What Would a Floating Sheep Map? The Manifesto

After six years of doing this FloatingSheep thing and something like 400 different posts, we finally decided to summarize a bit of our research in our newly published essay/manifesto, entitled "What Would a Floating Sheep Map?". A collaboration between the five members of the FloatingSheep collective and Rich Donohue and Matt Wilson of the University of Kentucky, our manifesto tries to provide some context to the broader debates about mapping that we've been participating in for the last several years.

Our manifesto is about maps, but it's not just about maps. We like maps and celebrate high-quality, aesthetically-pleasing, intellectually engaging and (yes) amusing maps. Our aim, however, is to do much more than express an appreciation for maps. We also want to challenge people to think deeply about space, construct maps that demonstrate an awareness of social contexts, and critique these very same maps. Thus, our manifesto focuses not on the specific techniques or technologies of map-making, but on the societies, spaces, and places that are being mapped, and from which maps emerge.

While we'll leave you to actually read the entire thing, as a bit of a teaser, here are the six key points that summarize our manifesto, and, ultimately, the last six years of FloatingSheep research that we've shared with you here on the blog.

#1a. Maps help us to understand the world.
#1b. But maps also produce the world as we know it. 

#2a. All maps lie.
#2b. But lies are the source of maps' power. 

#3a. Maps are now fundamentally different.
#3b. But maps are still fundamentally the same. 

#4a. Pretty maps are better than ugly maps.
#4b. But ugly maps will do in a pinch. 

#5a. Map or be mapped.
#5b. But not everything can (or should) be mapped. 

#6a. Maps of the digital world are a reflection of the material world.
#6b. But this reflection is imperfect and distorted.

Zook, Matthew, Taylor Shelton, Ate Poorthuis, Rich Donohue, Matthew Wilson, Mark Graham, and Monica Stephens. What Would a Floating Sheep Map? Lexington, KY: Oves Natantes Press, 2015. http://manifesto.floatingsheep.org.

Floating Sheep in Comic Form

Have you ever wondered what it is like to take a classes at one of home institutions of Floating Sheep? In this case, the Department of Geography at the University of Kentucky, a place where the land flows with milk, honey and cutting-edge insight with giant herds of critical GIS researchers roaming the tundra and bellowing out challenges?

OK, perhaps not. 

But it is a place where we have stock-piled Floating Sheep stickers and tattoos for the coming apocalypse. And sometimes these artifacts emerge and are reused and parodied by the denizens of the department.

Ergo [1], the following cartoon [2] has appeared in Kentucky, the work of the talented Emily Kaufman, Ph.D. student. And while the Floating Sheep collective would love to have come up with the "Computer is not a banana" as a metaphoric/allegoric/crazed bon mot -- if for no reason beyond its mysterious incomprehensibility -- we cannot take credit. This is the idea of Jeremy Crampton, who is actually the professorial figure featured in the cartoon.

And no, we don't under why a "computer is not a banana" [3] either.

----------

[1] What a great word, love to use that word. 

[2] Technically a mixed media collage of paper, ink and tattoo.

[3] We are in cautiously in agreement with this statement although we think the idea of a banana-based computer is great idea.

The Prime Minister and the Pig: Mapping #piggate

“The creatures outside looked from pig to man, and from man to pig, and from pig to man again; but already it was impossible to say which was which.” - George Orwell

It has recently been revealed that David Cameron, the Prime Minister of the United Kingdom, has potentially engaged in sexual acts with the head of a pig. Yes, really [1]. We assume the technical term would be beastialnecrophiliality. Or Necrophilibeastialogy. Or Ick!

Because of our deep commitments to both data science and animal welfare here at Floatingsheep, we decided that we needed a better understanding of how the conversation around #piggate is spreading geographically. Also, how we can resist such a wonderful story about a "debauched and secretive society" at one of the home institutions of FloatingSheep. Hint: it is NOT the University of Kentucky.

In the map above, you can see that people in the UK - for obvious reasons - are most likely to be tweeting about the Prime Minister and his, erm, attachment to pigs [2].

The French, interestingly, seem to be largely ignoring the story. Whereas the Spanish, Irish, and Americans all seem to be highly tuned-in (perhaps because of the love of bacon and ham in all three countries).

The spatial dynamics of #piggate will, of course, evolve over the next few hours and days; but until then, this is perhaps the obvious next addition to any conversation that begins with 'where were you during #piggate 2015.' What a time to be alive!

Unless of course you happen to be the severed head of porcine.

--------
[1] Yes, that is actually the accusation. We're still have trouble processing the news as well.
[2] Perhaps rapprochement is better term. After all it's a French word and makes it sound somehow better. And frankly this needs all the help it can get.

