This exploratory visualization is an interactive infographic about New York City’s subway system and the problem of income inequality. It shows median household income along each stop of the line, which vary from poverty to wealthy. Users may interact with the visualization by choosing which line they’d like to explore; the lines are indicated by the actual colors and symbols used in NYC’s subway system.
The juxtaposition of the line sprawled on a map of NYC with the median household income below was effective for me, as it gave me a sense of where such patterns are taking place. I thought it was interesting, but perhaps no surprise, to see the stark contrast of earnings between Manhattan, Brooklyn, Queens, and the Bronx. I liked the separation of rail lines based on what borough they were running through. However, I thought the shorthand labels for the boroughs such as “MAN” and “BRK” are not intuitive, particularly for people who have never traveled or lived in NYC. In all, an interesting exploratory visualization on NY’s subways.
I always find me confused with all the foreign names of the artists and their artwork and mutual influence to others in their work in art. I found this interactive visualization from MoMA as tool to help people want to know more about the art.
It is an exploratory visualization of graph of the abstractionism artists and their representative artwork with brief history. An art movement can’t be understood with only one artist but with a group of artists and its historical context. In this sense, this visualization is giving a chance to expand their understanding to a larger pool of artists by comparing their style and the influence they’ve exchanged.
I personally think this visualization could be a lot more interactive and educational. If a snippet of work of the artist would pop up it must have been easier to match the his/her name and the work. Also, in the graph, if there was some weight difference to show who interact with whom more often, it would have been great and fun to compare their artwork.
This week was one of travesty in the city of Boston. As you all are aware of, there was a bombing at the Boston Marathon. Three people died on that tragic day.
The above visual is from one of my favorite news websites, QZ.com. The visual maps out the sequence of events that unfolded on the city’s streets throughout the day. I think this is a superb visual because it clearly answers the following questions: What happened? Where did it happen? When did it happen? From the beginning to end, there’s complete clarity in the author’s narrative.
I honestly cannot offer substantive criticisms regarding this visual (I’d make different color choices, but I don’t think that is all important here because I got the information I needed). The folks at QZ.com just have knack for developing great visuals and delivering news content in a concise manner (usually 2-4 paragraphs).
The website, DataGenetics, often displays quirky or amusing analyses for the enjoyment of viewers. The blogposts found here are often used to communicate powerful computer science theories or problems in compelling and easily accessible mediums. One that I particularly enjoyed was about Pin Numbers and their predictability. The visualization that you see above is a heat map of almost 3.4 million 4 digit passwords that he found by executing some SQL injection.
The heat map is one of several visualizations that he uses to analyze the problem and then educate his audience on how best to select a Pin number. I liked the heat map the best because it displays the serious lack of balance in Pin numbers that exist. Over 25% of all pin numbers could be guessed with only 20 combinations!
The heat map I attached shows the number of first two digit combinations on the x axis and number of second two digit combinations on the y axis. So at the bottom left is the Pin ‘0000’ and the top right is ‘9999.’ This heat map quite ably demonstrates the lack of spread among Pin numbers, and his box around the high concentration of numbers in the bottom left shows how easy it could be for a criminal to make an educated guess at people’s Pin Numbers.
This is a visualization project shown by video from Autodesk Research. It visualizes the evolution of the company’s structure over time from May 2007 to June 2011 as a tree structure. It took a snapshot of the organization structure everyday, and every second in the video represented approximately one week of the time period. There are about 8000 employees shown in this graph. In the beginning of the video, it illustrates three types of interaction (connection) between every nodes and links. The link between the nodes means a hierarchy of manager and employee. An employee may join, leave the company, or change the manager.
It is astonishing to see how much a company changes over time. However, the color code confuses me. I thought it represented each independent department (tree) at first, but would make no sense if they all linked to the same CEO.
On the other hand, the project group seems to release only the video version of this visualization. It would be much more interesting if we can play with an interactive version, such as to pause at a particular timestamp, filter out some particular departments that have more than 1000 employees, or focus on a/several specific node(s) and see the changes. Nevertheless, to see the changes over time in a video seems to be a nice way to see the changing as a whole (maybe I’m just not the right audience).
The objective of our visualization was to investigate the possible “culprit” in the changes within average income, share of income, share of federal tax revenue, and household income (before taxes) across different sectors of the United States population. When we were brainstorming, we originally thought this infographic would be great for middle-income household workers in order to educate them on who could possibly be responsible for the state of the economy now. We decided to focus on four key presidents: Ronald Reagan, George H. Bush, Bill Clinton, and George W. Bush. We split up our graphs into four key time frames that each president was in office for, and analyzed any changes that occurred specific to that president. After we picked our key demographic, we decided to pick and choose which graphs were needed and which weren’t, since we thought some weren’t relevant to the time frame and objective.
