Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu, Yang; Wu, Lun; Liu, Yu; Kang, Chaogui

doi:10.3390/ijgi6110345

Cited by 58 publications

(37 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several alternative sources of social sensing data, including POIs [30], check-in data with location information [40,41], mobile phone data [25], and trajectory data [42], can then be applied to replace the MPPD data. A similar approach that fuses remote sensing and various social sensing data can be used to obtain a land use map.…”

Section: Advantages Of the Fused Land Cover Map And Future Workmentioning

confidence: 99%

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

show abstract

Section: Advantages Of the Fused Land Cover Map And Future Workmentioning

confidence: 99%

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Previous studies that utilize online photographs for inferring nature-based human activities have a diverse research focus. Some examples include, but are not limited to, identifying character of landscapes [4], land cover and land use [5], recreational demand in water resources [1], events and tourist hotspots [6][7][8], impact of rare species in tourism [9], common tourist trajectories [10,11], recreational visitation at national parks [3], the perceived aesthetic value of ecosystems [12,13], as well as the relationship between cultural ecosystem services and landscape features [14,15].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

Koylu

Chang

Shao

2019

IJGI

View full text Add to dashboard Cite

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.

show abstract

“…From posts on the NAVER blog in Korea, it was found that London, Paris, Venezia, and Firenze were key cities where Korean backpackers tended to enter Europe [26]. Tourist trajectories were extracted from Flicker's geo-tagged photos and then motif tourist mobility patterns were detected [27]. Wu et al introduced a tourism hotspot network approach to investigate travel patterns from social media data for tourism resources planning [28].…”

Section: Introductionmentioning

confidence: 99%

Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs

Gao

Zhong

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for revealing comprehensive spatial patterns of tourist movements. Instead of concentrating on a single city or attraction in previous research, this work investigates the intercity travel flows extracted from the online travel blogs in China from 2012 to 2016. The descriptive statistics of travel flows are first analyzed. The distribution of travel volume is found to satisfy the power-law distribution. Based on the intercity travel flows, a network structure is then constructed to investigate tourism interactions between cities. After four communities and 14 sub-communities being detected from the network, a tourism spatial layout with regional agglomeration effects are recognized. This research concludes that distance is essential in determining tourist movements based on a spatial interaction model. Intercity travel flows decline with distance under a power-law function. These results reveal the spatial patterns of tourist movements at an intercity scale. It will be helpful for arranging tourism resources, predicting tourist flows, and maintaining sustainable tourism.

show abstract

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Cited by 58 publications

References 33 publications

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs

Contact Info

Product

Resources

About