Month: January 2017

As the world becomes increasingly interconnected and interdependent, opportunities to generate value through data visualization will only increase. The Internet of Things will have a profound effect on the role that data visualization can play in organizations and society, improving our ability to understand how humans and machines interact with each other and the environment.

Cisco predicts that there will be 50 billion connected devices by 2020, each being able to transmit and collect data. It will give us the most in-depth opportunity to see relationships between different ‘things’ in human history. However, tracking and analyzing these different datasets is only going to be possible with the use of powerful and simple data visualizations.

Alongside this huge influx of data is the seemingly unstoppable increase in the speed of analysis available due to accelerations of processing speeds. We have seen through the use of current technologies like in-memory databases and Apache Spark, alongside those for the future like quantum computing, that the ability to collect, process, and analyze huge datasets is increasing. As these technologies become more prevalent and the use of real-time analytics allows more and more companies to react to issues instantly, visualization is going to be the key that gives companies the opportunity to quickly identify and then act upon it. Without this ability, it would be almost impossible to quickly make decisions on data.

The human brain has evolved to be adept at noticing differences in patterns and, although the AI and machine learning have huge implications in a number of areas, they still lack some of the most important contextual elements in decision making. Therefore, having the ability to quickly and easily notice, and then act upon, patterns in data still falls within the realm of humans, and data visualization is the most powerful tool that allows us to do this. The IoT will certainly run without the use of data visualization, but without it, many of the possibilities that the connected world offers will be missed.

References:

https://channels.theinnovationenterprise.com/articles/the-internet-of-things-and-data-visualization

http://analytics-magazine.org/data-visualization-the-future-of-data-visualization/

 

Today’s class was a little difficult and I wanted to do some research on the concept that professor was lecturing. Toulmin, an English philosopher and logician, came up with a creative idea called Tolumin’s Argument Model. It not only helps writers’ to have better argument in writing but also benefits someone who wants to make a good visualization. He conveys his message about how a good argument should be analyzed by the model.

In the reference 1, it shows that a claim without a warrant is not a very supported claim. Warrant, as chain of reasons bridges between the data and claim and answers, “how did the argument get there” question.

Further in the reference 2, there is explanation about the argument model based on a hearing aid data. It clearly tells that qualifier is specification of data to claim and it measures the power of warrant. Rebuttal, in another word it means a condition of exception to the original claim and it strengthened by warrant. We should always have backing in our data to claim process to support the warrant.

Please have a look at  reference 2 since it helps us to have better understanding about future project.

Reference 1: http://individual.utoronto.ca/ecolak/EBM/evidence_and_eikos/models_of_argumentation/toulmin/toulmin.htm

Reference 2:

https://courses.lumenlearning.com/englishcomp2kscopexmaster/chapter/toulmins-argument-model/

As I’m working on my first dashboard project recently, I pay great attention to building DOM elements with animations.

Here I have found a great website about simple percentage circle: http://circle.firchow.net/. You can also check out the source code on Github: https://github.com/afuersch/css-percentage-circleA few things you need to know before starting to build a percentage circle by yourself:

A few things that you need to know before starting to build a percentage circle by yourself: clearfix, transform, hover, before&after. “clearfix” is a class when you are using float layout, to automatically clear all child elements of one element. “transform” is the degree to rotate a CSS element. “hover” is when your mouse just moves onto one element, it may render some changes. “before” and “after” are CSS pseudo elements, which are not real elements on index.html, and you can induce some actions for them.

A complete animated percentage circle includes its outer circle, the bar, the percentage number, the filled color inside of the circle, before and after elements, and the clearfix keyword.

Right now I myself is pondering over the source code to understand each line of it. I know that I can just use it instead of reinventing the wheels, but I think it’s better to know the fundamentals and then build fancy visualizations myself.

 