Once we were able to place all of the charts and pieces together, we discovered interesting facts about the data from each president’s time in office. While the general population stayed relatively consistent over the course of ~27 years, the data from the top 1% of the population fluctuated greatly with constant rise and falls in all categories. Based on this data, we determined that the most drastic increase in average income, share of income, and household income for the top 1% of Americans occurred during the Clinton administration; we then deemed him the “culprit” of this eventual trend.
By Julia Kosheleva-Coats , Kate Hsiao, Seongtaek Lim, Kiran Chandramohan, Shreyas
Visualization Name: “Making it to the Top”
Audience: “iSchool New Admits”
Our visualization provides a narrative about how to make it to the top 1% of the economic tier of US. Meet David, who wants to make it big and has dreams of being rich. Sure he could be a pop star or a model, but he decides to bet it all on education. David enrolls himself into a school and soon enough he has gotten a bicycle. Enter David the teenager who now can work part time jobs and can save up enough to zoom around in his cool skateboard and flashy hairstyle. Soon he graduates from high school and enters college. He works summer jobs and now he can afford a motorbike. After graduating from college he can now afford a car. Life is now steady, he now has a girlfriend but still isn’t in the top 1%. While researching on the future possibilities he finds information about the iSchool at UC Berkeley. He joins in, slogs it out and by the time he graduates he can now afford a house. But that still just takes him to the top 10%. He keep persevering and his hard work pays off. Fame, Money and a beloved Ferrari are queueing up as he is now the best data scientist in the country.
Team: Charles Wang, Sonny Vernard, Sonali Sharma, Seema Puthyapurayil
Our objective was to show the unfairness of the richest 1% in America paying decreasing amount of taxes while their income had increased by 240% from 1965 to 2007. We applied a 3-step approach in our narrative. In step 1, a cash sack was drawn to illustrate the total $54 trillion national income. The richest 1% was highlighted as our target group. In step 2, we used a line chart to show the income distribution from 1960’s to 2007. We observed dramatic increases of income for the richest 1%, whereas the average income for low-wage earners had pretty much remained constant. In step 3, we applied a unique visualization that indicates not only the wealthy had become richer over time in absolute dollars, but also captured the story of America’s richest paying a lesser share of taxes as their incomes increased. Furthermore, two arrow indicators beside the bar chart expressed the obvious opposite trends of taxes paid by the richest and the income they made. The decreasing amount of taxes paid by the 1%, our biggest concern, was emphasized with red color.
We toyed with several iterations on the increasing disparities in income and taxation levels during the past 30 years, but it doesn’t seem to us that the main source of rising inequality was the rising wealth of the top 1 percent per se. We liked the chart showing how average salaries since 1970 had been relatively stagnant while productivity had grown substantially. The wealth of Americans, it seemed, had become unhitched from the great successes of corporate America during the past few decades of globalization.
With this as a point of departure, we came up with the scale as a succinct metaphor for the divergence of economic fortunes. The idea is to split the population in two groups; the 99 percent (other) and the 1 percent (wealthy) and show a scale balancing different economic distributions. The first one would just compare count of people; the second the income share to each “group”, the third the share of taxes. For each variable, the scales would
balance in different ways. To be fair, we would include the share of the federal income tax which would tip the scale in the other direction, as the top 1 percent paid a full 28% of the Federal
Tax Liabilities for All Households.
We would implement this as an interactive visualization with two variable features:
(1) a time slider, allowing the user to see change over time, and
(2) an equalizer, allowing the user to see how the two groups would need to change in size to give an equal balance
On the right hand side, we would have linked decorations/charts, enhancing the main core of the story though additional and relevant details.
Early on, our team decided on young children as our audience for telling the story of growing wealth inequality in the U.S. Being that charts and graphs can be difficult for even adults to understand, we spent a lot of time discussing what kind of metaphor we wanted to use, and then lastly, how that metaphor would aid our message.
The story is relatively simple: If the kind of animal you could afford was a direct correlation to how well off you were, then this is how that wealth has been changing over time. While in a child’s grandparents’ time, they were able to afford relatively decent pets in all walks of life, the disparity has greatly increased in a child’s parents time in the present day. Furthermore, when said children came of age, that difference would be even more apparent where most everybody would only be able to have an ant as a pet save the top 20% of the wealthiest people who could own a whale, which is indisputably way more awesome.
While these are not the most scientifically sound metrics, it speaks to the inequality in a way that actual numbers cannot.