Data visualizations

General principles

From Tufte:

Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency. Graphical displays should:

  • show the data
  • induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
  • avoid distorting what the data have to say
  • present many numbers in a small space
  • make large data sets coherent
  • encourage the eye to compare different pieces of data
  • reveal the data at several levels of detail, from a broad overview to the fine structure
  • serve a reasonably clear pupose: description, exploration, tabulation, or decoration
  • be closely integrated with the statistical and verbal descriptions of a data set

Graphics reveal data.


Visualization types

There are many conventional kinds of visualizations: tables, graphs, charts, maps, timelines &c. Existing visualization types provide a solid base to begin analysis, but we are interested in quickly moving to hybrid and invented representations that capture spatio-temporal aspects of the site. To this end, vector fields, heatmaps, and cinematic overlays have been utilized as static drawings that capture forces existing in time and space. Ultimately, however, it is our objective to move beyond these existing visualization types to truly animate analyses.

  • Some conventional graphs, charts, stats, and maps. Note the use of superfluous graphic elements that do not add value to the visualizations.
       

  • ‘Small multiples’: using the same visualization on several related data sets invites easy comparison. Depending on the nature and scale of your media, however, small multiples can be taken too far.
       


Environmental visualizations
  • Abstract diagrams
       

  • Site-based maps
          

  • A generative process
             
  • Alternative ‘environmental’ factors
          


Compound systems

To become increasingly critical of the sources, natures, and meanings of data, we will seek relationships between outwardly independent datasets. While even raw data is rarely truly objective, it is at this stage where the interpretive nature of our analyses becomes apparent and where the first seeds of design are sown. We are specifically searching for novel, innovative, and/or polemical analyses which shun conventional readings of site and context and begin to illuminate the fringes of potential for the ultimate project.

  • Basic vector fields
       

  • Complex vector fields
          

  • Compound systems in art
       
       
       

  • Compound systems in architecture

       

  • Beginning to temporalize
          


Temporal narratives

Interpreting our site over time is necessarily a speculative endeavor. To envision scenarios of future development is certainly a starting point to the creation of a robust system that responds to uncertainty, but such deterministic prognostication limits the system’s performance to conditions that are imagined a priori. Learning from physical systems of self-organization and adaptive mechanisms of biological growth, we intend to design non-deterministic engines for future development that have the capacity to thrive even in situations that are unanticipated.

  • Agent-based simulations
       
       
       

  • Output possibly as mesh geometry
       
       

  • Analytical models
             

  • Cinematic multiples

  • From map to project
       
             
       
          


Bibliography

http://www.aaschool.ac.uk/lu/

http://projectsreview2010.aaschool.ac.uk/beta/

http://projectsreview2010.aaschool.ac.uk/html/units.php?show=81


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