Visuality

The Practice & Art of Thinking


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Problem Solving – Learning and Sense Making

Explaining how something works is important, not only for the sake of knowledge itself, but because explanations can lead to solutions and improvements of our understanding of how something works or behaves.  You cannot fix something if you do not understand what has gone wrong.  You can’t prevent cancer cells from growing if you do not know how and why they started to multiply in the first place.  To interfere in the process, you must understand the process in all its enormous complexity. You need to start somewhere and a theory is the beginning.

This post is a hybrid; it uses observations and years of teaching and solving problems as the source to attempt to explain problem solving.

A large component of problem solving is learning. When we solve problems of relative complexity, we have to go through a stage of learning. I will explore this within the context of problem solving in a future post. For now, let us accept that a good chunk of what we do, when we solve problems, involves learning. The link between learning and sense making is also made.

Learning is a natural process that originates from us and enables us to interact with the environment. Learning is something we ‘do’ – we do not acquire ‘learning’. Like breathing, learning is a normal function of living. The activity of learning originates from our desire or need to make sense of our experiences; to manage the unknown and uncertain aspects of life; and to take action in the best possible way to ensure our survival and security.

Humans are constantly making meaning. As William Perry said: we are wired to organise meaning. We make sense of our experiences and give them meaning.

We learn to make sense of the chaos and confusion of the raw uninterpreted ‘data’ surrounding us and we learn to develop ways (methods, heuristics etc.) to best respond to and interact with the environment (external and internal). We also learn to define who we are and our personal view of the world. This filters and conditions how we interact with the ‘world’ and how we choose to ‘see’ and make sense of it.

We do this thing called learning instinctively. Current research proposes that our brain is intensely aggressive and is designed to learn throughout life – learning is an inbuilt survival strategy of our species. We create meaningful patterns from the environment that we then use as constructs that make sense to enable our survival.

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Sense making (as explained by Karl Weick) refers to how we structure the unknown so as to be able to act in it. Sense making involves coming up with a plausible understanding of our perceived reality of the ever-changing world around us. It is a mapping process that attempts to give structure to the unknown.

This process enables us to “to comprehend, understand, explain, attribute, extrapolate, and predict”. It is a cyclical process that we go through, as we obtain more information. We refine our understanding through the patterns and associated meaning we have created and which we constantly test, verify and refine. Eventually, the complex unknown situation becomes ‘tamed’ and we reach a higher level of understanding at this point. We have learned and created new knowledge that we can call on to solve our problems. Learning from a constructivist point of view is essentially a sense making process.

From what we have seen, there are three conditions required for learning:

  • Enough raw data or experiences must be available with enough repetition and variations on themes to allow for the differences in patterns to emerge;
  • Enough time for the patterns to emerge naturally; and
  • Sufficient, prior meaningful perspectives to be able to handle new experiences productively. If these do not exist, then a longer learning process is required or this can be acquired from other people we trust (we do this with caution).

Learning is dialectical. It is a process that involves interaction through discussion and reasoning by dialogue, whether carried out internally with oneself or eternally with others. This dialectical process explores alternative viewpoints in order to develop an integrated point of view, resulting from the best aspects of all the alternatives we have been exposed to up to that point. The process goes on as new alternatives emerge and it is interactive because we generate meaning by exchanging information with the environment and integrate the meaning into a constructed whole. We construct our knowledge.

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There are many aspects that affect our learning, such as past experiences (these can act as a barrier or can enhance our learning). We also have preferred learning styles (a fascinating topic on its own, which I will try and explore in a future post); cognitive styles (a term referred to more by psychologists); preferred learning strategies (visual, auditory and Kinesthetic) and our mental models, to name a few.

Learning is central to problem solving. Our ability to solve problems is affected by some of the aspects listed below:

  • Independent learners – the degree to which we are able to communicate and learn from the meaning created by others – we create meaning for ourselves without reference to others. Some people need to learn things for themselves, whilst others are quite happy to develop a shared meaning through interaction with others;
  • Ability to communicate – learning is dialectical –our ability and how rich our vocabulary is and other non-verbal expressions such as drawing gestures etc. Our ability to understand what needs to be stated and how best to communicate that.
  • Mental flexibility – how prepared we are to adjust our own learning and theories of the world around us. Some people can be quite stubborn (or mentally lazy) and are not flexible or open to adjust their opinion about their worldview. Good problem solvers are highly flexible but critical and keen to reassess their mental models. They are agile and curious. They know that the world is in constant change and, the best strategy to survive, is to make sure they adjust their knowledge to meet the new changes. Their mental models are constantly being renewed and tested against new data and information;
  • Ability to actively re-examine personal constructed theories – we place our ‘self’ at the centre of our reality. The meanings we assign to reality, together with our constructed theories about life and reality, enables us to operate in the environment – without these we are lost and paralyzed. However, at the same time, a good learner and problem solver is very mindful of the fact that these maps of reality are only temporary and approximate reality. The good problem solver will not objectify these maps but, instead, will re-evaluate them eagerly when presented with new information and patterns that emerge and will adjust reality to match the new meanings.
  • Critical thinking – the rigour in critical thinking allows us to evaluate the data, make sense of it and assess its usefulness within the context of the problem to be solved.

The above aspects are important for us to be aware of when we solve problems, especially if we want to be good at solving problems. The objective of this post was to provide background and I will be referring to some aspects covered in future posts. It was also important to make the link to sense making, as I often use the term and will be using it frequently, especially as I start to elaborate on a problem solving method we have been developing.


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Heat Maps

Heat Maps is a simple yet powerful technique that leverages the human’s superior visual cognitive capacity to gain deeper and faster insights into data and information.

Cormac Kinney coined the term in 1991 to describe a 2D-display depicting real-time financial market information.

A heat map is any data visualization that uses color to represent data values in a two-dimensional image. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.

There are many different types of heat maps used in different disciplines, each referred to by the term “heat map”, even though they use different visualization techniques.

The reason heat maps work so well is because of our ‘Pre-attentive Processing’ ability. The term refers to the ability of the low-level human visual system to rapidly identify properties, such as color and size, in less than 250 milliseconds.

According to Van der Heijden, we unconsciously accumulate information from the environment ,which is pre-attentively processed. The brain filters and processes what is important based on what stands out the most. Once the individual’s attention is captured (based on what is of relevance to what the individual is thinking), at that point the information is selected for further and more complete analysis by conscious (attentive) processing.

It is a two-stage process, which happens in seconds, and this is why heat maps are so powerful.

Heat maps are being used in many sectors and that is great. The best way to demonstrate these is to show you examples. I use some examples as shown by John Brandon in a slide presentation.

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Marketing people created the map above. By using eye-tracking devices, a heat map is created showing where attention of the user is focused on display pages. The map above is a sample heat map of a Google search result.

A dominant pattern for search engine results is the “F” pattern showing the eye being drawn to the upper left (hot colors) and then moving down and across from there (shown as the blue colors). There are, however, factors (such as the inclusion of images, graphics, and additional columns) that can significantly alter this pattern.

This heat map displays risk by location. It was created by RMS (risk management company) to show risks related to catastrophic events: Earthquakes, hurricanes, severe storms (including tornados and hail), wind storms, wildfires and volcanoes. An insurance company might use it to determine the “probability of loss” related to such an event.

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This heat map shows the reality of fraud attempts in real-time using live data. A red dot pops up to show a fraud attempt. ThreatMatrix culls the data from 1,950 customers, which includes about 9,000 websites, and tracks about 360,000 cyberattacks per day.

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This heat map from MarketProphit shows buzz and sentiment around specific stocks. The larger blocks indicate the most buzz (or discussion) around a stock as culled from Twitter. The colors show sentiment; the red blocks denote negative comments and the green denotes positive comments. In an instant, financial planners can see general trends with stocks based on social media posts.

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This heat map shows the movements of customers in a retail store aisle. Red areas represent the spots where most customers shop. Retailers can use the heat map for product placements and to see whether a sales campaign was successful.

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This heat map shows the age of buildings in the Portland, Ore. area. About 544,000 structures are represented, including about 4,500 erected in the 1800s and 10,265 buildings constructed in 1978 alone.

Justin Palmer created this heat maps based on public data released by the City of Portland. It shows the age of buildings. This can help municipalities see which neighborhoods hold the greatest concentration of structures that may need repairs.

