Guochen Cai

Exploration of geo-tagged photos through data mining approaches

With the development of web technique and social network sites human now can produce information, share with others online easily. Photo-sharing website, Flickr, stores huge number of photos where people upload and share their pictures. This research proposes a framework that is used to extract associative points-of-interest patterns from geo-tagged photos in Queensland, Australia, a popular tourist destination hosting the great Barrier Reef and tropical rain forest. This framework combines two popular data mining techniques: clustering for points-of-interest detection, and association rules mining for associative points-of-interest patterns. We report interesting experimental results and discuss findings.

Mining Points-of-Interest Association Rules from Geo-tagged Photos

The advent of photo-sharing services results in massive user-generated geo-tagged photos. These photos implicitly and explicitly indicate points-of-interest and their associations. This study aims to combine two data mining techniques: clustering and association rules mining to mine areas of attraction, and their associative patterns. We analyze photos from Flickr in the area of Queensland, Australia, a popular tourist destination hosting the Great Barrier Reef and tropical rain forest. We report interesting experimental results and discuss findings.

Sequential pattern mining of geo-tagged photos with an arbitrary regions-of-interest detection method

Propose an arbitrary shape detection regions-of-interest mining algorithm.Propose a sequential trajectory pattern mining framework for Flickr geo-tagged photos.First time analyzing geo-tagged photos using sequential trajectory pattern mining.Experiments supporting the applicability, and effectiveness of our proposed algorithm. Geo-tagged photos leave trails of movement that form trajectories. Regions-of-interest detection identifies interesting hot spots where many trajectories visit and large geo-tagged photos are uploaded. Extraction of exact shapes of regions-of-interest is a key step to understanding these trajectories and mining sequential trajectory patterns. This article introduces an efficient and effective grid-based regions-of-interest detection method that is linear to the number of grid cells, and is able to detect arbitrary shapes of regions-of-interest. The proposed algorithm is combined with sequential pattern mining to reveal sequential trajectory patterns. Experimental results reveal quality regions-of-interest and promising sequential trajectory patterns that demonstrate the benefits of our algorithm.

Mining Frequent Trajectory Patterns and Regions-of-Interest from Flickr Photos

Flickr represents a massive opportunity to mine valuable human movement data from geo-tagged photos. However, existing Flickr trajectory data mining research has not considered mining frequent trajectory patterns whilst also considering the temporal domain. Therefore, a significant opportunity exists to demonstrate the application of a pattern mining algorithm to a large geo-tagged photo dataset. Thus, we present a novel application of the trajectory pattern mining algorithm to a 2012 Flickr dataset of Australia and encompassing state, Queens land. In our experiments we show that many interesting, previously unknown patterns discovered through our framework. Our framework is able to discover expected major landmarks such as cities and tourist attractions. In addition, we make the notable discover of what is theorized to be valuable tourist travel information about sequential movements between hot-spot attractions.

Points-of-Interest Mining from People's Photo-Taking Behavior

Millions of geo-tagged photos are becoming available due to the widespread of photo-sharing websites. These social medias capture attractive points-of-interest and contain interesting photo-taking patterns. Massive amount of these user-oriented data produces new challenges and understanding peoples photo-taking behavior is of great importance for local tourism-related businesses. This paper analyzes geotagged photos from Flickr for Queensland, a tourismintensive and the second largest state in Australia. We report interesting points-of-interest patterns and discuss these findings.

Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos

A large number of geo-tagged photos become available online due to the advances in geo-tagging services and Web technologies. These geo-tagged photos are indicative of photo-takers’ trails and movements, and have been used for mining people movements and trajectory patterns. These geo-tagged photos are inherently spatio-temporal, sequential and implicitly containing aspatial semantics. and recommender systems are collaborative filtering based. There have been some studies to build itinerary recommender systems from these geo-tagged photos, but they fail to consider these dimensions and share some common drawbacks, especially lacking aspatial semantics or temporal information. This paper proposes an itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos by discovering sequential points-of-interest with temporal information from other users’ visiting sequences and preferences. Our system considers spatio-temporal, sequential, and aspatial semantics dimensions, and also takes into account user-specified preferences and constraints to customise their requests. It generates a set of customised and targeted semantic-level itineraries meeting the user specified constraints. The proposed method generates these semantic itineraries from historic people’s movements by mining frequent travel patterns from geo-tagged photos. Experimental results demonstrate the informativeness, efficiency and effectiveness of our proposed method over traditional approaches.

Discovering Common Semantic Trajectories from Geo-tagged Social Media

Massive social media data are being created and uploaded to online nowadays. These media data associated with geographical information reflect people’s footprints of movements. This study investigates into extraction of people’s common semantic trajectories from geo-referenced social media data using geo-tagged images. We first convert geo-tagged photographs into semantic trajectories based on regions-of-interest, and then apply density-based clustering with a similarity measure designed for multi-dimensional semantic trajectories. Using real geo-tagged photographs, we find interesting people’s common semantic mobilities. These semantic behaviors demonstrate the effectiveness of our approach.

A framework for mining semantic-level tourist movement behaviours from geo-tagged photos

This study investigates tourist movement patterns on the type of place semantic-level. We extract the semantic common movement patterns that a group of tourists have similar movement trajectories on the semantic level, and find out semantic trajectory patterns which are sequences of the type of place objects with transit time. Using real geo-tagged photos, we find out interesting common movement patterns and trajectory patterns. These results provide richer information and understanding of tourist movement behaviour on the type of place semantic-level.

A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories

There is an increasing need for a trajectory pattern mining as the volume of available trajectory data grows at an unprecedented rate with the aid of mobile sensing. Region-of-interest mining identifies interesting hot spots that reveal trajectory concentrations. This article introduces an efficient and effective grid-based region-of-interest mining method that is linear to the number of grid cells, and is able to detect arbitrary shapes of regions-of-interest. The proposed algorithm is robust and applicable to continuous and discrete trajectories, and relatively insensitive to parameter values. Experiments show promising results which demonstrate benefits of the proposed algorithm.

Mining Semantic Sequential Patterns from Geo-Tagged Photos

Social media data associated with geographic location and time information reflect people footprint in real world. Abundance of geo-referenced content represents a massive opportunity to understanding of human geographic mobility behaviors. Most trajectory mining research from geo-enabled social media data focus on spatial geometric features. Integrating trajectory analysis with semantic information can implicate human movement behaviors on semantic levels. In this work, we illustrate a study on mining semantically enriched trajectory patterns using geo-referenced content especially using geo-tagged photo data for case study. We first propose a semantic region of interest mining technique to extract reference regions with semantic information. We then present a multi-dimensional sequential pattern mining algorithm to find trajectory patterns with various semantic dimension combinations. We apply our method to real geo-tagged photo data to discover interesting patterns about sequential movement related to multiple semantics. Experimental results show that our method is able to find useful semantic trajectory patterns from geo-tagged content and deal with multi-dimensional semantic trajectories.