This page is a listing of all of the final projects done for CS 465: Information Visualization in the Spring of 2014.
Our goal with this visualization was to create an interactive map that could be used to show where the fires are burning relative to where the lookouts are located. We were hoping to create a tool that could show whether or not a lookout is well-placed and worth spending the money to maintain. As such, our target audience was the USFS employees, particularly those at a supervisor level who are deciding where to spend money, but the visualization is also interesting for anyone who is curious about forest fire locations and history.Questions we asked included: Where are the fires occurring? Have those locations changed over the last 50 years? Are the fires occurring close to the lookouts? Are there lookouts that don’t have any fires starting nearby? Are there lookouts that are surrounded by small fires? Are there lookouts close to the largest fires?
The visualization is about liberal arts college disciplinary violations over the past 12 years. The data set is a multivariate data with different types of campus disciplinary violations.
The goal for this project was to create a tool to help people studying the bible to more easily explore word occurrence patterns in the Bible. Biblical scholars often use word choice to determine chronological continuity (whether a chapter or even a whole book were actually written as a single work or whether they were pieced together or added onto later). Therefore it is useful to have a program that will quickly graph the occurrence of a word over the course of the bible, then allow the user to quickly navigate to points of interest where the frequency of the searched terms is either higher or lower than expected.
This visualization allows the user to interactively see real-time flight data originating from a single airport. Our visualization is designed for a user looking to take a spontaneous vacation, or anyone interested in flight trends based on route, price, or time of day. It is designed to answer the following questions: What flights are departing in the next 6 hours? Are there flight path trends for certain airports at certain times of the day? What are the previous price trends of a specific route? Should I buy now or wait a few months, based on historic data?
This is a visualization of CVE and CPE data feeds from the National Vulnerabilities Database. it is meant to be used by a system administrator (especially at the enterprise scale) who wishes to install a particular type of product and has a number of candidates in mind after taking into account external factors such as budget and features, or a curious individual or researcher wanting to gain insight into the vulnerability history of particular vendors and products (especially within a particular time interval).
The data for this project came from whoscored.com, an online soccer statistics website and database. This website has statistical data for the English Premier League for each season, dating back to 2010. The visualization allows for these individuals to quickly filter players by desired attributes. Hopefully this tool will allow managers to identify up and coming players who do not receive much media attention and recruit them to their teams. The historical aspect of the visualization is intended not only for coaches but also analysts and fans who wish to examine trends in an individual’s performance.
The purpose of our visualization is to look at commuter trends, identify commercial and commuter centers and increase the visibility of Citibikes. We decided on a multi-graphic visualization in order to improve the visualization’s that Citibike already provides. Currently they have a few difficult to read bar charts and a map of the station locations. We chose to create an interactive chloropleth map and chord diagram supplemented by two bar charts in order to convey information that we deemed valuable.
The data came from a sociolinguistics project focusing on the LGBT community. There were multiple aspects of the original project, but the portion I focused on was social attitudes towards reclaimed gay slurs.The data was collected through an online survey posted to various LGBT forums online, as well as multiple Middlebury Facebook groups, with about 200 responses from the “public” and 50 from Middlebury students. The visualization represents these responses as one large dataset. The survey first asked for demographic information, that is sexual orientation, age, gender. It then asked people to rank the appropriateness of ‘queer,’ ‘dyke’ and ‘fag’ in different social contexts, namely when used in self-reference, when used in reference to others while also in the community, and when used in reference to others while outside the community.
This project provides a tool for people who are interested in the precipitation in Vermont to learn more about it. In the visualization, viewers will be able to know the annual precipitation in Vermont and its counties over the past 30 years, compare precipitation of different counties and of different years, and compare precipitation in a specific region of a specific month across the past 30 years.
This project visualizes people’s geographic knowledge of United States cities. The data for this visualization will be collected from a survey where users drag and drop U.S. cities onto a map of the continental United States. Users position the cities the best of their ability using only the outline of the U.S. as a reference. The users submit their guesses along with some demographic information: their home state, age, and gender. How good is your geographic knowledge?
The visualization is for players who want a tool to compare different champions in head to head match-ups (or multiple at once) in order to see differences in attribute values as they progress in levels. In order to understand the usefulness of the visualization it is important to know how a game of League of Legends works. At the beginning of each 30 minute game, each player selects a character (champion) that starts at level 0 and progresses to level 18 by the end of the game. As champions level up, their attributes, such as attack damage and health points, increase. Much of the game is being aware of how strong your opponent is at any specific moment in order to know when it is safe to engage them or not.
After reading an article about new research that has shown a positive correlation between women in management positions and economic productivity and success in a given company, we were interested to see how the economic, social and familial role of women in nations across the world correlate to the national per Capita GDP of that given country. We imagine that our visualization would be of use to economists and politicians who are interested in how modern gender roles affect the world economy and political structures, and to curious readers like us who are just interested in becoming more knowledgeable about gender roles and their consequences globally.
Our visualizations are designed for people who are interested in life logging or keeping a journal and tracing their thought patterns across time and location. Traditionally, such an audience uses a normal paper or software journal in which they record their thoughts. However, this serves the cathartic purpose of writing down thoughts, but is not very good for understanding thought patterns or trends. Therefore, the benefit of our app as a journal over writing all of your thoughts down on paper is that you can instantly view old records, or examine trends and patterns in your posts using visual analytics. Our visualizations mainly answer questions such as “Where am I happy or sad?”, “When am I happy or sad?”, “What words do I associate with positive or negative feelings?”, and “What are my thoughts when I am happy or sad?”
This visualization was created to help people find interesting in correlations between various health and population statistics over time as well as trends between countries in certain statistics. The exact audience includes both then mildly interested and inexperienced as well as those whose profession requires them to study these statistics.
This visualization is for people who want to pick out relationships between census variables while getting an idea of the geographic representation of those variables. It should answer questions about how two different statistics are correlated and and the geographic trends associated with those data. Doing both together is helpful because some patterns are recognizable only on one of the two levels.