Click on the link (http://citysdk.waag.org/buildings/) to see the map in large scale, iit s beautiful. It shows the nearly 10 million buildings in the Netherlands; some in central Amsterdam are more than 1,000 years old.

Some would argue that heat maps are very specific – “the heat map is a treemap-like graphical technique used to represent a two-dimensional array of data” as shown in the example below.

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Many techniques illustrated in this post, as argued by the purists, are not heat maps. I agree and I disagree. The principle of using color to very quickly highlight the issues of concern, attention or importance is extremely valuable and it is a perfect example of how we work better with visuals than with coded jargon. The fact that it is called a heat map – well, does it matter? Notionally, it is a perfect description even though it might not be as it was originally labeled – I think this is a good and natural evolution of the principles. I will call these techniques ‘heat maps’ – it works and it is powerful.


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WHAT DO WE REMEMBER VISUALLY?

There is much debate as to what we remember and why we remember as far as visual information is concerned. The age of visualization has arrived. Now the question is: what makes a good visualisation memorable and what makes a bad one – the ones we forget easily? What are the elements, structure and colors in a visual that makes us remember it?

This question is important, especially if we are trying to design something that people will vividly remember and something that becomes useful. There is little point spending time creating a visual that would fall under the category of ‘chart junk’ as Edward Tufte calls it.

The debate is a serious one amongst visualization experts. The point of the debate is obviously about trying to get a systematic set of principles that would allow us to guarantee the creation of an excellent visual each and every time. The reason is that this is a relatively young discipline – the discipline of visual thinking – and we still are very much at the exploratory phase.

In an attempt to cast some light on the debate, research led by Michelle Borkin from Harvard, together with Aude Oliva from MIT, was presented at the 2013 IEEEE INFOVIS conference.

visualizationsThey designed a large-scale study in the form of an online-game to measure what visuals participants remembered. _They collected more than 5,000 charts and graphics from scientific papers, design blogs, newspapers, and government reports and manually categorized them by a wide range of attributes. By showing these to participants in one-second glimpses (using Amazon Mechanical Turk), the researchers tested the influence of features such as color, density and content themes on users’ ability to recognize which ones they had seen before.

The findings showed images that contained photographs, people, cartoons and logos were easily recalled because these elements are not abstract. A visual that contained some image of a recognizable object to a human was remembered more easily.

Images that were visually dense and used more colors were also more memorable as well as unusual types of charts such as network diagrams, tree diagrams and grid matrices. The surprising result was that simple bar charts, pie charts and scatter plots were not easily recalled.

Images that were natural to humans like branching of trees, as well as images that combined familiar and unique elements were very memorable.
“A graph can be simple or complex, and they both can be memorable,” explains Oliva. “You can make something familiar either by keeping it simple or by having a little story around it. It’s not really that you should choose to use one color or many, or to include additional ornaments or not. If you need to keep it simple because it’s the style your boss likes or the style of your publication, you can still find a way to make it memorable.”

If we could systematize visual thinking then we would be able to use it more effectively, faster and efficiently. Unfortunately, the researchers did not issue a set of rules for us to follow – that might be good – forcing us to use our imagination. They confidently state that all visualizations need to be accurate, easy to comprehend, aesthetically pleasing and appropriate to the context. Together with the findings mentioned above, I think we have some good guidelines.


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Revealing Hidden Knolwedge

This Ted talk by David McClandless is worth watching. I agree with his observation that data visualization is really the process of using Design as a means of solving problems and creating elegant solutions. He points out that information overload is now a very serious problem and data visualization is one powerful way to make sense of it.

I agree and will even go further and suggest that without data visualization (an important new emerging industry), we will experience paralysis.

Enjoy – as with all TED talks it is no longer than 18 minutes.


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Visual Map of Abstract Artists

Watch the video of an exhibition at the MoMA – Inventing Abstraction 1910-1925.

The primary thesis of the show is that abstraction is about relationships.

How does one best depict this?

In text?

Cumbersome.

Visually?

Not only is it beautifully done. It shows the dynamics and complexity with great elegance and simplicity – allowing the observer to read the layer he/she wishes to and freely explore connections and create their own meaning from the map (they are free to tell their own story). This would not be possible if the information was presented linearly in text format.