Data Visualisation Techniques

These three design issues—data collection, representation, and navigation—can be considered as significant building blocks that constitute an overall design process of data visualization, not necessarily put in a linear sequence.”
Mao Lin Huang and Weidong Huang
(eds) in their book, “Innovative Approaches of Data Visualization and Visual Analytics

With the amount of data being processed by OSS tools ever on the increase, data visualisation and interaction techniques become increasingly important as a means of identifying valuable insights. Over the next few days, we’ll report on some of the innovative techniques proposed in Huang and Huang’s book, which includes a compilation of work by others.

In 2009, 160 volunteers were given smart phones for a year and data was collected / collated from all the sensors within the phones. This data was subsequently made public for the Nokia Mobile Data Challenge.

For example, team of Jung, et al categorised the following as a means of showing a storyboard of a single user’s activity:

  • Inter-Personal Communication Data: Voice call logs and text message logs
  • Physical Proximity Data: The Bluetooth IDs of mobile phones scanned within a physically close distance
  • Location Data: GPS, WLAN access points, and cell network information (in the order of precision)
  • Media creation and Usage Data: Logs of photo taking, video shooting, music play, web browsing, alarm setting, etc.

The following graph is their representation of a single day’s storyboard from a single user’s phone. From the mountain of source data, this storyboard is able to present a number of insights, which a CSP or marketer may be able to derive a set of actions from.

Basically, there are four factors that are being displayed:

  1. An x-axis showing time-scale of a single day from 0:00am to 23:59pm
  2. A grey-scale background shading that shows the number of GPS move statuses
  3. Grey-scale circles above the x-axis showing the number of interactions with near-by Bluetooth devices. The darker the dot, the more interactions
  4. Coloured squares below the x-axis showing the type of application usage (ie call, SMS, web, music,

The following are a few take-aways that could be assumed from looking at the data sets:

  • The grey-scale background shows movement from 6:30 til 8:00am, which probably corresponds with the daily commute to work.
  • The grey circles indicate many more people near-by during typical working hours, implying this person is working in an office environment
  • From noon to 12:30 there is a smaller number of Bluetooth contacts, but a stronger dot indicates a close association, possibly eating lunch together with a small group of friends
  • The blue squares indicate listening to music first thing in the morning and last thing at night
  • The user utilised all of the applications that were being recorded (ie SMS, web, etc) scattered throughout this given day

This particular graph is a quite ingenious way of showing a rich set of smart-phone data, allowing for easy cross-referencing or overlay of data.

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