General principals of designing effective visualization

• Affordances: In the field of design, experts speak of things having affordances – characteristics that reveal how they’re to be used. A teapot has a handle. A door that you push has a push plate. The design of an object should, in and of itself, suggest how the object should be used. The same is true of your graphs, tables, and slides. Lead your audience through your visual – make it easy on them! Provide a visual hierarchy of information, these are visual cues for your audience so they know where to direct their attention.
• Accessibility: Designs should be usable by people of diverse abilities. Example of good design by this measure are Apple products: my mother can barely send an email, but put her iPhone or iPad in her hand and it’s so intuitive that she doesn’t feel overwhelmed by the technology. Work to make your data visualizations similarly straightforward and easy to use. Don’t overcomplicate. Use text to label, introduce, explain, reinforce, highlight, recommend, and tell a story.
• Aesthetics: People perceive more aesthetic designs as easier to use than less aesthetic designs whether they are or not. Specifically, studies have shown that more aesthetic designs are perceived as easier to use, more readily accepted and used over time, promote creative thinking and problem solving, and foster positive relationships, making people more tolerant of problems with design (this is crazy, right? leverage it!). Use a pleasant color palette (personally, I tend to do everything in shades of grey with strategic, explicit use of bright blue to draw my audience’s eye). Bring a sense of visual organization to your design (preserve margins, align things visually), showing attention to detail and a general respect for your work and for your audience.

source: http://www.storytellingwithdata.com/blog/2011/07/what-makes-good-data-visualization

The two visualizations show the color analysis of three Netflix original series in 2013. The sole purpose of this visualization is to derive customer insights and attract more users to watch the shows.

View post on imgur.com

This analysis is helping the company to find out the distance between users and further develop an algorithm to determine the  average color of titles of each user over a period of time which will help improve their personalized recommendation system. Eventually Netflix will be able to track if there is an ideal color for an original series or if different colors can be used for different audiences. The first visualization shows that House of cards and Macbeth has a lot of similarity based on the cover.

View post on imgur.com

Again, the color pallet used for House of Cards and Hemlock Grove is again very similar which may be misleading since differences do exist based on genre, cast, plot, development and production. However they have a huge contrast in comparison with Arrested development. These visualizations are alone not self-explanatory. A dashboard along with customer’s viewing habits, recommendations and ratings can convey the required insights needed to make important business decisions.

Source: techblog.netflix.com

As the amount of data available for comprehension increased exponentially, the need to represent the same in a coherent, concise and simple manner popped up as well. This resulted in the emerging field of data representation and this has a dynamic effect on our society. The evolution of data representation from a something as simple as a graph to interactive applications and advanced 3D representations has completely changed the face of the data analysis. The advances made in the field may be immense but there still remain a few stumbling blocks that has to be overcome in order to maximize its potential.

Paradoxically, it is the advancements made in the made in the field of computers and animations that are proving to be a hurdle now. Virtual reality, for example, has the potential to augment the existing ways of data visualization and elevate it to a superior level. This has been put to test by Goodyear tyres and used the interpretation to enhance the performance of their F1 tyres. But VR has been associated with the entertainment sector for so long that efforts to incorporate the same technology for data analysis in say, a Fortune 500 company has met with ridicule. Research to make the VR headsets compact are ongoing but it might take a few years to come to fruition.

Augmented reality is another technology making waves in the market currently, not least because of the popularity of Pokemon GO. Among all the new technologies, AR has the best and immediate chance of improving on the existing data representations. The challenge though lies not so much in its implementation as its augmentation. The overlaying data used to augment the representation should be clear, concise, and non-befuddling and should serve the purpose of augmenting, not distracting.

The other challenges of data representation are contingent on the users and developers. The majority of data representations are still done in 2D and as such, banal. Hence, to make the data representation distinct and interesting, the developers would have to resort to innovative ways of data representations. This could include vivid colors, interactive applications and collection and representation of more interesting data. As such, there is an increased demand for technical expertise and a channeled scientific approach in processing data representations. There is a dearth of data scientists currently, something that a lot of universities are trying to combat by offering new courses pertaining to data analysis. The differing levels of comprehension among the group the data representations target is another challenge presently faced by data representation. This particular problem is much more difficult to tackle as there is no individual solution to this. The best way to tackle this would be to form a protocol for interpreting data representations.
Even though there are challenges facing data representation these days, they pale in comparison to the progress made in the field in the past decade. Judging by the pace with which the field is evolving, it wouldn’t be surprising if the challenges listed above do not exist anymore by the next few years.

