April 04, 2015

Cats and Badgers: Mapping the Final Four

Later tonight, the beloved Wildcats of the University of Kentucky men’s basketball team face the University of Wisconsin Badgers in the Final Four. And while the score will ultimately be settled on the court, this (sort of) rivalry can’t help but spread to other arenas. You’ve got the greatest tradition of cheese-making and beer brewing in the United States up against the greatest tradition of burning couches, making (and drinking!) bourbon and betting on horses. You’ve got those former Wisconsin-ites of Maptime Boston starting stuff with the good hearted Kentucky folks at MaptimeLEX. You’ve got new maps versus old maps.

Indeed, the map itself offers an important terrain on which to fight the battle between Kentucky and Wisconsin. The unsourced map below represents just the first cartographic shot fired in this war. While Wisconsinites might like to think that only their Minnesotan rivals to the west are the only ones that would side with Kentucky in this match-up, how are we to believe such cartographic assertions without interrogating the context of the map?

From what dataset is this map constructed? Why is this data aggregated to the state level, anyways? Why isn’t Canada similarly disaggregated into province-level data? Are we really to assume that such administrative regions are internally coherent in terms of their sports-rooting interests? I mean, UK even has a half-Canadian player! Even if we are assuming that, why are we assuming that Mexico doesn’t like college basketball, too? Similarly, why are there no gradations to reflect those places that might be relatively conflicted about who they’re rooting for? Why is there no context regarding the motivations behind such rooting interests (e.g., Michigan State and Duke fans who would much rather play Wisconsin than Kentucky in the championship game)?

Quite obviously, we can’t trust this map. Maps are fallible and power-laden and subject to the whims of the always-partial cartographer, in this case some Wisconsin fan who needed to make themselves feel good before a second-straight Final Four defeat at the hands of the greatest program in the history of college basketball. What we can trust, however, is that always transparent window into the collective social psyche of 21st century America: our database of geotagged tweets. This kind of data is objective and apolitical, a true representation of the world as it actually is. So, collecting nearly a month’s worth of tweets in the continental US from March 1 thru March 30 for the more-or-less official school hashtags of #BBN and #OnWisconsin, we sought to create a more objective, data-driven representation of the geography of Saturday’s matchup, as seen below.

Comparing #BBN and #OnWisconsin Tweets [1]

At the broadest level, #BBN tweets outnumber #OnWisconsin tweets by over 5x: 5,707 to 1,039. For a state that’s only about three-quarters as populated and a university that has only about two-thirds as many students, I’d say that’s a resounding victory for the Big Blue Nation. While each team dominates its own state (though we should note, there are some parts of Wisconsin without any pro-Wisconsin tweets at all!), those areas with more #BBN tweets than #OnWisconsin tweets are a bit more numerous throughout the country. Importantly, Kentucky fans have taken over key outposts such as Chicago and New York City, along with Nashville (the site of this year’s Southeastern Conference tournament), Cleveland (the site of the Midwest Regional) and Indianapolis, the site of the Final Four itself, not to mention much of the south, more generally. These are important strategic victories in the geography of basketball fandom this week, as the only other state Wisconsin fans have dominated apart from their own is Minnesota, in clear contrast to the stylized representation seen up above. Indeed, it’s quite telling that based on our hexagonal aggregation, no place in the United States has more than 100 more #OnWisconsin tweets than #BBN tweets: not even Madison, Wisconsin!

While there are still a number of places throughout the country where Wildcats and Badgers seem to be coexisting peacefully (thus far, at least!) -- and a good number more that seem not to particularly care -- we can say with some level of certainty that the members of the BBN are crazy and the Cats won’t be stopped on their way to another national championship. At least those folks in Madison are good at making maps! [2]

[1] Many thanks go out to Eric Huntley, PhD student in the Department of Geography at the University of Kentucky, for his help with this map!
[2] No hard feelings, Wisconsin fans. We love y'all. But seriously, GO CAYTS! And may we have another occasion to publish an academic paper about UK fans celebrating a national championship!

