Speaker
Description
Until now, tourism statistics data have been limited to metrics such as the number of visitors to attractions, international tourist card sales, and the number of inbound and outbound tourists. However, recently, the advent of Origin-Destination (O/D) data in the South Korean Capital Region for tourism-related population movements have begun to be provided, it has become possible to analyze tourist mobility patterns at a micro-spatial scale and identify temporal tourism trends that were previously unattainable. Therefore, this study aims to overcome the limitations of traditional tourism research, which has primarily relied on surveys and qualitative methods, by utilizing tourist O/D (Origin-Destination) data extracted from urban datasets, this research conducts a quantitative and empirical analysis to explore these novel dimensions of tourism dynamics.
First, the study examine tourism flows and patterns from multiple perspectives by conducting gravity modeling and network analysis sing O/D data from South Korea. The gravity model generates a matrix to quantify the scale of inter-regional tourism flows, and then the structural relationships (connectivity, centrality, and betweenness) and patterns within these flows are visually identifies through network analysis. Additionally, the spatiotemporal panel analysis is conduct to uncover the temporal and spatial trends of tourism flows and causal relationships. Through these analysis processes, this study aims to comprehensively understand the tourism flows, spatio-temporal patterns and trends, and inter-regional connectivity.
The study integrates theoretical frameworks specifically the theory of tourist destination attributes, central place theory, and gravity models. As such, it employs gravity model analysis, network analysis (including centrality analysis), and spatiotemporal panel analysis. The study replaces qualitative variables commonly used in traditional tourism research with quantifiable variables for empirical evaluation. The dependent variable is the movement volume of international tourist. Key independent variables include destination choice attributes categorized as cultural factors (tourism facilities, historical and cultural resources, and festivals), natural factors (green spaces and waterfront areas), service factors (accommodation facilities and convenience stores), accessibility factors (public transportation density and proximity to airports), and
prices factors (average price of goods or services at destination). Control variables include climatic characteristics (maximum temperature, diurnal temperature range, snowfall, and precipitation), demographic characteristics (gender and age of tourists), temporal characteristics (seasons, holidays, and weekends), and land use characteristics (developed commercial districts, local markets, traditional markets, and designated tourism zones).
This study found that, first, gravity model analysis identified regions with high and low tourism density. Second, network analysis assessed the centrality of regions frequently visited by international tourists, such as tourism central areas (degree centrality and eigenvector centrality) and tourism transfer areas (betweenness centrality). Visualizations further revealed regions with high and low inter-regional connectivity. Lastly, through spatiotemporal panel analysis, the study uncovered the spatiotemporal factors influencing international tourist movements and evaluated the spillover effects of tourist movements from specific regions to neighboring areas.
The significance of this study lies in overcoming the limitations of prior tourism research, which predominantly relied on qualitative or survey-based methods, by quantitatively analyzing objectively measured tourist movement patterns and flows. Specifically, this study utilizes O/D data to provide analytical and visual finding into the movement patterns of international tourists. These findings will provide actionable policy insights for enhancing international tourist activities by identifying which the destinations preferred by international tourists and to identify the attractive factors of these destinations.
| Keywords | Tourism-Trends; Origin-Destination-Data; Central-Place-Theory; Gravity-Model; Network-Analysis; Spatiotemporal-Panel-Analysis |
|---|---|
| Best Congress Paper Award | Yes |
