Tourist Movement Patterns Research Articles

Travel patterns and spatial structure: understanding winter tourism by trajectory data mining

ABSTRACT Mining tourists’ spatiotemporal patterns are fundamental for comprehending and applying spatial structure theories in tourism research. This study mines 27,981 tourist-generated trajectories in Beijing winter tourism to reveal the spatiotemporal patterns and spatial structures using a framework combining similarity measures and improved density-based spatial clustering of applications with noise algorithm. The findings indicate that Beijing winter tourism has established a spatial structure consisting of “three poles-two axes-two secondary poles.” A total of 50 popular routes and 12 patterns of winter tourism can be categorized into three main types and six subcategories: winter tourism leading (skiing oriented, skating oriented), winter tourism with sightseeing (urban snow scene sightseeing, mountain snow scene sightseeing), and winter tourism with leisure (skiing + hot spring therapy, skating + city walking). The findings demonstrate that identifying spatial structures through tourism pattern mining is scientific and feasible. This study examines the movement patterns of tourists to reveal tourism spatial structure and incorporates spatial geography theory to assure the accuracy and scientific validity of the identification process, which enables a comprehensive knowledge of the spatial structure of Beijing's winter tourism industry and broadens the application scenario of spatial geography theory in the era of big data.

How Social Media Data Mirror Spatio-Temporal Behavioral Patterns of Tourists in Urban Forests: A Case Study of Kushan Scenic Area in Fuzhou, China

Exploring the spatial distribution of tourist attractions and comprehending the spatio-temporal behaviors of tourists within tourist attractions can provide local planning agencies, destination marketing organizations, and government departments with essential evidence for decision-making processes. This study examines the spatio-temporal behavior patterns of tourists in the Kushan Scenic Area by analyzing GPS trajectory data acquired from social media platforms. The investigation primarily utilizes three research methodologies: grid analysis, Markov chain, and K-means clustering. The grid analysis results reveal three spatial distribution patterns within the scenic area, while the outcomes from the Markov chain and K-means clustering delineate six tourist movement patterns, along with three choices regarding travel time. This finding holds significant practical implications for enhancing the attractiveness of scenic areas, optimizing spatial layout, and improving tourists’ experiences.

빅데이터 기법을 활용한 외식관광이동패턴 연구

This study explored the movement patterns of tourists’ behavior concerning their food and beverage product choices, which differs from previous studies focusing on tourist movement patterns related to tourist products such as tourist attractions or accommodations. Navigation data of Jeju Island tourists visiting restaurants and cafes were analyzed through descriptive analysis, heatmap analysis, and social network analysis, which helped identify the movement patterns of tourists related to their food and beverage product choices. The heatmap analysis revealed that the movement patterns of tourists who visited restaurants were mostly centered around the urban areas of Jeju-si and Seogwipo-si, while the movement patterns of tourists who visited cafes were observed mostly in outlying areas such as along seasides or mountain areas. Additionally, the social network analysis revealed that the central nodes of restaurant networks were located near urban areas, while the central nodes of cafe networks were located further away from urban areas. This study can contribute to the theoretical understanding of differences in tourist movement patterns based on the purpose of the tourists’ visits. Furthermore, the study provides important insights for DMOs to efficiently operate and effectively develop and promote food and beverage products at destinations.

Tourist Movement Patterns and the Effects of Spatial Configuration in a Cultural Heritage and Urban Destination: The Case of Madaba, Jordan

This study investigated the impact of spatial configuration on tourist movement patterns and its role in managing cultural heritage destinations in the city of Madaba. Understanding tourist movement patterns is important for the development of sustainable tourism practices in cultural heritage destinations, as it can help to identify attractions of high tourist impact and inform the development of strategies to mitigate the negative impacts on site preservation and management. Madaba is one of the largest homes to ancient mosaics and is one of Jordan’s most important cultural heritage tourism destinations. This study was carried out using a variety of methods, including observation, site exploration, tourist diaries, questionnaires, and interviews. Space syntax was one of the spatial analytical tools used to assess connectivity and integration. The study found that the arrangement of attraction points and routes, physical and visual connectivity, accessibility, and the location of tourist activities all influenced the spatial behavior of tourists. The findings of this study can be used to guide management and development policies such as redistributing tourist flow, managing and developing tourism resources, and promoting the adaptation of sustainable tourism.

