Ash at CIID

Ashwin Rajan's blog while at the Copenhagen Institute of Interaction Design.

Posts Tagged ‘data visualization

‘Elevator Buzz’ Concept for Intel

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Here’s the presentation for the Elevator Buzz concept that my team worked on for the Intel-CIID industry project. The storyline for the scenario is as follows: three office goers step into the office elevator, as as they arrive at differnt floors on their way up, a visualization inside the elevtor shows the energy efficiency of the floor. The performance of the floor is reflected in the expression of the person who works there, ranging from embarrassment to elation. At the end of the piece, the worker on a floor with low energy efficiency makes a small behavioral change (switches off the light in an unused office space) for the better.

The panels were then drawn up in a neater, crisper scanario to use as context-setting probes for acquiring later user feedback on the concept. The reactions and feedback gathered from users were used to define next steps, setting the agenda for the overall design direction of the project.


Written by Ashwin Rajan

May 17, 2009 at 8:07 pm

IXDI Cluster Map – 04.23

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Blogging about a cutting-edge subject is so much fun, especially with web 2.0 tools that can potentially tell you interesting stuff about whats going on in the works. I started this blog since moving to Denmark in Sep ’09 , and I just had a look today at the Cluster Map app installed on my blog. Here is the verdict – visitors come from everywhere! I would love to see some big red dots on North and central Africa, Russia and Central America as well, though.


Incidentally, I just ran into a map of members on the Interaction Design Association ( website. Its here below; see any patterns?

Written by Ashwin Rajan

April 23, 2009 at 3:17 pm

Intel Project – The Social Collective as an Agent of Behavioral Change

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Working with Intel design researcher Jay Mellican (of Intel’s Digital Home group) and CIID faculty Vinay Venkatraman, we explored the role of the social collective in achieving sustainable behaviors towards the effective management of energy.  Here I will discuss the context of the project, and go into our process and solutions in future posts.

The focus for Intel was the emerging “smart grid” – the efforts promoted by many governments and utilities to modernize, from the bottom up, the integrated system by which energy is collected and distributed. In bringing the energy grids of yesteryear into the digital age, many of the technologies and standards that will make up the “smart grid” are yet to be defined, and the implications they will have on our patterns of daily living, as well as their likely success, will depend heavily on how they are defined.

The idea of the “smart grid” is a really interesting one. Here is a really nice video that covers the main aspects of the concept well:

Its hard to discuss the “smart grid” for long today without running into GE’s efforts in that space. As part of GE’s ecomagination campaign, the company has created this engaging augmented reality web object, as shown in the video below:

The virtual energy farm object can be accessed here. Nice website too.

Amongst others, Google has been working to bring visibility of energy use to the notice of individual customers; check out its Power Meter tool.

Given all that, in trying to envision scenarios and solutions for behavioral change for this emerging and very complex space, my team (classmate Mimi and I) quickly realized that the problem of visibility of use was a crucial one. According to the Environment Change Institute, for instance, “most householders have only a vague idea of how much energy they are using for different purposes, hence the importance of making energy flows more visible and controllable. There is a lot of interest in the potential for better feedback using improved (‘smart’) metering, more informative billing and direct display panels.”

As my team’s interest was in understanding the behaviors and influence of the “social collective” – networks of people connected by social technologies – in the context of smart energy use, we decided to explore the space beyond the use of individual control devices such as energy meters, and look at “visibility of use” aspects for groups of people. More on our contextual research and enquiry in posts to come.

Global Tourism – Interactive Visualization

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Project Context
The brief for this class project (with Shawn Allen of Stamen Design, SF) was to develop interactive data visualizations based on UN Data as available on On this website, the UN provides downloadable data in various ready-to-use formats on a wide variety of issues and themes – demographics, global indicators and statistics on commodities and trade, energy, population and gender, industries, children, health, tourism etc.

Choosing a data set
We were interested in several data sets to begin with, but common underlying themes seemed to dominate across our choices. In order to be able to think freely about the kinds of data that we would like to work with, we also explored data sets external to those provided by the UN. These included experimenting with data from (a streaming internet radio service) and with live feed of statistics describing developments on Second Life (a globally popular virtual world).

Finally, we decided to go with a data set that provided ‘Tourist Arrivals by Region of Origin’ for the years  2001-2005 from the UN database.

It is usually easy to find information about the most popular global tourist destinations. What is less understood is ‘who travels where’, or to put it in broader terms, which travelers place the world’s top tourist destinations at the top of the charts. Since we were focused from the  first on exploring the ‘supply side’ of the business, we decided to include only the world’s top twenty five tourist destinations (by revenue from tourism).

The Visualization

Global Tourists - Map View

Global Tourists - Map View

Global Tourists - Stack View

Global Tourists - Stack View

We chose to use the metaphor of a world map for this visualization to be able to simultaneously represent both a region of tourist origin as well those tourism hotspots most frequented by its travelers. The region of origin chosen is depicted by a color, while circles of the corresponding color represent the places visited by its native tourist population. The size of the circle indicates volume of tourist traffic.
Viewers can choose to see the visualization in either a ‘map’ (Image 01) or a ‘stack’ (Image 02) view. Stacks can further be organized by ‘Destination’ or ‘Number of Visitors’; switching between these two modes reveals interesting trends across the years.

