Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Data matter(s): legitimacy, coding, qualifications-of-life


Published on

These slides were presented on 14 April 2010 at the Association of American Geographers Annual Meeting in Washington D.C.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Data matter(s): legitimacy, coding, qualifications-of-life

  1. 1. Data matter(s): Legitimacy, coding, qualifications-of-life Matthew W. Wilson Assistant Professor of Geography Ball State University [email_address] 14 April 2010
  2. 3. Overview <ul><li>How are concerns about quality-of-life geocoded and made legitimate? </li></ul><ul><li>How do these data matters diversely matter in urban assessment? </li></ul>photos from SUNI surveys, courtesy of Sustainable Seattle
  3. 4. Indicators constitute the urban as a space of calculability.
  4. 5. Within the ComNET system, information originates in the street.
  5. 6. Objects become the point of focus. (The Fund 2008)
  6. 7. Transduction of space by data
  7. 8. Categorizing life: ‘suspicious activity’ “ An example of a suspicious activity is if you see an abandoned shopping cart. Or like beer cans, let’s say, on a tree. We’ve had that happen before, where someone saw a beer can on a tree.” Tonya Oriega, Sustainable Seattle surveyor
  8. 9. Hard data matter “ Hard data, where it may or may not be as important to the community members, is extremely important to policy makers and funders, who would then give resources towards the particular issues that came up.” Sandy Weng, Department of Neighborhoods, City of Seattle
  9. 10. Data fatigue Sunders: “I remember a person on our group was so compulsive, and would count everything! And I was getting really tired, it was cold, rainy, and they kept counting every little thing … It wasn't like a general sense of how much graffiti there is, there was a counting of every little piece of it .” Graves: “Yeah and I mean, data is always interesting in that way, and [I’m] just reiterating that my projected concern is that that particular item is going to get a lot of attention , and there are so many other street-level components that are, you know, [important]… So I’m just super worried about that deal…”
  10. 11. Counting problems Graves: “One of my theories is that graffiti, and occurrence of graffiti, is directly proportional to youth habitat and whether there is youth habitat or not, and so somehow I want to be able to … use [this data] as a tool to get youth habitat and provisions…” Griffin: “… So if you can’t—if you don't articulate that kind of concept … then the standard practice is this is a sign of disorder, this is a sign of a problem, as opposed to this is a habitat for teens. It's a different problem, this [graffiti] is a symptom of a problem.”
  11. 13. Standardizations and flow <ul><li>Objectified observations avoid ‘emotions’ </li></ul><ul><li>Data in isolation lead to problematic associations </li></ul>(The Fund 2004)
  12. 14. Objects and visioning <ul><li>Databases disenabled discussion </li></ul><ul><li>Bodies treated as broken windows </li></ul>(The Fund 2004)
  13. 15. Conclusions <ul><li>Data practices involve the standardization of the visual </li></ul><ul><li>Data constitute the space of the objectified city street </li></ul><ul><li>Data and data-based thinking is far from politically neutral </li></ul>
  14. 16. Thank you.