POLA PERGERAKAN WISATAWAN NUSANTARA iGENERATION BERWISATA DI BALI PADA MASA PANDEMI COVID-19

This writing analyzes how the movement patterns of iGeneration Nusantara who visited Bali during the COVID-19 pandemic. The purpose of writing is to determine the characteristics of iGeneration domestic tourists, and to analyze the movement patterns of iGeneration domestic tourists. In this paper the researchers collected data through questionnaires, and literature review. The type of data used is in the form of qualitative descriptive data by 100 respondents who are generated based on a questionnaire with the criteria set are iGeneration domestic tourists who travel in Bali with a maximum age of 27 years. The patterns produced by iGeneration domestic tourists based on 100 respondents, will get the patterns and characteristics. This tourists are dominated by male visitors as much as 65% and women 35%. The trip carried out with friends/partners and family in this writing the dominant iGeneration domestic tourists generally use car and plane transportation when going to Bali. Length of stay for 3-4 days using hotel accommodations, villas, homes of relatives and getting through social media and verbal information. The pattern obtained is dominated by 77% Complex Neighborhood pattern, 11% Destination Region Loop 7%, Chaining Loop, and 5% Stop Over pattern. This finding is expected for industry players and developers to further promote other tourist attractions so that they can be evenly distributed. For the government in the future, public transportation routes can be made to connect tourist attractions, so that it can make it easier for tourists who use public transportation.
 
 
 

A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis

Taxis are commonly used by tourists to travel around unfamiliar cities they visit. These taxis today have GPS devices, which can then be used to collect a significant amount of data on the movement of tourists. One problem with this idea, however, is the question of how to extract that movement data from the raw GPS data, which includes a lot of other data, such as vehicle IDs, timestamps, and speeds, etc. The purpose of this research is to propose a data management platform to process heterogeneous data including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to extract the tourist trajectory data from the raw GPS data and produce a data integration module enriched with a knowledge base of tourist trajectories. This knowledge base is constructed through the extension of semantic trajectory ontology (STO) and mobility behavior ontology (MBO). The second objective is to extract tourist activities/point of interests (POIs) from geo-tagged Twitter data. The results of the data pipeline can readily be used for tourist behavior analysis, such as tourist descriptive analysis, popular tourist destinations/zones, and tourist movement patterns identification. We leverage the study’s results to demonstrate the real-life case study in Bangkok during the Songkran Festival in 2019. Thus, we could precisely identify tourist movement during various periods, determine popular destinations/zones, discover high density density of taxi destination points for a given trajectory type, and display the top ten tourist destinations, as well as prominent tourism keywords or trends at the time. This can provide insight to governments and businesses related to tourism regarding the trajectories and activities of tourists, and it will help predict future tourism trends.

Destination attraction clustering: segmenting tourist movement patterns with geotagged information

This study used geotagged Instagram information to analyze tourist movements in Hong Kong. Data were collected over a four-year period from over 600,000 posts at 202 attractions. Destination-wide analyses of cluster attractions can illustrate relationships between attractions and facilitate comprehensive multi-destination planning. Using geotagged information for tourist movement patterns between attractions, we connected attractions based on tourists’ common motivations and described the corresponding clustering effects by sorting the attractions into four clusters. A new framework was used to reveal the characteristics of these intra-cluster attractions through three dimensions: theme, visit volume, and importance level by attractiveness propagation rank.

소셜 네트워크 분석을 활용한 울산 방문 외래관광객의 이동 패턴 분석

In order to analyze the movement patterns of foreign tourists visiting Ulsan, this study conducted social network analysis using the raw data of a 2019 foreign tourists survey. Through this, we tried to identify the tourist destinations they visited and to understand the characteristics of the network structure between the destinations. Furthermore, in order to understand the travel characteristics of foreign tourists that affect the travel planning, an analysis of differences between groups was conducted. As a result of the study, foreign tourists visiting Ulsan were largely classified into two patterns, one via Incheon International Airport and one via Seoul and Busan. Because the centrality index of Ulsan tourist destinations is low, indicating that attempts to improve centrality index such as increasing international air routes and charter flights are necessary. In addition, as a result of the analysis on the characteristics of multi-destination travel, the purpose of visit, travel type, period of stay, and nationality had a significant impact on visits through Seoul. Furthermore, on the number of destinations visited, the purpose of visit, type of travel, period of stay, and type of visitor had a significant effect.

A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China

Insights into the association rules of destinations can help to understand the possibility of tourists visiting a destination after having traveled from another. These insights are crucial for tourism industries to exploit strategies and travel products and offer improved services. Recently, tourism-related, user-generated content (UGC) big data have provided a great opportunity to investigate the travel behavior of tourists on an unparalleled scale. However, existing analyses of the association of destinations or attractions mainly depend on geo-tagged UGC, and only a few have utilized unstructured textual UGC (e.g., online travel reviews) to understand tourist movement patterns. In this study, we derive the association of destinations from online textual travel reviews. A workflow, which includes collecting data from travel service websites, extracting destination sequences from travel reviews, and identifying the frequent association of destinations, is developed to achieve the goal. A case study of Yunnan Province, China is implemented to verify the proposed workflow. The results show that the popular destinations and association of destinations could be identified in Yunnan, demonstrating that unstructured textual online travel reviews can be used to investigate the frequent movement patterns of tourists. Tourism managers can use the findings to optimize travel products and promote destination management.

The structural changes of a local tourism network: comparison of before and after COVID-19

ABSTRACT The COVID-19 pandemic has caused dramatic changes on a global scale. According to the United Nations World Tourism Organization (2020), the restricted movement that has arisen from the pandemic is expected to reduce the national tourist market to 180 million visitors, a 44% decrease from 2019. Subsequently, alternative tourist activities have been promoted to satisfy the suppressed demand. This study examined the structural changes of a local tourism network focusing on Gangwon Province, in the Republic of Korea, which has experienced a rise in tourist demand following the COVID-19 outbreak. This study composed a matrix using the movement patterns of tourists who visited Gangwon Province during corresponding periods before and after the outbreak and then conducted a network analysis. The results demonstrated that as tourists travelled, they focused their movements on local areas and simplified travel routes. Moreover, our findings revealed that the demand for tourism was concentrated on beaches, which previously tended towards a low tourism density. As COVID-19 is expected to become a pervasive reality for the foreseeable future, this empirical study on the corresponding changes in tourist behaviour has implications regarding the tourism industry’s direction.

GIS in Tourism: A Review of Tourists’ Movement Behaviour

Tourism is a wide, spirited, dynamic and growth-oriented industry. It has a great impact on strengthening a country's economy, creation of employment, development of the country etc. However, the tourism industry is facing many challenges such as lack of graphical tourists guides and maps, deficiency of digital information for tourism facilities, lack in understanding tourist behaviour, poor travel experience, destination management and many more. Moreover, the information regarding the movement and behaviour patterns of tourists plays a major role in widening the tourism industry. In this context, the Global Positioning System (GPS) and Geographic Information System (GIS) technologies help to better understand the spatial pattern and behaviour of tourists. The enhancement in such types of technologies related to tourist tracking has empowered more exact traveller social information than ever before. In this manuscript, a review and comparative analysis have been conducted on the movement patterns of tourists and their behaviour across various destinations.

