An approach to scenario analysis of the sustainability of an industrial sector applied to clothing and textiles in the UK

Companies aiming to be 'sustainability leaders' in their sector and governments wanting to support their ambitions need a means to assess the changes required to make a significant difference in the impact of their whole sector. Previous work on scenario analysis/scenario planning demonstrates extensive developments and applications, but as yet few attempts to integrate the 'triple bottom line' concerns of sustainability into scenario planning exercises. This paper, therefore, presents a methodology for scenario analysis of large change to an entire sector. The approach includes calculation of a 'triple bottom line graphic equaliser' to allow exploration and evaluation of the trade-offs between economic, environmental and social impacts. The methodology is applied to the UK's clothing and textiles sector, and results from the study of the sector are summarised. In reflecting on the specific study, some suggestions are made about future application of a similar methodology, including a template of candidate solutions that may lead to significant reduction in impacts. © 2007 Elsevier Ltd. All rights reserved.

The Trade-offs with Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Fast and correct analysis of such information is important in for instance geospatial and social visualization applications. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a dataset to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap we report on a between-subjects experiment comparing novice users error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the dataset, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users when analyzing complex spatiotemporal patterns.