Students for Urban Data Systems

at Carnegie Mellon University

October 31, 2016
by SUDS Admin
0 comments

Data Day 2016

by Eric Darsow

Our digital age birthed another unusual occurrence: a tabling event devoted entirely and exclusively to the idea of data. Organizations of all sizes and girths carted in maps made in several centuries, charts of dazzling design, and slews of glimmering screens. A 3D printing robot was even spotted spewing layers of plastic into cute shapes. Amid this flurry of patterns and coefficients, the Students for Urban Data Systems (SUDSers) teamed up with nerds from CMU’s CREATE LAB to referee the pesky spar between the number crunchers and the story tellers.

The so-called “numbers and narrative” divide is turning out to be a chasm of our own making. While the process of regressing a spreadsheet full of figures obviously lacks a well-told story’s emotional pin-pricks, cryptic tabular outputs can, in fact, add dimensions of extent and intensity to an issue first illuminated by a personal narrative.

For example, how are we to make sense of, say, a sudden drop in high school test scores without talking to some teenagers about their experience bubbling in answers to mind-numbing test questions? The other direction works, too: few folks would rebuff a decision to augment an angry biker’s story about getting run off the road by a texting driver with a map showing ten years of bike crashes in Pittsburgh.

The SUDS + CREATE exhibit facilitated a safe crossing of this oft-feared number/narrative gap by displaying a few statistics about a central topic—such as transportation—and then inviting folks to write and physically connect a story or question to an otherwise lonely and contextless number.
data-day

One attendee affixed a short story about his personal experience with skyrocketing housing prices in his home city of Seoul. Pinned and ready for connections, another visitor complemented the narrative account with satellite images (pixel data) showing the Korean capital’s stunning vertical growth since the mid-1980s. Adding some sky shots of Austin, Texas’s metastasizing suburbanization over the same time period couched the sky-high rent story into a global context.

Even young people (perhaps less demoralized by hours of myopic method design meetings) sense intuitively the value of a well-told story alongside a chart or graph. One 9 year-old who visited our station looked over a bar graph depicting the average number of bicycle crashes by hour of the day. After a few minutes of thinking and talking aloud about the bars and axes, he used a marker and construction paper to ask all future board viewers: Why are there so many more bike crashes at midnight than 4:00 am? With an average bed time of 9:15 pm for children under ten in the United States, his wonder was about as genuine as it comes.

An enthusiastic transplant to Pittsburgh, Eric explores how the computerization of society impacts our geographic communities, social landscapes, and work identities. Eric is eagerly wrapping up his grad program in information systems at CMU and actively balances his screen-based life with wood carpentry and trying his hand at “installation art.” He serves as SUDS’s Assistant Director of Outreach, and interned at the CREATE Lab last summer.

October 21, 2016
by SUDS Admin
0 comments

Criminal Justice Work Night highlight: do police from smaller units use force more often?

At our first Work Night a few weeks ago, SUDS members dug into data on crime and criminal justice–particularly from the 2013 Law Enforcement Management and Administrative Statistics (LEMAS) survey. One student, Kee Won Song, pulled together some interesting initial insights and a sweet chart in just a few hours. He writes:
I am interested to see if we can identify factors that contribute to use of force incidents.  Specifically, I am interested to see if factors like employee demographics, education level of employees, size of department, participation in academic research (which we might also use to assign a score for ‘transparency’), budget, training methods, number of specialized units, use of data/computers in evaluating performance etc. have any affect on the frequency of use of force.  I did not get to analyze many of these factors, however, this is one figure that I produced that plots use of force incidents (expressed as incidents per employee) against total employees (full-time plus part-time):

LEMAS surveyIt’s hard to say that anything substantive can be gleaned from the visualization but it might allow us to further focus on smaller departments that have a high use of force rate (or identify outliers for further analysis).

Kee Won Song is a full-time MPM student at CMU, who is also completing Masters of Sustainability at Chatham; his interests include researching the impacts of unconventional oil and gas extraction on air quality, particularly in underprivileged communities.

October 12, 2016
by Lauren Renaud
0 comments

Work Night: Hacking & Fracking

Environmental Sensors HackNight
We are going to be using the ESDR dataset collected by various sensors across the country. The data is collated by CMU Create Lab.
  1. Follow this link, and download the dataset (or from here and select PGH_Sensors_Data.csv if that link doesn’t work).
  1. For simplicity, we filtered it only for Pittsburgh and only the sensors that have been active. If you need more data, it can be collected through  https://esdr.cmucreatelab.org/browse/
  1. The file is a CSV file, with columns indicating the name, id , location of the sensor, and the observations it has been collecting, and at what times.
  1. Load the file into R, iPython, or wherever.
You can also check out other cool visualizations on this data here – http://explorables.cmucreatelab.org/
Discuss your ideas, cool observations here: Environmental Sensors HackNight
Sample Ideas for the ESDR data
  1. Mapping the sensors, and visualizing the pollution levels based on the neighborhood.
  1. Finding which neighborhoods are the worst in air-quality
  1. Combining with 311 data from WPRDC to figure out some cool stuff.
  1. Talk to people, and think what more can be done.
PS. If you want some tips on using Tableau to visualize data, this tutorial from a former SUDSer can get you started. The video starts with pulling data from a public API, which we’re not doing here, you’ll have to bring the data in manually, but after that follow along.
PPS. If you tweet / insta / etc… #SUDSWorkNight