Category Archives: Data Journalism (academic)

Data Journalism Reading Wk 13

http://www.newyorker.com/reporting/2010/12/20/101220fa_fact_paumgarten?currentPage=all

This is a fascinating article about a legend in the video game industry, the man behind the fabled Super Mario Bros, the Nintendo and the Wii.

There were a few points to take away from the reading that would definitely serve us in our roles as developers.  The first is the ability to design mostly in one’s head. I think that’s why the story of the cave is so essential. When you sit and start designing with what you have or trying to make something a derivative of the other, the design you come up with won’t be original and may not even be a substantial improvement on anything. To be truly revolutionary, one really has to design in their head, free from the constraints of possibility and current knowledge.

Throughout the article there is a constant regard for simplicity.  For a design to be effective, there need be only a few dynamic elements, only a few moving parts for the user to comprehend. Finding that limit of the users capability is the challenge. In all of Miyamoto’s games, the concept is always simple. Get the girl, find the treasure etc. They show us that even with just a few elements, the user experience can vary greatly and be unique to each person which is what you want your design to do so that it’s fun. In fact the user experience differs every single time the interaction occurs and that’s the key to making something addictive. Its a similar concept to gambling. “This time, things could be different”

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Data Visualization Critique Wk13

This chart comes from NPR and it uses government data to chart out what the average American household is spending their money on. It’s a simple enough visualization, with the biggest bubbles illustrating the biggest percentage being spend. It’s also useful to color code the bubbles according to type of expense.

However, my gripe with bubbles being used to illustrate differences in quantity is that it becomes difficult to tell the difference. Sure the change is apparent when viewing a really large bubble next to a small one, but all the ones in the middle are pretty much the same size..give or take. I would much rather use a bar chart in this case, with the values going in ascending order because then it would really illustrate the priorities of spending correctly. Right now, with all the bubbles floating arbitrarily, we have to look for the story and the free form means that our eyes will gloss over some of the more interesting figures.

Data Reading: Wk 12

Emotional Design

The reading introduces the concept of “attractive design” not just being an aesthetic requirement but essential to the usability of a device. Once again, Apple designs immediately come to mind. To me, the most eye-opening revelation was in the opening chapter where the author tells us why attractiveness helps people perform certain tasks. Seeing or interacting with an attractive design is pleasurable, it puts us in an elevated mood. This elevated mood is most conducive to creativity. Creativity allows us the flexibility to figure out how to use a new object. By contrast, the anger we feel when an ugly object does not respond to us makes us want to increase our level of effort, which inevitably results in rage induced destruction.

One can’t help but think of Apple designs. Does Apply have the best functionality? Other designs may allow more options, more control and more customization. But the sheer beauty of the touch screen or the one touch button on the iPad or iPhone causes us to overlook those defects. Or are those defects negligible in the first place because of the superior aesthetic. Whatever the case may be, Steve Jobs understood the users desire to be immersed in a beautiful experience. He understood the pleasure of “cool”, the tiny jolt of delight one gets from a novel immersive experience. It is a powerful thing, one only has to take an iPad in front of someone who has never used it to truly observe the power of aesthetic pleasure.

There are of course different types of aesthetic enjoyment which the reading discusses. Along with pure visceral enjoyment, there is also the addition of aural pleasure (accompanying music), competition or novelty factor and then of course pure emotional attachment which evolves after prolonged use. To develop that trust between object and user is the gold standard every programmer/designer must aspire to.

Data Visualization Critique: Wk 12

http://www.nytimes.com/interactive/2012/02/12/us/entitlement-map.html

 

We’re looking at yet another map of America visualization with regards to the economy. There definitely seems to be a high demand for facts and figures about unemployment, government spending etc, especially since it’s an election year.

On the surface, this map contains a lot of information about the different kinds of social programs Americans receive. But there’s really only one stat we’re interested in looking at which is the figure for unemployment benefits. We can naturally infer that the states with the darkest regions, or the most unemployment benefit claims are the ones that have been hit hardest by the economy. What’s cool about this map is that you can toggle between the different decades to see if it’s always the same areas that get affected when the economy goes south and it looks like that’s the case.

I would have done away with the “play” button that shows up when the page loads. It isn’t adding anything we can’t already do with the left/right buttons and besides, the transitions occur too fast for us to observe any impact or trend.

The popup graphs that appear when you mouseover a county are great because they are uniform and it is easy to see the story of each county through the dips and spikes. If you gradually mouseover entire areas, it’s pretty easy to see which counties and states were drastically affected and which ones remained relatively stable.

The color scheme also works, with the dark red signifying increased government spending, the gradation of the colors are smooth yet distinct enough for us to see a clear difference. The numbers accompanying the scale are also helpful and the chart does one thing lacking in so many US maps which is to make all the information uniform and comparable as you go from state to state.