Key Learnings from Visualization
The key learning that is evident from browsing the visualization is that global tourism is  still largely regional. Tourists from the Americas travel largely within the Americas, with some concentrated bursts of travelers to some parts of Europe. Europeans travel mostly within Europe, as do Asians and Africans within their own regions. If there are far-off exceptions to this overall trend, they are limited in number and usually to a very specific set of destinations depending on the region of origin.
Since data was only available for a six year period, we (predictably) saw no huge variations from year to year, especially in the map view. In the stack view however, one notices that competition for tourist revenue is fierce amongst the top twenty five destinations, indicated by the quite frequent shifts in place among the contenders under the fifth spot.

Written by Ashwin Rajan

January 11, 2009 at 5:56 pm

History of Religion Visualization

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Check out this compelling information visualization on the history of religion in movie format. The movie approach to presenting visualizations is very effective for communicating encapsulated information about limited and specific parameters – in this case WHEN, WHICH and HOW MUCH five dominant religions flourished.

A visulization of the history of religion in movie format

A visulization of the history of religion in movie format

This mode is the opposite approach to an interactive timeline/map approach, where all of the information would be available upfront, and the user learns primarily by exploring the interface. In which case, the customization made available to users is gained by trading off the goal to communicate something very specific as is achieved by this visualization.

Come to think of it, it is surprisingly difficult to go beyond the handful of infoviz presentation formats in currency today.  Maps, charts, graphs, clouds, trees and network diagrams seem to dominate in different forms and variations. And this is true for two reasons – its incredibly hard to find new metaphors that do a great job of representing qualitative information, and secondly, I suspect it has a lot to do with our own preferred ways of ‘seeing meaning’ – the information scanning, browsing and seeking behaviors we are most attuned to.

For instance, this is a list of Visualization Types provided by IBM’s ‘Many Eyes’. While the formats are decidely limited, the possibilities of exploiting these formats to present various types and degrees of qualitative information (as suggested by the titles they are grouped under) catch my eye.

1. See the world:
-World maps
-Country maps

2.Track rises and falls over time:
-Line graph
-Stack graph
-Stack graph for categories

3.Compare a set of values:
-Bar chart
-Block histogram
-Bubble chart
-Matrix chart

4.See relationships among data points
-Network diagram

5.See parts of a whole
-Pie chart
-Tree map
-Change tree map

6.Look for common words in a text
-Tag cloud
-Word tree

Written by Ashwin Rajan

January 2, 2009 at 4:38 am

Visualizing Wind

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This mini-project was executed as part of the Computational Design course at CIID some weeks back. My objective was to use the Nintendo Wii to record simple wind data by suspending the Wii in an open location, and using the recorded data to create visualizations drawn by programming in Processing. More details follow.

The Concept:
This intent is to show  a concept for visualization of simulated wind data as would be available from a sensor located on a windmill.
A real stakeholder – wind data analyst – from a leading Danish consultancy was interviewed to understand key challenges in wind turbine design.
It is common in the Danish wind industry (and elsewhere) to record wind data for very small intervals of time. By understanding wind patterns in terms of its properties such as acceleration and constancy, engineers are able to go beyond physical limitations of turbine design to evolve increasingly efficient and productive systems.
With over 20 sensors recording different parameters on a single windmill, analysts often face a veritable mountain of data (down to individual seconds). In this context, visualization of such data in a manner that facilitates comparison, causality and multivariant evidence becomes key. The poster describes briefly how some of these goals were met.

The delightful ‘Wiimote’ was used in this experiment to mimic the sensor.

Design Context: A real world scenario 
Location: a wind farm out in the North Sea
o   72 turbines
o   20 sensors on each turbine
o   Each sec of wind data recorded
o   Data from 8 years archived for analysis

·      The peak production of windmills of all capacities is 60% of full capacity due to physical limitations
·      Measuring constancy of wind is of most interest to wind analysts and windmills designers
·      Acceleration of wind is most deterrent to wind production as it wears out the material most, and least load on the material comes from constancy of wind
·      The main difficulty in real-world wind turbine design isn’t generating the most electricity at a given speed – it is making blades which will work across a range of wind speeds
·      The design challenge is to be able to measure and visualize wind data in a way that can help engineers interpret the ‘acceleration’, ‘lift’ and ‘orientation’ of wind.

Using the Wii Remote as principle sensor
Wii remote sensors simulate the acceleration of wind by it’s x, y, z acceleration coordinates
·      In order to holistically render the acceleration of wind in a visual manner, we have focused on gathering data in coordinate directions x and y, and rendered the z axis insignificant – by suspending the sensor (in this case, the Wiimote) with a cord of fixed length.
·      Due to the position of the Wii remote when recording the raw wind data, the x and y coordinates are principle axis in this experiment. This has two benefits
o   The z sensor is rendered insignificant in terms of contributing to measuring acceleration, thus rendering it simpler by reducing the number of variables required in its calculating
o   The z axis becomes dedicated to measuring ‘lift’ and denoted here by the small series of arcs at the bottom.

Anatomy of a second - Composite View
Anatomy of a second – Composite View


And another mode of visualizing the same data set …




Written by Ashwin Rajan

November 30, 2008 at 9:00 pm