This page is a listing of all of the final projects done for CS 465: Information Visualization in the Spring of 2016.
This is visualization is for anyone interested in exploring the nature of Health Insurance in the United States. Our goal in building this tool was to show how access to health insurance has changed over the years. In particular, we wanted to see whether the Affordable Care Act (Obamacare) had any impact on the level of access of insurance. We use the percentage of uninsured people as our measure.
Our visualization attempts to make the college search more accessible. We created it with our experience as a highschooler searching for a college. By allowing the user to select for size, location type, and degree level, and to see these variables visually on a map, we allow for much more personal exploration. In addition to this feature, the heatmap allows the user to compare their own desired characteristics with the colleges they are looking at. Overall, we wanted to make the college search much more accessible.
The concept of this visualization was born from the observation that in America, country-wide races are about more than a candidate’s experience, their record or their position on issues. If that were true, why would candidates pay college graduates to run their twitter and facebook accounts? Why would President Obama appear on “The Tonight Show” four times as a sitting president? It seems that American voters, that varied red, white and blue mass, need to be interested. To earn this “American” vote, candidates must be talked about. They must be engaging, funny, charming, attractive, disgusting—anything, as long as someone’s talking about it. Candidates must generate a buzz.
The visualization answers the questions: What is the busiest time of any given day of the week in the lab? What is the busiest time of the semester? What day of the week is busiest? Which computer is busiest on any given day and where is it in relation to the other computers? How many minutes are spent SSHing into computers at any time of day on any given day of the week?
The biggest question that our visualization can answer is how has the number of crimes in the United States changed in the last 10 years both on a countrywide basis, but also on a state by state basis. Another question that the visualization seeks to answer is: What crimes are committed in the U.S. every year (or set of years) and how do they relate to other crimes committed both categorically and numerically?
The musiXmatch dataset is a bag of words database that contains lyrics and their frequency in each song. We grabbed every word from every song in a given year, and calculated the TFIDF for those words. We then created some interactive visualizations to go along with the dataset we had created in order to allow our users to explore trends in lyrics both within years and over time.
This visualization is geared for people who want to visually explore the relatedness of different eukaryotic species. This visualization could be helpful for kids interested in biology. The visualization is designed to show how related different species are by visually tracing paths from selected species to their most recent common ancestor. It's also designed to show this relatedness in a quantitative manner through the bar chart that visualizes how much each species has diverged from its last common ancestor. These data were calculated using branch lengths included in the original data, which represent degree of genetic differences between parents and children. Generally, this visualization shows where species appear in the Eukaryotic tree of life, and it can do this specifically through the search function.
The purpose is to display where all the professional basketball players in the American Basketball Association and National Basketball Association went to high school in the United States. This is largely intended for fans of the NBA to explore the American regions that produce the most elite level professional players. Some players who did not attend school domestically in the US will not be included in the data. The main question is where did professional basketball players go to high school. However, one of the questions we hoped the visualization may answer was whether there were or are any location trends in producing professional basketball players. With any discovered trends, this visualization could be useful to USA basketball talent development groups or even basketball talent recruiters.
Our visualization is for anyone interested in baseball, especially in either a team-building or a fantasy-team building setting. The Visualization has access to all players who played in either 2015 or 2016, so it is helpful for current baseball analysis, not historical. Our question helps answer the question of "where are my team's weaknesses and strengths" in one polygon. It also can help answer the question "what does my team look like if I add X player"
Over the past couple of decades gentrification has become one of the most debated topics amongst urbanists and geographers. While some understand gentrification as the natural process of neighborhood transition and change, others view it as whitecolonization of poor minority neighborhoods. This visualization serves as an exploratory tool allowing researchers, urban planners, city officials and curious townspeople to investigate gentrification in New York City. Its functionality will allow its users to explore demographic conditions that may precurse gentrification and notice any geographic trends in its expansion. This visualization serves as a research tool due to the ability of the user to download a geoJSON file of a set of tracts that they have selected. This download ability will benefit any city planner or researcher that wants to explore the phenomenon of gentrification in New York City, group census tracts by trends and patterns, and then export all of the demographic and geographic data of those tracts in order to create their own visualization or engage in further research.
This visualization is for people who are looking to enter DraftKings contests, but it can also provide some decent insight about NFL performance in general, as fantasy output and overall football output are definitely correlated. The visualization is specifically supposed to help people determine which offensive players have been consistent performers, how each position stacks up against the others in terms of expected output and cost, and how each individual game played out. In the context of trying to select a good lineup before a week’s contests, a person could use this visualization (assuming it’s updated with the most recent data) to quickly understand the recent history of two player he/she might be weighing against each other. Our data is extremely dense in that there are many variables, most of them have complex meanings, and there are definitely strong relationship between many of the data points (i.e., a QB always has a large effect on the performance of his WRs, as he’s the one throwing them the ball).
This visualization would be used by anyone, from portfolio managers to people who simply trade stocks in their free time. It is designed to show the value of your portfolio over time, as well as what stocks in particular have led to its growth. As well, it could be used to analyze a particular sector of stocks, to see which individual stocks are performing well and which have the largest market share.
The visualization is meant for people who are interested in seeing how demographics compare across different regions in the United States. The idea is that our users will see the map, a visual representation of the the United States’ geography, and then decide which particular city’s demographics they are interested in visualizing in parallel coordinates.
flyght is for people interested in exploring flight patterns withint he United States. It is a good way to see flight paths from United States domestic commercial airports. It also shows the large volume of small airports distributed across the country. flyght is designed to answer how connected is the US, where can I fly to from a given airport and at what time of the year, and on average - how late is a given flight. It is a good way to visualize the connections between airports as well. [flyght requires a special server setup, so it is currently unavailable]
Our visualization is targeted towards a high school student who is investigating the diversity of the student body at the colleges they are applying to. It is designed to show the ethnic breakdown of each NESCAC college so a student can compare the diversity of each school, and show the ethnic breakdown of a college over time so a student can see how diversity has changed over time.
The description is for people ranging from the environmental scientist to the curious procrastinator looking to explore some interesting data. It answers questions regarding the current state of the world environments and the seriousness that is the reality of climate change. How has this country changed over time in terms of this data? What regions are generally worse/better when it comes to this environmental data? Thanks to our choropleth map, you can easily tell by color which areas and countries contribute the most or least to climate change causing phenomena.
We approached our visualization with the basic question, “Who fouls the most in the NBA?”. We took this question further as we aimed to create a true data exploration tool for NBA foul statistics. Our visualization allows any casual fan to explore such data. The tool allows you to filter by team and year. Brushing on our scatterplot allows for more filtering by the correlation of two basic stats. The axes on our parallel coordinates can be reordered to show different relationships and trends.