Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour

Abstract

Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study.

Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area

“…Tourist flow can depict the spatial distribution of tourists from a dynamic perspective. Scholars usually use Markov chains [13], spatial clustering [14], kernel density estimation [15], exploratory spatial data analysis [16], and other methods to detect the spatial patterns of tourist flows. In recent years, the introduction of network science has provided a new way to analyze tourist flow [17].…”

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

“…The researchers in [13] used spatial clustering methods to mine tourist destinations and preferences, in which the regions of tourist attractions for each tourism category were derived by the clustering algorithm. The researchers in [14] used a density-based spatial clustering algorithm to study tourist behavior, and by extracting the tourist behaviors, the tourism hot-spots were extracted as they related to tourist behavior. In [15], the clustering algorithm was used to generate tourist-attraction clusters via network and geographic information system (GIS) analyses, and three tourist-attraction clusters were extracted.…”

“…As seen in [1][2][3][4][5][6][7], clustering algorithms have been used in tourism research for POI extraction, data mining, algorithm modeling, transportation behavior, etc. The other clustering methods in [8][9][10][11][12][13][14][15] indicated that spatial and attribute data of tourist attractions were the main targets that were used to generate proper tourism categories, extract tourist preferences, and recommend appropriate tourist destinations. The studies concerning tourist-attraction data extraction and tour-route algorithms that were used in [16][17][18][19][20][21][22][23][24][25][26][27][28][29] focused on three specific aspects.…”

A New Methodology to Study Street Accessibility: A Case Study of Avila (Spain)

“…As already mentioned, accessibility has great impact on a key economic driving sector such as tourism [8]. Many scientific studies focus on tourist trends, proposing theories about their behavior to predict even their seasonality [9,10]. Within the different typologies of tourism, the following research refers to cultural tourism, studying behavior through images of geolocation techniques to even propose tourist routes within the different cities [11].…”