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Team #13

June 26, 2011

Disaster Strikes: A World In Sight

Disaster Strikes  is an exploration of disaster frequency and intensity from 1900 to 2010.  As you watch time go by, observe that the increasing frequency and impact of disasters are magnified by the modernist international development movement’s concern with recording and sharing the disasters that affect the world.  Natural disasters are beyond the power of man, but as our dynamic timeline shows, we only remember the volcano eruptions, earthquakes and floods recorded by our institutions.  Countries affected by similar types of disasters are clustered over time; where the size and color represent the disaster impact on mortality or cost.

☠ Over 38 million deaths are represented in this dataset (average 2,700/event)
$ The total cost of 17,832 disaster events in the dataset is $6,192,871,321.00
Droughts, epidemics, and floods were the most deadly
Floods, droughts, and storms were the most costly

Disaster Strikes


We are Disaster Masters, a spontaneously assembled group interested in the visualization of compelling data:

Barret Schloerke
Edward Fine
Mariana Anderle
Mary Becica
Norman Klein

data, software, APIs

We have used the following data from infochimps: M-DAT was created in 1988 at the Université catholique de Louvain by researchers at the Centre de Recherche sur l’Epidemiologie des Desastres – Centre for Research on the Epidemiology of Disasters (CRED).

And we contributed an excerpt of the following to Factual: Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 7.0, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, May 2011 (PWT 7.0).

We added portions of the Penn World Table to Factual, which were used in information gathering but not in our visualizations.  We did not work with this data prior to the competition.

Analysis was performed in Google Refine, Excel, R.  Visualization was performed in Node.js, d3.js and DVL.  We used the Infochimps API.


Team #12

June 26, 2011
  • Visualizing the Data Visualization Process
  • After many pivots, members working in parallel on different stages of the visualization process, we decided to create a visualization of our process and lessons learned.
  • Team member names: Sam Ho, Martha Pettit, Siamak Faridani, Rachel Binx, Edwin Chen
  • Affiliation of team members: Met each other at conference!
  • Datasets used: FEMA dataset, Disaster Data [Infochimps] and U.S. Population over time by county [Infochimps]
  • Excel, Python, R, Processing, Adobe Suite, Coda, Omnigraffle, Imagemagic
  • APIs explored: YouTube, Google News
  • Work done with the dataset before the event: none
  • Link to the result:

Team #15

June 26, 2011

TITLE: Academia is an Iceberg

DESCRIPTION: Data on research paper readership was obtained from Mendeley and profile information for matching authors was obtained from LinkedIn, showing the relative under-exposure of academia on LinkedIn.

TEAM MEMBERS: Giorgio Caviglia (DensityDesign Lab, Politecnico di Milano, visiting at Stanford), Pino Trogu (SFSU Assistant Professor of Information Design), William Gunn (Mendeley)

DATASETS: Mendeley Readership Stats, LinkedIn profile pages (scraped).

SOFTWARE: PHP, Javascript, D3

APIs: Mendeley, LinkedIn

PRIOR WORK: Light cleanup of the Mendeley dataset.


Academia as an Iceberg

Team #7

June 26, 2011
  • Data viz project title:  Earthquakes 2011
  • Earthquakes are happening all around us, all the time, we wanted to reveal the quantity and magnitude of the seismic events of 2011 and correlate with their human consequences on the ground by showing how normal lives were transformed in minutes as Earthquakes hit.
  • Team members names: Julian Gay, Philip Nuzhnyi, Paul Pettengill
  • Affiliation of team members:  Fellow humans
  • Datasets used: Factual Earthquake data (, USGS
  • Software used:  WebGL Globe, Google App Engine, JQuery, HTML5, Python, Excel
  • Work done with the dataset before the event (describe if any): None
  • Screenshots and/or video of the results: (need a WebGL capable browser such as Chrome and machine with appropriate graphics chip)
  • Link to the result (if applicable):

Team #11

June 26, 2011

Title: Silenced

Brief Description:

We examined data on child abuse, with the focus of raising awareness by allowing the user to interact with the data. Creating an experience of the behaviors associated with the types of abuse.

The Team: Kristen Chan, Ian Johnson. David Zhang, Jacob Esparza

Affiliation of Team Members:Kristen & Jacob know each other from Data Visualization Meetup Group.

Dataset used:

Software Used:

APIs Used:

Work Done with the Datasets before the Event:


Screenshots and/or Video of the Results:

Link to the Results:

Team #20

June 26, 2011


The idea behind this project was to get as much data as possible to push at the browser and test the limits of what it’s capable of displaying. The data used is the twitter streaming API, and these realtime events are displayed on a 2D map in a visually stripped-down format to give the impression of activity, and let the data speak for itself.

Future improvements:
    – websockets for persistent data stream (currently ajax polling every 10s)
    – more data faster (currently 10tweets/s)
    – show spatial relationships (@mentions, WOEid text queries as animated great circle arcs)
    – sliders to customize your screensaver (colors, speeds, #s, times)
    – falling sand type animations ( ) vs. missile command
    – other spatial activity inputs
    – 3D world browser

Team members: Erik Swedberg concept+front end, Jesse Zbikowski back end

Datasets used: canned US states geoJSON from d3.js examples
Software used:
        – front end: html/css/javascript, jQuery, d3.js (
        – back end: python, pycurl, mysql

APIs used:
Work done with the dataset before the event: none.

This has been tested on the latest versions of Firefox, Chrome, and Safari.
Code on:

Team #18

June 26, 2011

Crimes & Lines lets you see the MUNI routes as they were meant to be seen– with a heat map of nearby crimes. Discover the perils of your daily commute!

Datasets used:

  • Table of crimes and locations since 5/25
  • Table of MUNI routes

APIs used:

Created by:

  • Scott Tran (Programmer)
  • Ceasar Bautista (Programmer)
  • Holly Hagen (Designer)
  • Ed Salvana (Designer)
  • Dave Shih (Designer)