Data viz project title
- An iPhone app that draws multiple paths of different potential bike routes over a map of San Francisco. The elevation of the different route segments are shaded in different colors to represent how steep of climb the hills are, and, uniquely, the segments are colored by incline only in the direction of travel. By showing multiple routes at the same time, the hills are visible and your desired type of of ride to your destination can be determined. Do you want to climb hills or ride around them? Now you can see what your route looks like before you go.
Team members names and affiliations
- E.O. Stinson
- Steffen Frost
- Julia Klein
- Kathleen Kegaarn
Data.SF street data (found via InfoChimps), uploaded to: http://www.factual.com/t/huU9fQ/SF_Bike_Routes_with_autoID
Factual iPhone Framework/iPhone SDK
Work done with the dataset before the event (describe if any)
Screenshots and/or video of the results
Link to the result (if applicable)
This project is a detailed analysis of how low- and high-performing schools have behaved over time. We analyzed these groups under the lens of teacher experience, charter school percentage and raw performance scores.
Team Member Names: Shan Lu, Sameer Farooqui, Jon Bender, Mauricio Vacas, Hareesh Ganesan
Affiliation of Team Members: Accenture Technology Labs, Duke University
Data Sets: http://www.factual.com/t/mEtm9G/US_Schools_Merged
Software Used: Python, Excel, Tableau, Photoshop
APIs Used: Factual
Work Done Before: Downloaded API data and uploaded to Factual
Marvel Universe Social Graph
Brought to you by team Xavier Institute for Higher Learning, X-Maps analyzes damage from leaked professional and interpersonal connection information centered on a social network of heroes from Earth-616 which extends across the multiverse.
The final results are posted here.
Data set used: Marvel Universe Social Graph
Team members (no previous affiliation):
- Kai Chang, data morpher
- Tom Turner, Gephi explorer
- Jefferson Braswell, network analyst
- Google Refine and Python for data cleaning and transformation
- Gephi for data visualization and analysis
- Seadragon export for interactive web visualization
- None. We used a dataset specifically for the competition.
Project title: UFO Siter
This website plots UFO sighting data onto a map of the U.S. and allows the user to interact with it in various ways. A visitor to the site can see all sightings for all dates, and search for specific sitings by location –“Wichita, KS”, or “Whichita” or “Kansas” for example– or choose from a drop-down menu of State names and saved locations. Clicking on any sighting data point will pop up the full sighting record from the database. Media Search panel preferences allow this sighting’s record details to automatically generate a Search listing from YouTube or Google, and allow the visitor to modify the search terms and rate the search results for relevance. Time line data along the bottom allows the visitor to search for sightings by date, date range, or see a chronological “movie” of sightings as they occurred. Finally, the visitor can enter a new sighting record, ideally with photos and/or video of the sighting if they have any. “Community” aspects of the website (top nav) allow the visitor to read more about sightings and connect with others interested in the subject.
Map with location of 1000 UFO sightings in the U.S.
Team members names
Mark M. Muskardin: http://www.linkedin.com/in/markmuskardin, email@example.com
Affiliation of team members
Met on LinkedIn prior to event.
UFO sightings from Infochimps:
Geonames list of U.S. cities and coordinates:
Ruby on Rails
Google Maps API
(Github, TextMate, Photoshop)
We downloaded the datasets. We used Google Maps API to plot.
Work done with the dataset before the event:
Screenshots: (from presentation)
Implementation so far:
Where are Uber cabs taking people in the city? How many of them frequent the Tenderloin?
Brandon Liu, Zach Margolis, Diogo Monica, Billy Roh, Ian Wong (from Square)
Dataset used: Infochimps Uber GPS logs
Software used: node, d3, polymaps, processing, sqlite, (python, ruby and r for data manipulation)
No work was done with the dataset before the event. In fact, we switched ideas three times yesterday😛
The real-time query and display of parking meter availability and price within San Francisco.
Infochimps (SF Parking Meter)
SFgov.org (neighborhood shape data)
OpenLayers for OpenStreetMap
Particular points of pain: everything realting to GIS.
Remember back when you were a kid? You asked questions. You asked so many questions that your mother had to try very hard not to snap at you. What happened now?
Well, some adults are still curious fellows who asks questions. Just they don’t ask their mother any more. There’s something better now: the internet.
This visualization is a celebration of the sometimes fascinating, sometimes dull, and the sometimes outright disturbing questions people ask. Beware, the questions are uncensored!
Fravic Fernando (U. of Waterloo)
David Ma (U. of Waterloo)
David Townsend (loyalize.com)
Lisa Zhang (U. of Waterloo, ContextLogic, dataInColour.com)
AOL Search Queries
Work done with dataset before the event: