Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Hasnat, Mehedi; Hasan, Samiul

doi:10.1016/j.trc.2018.09.006

Cited by 85 publications

(57 citation statements)

References 47 publications

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“…Therefore, the positional changes in the tweet points may affect the formation of the clusters. Many previous studies (e.g., [17,18,50,58]) which use manually selected static values for the clustering parameters may not adapt well in such cases. However, our proposed approach works well even in such scenarios because the parameters for clustering algorithm are estimated by using objective functions StrictParameters and MaxParameters.…”

Section: Impact Of Location Accuracy Of Geo-tagged Tweetsmentioning

confidence: 99%

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

et al. 2019

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Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.

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Section: Impact Of Location Accuracy Of Geo-tagged Tweetsmentioning

confidence: 99%

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

et al. 2019

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“…Therefore, social media platforms are considered to be human probe data sources, while radiofrequency detectors serve as the means of extracting digital footprints from wireless communications. According to social media tracing [17], various past studies have used social media platforms, such as Twitter and Flickr, to explore traveler behavior and trajectory patterns, with a focus on smartphone users in wide tourism areas [7,8].…”

Section: Digital Footprints In Tourism Studiesmentioning

confidence: 99%

“…In recent years, numerous probing studies [7][8][9][10][11] have been conducted on tourism and visitor destinations. However, data collection has presented several constraints, such as survey expense, the bias of questionnaire results, and the accuracy of the measurement devices used for observation.…”

Section: Introductionmentioning

confidence: 99%

“…Innovative technologies for conducting surveys are introducing a new era of data collection and superseding conventional survey methods, such as traditional paper-based or call-based questionnaire methods and email-based interviews. These new technologies are capable of capturing high-resolution data on the behavior of individual travelers in a large-scale population [7]. These tools also provide greater accuracy and reliability than conventional methods.…”

Section: Introductionmentioning

confidence: 99%

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Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

et al. 2019

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The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used a Wi-Fi scanner device to track tourists’ traveling behavior in Hokkaido’s tourism area, which occupies a large region that features a unique natural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important for maintaining the tourism atmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARM method integrated with a Wi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies.

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“…The easy availability and wide range of applications have made the LBSN data valuable for researchers in various fields to better understand different aspects of mobility and urban activity patterns. The LBSN data has been used by transportation researchers in land-use type identification [12,13], urban travel demand estimation [14,15], passenger flow prediction [16], trip purposes inference [11,[17][18][19] and etc. For instance, one study explored the spatiality of destinations and social network influence on travelers' destination choice in Chicago [17]; another study used a topic modeling method to infer individual activity patterns using location-based social network data [18]; and another study conducted in Florida proposed a method to build individual-level tourist travel demand models [19].…”

Section: Introductionmentioning

confidence: 99%

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Yang

Ding

2019

Sustainability

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Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

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Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Cited by 85 publications

References 47 publications

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

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