Source : https://channels.theinnovationenterprise.com/articles/the-5-biggest-challenges-facing-data-visualization

In class and while working we have come across many ways of making an effective visualization. The visualization can be made more effective by keeping in mind few simple points-

1. Displaying too much data: It is extremely important to not confuse the reader with bombarding of information. The data must be precise and correct. It should be easy to summarize for the reader.
2. Oversimplifying data: Complex data should not be oversimplified for visualization purpose. This can change the entire meaning of the data and the reader might interpret it in a wrong way. Hence oversimplification of data must be avoided.
3. Choosing wrong visualization: The designer must understand which king of chart would be useful for which kind of data. The use of pie charts and donut charts must be completely avoided as they give the wrong interpretation of information. Also 3D techniques must necessarily to make the visualization more appealing.
4. Not following conventions: It is necessary to follow correct conventions while designing any visualization. The x and y axes should always be present while quantifying the data. Correct annotations must be used wherever needed. The graph should always be labelled to understand the correct meaning of it.
5. Don’t use hard to compare data: While creating visualization it should always be kept in mind that is there is a comparison, it should be of similar nature. Non related data must not be used for comparison as it does not make any sense.

By following these simple steps an effective visualization can be achieved.

Reference: http://www.techadvisory.org/2015/07/data-visualization-mistakes-to-avoid/

http://designroast.org/the-7-most-common-data-visualization-mistakes/

During the course of our daily lives, we all perform several tasks each day. However, most of our time nowadays is spent on our personal computers and laptops. The data visualization I am talking about in this post turned into a stunning piece of art. Margin Ignac created a data visualization from his activities on the computer spanning around 2.5 years-web surfing, working on excel sheets, playing games etc.
He created this stunning visualization of the data collected from his computer over the course of two and half years documenting every small detail. He documented each activity usage that happened on his computer in every minute of the two and half years including the time when his computer was switched off.

He represented each day as a line with a different color for each different application that was running in the foreground at that moment on a black background.

ta The data visualization looks well documented with a clear distinction for all the applications and patterns. We can easily make out different patterns like sleep hours, travels, holiday times from the distinct black areas between the colorful lines. Along with this, he further documented details like mouse clicks and keyboard hits. Although the data visualization isn’t of any prominent commercial use, the idea is well implemented and the results are stunning. His final info-graphics were shown at the Click Festival in Denmark in 2012.

References:       http://thecreatorsproject.vice.com/blog/25-years-of-computer-usage-turned-into-a-stunning-data-visualization

 

Scientific meaning of Vaccination states that it is “the injection of a killed or weakened organism that produces immunity in the body against that organism”. An immunisation is the process by which a person or animal becomes protected from a disease. Measles, causing skin rashes and flu like symptoms including fever, cough and running nose. It is a highly contagious disease caused by the virus. Even though it is rare, almost 20 million cases of measles happen every year worldwide. 90% of the people who haven’t been vaccinated will catch up measles if they are near the any measles infected person. Nowadays, Measles vaccines is given in combination with mumps and rubella and the type of measles vaccines available are as follows is in the form of MMR – II (Measles mumps and rubella vaccine). MMR vaccine is a live attenuated viral vaccine used to induce immunity against measles, mumps and rubella. Giving two full doses will make sure that 99% of the people will be protected against the disease. Sometimes cases may happen that in spite of people taking vaccinations end up being infected and don’t develop immune response to the disease thus leaving them vulnerable.

The chart appropriately highlights the percentage of VAX rate, an indication of whether or not the person may end up being infected by measles. The chart also demonstrates that the states having a particular percentage of VAX rate. Finally, it shows that the vax rate should be at least 90% for an individual to be safe against measles.

Let’s firstly look at why we use charts in the first place

• Charts are help audience to more understand data information.
• Charts are help audience easier to compare different sets of data.
• Charts are help to simplify conveyed information.

Most time we use a pie chart to show relationship of parts out of a whole. However, pie charts is always make problems more complicated. Take a look at these three pie  charts.

Let’s say that they represent the polling from a local election with five candidates at three different points A, B, an C during an election:

Since these are the shares of the votes that each candidate will get, it should be easy for the reader to figure out what is going on in this race, such as is candidate 5 doing better than candidate 3? or Who did better between time A and time B, candidate 2 or candidate 4? However, this pie charts doesn’t achieve that.

Look at how much clearer that will be if convert to a bar chart. We can exactly see what is going on with each candidate at every point in the race at first glance.

Let’s look at another shortcoming of a pie chart. Here’s a pie chart of the party breakdown of the European parliament:

From this pie chart, we can only tell that EPP is bigger than S&D, but we are not able to compare the slices to figure out distinctions in size between each and every pie slice. The chart is only useful if we’re able to compare each and every element within it. Besides, humans are not very good at comparing slices of a circle when it come to size.

By using a simple bar chart and comparing the length of rectangles, we can compare each and every party to each and every other party.

 

Reference:

http://www.businessinsider.com/pie-charts-are-the-worst-2013-6