Editorial

Abstract

Introduction

Visualising Mobility

Visualising transitions

Visualising trajectories

Discussion

Conclusions

Acknowledgements

References

Case Studies Index

1. Introduction

Traditionally, quantitative social scientists have gathered data primarily through cross-sectional sample surveys carried out at single points in time, and have relied on aggregate time series primarily constructed by government statisticians to study change. Methods for both analysing and visualising such data are well-established: for cross-sectional data, the standard inferential methods, and the graphical toolkit which is included in every spreadsheet programme; for time-series data, methodologies for time-series regression which have been particularly highly developed by econometricians, and the standard time-series graphs which are again widely available.

Table 1: Statistical and Visualization methods relevant to chronological data.

However, in the last decade a great deal of research has focused on tracing individuals over time, empirically through the gathering of data through panels of individuals who are periodically re-interviewed, or asked to maintain a diary; theoretically via micro-simulation studies. There is by now a well-established body of statistical techniques for analysing such datasets -- methods for event history analysis and survival analysis (see Mayer & Tuma, 1990; Courgeau & Lelièvre, 1992). However, techniques for visualising such data are far less well developed, and researchers tend to fall back on aggregating their data to create conventional time series data, or present arbitrarily chosen individual cases -- in other words, we tend to have to choose between losing all the detail we have gone to such effort to assemble, or seeing just the individual trees but no wood.

Given that projects assembling life-course data are among the most expensive in British social science such as the 1958 National Child Development Study, the ONS Longitudinal Study 1971-91 and so on, it is essential that findings be not only statistically significant but communicable to a broad audience. Unless we can convey the diversity of life-course experience, popular notions will tend to be based on a life-cycle model derived from averages. One example of how misleading this can be is that in the mid-19th century life expectancy at birth could be below 50, but this was due to high infant and child mortality, not to any significant fraction of the population dying in their 40s.

This Case Study explores methods for visualising the sequence of events that make up a life, and in particular a particular form of visualisation which is seems to be generally known to its scattered users as a lifeline diagram. The precise origins of both this diagram and its name have not been established, but many ideas in this area can be traced back to Time Geography, and through that to Swedish geographers and in particular Torsten Hägerstrand. Time geography was, in retrospect, perhaps something of a passing fad within human geography of the late 1970s and early 1980s (eg Carlstein et al, 1978; Parkes & Thrift, 1980), concerned not so much with the long time spans of historical research but with daily, weekly and seasonal rhythms within human behaviour over space. Its approach was often highly conceptual, actual empirical studies were often on a relatively small scale. Its importance here lies mainly in the range of ideas it came up with for graphically portraying individual-level data involving a time dimension.

As far as we have been able to discover, the vast majority of the visualisation created by time geographers were executed entirely manually, created not by the researchers themselves but by the cartographers their departments employed at the time. The fact that geographers are so heavily involved in current visualisation research in the social sciences partly reflects the relatively generous equipment grants they enjoy, but also a longer tradition of specialist support for non-computer graphics. Even with such support, many of the visualisation methods developed by time geographers were enormously time consuming, taking days or even weeks to draw, and in practice they seem to have been rarely used.

Like the work of time geographers, the examples used in this essay are all essentially manually drawn, and this requires some defence in an initiative concerned with computer graphics. In a strict sense, most of the graphics here are computer-generated, in that they were drawn using Adobe Illustrator, but often starting from a scan of an original drawn with pen and ink. However, again as far as we can discover, no software exists to create a final lifeline diagram from raw data, and the partial solutions discussed create poor quality output. Given that the central end-product was to be a conventional essay, where the software used would be invisible; given that one of the two other non-GIS case studies within this Initiative was concerned with the creation of Lexis pencils (a form of lifeline diagram), and given that the time available meant that even if we had spent all our time developing software its functionality would have been very limited, it was decided to concentrate on exploring ideas rather than developing software.