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Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform
Abstract The number of geo-tagged digital photos has grown exponentially in the past decades. Increasing numbers of digital photos with geo-tags are available on many photo-sharing websites such as Flickr and Instagram. The proliferation of online photos offers great opportunities to study people's travel experiences and preferences. Mining tourists' behavior and city preferences has become popular in recent geographic information system (GIS) research. However, the huge amount of data also poses challenges in spatial analytics. In this study, we automate the detection of places of interest in multiple cities based on spatial and temporal features of Flickr images from 2007 on. We also speed up the process by running jobs on top of the RHadoop platform. This project provides fast and accurate tourist destination detection by mining large amounts of geo-tagged Flickr images. In addition, this study provides insight in applying the RHadoop platform to strengthen large geospatial data analytics. Our methods can be applied to many other cities, and results are valuable for tourism management.
Highlights This paper effectively detected and ranked popular tourism destinations in multiple cities. This paper also leveraged cloud computing to expedite Flickr tag processing and similarity graph preparation. Computation speed for single machine, multiple threads, and cloud platform were compared for different amount of data.
Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform
Abstract The number of geo-tagged digital photos has grown exponentially in the past decades. Increasing numbers of digital photos with geo-tags are available on many photo-sharing websites such as Flickr and Instagram. The proliferation of online photos offers great opportunities to study people's travel experiences and preferences. Mining tourists' behavior and city preferences has become popular in recent geographic information system (GIS) research. However, the huge amount of data also poses challenges in spatial analytics. In this study, we automate the detection of places of interest in multiple cities based on spatial and temporal features of Flickr images from 2007 on. We also speed up the process by running jobs on top of the RHadoop platform. This project provides fast and accurate tourist destination detection by mining large amounts of geo-tagged Flickr images. In addition, this study provides insight in applying the RHadoop platform to strengthen large geospatial data analytics. Our methods can be applied to many other cities, and results are valuable for tourism management.
Highlights This paper effectively detected and ranked popular tourism destinations in multiple cities. This paper also leveraged cloud computing to expedite Flickr tag processing and similarity graph preparation. Computation speed for single machine, multiple threads, and cloud platform were compared for different amount of data.
Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform
Zhou, Xiaolu (author) / Xu, Chen (author) / Kimmons, Brandon (author)
Computers, Environments and Urban Systems ; 54 ; 144-153
2015-07-24
10 pages
Article (Journal)
Electronic Resource
English
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