The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Computational Statistics & Data Analysis - Data visualization
GeoTime Information Visualization
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
Space, time and visual analytics
International Journal of Geographical Information Science - Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization
Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
How many makes a crowd? on the evolution of learning as a factor of community coverage
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Mining smartphone data to classify life-facets of social relationships
Proceedings of the 2013 conference on Computer supported cooperative work
Experiences in involving analysts in visualisation design
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
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We designed and applied interactive visualisation techniques for investigating how social networks are embedded in time and space, using data collected from smartphone logs. Our interest in spatial aspects of social networks is that they may reveal associations between participants missed by simply making contact through smartphone devices. Four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data, along with demographic and attitudinal variables, helped add context to the behaviours we observed. Using these techniques, we were able to characterise spatial and temporal aspects of participants' social networks and suggest explanations for some of them. This provides some validation of our techniques. Unexpected deficiencies in the data that became apparent prompted us to evaluate the dataset in more detail. Contrary to what we expected, we found significant gaps in participant records, particularly in terms of location, a poorly connected sample of participants and asymmetries in reciprocal call logs. Although the data captured are of high quality, deficiencies such as these remain and are likely to have a significant impact on interpretations relating to spatial aspects of the social network. We argue that appropriately-designed interactive visualisation techniques-afforded by our flexible prototyping approach-are effective in identifying and characterising data inconsistencies. Such deficiencies are likely to exist in other similar datasets, and although the visual approaches we discuss for identifying data problems may not be scalable, the categories of problems we identify may be used to inform attempts to systematically account for errors in larger smartphone datasets.