Conference Paper

Event-Based Analysis of People's Activities and Behavior Using Flickr and Panoramio Geotagged Photo Collections

Univ. of Konstanz, Konstanz, Germany
DOI: 10.1109/IV.2010.94 Conference: 14th International Conference on Information Visualisation, IV 2010, 26-29 July 2010, London, UK
Source: DBLP

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.

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    • "Visitor's photographs have been employed together with interview by previous studies on landscape and nature perception, proving a suitable way to analyse different environmental aspects which may attract visitors (Dorwart et al., 2009;Taylor et al., 1995). The analysis of photoseries from platforms such as Flickr and Panoramio has already been shown to be a suitable proxy for the empirical estimation of visiting frequency (Da Rugna et al., 2012;Kisilevich et al., 2010;Produit et al., 2014;Sun et al., 2013;Wood et al., 2013). More recently online photo libraries have been used to assess CES (Allan et al., 2015;Arkema et al., 2015;Casalegno et al., 2013;Martínez Pastur et al., 2015;Keeler et al., 2015;Nahuelhual et al., 2013;Richards and Friess, 2015;Willemen et al., 2015). "
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    ABSTRACT: Integrating cultural dimensions into the ecosystem service framework is essential for appraising non-material benefits stemming from different human–environment interactions. This study investigates how the actual provision of cultural services is distributed across the landscape according to spatially varying relationships. The final aim was to analyse how landscape settings are associated to people's preferences and perceptions related to cultural ecosystem services in mountain landscapes. We demonstrated a spatially explicit method based on geo-tagged images from popular social media to assess revealed preferences. A spatially weighted regression showed that specific variables correspond to prominent drivers of cultural ecosystem services at the local scale. The results of this explanatory approach can be used to integrate the cultural service dimension into land planning by taking into account specific benefiting areas and by setting priorities on the ecosystems and landscape characteristics which affect the service supply. We finally concluded that the use of crowdsourced data allows identifying spatial patterns of cultural ecosystem service preferences and their association with landscape settings.
    Full-text · Article · May 2016 · Ecological Indicators
    • "Visitor's photographs have been employed together with interview by previous studies on landscape and nature perception, proving a suitable way to analyse different environmental aspects which may attract visitors (Dorwart et al., 2009;Taylor et al., 1995). The analysis of photoseries from platforms such as Flickr and Panoramio has already been shown to be a suitable proxy for the empirical estimation of visiting frequency (Da Rugna et al., 2012;Kisilevich et al., 2010;Produit et al., 2014;Sun et al., 2013;Wood et al., 2013). More recently online photo libraries have been used to assess CES (Allan et al., 2015;Arkema et al., 2015;Casalegno et al., 2013;Martínez Pastur et al., 2015;Keeler et al., 2015;Nahuelhual et al., 2013;Richards and Friess, 2015;Willemen et al., 2015). "
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    ABSTRACT: The realization for the growing demand for cultural services (CS) calls for methods to identify and quantify them in order to plan for the provision of such services. Assessing revealed preferences of CS is challenging, especially when considering the spatial dimension needed for planning at the regional level. Most of the current studies on CS are based on socio-economic data or specific surveys which are collected on a declarative basis. Spatially explicit data on location for nearby recreation are especially difficult to obtain. The analysis of photo series and data mining from social media can be used as a surrogate of interviews or surveys to assess recreation behavior, perception and preferences, assuming that visitors are attracted by the location where they take photographs. We will present a case study using Crowd-sourced information as a suitable proxy for the empirical estimation of visitation. The analysis of photo series is a pragmatic, cost effective way of gathering space-and time-referenced data on visitation which can be used to represent observed people preferences. The photos were analyzed in order to identify hotspots of service deliver and landscape properties which represented the major predictor of nearby recreation activities.
    No preview · Conference Paper · Oct 2015
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    • "For instance, Li and Goodchild (2012) studied point patterns of georeferenced Flickr imagery in conjunction with toponyms in their metadata to identify places through user references to them. Other efforts attempt to recognize activity and behavioral patterns by analyzing these spatiotemporal points of geotagged entries, such as identifying attractive destinations (Kisilevich et al. 2010) or constructing travel itineraries (De Choudhury et al. 2010). Despite these efforts, the multimedia content of social media remains underexplored. "
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    ABSTRACT: The analysis of social media content for the extraction of geospatial information and event-related knowledge has recently received substantial attention. In this article we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses both Twitter and Flickr content in an integrated two-step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient Flickr imagery accordingly and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The article presents our approach and demonstrates its performance using a real-world wildfire event as a representative application case study.
    Full-text · Article · Dec 2014 · Transactions in GIS
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