PG04: Journal

Review and  Learning Journal

Week 1:

Wednesday, 4th February 2015


The first week is overview of the course and to present ideas, concepts, a review of research materials and initial findings.

 To do lists for next week:

  1. Practice based workflow
  2. Decide on a project for next week
  3. Draw up a template of ideas
  4. Methodology of the Proposal
  5. Themes and project timeline
  6. Consideration & of emerging technology, hardware and & software
  7. Exhibition of works
  8. Post ideas and inspiration onto the pinterest board


Week 2:

Wednesday, 11th February 2015


I continue my  research into  data visualisation, online learning materials, tools and technology needed to implement a data visualisation project, and analyses and dealing with current events and future  challenges facing big data implementation. One of the main questions I have to deal with this week, is how  to source appropriate information  that is relevant to my research and review.



  • Cargo platform, [Accessed 14 February 2015]
  • Data Fashion Wellbeing:Experimenting Speculative design with data – Carolin Yan Zheng  (2014). Available: [Accessed 17  February 2015]
  •  Fashion, Data and QS  –  Ken Snyder (2015) [Vimeo]. Available: [ Accessed 1 7  February 2015]
  • The Studio. Available: [Accessed 1 4 February 2015]
  • Values at Play. Available: [Accessed 8 Feb 2015]
  • Visualizing Eleven Dimensions  – Thad Roberts (2010) [Youtube]. Available: [Accessed 17 February 2015]
  • Wearable Data Project [ Accessed 14 February 2015]
    The Practice of Mindfulness – Diana Winston  (2012)[Youtube]. Available: [Accessed 17  February 2015]

    Week 3:

    Wednesday, 18th February 2015


    I continue to research and review into data visualisation, I came across various projects by Aaron Koblin, an award winning Artist and programmer. One of the best one is the illustration of the global exchange information from New York in real time by visualising volumes of long distance telephone and Internet Protocol (IP) data flowing between New York and cities around the world. See figure 1. This is  dynamic, lively and beautiful.

    NYTENYTESource: Aaron Koblin  Visualisations  for the New York Talk, a project by the Senseable City Lab at MIT for the MoMA.


    Also, the Flight  patterns: Figure 2:

    Screenshot 2015-06-10 18.27.23A collaboration with Wired Magazine and FlightView Software showing flight path and altitudes in August 12, 2008

    A Flight Patterns Color – HD  [Youtube]

    Arcade fireArcade Fire, A virtual projection by Vincent Morisset

    The ‘Just a Reflektor’ is an interactive short film combining physical control via mobile device and hardware accelerated video effects, and also utilises many modern browser features.  I liked  the  above idea  as  being able to use a mobile device/ tablet to control the ‘user interaction’ and events on this website. My proposal is to incorporate this concept (WebGL) into my final showcase.


    Arcade Fire’s Just A Reflektor: Behind the Scenes (2013)

    A documentary of the process, behind the scenes commentary and  technology used in making this interactive site.

    The  imageries, videos and websites  were  inspirational,  motivational and conceptual ideal for my project.



    Week 4:

    Wednesday, 25th February 2015


    I spent more time exploring data visualisation concept, theory and history. The first step is exploring the history of infographics and data visualisation. What is the first experience of using data graphics and what kind of lesson could be learn and what impact did they have?

    The first interesting video is by Thad Roberts about the visualizing eleven dimension. What you have to say ‘storytelling’ and how. Understanding a lot of human unconscious decision making, recognition of patterns in order to survive and take advantage in design.

    Some of the key things that data visualisation can help with are:

    • To inspire new questions and further exploration

    • To helps identify sub-problems

    • Identifying trends, outliers, discovering or searching for interesting or specific data points in a larger field

    “Data is just a clue to the end truth.”
    – Josh Smith, Hyperakt

    Genealogy of Pop & Rock Music


    “Every single Pixel should testify directly to content.”
    — Edward Tufte, Data Visualization Pioneer

    In that we got to get the design out of the way, because it the relationship of the ‘viewer’ and how they reason about the content. The style and aesthetics cannot rescue failed content, if the words are not truthful, the finest letter space (typography) would not turn lies into truths. There are enormously beautiful visualisations, but is the byproduct of the truth and the goodness of the information.

    A brief History of Data Visualisation:

    The big steps in showing information began all with cartography (drawing maps), about 6,000 years ago, when the first map was scratched into a piece of stone.
    Screenshot 2015-06-11 21.56.15

    Fra Maurol


    And that has wound up now with the most widely seen visualization in the world, which is Google Maps, where people are using a visualization to actually do something.

    Street View of 02

    Source: Google map


    Source:Bibliothèque Nationale de France

    The next development is real science by Goalie August Telescope. He saw things that have never being see before and makes beauty drawing of the sun (sunspotting). He visualised what he saw, and this led to the visualisation of data, hence history of science.

    The three basic principles of data visualisation are:

    • One is you as the designer and What you had to say and what you want to communicate
    • Two is the reader, the reader is not you , the reader is coming with it own context, and their own biases, and their own assumptions . You need to account for that in your design thinking and development.
    • And finally, the data itself, what information has to say , how that informs the truth

    In conclusion and reflection which led to redefining my aims and objectives for this project. What are my motivational factors? Is this for societal changes, educational and the social economic impacts of poverty, population and environment on the planet. By having a better understanding of data visualisation principles and design concept, would led to creating a successful visualisation project.



