Event-Based Analysis of People's Activities and Behavior Using Flickr and Panoramio Geotagged Photo Collections.
ABSTRACT Photo-sharing websites such as Flickr and Panoramio contain millions of geotagged images contributed by people from all over the world. Characteristics of these data pose new challenges in the domain of spatio-temporal analysis. In this paper, we define several different tasks related to analysis of attractive places, points of interest and comparison of behavioral patterns of different user communities on geotagged photo data. We perform analysis and comparison of temporal events, rankings of sightseeing places in a city, and study mobility of people using geotagged photos. We take a systematic approach to accomplish these tasks by applying scalable computational techniques, using statistical and data mining algorithms, combined with interactive geo-visualization. We provide exploratory visual analysis environment, which allows the analyst to detect spatial and temporal patterns and extract additional knowledge from large geotagged photo collections. We demonstrate our approach by applying the methods to several regions in the world.
- SourceAvailable from: Thiago Henrique SilvaUbiquitous Social Media Analysis, Edited by Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas, 01/2013: pages 63-87; Springer Berlin Heidelberg., ISBN: 9783642453915
Conference Paper: Multi-day and multi-stay travel planning using geo-tagged photos[Show abstract] [Hide abstract]
ABSTRACT: By utilizing large amount of crowd volunteered geo-tagged photos, existing research can successfully discover landmarks or attractive areas, mine travel patterns, find classical travel routes and recommend travel destinations or routes for inexperienced tourists. However, few of them focuses on a complicated real-life travel planning problem--planning multi-day and multi-stay (different places of accommodation) travel for tourist. By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information; 11/2013
Conference Paper: Exploratory analysis of OpenStreetMap for land use classification[Show abstract] [Hide abstract]
ABSTRACT: In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas' values are remarkable and promising results that encourages us for future research on this topic.Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information; 11/2013