April 01, 2015

New paper accepted – Mapping Information Wealth and Poverty: The Geography of Gazetteers

  Spatial distribution of placenames in the GeoNames gazetteer

  Spatial distibution of placenames in GeoNames included in the dataset of populated places with more than a thousand inhabitants, compared to the spatial distribution of population

Mark and his colleague Stefano have put together a short paper that will be forthcoming in Environment and Planning A. The paper focuses on the geography of geographic information, and builds on Mark's work into the uneven geographies of information. It highlights how the very information systems that we use as 'ground-truth' are themselves characterised by significant biases.

Gazetteers are dictionaries of geographic placenames that have important implications far beyond the worlds of geographers and cartographers. By containing ‘definitive’ lists of places, gazetteers have the ontological power to define what will and won’t be geocoded and represented in databases, maps, search engines, and ultimately our spatial understandings of place.

The paper focuses attention on GeoNames, which is the world’s largest freely available and widely used gazetteer. It illustrates how content in GeoNames is characterised by highly uneven spatial distributions. There are dense clusters of placenames in some parts of the world and a relative absence of geographic content in others. These patterns are related to not just the wealth and population-size of a country, but also its policies on internet access and open data. The paper then traces some of the specific implications of this information inequality: showing how biases in gazetteers are propagated in a variety of geographic meaning-making.

Graham, M. and De Sabbata, S. 2016. Mapping Information Wealth and Poverty: The Geography of Gazetteers. Environment and Planning A. (in press).

March 11, 2015

New paper accepted in Landscape and Urban Planning!

We're very happy to announce that three-fifths of the collective have recently had a new paper accepted for publication in the journal Landscape and Urban Planning, as part of a special issue on critical visualization edited by Annette Kim, Katherine Foo, Emily Gallagher and Ian Bishop. Taylor, Ate and Matt's paper, "Social media and the city: rethinking urban socio-spatial inequality using user-generated geographic information", builds on our earlier calls to go 'beyond the geotag' in order to develop alternative conceptual and methodological approaches for the use of geotagged social media data, drawing attention to the variety and complexity of socio-spatial processes embedded in such data.

Using Louisville, Kentucky as a case study, our paper examines the socio-spatial imaginary of the '9th Street Divide', separating the city's largely poor and African-American West End from its more affluent and predominantly white areas to the east.

While a more conventional analysis of these inequalities as reflected in geotagged tweets might look a bit like the map above, we argue that such maps of isolated, atomistic dots do little to reveal the nature of inequality between places, and do a disservice to the data itself by stripping it of much of its context. So, rather than just arguing that the West End seems to have a relative lack of tweeting activity compared to other parts of the city -- and thus deducing that the digital divide is persistently reflected in this data -- we put these different areas of the city in comparison to one another in order to understand how both individuals and groups move through the city and (re)produce landscapes of segregation and inequality through their everyday practices and mobilities.

Using a novel method for analyzing this data, we attempt to demonstrate how the idea of the West End as a separate and apart from the rest of the city is challenged by the realities of people's everyday movements. Rather than being isolated, West End residents are actually much more spatially mobile within the city, while East End residents tend to be much more confined to their own neighborhoods.

So while the 9th Street Divide remains a key way of understanding and highlighting the spatial dimension of urban inequality in Louisville, we tend to think that this framing actually reinforces the understanding of the West End as a kind of 'problem area'. And while only a partial contribution to this argument, we hope that understanding the West End through its relations with, and connections to, other spaces and places ameliorates the vilification and pathologizing that is so common in discussion of racial and socio-economic inequality in highly segregated cities.

Ultimately, we hope this paper can allow for an alternative conceptualization of urban inequality in Louisville and the West End, while also demonstrating the utility of a situated and contextualized, mixed methods approach to the study of geotagged social media data, emphasizing the full range of socio-spatial processes embedded in this data that can't be captured in just a single point on a map.

The full citation for our paper is below:
Shelton, Taylor, Ate Poorthuis, and Matthew Zook. (Forthcoming) Social media and the city: rethinking urban socio-spatial inequality using user-generated geographic information. Landscape and Urban Planning.