I might have chosen to not represent any of the other government social programs since unemployment is such a crucial piece of the pie but I can see the value in having them there. The map allows for a great degree of personalization as you can zoom into the county you live in to get the exact figures and history of the area.

Adding a link to the NYtimes story about how government spending has increased was also a nice touch, something I would definitely click on to give this information some context. A great example of the symbiotic relationship between data and story.

Data Visualization Critique: Wk11

http://www.usatoday.com/money/economy/story/Jobs-Forecast-2011/34083932/1

This is a really fantastic visualization from USA Today.  It’s sophisticated, uncluttered and conveys several stories without overwhelming the viewer.

There isn’t a map key or a set of instructions but you intuitively mouseover the map and click on the state you want.  For e.g. lets say you click on Texas. Not only do I have a graph that shows up on the left telling me what the percentage change in job growth has been for the last couple of years I can also see a trend. The jobs are predicted to rise, but it’ll be slower than predicted. Below I can match up Texas’ performance with the other states.

With the purple bars clearly showing where job growth is negative, it’s very easy to create a narrative in your mind about what happened to Texas when it came to employment. But the best part is yet to come.

On the far left, you can click on different job sectors to see exactly which jobs were the most affected by the recession. The states then reflect the job growth in very clear terms, purple being jobs decreasing, and green to dark green meaning an increase. If you click on “construction” and “government services” you can see the very stark difference as the areas become mostly purple. Conversely, it’s easy to see that jobs increased in the educational and health sectors.

The visualization isn’t overly complicated, I can already imagine how we could create it using the simple for loops and swap map functions we have been working with. This is also data that is readily available from the Bureau of Labor Statistics so compiling it wouldn’t have been hard. They creators were also smart about using percentage change rather than just percentages so that we could have clearer, standardized comparisons.

Doing Data Journalism: Reading Wk11

http://cdn.livestream.com/embed/columbiajournalism?layout=4&clip=pla_a2481bff-f4cc-4bd1-a269-e9c3a00faf20&height=340&width=560&autoplay=false

Watch live streaming video from columbiajournalism at livestream.com
Quite possibly the best part about this panel discussion was finding out how data journalists in major news organizations still use basic journalism concepts when doing a data driven story.
Mo Tamman made an important point about not using data as an illustration or something to buttress the story. This is unfortunately a mindset that still exists out there as news organizations are slow to realize the potential of stories residing in the data itself. Old school reporters will often come back after anecdotally supporting their hypothesis only to find the data refuting or not fully supporting their claims. In this case, the text wins over data, exposing the publication to resourceful people dying to prove them wrong.
Another important concept is the hypothesis coming before the story or the data. Much like any journalism story, there has to be a central question you want to answer before you can progress.  In this way the data and the story emerge together rather than one coming before the other.
The panel showed that the imagined separation between the journalists and the data geeks is much less than imagined and increasingly disappearing. However,  you still get the feeling that there’s a long way to go when it comes to embracing data journalism as a legitimate storytelling medium, much like audio or video. It’s not just an embellishment anymore.
Courtesy a study by Bit.ly, we study how long a link ‘stays alive’, or how long it keeps  getting shared an accessed after it has first appeared. As we expect, there is an exponential decrease in how much the link is viewed after its initial spike. Bit.ly uses the concept of “half-life” to compare the duration of each link. The half-life of the link is the the amount of time at which this link will receive half of the clicks it will ever receive after it’s reached its peak.
Interestingly news items have a shorter half life than interest stories that aren’t timely (such as the baby otter link). This is probably an accurate reflection of our media consumption, where the 24-hour news cycle churns out so much news that any particular news item dies in the public consciousness pretty quickly.
The study also shows that Facebook gives links a longer lifespan than Twitter, definitely reflecting the design of each interface. Most of what everyone says on Twitter goes down the drain or the ‘firehose’ but on Facebook, there’s a greater chance of visibility, important things to note for online content creators looking to give their work the maximum amount of online exposure.

Data Reading: Week 10

The reading for this week focuses on Interactive Design, a concept we’ll have to keep in mind while designing how our web pages interact with users.

The reading talks about the different kinds of interactions, between humans, humans and machines and how they al overlap to varying degrees in interactive design.  It also stresses heavily on the process of coming up with a design, discussing prototypes, testing initial ideas and learning to adapt.

Several principles are also discussed including “conservation of complexity” which places the burden of dealing with complexity on the designer/engineer rather than the user and the “poka-yoke” principal which minimizes user error by creating a set of conditions that have to be met before the product can be used.

The factors contributing to a good design are also listed, including trustworthiness, appropriateness for the culture of the user,  intuitiveness and a pleasurable experience.

There is also a discussion on how the physical attributes of a design contribute to its ‘persona’ and relatability.  Many of the most iconic designs and forms we have come to accept came through years and years of generational testing and acceptance.

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