Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform
Computers Environment and Urban SystemsPublished 26 August 2015
Xiaolu Zhou, Chen Xu, Brandon Kimmons
Citations121
SJR quartileQ1
SJR score2.52
SNIP2.55
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TL;DR
This project provides fast and accurate tourist destination detection by mining large amounts of geo-tagged Flickr images from 2007 on and provides insight in applying the RHadoop platform to strengthen large geospatial data analytics.
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.
Keywords
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