Who decides the ugliness of a place?

Locations where flickr users identify urban decay in the contiguous US

I’m fascinated by how the meanings of places change from one person to the next. A given place could be sublime or mundane: it depends on who you ask. One example springs to mind. Much has been written (and photographed) of Detroit’s decline in recent years, so much so that although I’ve never visited the city it’s become linked in my mind to images of stagnation and urban decay (even though I recognize that isn’t necessarily the case). In some sense I have a feel for the place, even though I’ve never even come close to visiting it. I think there are some implications to my feeling as if I understand a place that I’ve never been to. You could certainly argue that anyone’s feelings about a place are always filtered and biased, and I would agree. But I think this is different.

How do I construct my feelings about a place that I have never visited? Increasingly, the internet acts as a significant source of our information about distant locations. With the emergence of crowd-sourcing and social media, we often tacitly assume that the content we find online is representative of a variety of viewpoints. As descriptions of places online become more and more specifically geolocated, they become vitally associated with the places they describe. I think, though, that we often fail to question whether what we see online is really representative of a place.

The internet isn’t nearly as open as many give it credit for being, and this extends to online portrayals of real places. The digital divide – differences in education, age, economic position, culture, location – affects who wants to/can/does author the descriptions of places we find online. I’m interested at getting underneath some of these issues, which was the motivation behind my making this map, showing concentrations of urban decay in the US as defined by the tags on photos uploaded by flickr users. Data-wise, I used the fantastic flickr API to mine lat/long coordinates of any photos in the US with the tag “urban decay”. I filtered the results to remove mass uploads by individuals, and (after some confusion) removed results that referenced an inconveniently named brand of makeup.

I don’t argue that this map provides an accurate portrayal of people’s opinions in broad terms. Instead, I wanted to embrace the biases inherent in the flickr user base to try to map how the photos there might represent (and perhaps distort) the world. You may be right to argue that this verges on triviality, but at the same time, maybe not. Flickr is a pretty popular website. I’d say that alone confers some significance to any content uploaded to it. To what extent could the patterns on this map affect how people who have never been to those cities think of them? Other types of media change how we think of places, but I think the faux-democratic ubiquity and dynamism of online social media makes it somewhat unique. I’m in the early stages of doing some similar work for my thesis, diving deeper into data from flickr to ask some questions about how the US/Mexico border is represented online. A lot of the inspiration for this map came from the floatingsheep folks as well as Eric Fischer’s flickr stream, both of which offer some interesting explorations of crowdsourced data.

Update: Interesting comparisons with the population change maps recently posted at Data Pointed…

28. July 2011 by dwtkns
Categories: Maps | Tags: , , , , , | 3 comments

Comments (3)

  1. Any hints on the flickr api calls necessary to return the lat/long for a given tag? I’m in an ornithology class and am interested in seeing if massive amounts of flickr data can give any sort of insight into bird migrations or at least birders.

    • The flickr.photos.search method should be what you need. Using has_geo=1 and tags=tag1,tag2,tag3 and extras=geo should return lat/long data for the tags you search for. You’ll likely have to make multiple calls to retrieve several pages of results, unless you are making a very specific search. You could trim these down by using bbox= to define a bounding box for your search.

      The API Explorer can be a great tool for perfecting a request (or for making small data searches without hassle).

      • Thanks! I figured out the extras option a couple of days ago, before that I had been trying some weird two step process using flickr.photos.geo.getLocation after first retrieving a list of photos. This way is working way better. I take the xml output, convert it into an attribute table and load it up into QGIS.

Leave a Reply

Required fields are marked *