;Luo, Yang, 2013;Kang, et al, 2014). To test null hypothesis which claims that feature values are randomly distributed across the study area Moran's score index is calculated.…”

“…Serbia, likewise other developing countries, embrace tourism to jumpstart its socio-economic development and to uphold sustainable regional development. In the more recent studies, spatial statistical techniques were used more extensively to analyze tourism development inequality and tourism flow patterns in various countries (see Klepers, Rozite, 2009;Chhetri, et al, 2013;Yang, Wong, 2013;Kang, et al, 2014;Xing-zhu, Qun, 2014).…”