Characterizing destination networks through mobility traces of international tourists — A case study using a nationwide mobile positioning dataset

Abstract This article demonstrates how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities. According to the results, the number of inbound tourists to destinations follows a log-normal distribution, which indicates a notable heterogeneity of destination attractiveness. Although this finding holds across different nationalities, we find that tourists from different countries tended to visit different places in South Korea. A community detection algorithm partitions South Korea into several tourism regions, each covering a set of destinations that are closely connected by tourist flows. The implications for transportation development and regional tourism planning are discussed.

Tourist perceptions and uses of urban green infrastructure: An exploratory cross-cultural investigation

Urban Green Infrastructure (UGI) serves both inhabitants’ and visitors’ numerous and various needs. This research aimed to enhance knowledge regarding the role of UGI in urban tourism. The research questions addressed tourists’ perceptions of UGI, their understanding and uses of UGI, and the ways that this understanding influenced their travel choice to specific urban destinations. A cross-cultural comparative study among urban tourists was carried out in eight European countries. The selection of case studies followed a roughly comparative logic, employing the same on-site questionnaire survey administered in a sample of large and medium size cities in Southern European, Central European and Northern European countries. Looking from the perspective of the tourists’ countries of origin, our findings validate a well-established trend in international tourism, namely the fact that neighbouring countries tend to be the most significant tourist markets of an urban destination. The other major finding confirmed the most well-known tourist movement patterns of Northern and Central Europeans travelling to the Mediterranean for tourism purposes. While the study revealed that the majority of the tourists interviewed were not very familiar with the term ‘Green Infrastructure’, nor with specific UGI features offered in the visited cities, the importance of UGI was acknowledged and viewed in a mostly very positive light. The majority of respondents enjoyed visiting UGI and used it for some light physical activity or for purposes of relaxation, socialization, and in order to explore the culture and society of the destination city. The fact that most UGI in the case study cities is located around or within a short distance from important heritage sites provided UGI with an indirect possibility of being included in the tourists’ visiting plans. In conclusion, the results of this study may prove to be very helpful to local and regional authorities in considering how to plan, manage and promote an urban tourism destination’s green infrastructure as part of the tourism offer.

POLA PERGERAKAN WISATAWAN PADA KAWASAN PARIWISATA PANTAI KOTA MAKASSAR

The city of Makassar has the advantage of a charming beach tourism destination and is an icon of the city located in the Losari Beach Region or known as the CPI Region (Center point of Indonesia). Losari beach area has zoning areas consisting of tourist areas namely Losari Beach Pavilion, Fort Rotterdam, Shopping Center and Ole-ole Somba Opu, and Culinary Tourism Areas that are expected to attract tourists to visit. However, visitor / tourist movements are concentrated only on the Losari Beach Pavilion. Through the cognitive mapping analysis method, this study aims to identify the characteristics of movement patterns of tourists in the Losari Beach tourism area and the factors that influence the pattern of tourist movements. The pattern of tourist movements is obtained from the recapitulation of index cards based on tourist routes. The results of this study are that the tourist movement pattern is dominated by a single point pattern with the tourist destinations of Losari Beach Pavilion and the least movement patterns carried out by tourists is the Chaining Loop pattern which visits four tourist spots namely Losari Beach Pavilion, Culinary Center for street vendors, Makassar, Fort Rotterdam, and Makassar Ole-Ole shopping center, and factors that influence tourist movement patterns are the classification of tourist interest, operational time and classification based on the ease of tourist movements.

Movement patterns of tourists

Previous studies have explored the movement patterns of tourists via interviews or observations. However, these studies failed to provide an overall picture that can facilitate the effectiveness of destination marketing. Given that Tibet has become more accessible at present and the important role of attractions in a destination, the present study calculates the probabilities of tourists' movement routes in different prefectures in Tibet through mobile phone signals. Specifically, the present study introduces a methodological approach by using Tibet as an example to identify the movement patterns of tourists from one attraction to another to examine the attractiveness of attractions/prefectures based on the extended contemporary urban transportation model. Findings indicate that tourists can visit remote attractions only if the destination has an absolute advantage/attractiveness. These findings can assist destination managers in exploring possible ways to enhance the attractiveness of a destination's attractions.