    Week 5:

    Wednesday, 3rd March 2015


    This is a continuous of the previous week – data visualisation and in-depth understanding and concept of visualisation as well as excellent examples and projects for my work.

    I found a tutorial on Interactive data Visualisation using Processing by Barton Poulson from and data Science Process:

    Data visualisation is viewed by many disciplines as a modern equivalent of visual communication. This involves the creation and study of the visual representation of data, meaning “information that has been abstracted in some schematic form, including attributes or variables for the units of information.” (Friendly, M, 2008)

    Data visualization is both an art and a science. The rate at which data is generated has increased, driven by an increasingly information-based economy. Data created by internet activity and an expanding number of sensors in the environment, such as satellites and traffic cameras, are referred to as “Big Data” . The processing , analysing and communicating of this big data present a variety of ethical and analytical challenges for data visualization .

    One of the primary goal of data visualization is to communicate information clearly and efficiently to users through the information graphics such as using tables and charts. indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but “stimulate viewer engagement and attention.” (Viegas and Wattenberg, 2011)

    Therefore, an excellent data visualisation project should aids in discovering trends, providing insights and exploring data sources, and finally to tell interesting stories.

    Historical context to Data Visualisation

    Most kinds of statistical graphs have a long history and they all have one important thing in common. According to John Tuft, the Spiritual father of data visualisation:

    “The greatest value of a picture is when it forces us to notice what we never expected to see.”

    The first known graphics is call the chart of ‘planetary movement’ from the 10th Century and is over a thousand years old.

    Multiple Time Series Chart

    This is showing us the positions of several celestial bodies over time and also making it the earliest version of what can be called a “Multiple Time Series Chart”.. This chart also been described as helping us in understanding the ‘nature of the solar system’.

    Moving forward over 800 or 900 years: two people made particularly important contributions to statistical graphics.

    The first one was William Playfair, who was a Scottish Engineer and Economist.
    William Playfair -Bar ChartSource:,/wiki/William_Playfair#/media/File:Playfair_Barchart.gif

    In this graph from 1786, Playfair created what is considered by many to be the first bar chart and still available in Excel software package today.

    William Playfair_TimeSeries-2Source:
    The Playfair’s trade-balance time-series chart, published in his Commercial and Political Atlas, 1786

    In 1801, Fifteen years later, Playfair came with another innovation call the pie chart :

    Pie-Chart_William Playfair_1801


    Playfair’s graphs were significant for getting the visualisation ball rolling, they simply communicated information and tried to do so in a clear and attractive way.

    The second person was the English Nurse Florence Nightingale who was first to use statistical graphics as compelling tools for persuasion and policy change .

    Nightgale's coxcomb diagram

    Her best-known chart was the 1858 Diagram of the Causes of Mortality in the Army in the East , which depicted causes of death among soldiers in the Crimean war in Turkey.

    It was reported that as a result of her presentation, Queen Victoria appointed a sanitary commission that came to Turkey and removed dead animals from the water, got rid of rotten floors and improved ventilation.

    As a result of this presentation the mortality rate dropped from 52% to 20% , making this perhaps the graph that saved more lives than any others.


    Playfair’s graphs were significant for getting the visualisation rolling, they simply communicated information in a simply clear and attractive way.

    These examples above, illustrated the importance of information Visualisation which refers to using graphics that are created to communicated information that is already understood by at least some people. However, Data visualisation on the other hand can be thought of as graphics that are designed to help researchers find the patterns in the first place.

    Modern day Visualisation in visual processing
    Aaron Koblin: This first was called Amsterdam SMS Messages and it’s a way of examine text message when and where they are sent.

    Ben Fry: The creator of Processing. This first one is genetic data and what he’s depicting is the genetic similarities between humans and other animals.
    Aligning Humans and Mammals
    Aligning Humans and Mammals

    Ben Fry: the second project is a depiction of the changes in Charles Darwin’s Book on the Origin Of Species. It’s actually an interactive graphic that shows you it’s a complete text and if you go the website, you can click on it and see exactly what parses and word were added or subtracted with each edition.

    The Origin of Species

    Brendan Dawes, is a larger piece called Cinemas Redux. What he’s done here is he’s taken a constant stream of still frames from several movies and simply arranged them in order, essentially re-creating the movie in very small images. Now if you go and see the entire collection, you can see very clear differences from one director to another. You see for instance here that Alfred Hitchcock had a much lighter palette and a much warmer palette than did William Friedkin in The French Connection, which was cooler and darker. You can also see that the vertigo was longer.

    Cinema Redux

    Max Planck Research Networks: This is created by Moritz Stefaner and Christopher Warnow, simply shows the interconnects of a series of researchers.

    Hamlet from Understanding Shakespeare by Stephen Thiel: selection of keywords arrange by scenes with the yellow highlighting the major characters.


    In this figure above , Hamlet where Hamlet speak all the way through. You can see for instance that he doesn’t speak very much in the second scene, but he comes back to the third and so on. And these also are produced wall size and wonderful ways for getting the general feel of what’s going on in a particular story.


    How could Data Science processing and methodology helps in big data and visualisation development?


    Data Science Processing

    Data Science Processing



    Week 6 :

    Wednesday, 10th March 2015



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Research, Review and Technology