Interactive tag maps and tag clouds for the multiscale exploration of large spatio-temporal datasets

'Tag clouds' and 'tag maps' are introduced to represent geographically referenced text. In combination, these aspatial and spatial views are used to explore a large structured spatio-temporal data set by providing overviews and filtering by text and geography. Prototypes are implemented using freely available technologies including Google Earth and Yahoo! 's Tag Map applet. The interactive tag map and tag cloud techniques and the rapid prototyping method used are informally evaluated through successes and limitations encountered. Preliminary evaluation suggests that the techniques may be useful for generating insights when visualizing large data sets containing geo-referenced text strings. The rapid prototyping approach enabled the technique to be developed and evaluated, leading to geovisualization through which a number of ideas were generated. Limitations of this approach are reflected upon. Tag placement, generalisation and prominence at different scales are issues which have come to light in this study that warrant further work

Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here

Incremental and hierarchical classification of a personal image collection on mobile devices

International audienceBrowsing multimedia collection on mobile devices raises the needs for new multimedia indexing solutions. In this paper, we focus on the management of personal image collections. We propose a method to simplify the browsing task on such a collection. The contributions reside in an incremental hierarchical algorithm, a method to provide a textual representation of the groups obtained and an algorithm to build a geo-temporal view of the collection. The proposed incremental hierarchical algorithm builds a temporal tree from the time stamp of each image. We opt here for a combination of a supervised clustering and an incremental algorithm based on mixture model. Good properties of the hierarchy are determined automatically thanks to the Integrated Likelihood Criterion (ICL). Based on the events obtained, a textual representation is proposed and then used to improve our temporal classification, combining geographical and temporal information. Results are validated on several real user collections with our prototype MyOwnLife

Extracting Touristic Information from Online Image Collections

In this paper, we present a Geographical Information Retrieval system, which aims to automatically extract and analyze touristic information from photos of online image collections (in our case of study Flickr). Our system collect all the photos, and the related information, that are associated to a specific city. We then use Google Maps service to geolocate the retrieved photos, and finally we analyze geo-referenced data to obtain our goals: 1) determining and locating the most interesting places of the city, i.e. the most visited locations, and 2) reconstructing touristic routes of the users visiting the city. Information is filtered by using a set of constraints, which we apply to select only the users that reasonably are tourists visiting the city. Tests were performed on an Italian city, Palermo, that is rich in artistic and touristic attractions, but preliminary tests showed that our technique could successfully be applied to any city in the world with a reasonable number of touristic landmarks

Investigating perceptions of a location-based annotation system

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