Aim: what kinds of new mapping techniques can be applied to the study of clusters, what new kinds of information does this produce and what kinds of visual representation is most useful.
The rationale for this approach can be found in ‘Mapping Primer‘ by Michelle Talbot from ARUP.
Typologies
The initial mapping of clusters in Shanghai using Google Map has generated much interests around the spatial mapping – how do we quantify ‘creativity’ in order to translate them into google maps?
Based on this initial groundwork, a standardised database has been used onto which the different characteristics of creative clusters can be placed. This will allow us to produce maps which might reveal different correspondences between clusters and other urban features. For example, are clusters located in certain areas; what do these connections tell us about the city and the evolving nature of the clusters. What typologies would we use?
See also: Typologies
GIS mapping
The usefulness of locational information will be greatly enhanced if we can map them onto other GIS based information. (see Mapping Primer). This is being explored by the team based at ARUP in Sydney (Dan Hill and Michelle Talbot), where they are looking at putting different layers of data alongside clusters. This is just beginning and the Australia based work will provide highly visual examples as to what might also be possible in China.
The key question: can we access GIS based information for Chinese cities and what sorts of information can we map onto it. Can we use GIS information to map clusters against other activities/information flows in Chinese cities?
Visual Database
Our team has used Flickr to store photos of clusters. We need to explore how this might become more navigable. It represents a great resource for the visual exploration of clusters – especially as we move into the qualitative work on design, identity, public space and ‘soft infrastructure’ in general.
Mapping and Evaluation
The database which underpins the mapping can also build in more evaluative categories. The discussion on evaluation below can feed into a more sophisticated mapping. So evaluative categories (e.g. diversity, connectivity, embeddedness) can be used to generate database ‘scores’ and represent these different dimensions of creative clusters spatially.
Some background and suggestions on all of this can be found in ‘Designing Creative Clusters and Mapping’.
