Visual analysis of social networks in space and time using smartphone logs

social network analysis
multivariate visualization
urban analytics

Aidan Slingsby, Roger Beecham and Aidan Slingsby (2013) “Visual analysis of social networks in space and time using smartphone logs”, Pervasive and Mobile Computing, doi: 10.1016/j.pmcj.2013.07.002


Department of Computer Science, City University of London

School of Geography, University of Leeds

Department of Computer Science, City University of London


July 2013



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.

Important figure

Figure 1: Screenshot of tool, with four coordinated views: [A] Zoomable map that shows all GPS points; blue and purple indicates where calls and texts were made. [B] Zoomable timeline (bottom) with one row per participant showing their GPS positions (yellow), calls (blue) and texts (purple). [C] List of participants where colours indicate gender (blue: male, pink: female), age (dark green: older) and a derived measure of social activity (dark red: more socially active). [D] Matrix (bottom right) of number of calls between participants.

BibTeX citation

    title = {Visual analysis of social networks in space and time using smartphone logs},
    volume = {9},
    number = {6},
    journal = {Pervasive and Mobile Computing},
    author = {Slingsby, A. and Beecham, R. and Wood, J.},
    year = {2013},
    pages = {848--864}