Representational strategies in visual analytics must respond to the nature of the data set and the problems that need to be addressed as well as the specific culture and understandings of multiple types of users. As Meirelles proposes, our challenge will be to bring together spatial and non-spatial phenomena to create understanding, continuous and non-continuous data. We undertook analysis of 2D and 3D strategies, including cartographic representation, using both forms of representation to create and test dashboards.

We contributed to the Urban Representation Ontology through comparative research and tool building, and a subset of related work through a taxonomy creation. We mobilized and extended the vocabulary of urban visualization and visual analytics drawing from other common visualization methods, such as stack graphs, to indicate the flow and differentiation of activities over time and from visual analytics in other fields. Research included the development of user integration into visual analytics research and usability testing. We integrated and tested a range of methods for engaging users in the design process for big data in the urban analysis context. The deliverables of this project include:

1. A taxonomy in written and diagrammatic form of common forms of urban representation, with a user-centric approach.

2. Identified software solutions that use common vocabularies for urban data analytics, and to provide a comparative chart of their application specifics.

3. Comparisons of 2D and 3D visualization solutions and their integration.

4. Explored blended infographics/models with embedded interactive charts, graphs in fly-through models, with prototypes, evaluation and user/usability testing.