February 19, 2015

Happy Year of the Sheep: Mapping "Literally" Every Sheep in the United States

The recent blog post by the Washington Post's Christoper Ingraham mapping “Literally every goat in the United States" was interesting in a number of ways. First, it continues the rhetorical trend of making maps (or at least claiming to make maps) that include everything.

If nothing else, it provides a compelling example of the current cultural capital value of "big data" in society. This is an interesting cultural moment in popular cartography, since the fundamental task of maps is abstracting and representing. And even the map that supposed showed "every goat" was actually a representation with each dot on the map standing in for 500 goats.

Secondly, we very much doubt that any map produced by the USDA Agricultural Census has ever received this much attention in the history of the agency.

Thirdly, why are the goats getting all the press? After all, isn't this the year of the sheep according to the Chinese zodiac [1]?  This is not the way Pan, god of shepherds, meant it to be. 

So being the ovis-chauvinists we are, we wanted to point out that there are actually twice as many sheep as goats in the US, and so the sheep population could probably take the goat population if it ever came down to hand to hand (or hoof to hoof) combat.

Also the USDA has made some fascinating interesting maps of sheep. Not baaaaaad.

Every Sheep in America

The USDA's Agricultural Census found that there were 5,364,844 sheep in the US in 2012. California was the sheepiest state in the USA with 668,517 sheep. However, Weld County, Colorado is the sheepiest county in the USA with 204,694 sheep reported in 2012. That's more sheep than Alaska, Delaware, Rhode Island, Connecticut, New Hampshire, Louisiana, Maine, Massachusetts, South Carolina, Mississippi, New Jersey, Florida, Vermont, Arkansas, Maryland and Alabama put together. Go Weld County [2]! We don't know about you, but we're booking our tickets and hotels to go visit ASAP.

However, Van Zandt County, Texas had the most valuable sheep compared to other crops with 68.38% of total market value of agricultural products sold originated from sheep, goats and their products (milk, wool, etc.). We've also found that the map of every sheep in the US opens up many perceptual rivalries with optical illusions hidden within. Yes, we are comparing our map to the illustrations of Sandro Del Prete. Please comment on this post--what do you see in the illustrations? The profile image of a lady in a bonnet? A man's naughty bits? A sheep?

 [1] We are aware of the sheep/goat confusion, but come down firmly on the side of the sheep.
 [2] Read more about Weld's extraordinary sheep processing.

February 11, 2015

IronSheep 2015. Be there! Or be an Iguana!

We are delighted to announce that we will be hosting IronSheep 2015 at TechNexus[1] in Chicago, Illinois on April 23 from 6-9:30pm. This is right in the middle of the AAG meetings so a great chance to get your sheep on during an academic conference.

If you are wondering "What is IronSheep?", the short answer is that it is our annual hackathon/mapathon modeled after the TV show IronChef (or project runway). Everyone gets the same set of data and competes to make the best map in a limited amount of time. And William Shatner will judge your map!!  Ok, the last bit is not true but we're working on it.

You'll be assigned a team with members who have a range of skills and all teams will have a mission to complete with common data.  

The best product at the end will win an award. The worst product at the end will win an award. Ok, almost everyone will win an award. We're just nice that way.

Anyone can join in the fun but we do ask that folks register for the event here:  http://goo.gl/forms/FMqxD6XFpv

There are also many AAG sessions relevant to our IronSheep event including a couple of "Future of Mapping" panels right before. We'll let you know in an upcoming post about those.

Check out our previous IronSheep events:

2012 at Pivotal Labs in New York
2013 at LARTA in Los Angeles
2014 at the Wave in Tampa Bay

[1] A big thank you to TechNexus who is sponsoring IronSheep by providing a space with wifi and breakout rooms.  We'll provide dinner and stickers, and you bring your laptop and your sheep (aka labor).

January 09, 2015

Mapping the Twitter Reaction to the Charlie Hedbo Attack

Following the attack on the offices of the French satirical magazine Charlie Hebdo, Twitter -- and those who make maps of it -- were all aflame with discussions, speculations and conclusions. In order to process the geographic extent of the reaction to the Charlie Hebdo attacks, we collected approximately 73,000 geotagged tweets created in a roughly 36-hour period from January 7th to noon (EST) on January 8th, that contained either of the hashtags: #charliehebdo OR #jesuischarlie (English translation: 'I am Charlie').

