Coastal Tourism Spatial Planning at the Regional Unit: Identifying Coastal Tourism Hotspots Based on Social Media Data

Abstract

There is an increasing need for spatial planning to manage coastal tourism, and applying social media data has emerged as an effective strategy to support coastal tourism spatial planning. Researchers and decision-makers require spatially explicit information that effectively reveals the current visitation state of the region. The purpose of this study is to identify coastal tourism hotspots considering appropriate spatial units in the regional scale using social media data to examine the advantages and limitations of applying spatial hotspots to spatial planning. Data from Flickr and Twitter with 30″ spatial resolution were obtained from four South Korean regions. Coastal tourism hotspots were then derived using Getis-Ord Gi. Comparing the derived hotspot maps with the visitation rate distribution maps, the derived hotspot maps sufficiently identified the spatial influences of visitors and tourist attractions applicable for spatial planning. As the spatial autocorrelation of social media data differs based on the spatial unit, coastal tourism hotspots according to spatial unit are inevitably different. Spatial hotspots derived from the appropriate spatial unit using social media data are useful for coastal tourism spatial planning.

Evaluating GIS as a Tool to Support Sustainable Coastal Planning: Climate Change Awareness as a Moderator

“…Although coastal cities are among the most beautiful, important, profitable, and popular, they are also inequitable and dangerous locations to live and work (Papageorgiou, 2016;Jarratt & Davies, 2020;Kim et al, 2021). Due to their diverse and intense usage in different economic fields, coupled with climate change-related consequences, they are increasingly threatened by coastal hazards, suffer ecological degradation, and face several obstacles in planning for and managing beaches (Irazábal, 2018;Hjalager, 2020;Kim et al, 2021). Urban planning for disaster prevention has become a critical concern for the socioeconomic development of coastal cities (Birkic et al, 2014;Papageorgiou, 2016).…”

“…The importance of tourism planning for coastal regions appears in its ability to meet the needs of tourism development on beaches while preserving the environment (Hjalager, 2020;Singh et al, 2021). This is achieved through development plans and decisions that are generated from planning and implementing policies that seek to enhance the coastal environment, taking into consideration the enormous challenges that the world has recently experienced due to climate change, such as coastal erosion and flooding (Irazábal, 2018;Kim et al, 2021).…”

“…Tourism planning for coastal regions includes a set of plans and strategies according to three levels: the national, the regional, and the local level (Prasetyo et al, 2018;Singh et al, 2021). Each level has a series of interconnected and complementary activities that aim to achieve the intended objectives of sustainable coastal planning (Norman, 2009;Irazábal, 2018;Hjalager, 2020;Kim et al, 2021). as demonstrated in the following table (1).…”

“…The GIS constructs consist of nineteen items to assess the availability of GIS tools at the local authorities, which were modified from Shyti and Kushi (2012) and Lee et al (2013). The sustainable coastal planning construct has been examined by using twelve items that were obtained from Irazábal (2018), Hjalager (2020), and Kim et al (2021). The last construct is "Climate Change Awareness" as a moderating variable, which comprises five items that were adapted from Fang et al (2018) and Arabadzhyan et al (2021), as illustrated in Table 2.…”

Application of Clustering Algorithms in the Location of Electric Taxi Charging Stations

“…Cluster analysis is a kind of unsupervised learning, and there are many kinds, such as the division-based K-means clustering algorithm [16], hierarchical clustering algorithm (agglomerative and splitting) [17], and DBSCAN algorithm based on density clustering, etc. At present, clustering algorithms are widely used in short-time traffic flow prediction [18], logistics center location selection [19], traffic flow speed prediction [20], and travel hotspot area research [21], etc., but they are less widely applied in charging station location selection. For example, Zhang et al [22] developed a siting model for electric cabs based on their dynamic distribution and charging demand using the K-means clustering method and the center of gravity method, and applied it to the problem of siting electric cabs in Chengdu, China.…”