research-article

Mining Travel Patterns from Geotagged Photos

Authors:

Tat-Seng ChuaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Issue 3

Article No.: 56, Pages 1 - 18

https://doi.org/10.1145/2168752.2168770

Published: 01 May 2012 Publication History

Abstract

Recently, the phenomenal advent of photo-sharing services, such as Flickr and Panoramio, have led to volumous community-contributed photos with text tags, timestamps, and geographic references on the Internet. The photos, together with their time- and geo-references, become the digital footprints of photo takers and implicitly document their spatiotemporal movements. This study aims to leverage the wealth of these enriched online photos to analyze people’s travel patterns at the local level of a tour destination. Specifically, we focus our analysis on two aspects: (1) tourist movement patterns in relation to the regions of attractions (RoA), and (2) topological characteristics of travel routes by different tourists. To do so, we first build a statistically reliable database of travel paths from a noisy pool of community-contributed geotagged photos on the Internet. We then investigate the tourist traffic flow among different RoAs by exploiting the Markov chain model. Finally, the topological characteristics of travel routes are analyzed by performing a sequence clustering on tour routes. Testings on four major cities demonstrate promising results of the proposed system.

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Index Terms

Mining Travel Patterns from Geotagged Photos
1. Information systems
  1. Information systems applications

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 3, Issue 3

May 2012

384 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2168752

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 01 May 2012

Accepted: 01 January 2012

Revised: 01 December 2011

Received: 01 September 2011

Published in TIST Volume 3, Issue 3

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https://doi.org/10.4018/979-8-3693-5956-3.ch006
Duan JLiang XLiao JNakashima RShi HHu CKumada TPeng KTong S(2025)“Big picture” predicts destination attractiveness: The role of physical breadth and contextual breadthTourism Management10.1016/j.tourman.2024.105114108(105114)Online publication date: Jun-2025
https://doi.org/10.1016/j.tourman.2024.105114
Oueslati WSami OBahri AAkaichi J(2024)Generic Semantic Trajectory Data Modelling Approach based on OntologiesJournal of Information & Knowledge Management10.1142/S021964922450083723:06Online publication date: 3-Aug-2024
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