We then aggregated these tweets to the country level and normalized these tweets by a random sample of tweets in each country during the same time period [1]. We excluded countries that did not meet a minimum threshold of activity (15 tweets) to exclude places with extremely low levels of engagement. The map below was created by Rich Donohue, a post-doc at the University of Kentucky Department of Geography, whose cartography will be showing up on the blog more in the near future. The interactive version of the map allows you to pan, zoom and select specific hashtags to reveal different patterns between the tweeting.

Normalized Distribution of Geotagged Tweets 
containing either #CharlieHebdo or #JeSuisCharlie
Click here for an interactive version of this map.

Those countries shaded in orange demonstrate a greater level of Charlie Hebdo-related tweeting than one would expect based on typical levels of tweeting [2], while those countries shaded in blue demonstrate a lower amount of tweeting than one might expect [3]. Countries shaded in grey failed to meet the minimum threshold of tweeting activity to be included, while the handful of countries in red -- France, Belgium and French Guyana -- have the highest relative number of Charlie Hebdo-related tweets.

As expected, the reaction to the Charlie Hebdo attack has mostly captured the public's attention in Europe, especially (and unsurprisingly) in France and Belgium, with a seeming distance decay effect as one moves away from Paris. But outside of Europe, one can see greater levels of tweeting about the attack in countries with historical -- often colonial -- ties to France, such as Algeria, Tunisia, Senegal and Canada, as well as French Guyana which has significantly more tweeting about the attacks than one would expect based on usual levels of tweeting [4]. Other countries, such as Australia, India and Pakistan, also demonstrate significant levels of tweeting about the attacks, but don't have the same kinds of historical connections to France that might explain such heightened awareness.

Countries with the Greatest Relative Number of Tweets 
containing either #CharlieHebdo or #JeSuisCharlie 
Note: A location quotient greater than 1 indicates a relatively higher higher level of tweets with hashtags relative to the normal amount of tweeting taking place. A location quotient less than 1 indicates a relatively lower higher level of tweets with hashtags relative to the normal amount of tweeting taking place. 

In addition, there are a number of noteworthy patterns that we wish to highlight although are not prepared to explain at this time.

While such patterns are fairly obvious and could easily be predicted, the data leave us with a number of lingering questions that we don't have ready answers for. For instance, why is there a greater level of attention to the attacks in India and Pakistan than in Turkey or Egypt, which are both nearer in absolute distance and, in some ways, social distance to the attacks in Paris? Why are Canadians more focused on the issue than people in the United States? Why are people in the United States roughly 15x more interested in the Charlie Hebdo attacks than in the attempted bombing of an NAACP branch in Colorado?

It's also interesting to explore the differences in how each hashtag is used, and how this effects the spatial distribution of the tweets. Is the use of #charliehebdo a simple indicator of attention to the event, while #jesuischarlie indicates solidarity with the magazine? For example, the UK has a relatively low amount of #charliehebdo tweeting (LQ = 0.84) but a much higher level of #jesuischarlie activity (LQ = 1.35). In contrast, other nearby countries such as Spain, Portugal, Algeria, Morocco, Tunisia have relatively more #charliehebdo than #jesuischarlie activity perhaps connected to a more fraught relationship with local populations and the satire contained within Charlie Hebdo cartoons. To be clear, the causes behind the observed patterns require much more in depth work than we can provide here and now.

Moreover, as always it's important to think about what kinds of discussions aren't captured in this particular dataset, such as discussions of the attacks in Arabic-speaking countries such as Saudi Arabia or Egypt, which use entirely different alphabets than we used in our search. While we don't want to read too much into these differences without further research, these issues do represent potentially interesting differences in the use of social media, both across space and different social groups.

It is also useful to track the distribution of tweets over time, which began shortly before noon Paris time and peaked approximately ten hours later.