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

A graph-based approach to detecting tourist movement patterns using social media data

ABSTRACTUnderstanding the characteristics of tourist movement is essential for tourist behavior studies since the characteristics underpin how the tourist industry management selects strategies for attraction planning to commercial product development. However, conventional tourism research methods are not either scalable or cost-efficient to discover underlying movement patterns due to the massive datasets. With advances in information and communication technology, social media platforms provide big data sets generated by millions of people from different countries, all of which can be harvested cost efficiently. This paper introduces a graph-based method to detect tourist movement patterns from Twitter data. First, collected tweets with geo-tags are cleaned to filter those not published by tourists. Second, a DBSCAN-based clustering method is adapted to construct tourist graphs consisting of the tourist attraction vertices and edges. Third, network analytical methods (e.g. betweenness centrality, Markov clustering algorithm) are applied to detect tourist movement patterns, including popular attractions, centric attractions, and popular tour routes. New York City in the United States is selected to demonstrate the utility of the proposed methodology. The detected tourist movement patterns assist business and government activities whose mission is tour product planning, transportation, and development of both shopping and accommodation centers.

Tourist movement patterns understanding from the perspective of travel party size using mobile tracking data: A case study of Xi'an, China

Travel party size has been shown to affect tourists' behavior. However, due to a previous lack of big-data analytical techniques, there remains limited research on the effect of party size on tourist movements from a large-scale perspective. This paper presents an empirical case study on the understanding of tourist movement patterns from the perspective of party size using mobile tracking data in Xi'an, China. A Fine-grained Travel Party Partition (FTPP) method is proposed to automatically distinguish accompanied tourists from a dataset of all tourists in Xi'an. After aggregating travel parties according to the size, tourist movement patterns are compared across different party sizes from demographic, spatial and temporal aspects. We further discuss how the obtained insights can help the stakeholders in travel package improvement, connectivity enhancement among attractions, attraction planning and management, and personalized next-attraction recommendation.

Tourist Movement Patterns between Tourism Sites in DKI Jakarta

Jakarta as the capital city of Indonesia is one of tourist destinations in Indonesia. Various attractions in this city is triggering tourists to visit, even when their movement is limited. This movement limitation is caused by traffic jam and the lack of supply from public transportation provided. This study aims to analyze the difference of tourist movement patterns based on distance, mode of transportation, and tourist typology in DKI Jakarta. The method used in this research is direct interview and observation which analyzed with spatial descriptive analysis. The result of the research shows that the majority of tourists in Jakarta only visit one tourist attraction or move in single point pattern with the type of individual mass tourist according to the distance among tourist attractions, time to pass by traffic jam point, and selected transportation mode.

Multilevel Visualization of Travelogue Trajectory Data

User-generated travelogues can generate much geographic data, containing abundant semantic and geographic information that reflects people’s movement patterns. The tourist movement patterns in travelogues can help others when planning trips, or understanding how people travel within certain regions. The trajectory data in travelogues might include tourist attractions, restaurants and other locations. In addition, all travelogues generate a trajectory, which has a large volume. The variety and volume of trajectory data make it very hard to directly find patterns contained within them. Moreover, existing work about movement patterns has only explored the simple semantic information, without considering using visualization to find hidden information. We propose a multilevel visual analytical method to help find movement patterns in travelogues. The data characteristic of a single travelogue are different from multiple travelogues. When exploring a single travelogue, the individual movement patterns comprise our main concern, like semantic information. While looking at many travelogues, we focus more on the patterns of population movement. In addition, when choosing the levels for multilevel aggregation, we apply an adaptive method. By combining the multilevel visualization in a single travelogue and multiple travelogues, we can better explore the movement patterns in travelogues.