Number of Geotagged Tweets Overtime (in ten minute blocks)

While we have surely raised more questions than we have answered in this post, hopefully this early attempt at mapping the response to the attacks provides some further food for thought for those wishing to delve deep into understanding the nature of the attacks and the response to them via social media.

[1] We used the following formula (location quotient) to normalize the data:

(# of tweets with hashtags in country / # of total tweets in country)
(# of tweets with hashtags globally / # of total tweets globally)

[2] With a location quotient greater than 1.
[3] With a location quotient less than 1.
[4] There were a number of Francophone African countries that had high location quotients but were excluded from this map because they did not meet the threshold of 15 tweets. This includes Côte d'Ivoire, Gabon, Burundi, Benin, Togo and Congo.  Other countries with strong ties to France -- New Caledonia, Fiji, and Saint Martin -- exhibited similar patterns.

December 31, 2014

The Best of Floatingsheep in 2014

With yet another year coming to a close, we thought it a good time to reflect upon yet another year of sheepish maps and blogposts, recounting what we have accomplished, perhaps mostly so that we don't dare attempt such goofiness again. And so we give you the Top 10 Floatingsheep posts of 2014, ranked according to the number of page views each received. Feast on these last remnants of 2014, and a happy new year to all!

#1 The Drama of Llamas vs. the Gloating of the Goats 
What was thought to be something of a throwaway post came from the shadows to become 2014's most viewed blogpost, largely thanks to some Redditors who took the map a bit too seriously, if we do say so ourselves.

#2 New Book Chapter on the Geographies of Beer on Twitter 
Based on some great work by Matt and Ate, the map below (and others from the same book chapter) has become a staple of Vox's explanations of alcohol this year... see here, here and here.

#3 Mapping Ferguson Tweets, or more maps that won't change your mind about racism in America 
The product of the Inaugural #IronWilson Map-a-Thon, this map and post was our attempt to counter some problematic uses of geotagged Twitter data in relation to the then-nascent protests in Ferguson, Missouri, and highlight the persistent limitations of this sort of work when dealing with issues as complex and fraught as violence and structural racism.

#4 Mapping the Seven Dirty Words 
One of the biggest missed opportunities from the 2014 IronSheep dataset, our series of maps of George Carlin's infamous seven dirty words didn't yield a whole lot except for excrement.

#5 Hashtags and Haggis: Mapping the Scottish Referendum
While the Scottish ultimately decided to remain a part of Great Britain, some of our maps helped to demonstrate persistent cultural divides between the English and the Scottish, and the fact that "the Scottish referendum [was] not just simply about 'yes' or 'no' but seemingly touche[d] on much more fundamental questions of ovis-based cuisine, men's wear and mythological creatures". Indeed.

#6 Artists, Bankers, Hipsters and the "Bro-ughnut" of New York: Mapping Cultural-Economic Identities on Twitter 
Some more work by Ate and Matt for a journal article yielded the discovery of what will surely be recognized in time as one of the most fundamental geographical phenomena known to humankind: the 'Bro-ughnut' of New York.

#7 Hey Y'all! Geographies of a Colloquialism
There are few places as distinct as the American South when it comes to cultural patterns expressed through geotagged tweets, as our mapping of references to "y'all" helped to confirm.

#8 Crowdsourcing Cake or Death?
While the choice between cake or death seems like an obvious one, our maps of references to these terms yielded a much different -- and troubling -- result.

#9 Are there really more juggalos than polar bears?
"As our analysis has shown, there is more to the story of juggalos and polar bears than meets the eye. Clearly, there are more references to polar bears than to juggalos, both globally and in the United States. But the relationship between these two is considerably more complex and contradictory than is assumed by David Cross and his ilk. Obviously more research is required as ten-second gifs are not up to conveying the complexity of the juggalo-polar bear ecosystem."

#10 The Epic Tweet Fight of Bronies and Juggalos
Despite Lexington, Kentucky being at the center of a online controversy around a Bronies vs. Juggalos street fight, the Floatingsheep home base didn't have much online activity around these two subcultures. In fact, when taking the epic street fight online and evaluating the epic tweet fight, we couldn't help but declare it a draw.