Punch Buggies vs. Perdiddles: The Geography of Road Trip Games

As the final days of summer come to a close, we start to wax nostalgic about long car trips from yesteryear. Sadly, the children of today -- hooked into their pads, phones and other devices -- will little understand the joys of being packed in the un-air-conditioned back seat with various siblings and/or cousins for 5+ hours with nothing to do but look outside and get on each other's nerves.

Of course, desperate parents came up with all manner of games -- license plate bingo, the alphabet game, twenty questions -- to try and keep the peace and a little bit of their own sanity. But truth be told, these were but stop gaps for the real games -- punch buggy and perdiddle -- which provide legally sanctioned channels for punching your sisters. While rules vary (the strip perdiddle game variant cited in Wikipedia is particular unnerving for family road trips), the basic rule is the first person who sees a "punch buggy" (a VW beetle) OR a "perdiddle" (a car with one burnt out headlight) "wins"; you now have tacit permission (according to the international charter of kid rules) to punch someone [1].

Over the years as multiple people stare blankly as I shout perdiddle, it has become apparent that these games (like most human culture) have lumpy spatial patterns, as is evidenced in our map of references to these games in geotagged tweets below [2]. 

Mapping Punch Buggy vs. Perdiddle Tweets

Using Twitter as a proxy for the preference for these road trip games, it is clear that "perdiddle" is mostly a midwestern to east coast phenomenon, with Ohio, Pennsylvania and Massachusetts being particularly prominent. While punch buggy had a greater overall number of references in our data (3612 total tweets, as compared to 1327 for perdiddle), it seems to be more spatially dispersed across the United States, with a somewhat greater prevalence in the west, and the west coast in particular. Perhaps because this is the natural habitat for VW Beetles?

We're curious if this distribution fits with your experience, so let us know.

------

[1] Of course, you might get in trouble with your parents but that's another issue.

[2] We ran searches for (1) padiddle OR pediddle OR perdiddle OR kadiddle, and (2) "punch buggy" OR "slug bug" for tweets sent from 2012 to 2015, aggregated them into hexbins and calculated a simple odds ratio to compare the two. This is basically the same approach we've done for awhile, so no need to rehash the details here. If you'd like to see more on this methodology, check out our forthcoming book chapter.

New job working with the Geonet team at the Oxford Internet Institute: 'Researcher in ICTs, Geography and Development'

Mark is now hiring a researcher to work at the Oxford Internet Institute to investigate low-wage digital work being carried out in Sub-Saharan Africa:

The Oxford Internet Institute is a leading centre for research into individual, collective and institutional behaviour on the Internet. We are looking for a full-time Researcher to work with Professor Mark Graham on the ERC-funded project Geonet: Investigating the Changing Connectivities and Potentials of Sub-Saharan Africa's Knowledge Economy. Combining archival research, surveys, and in-depth interviews, this ambitious project will critically assess the changing landscape of digital work in Sub-Saharan Africa, and ask who benefits (and who doesn’t) from those changes.

In this exciting role, the Researcher will carry out 9-12 months of fieldwork among digital workers and organizations in Sub-Saharan Africa, as well as working at OII’s premises in Oxford. The Researcher will also contribute to the dissemination of the findings through peer-reviewed academic papers, project reports, events, blogs and social media.

Candidates should have experience of social science research in Development Studies, Geography, Sociology, Social Anthropology, Communications, Organization Studies, Management or related disciplines, training and practical experience in qualitative research methods.

Based primarily at the Oxford Internet Institute (with periods of fieldwork), this position is available immediately for 3 years in the first instance, with the possibility of renewal thereafter, funding permitting. For qualified candidates, there may also be opportunities to teach course modules on our ‘Social Science of the Internet’ MSc course.

The application form and further details, including a job description and selection criteria, are available on Oxford University's recruitment website.

The closing date for applications is 12:00 BST on Thursday 3 September 2015 and only applications received before then can be considered. Interviews for those short-listed are currently planned to take place in the week commencing Monday 21 September 2015.

#BeerTweets

We love mapping beer, there's no secret about that. We've been making maps about the digital landscapes of beer across the world practically since we started this blog six years ago, and this work is consistently some of our most popular. This includes some maps on the geographies of beer-related tweeting in the United States, building from a book chapter by Matt and Ate.

Now we want you to join in the fun of exploring this liquid landscape. To celebrate the rollout of a new online graduate program in digital mapping (New Maps Plus at the University of Kentucky) we offer up this interactive visualization of America's beer-related tweeting.

(click the image above to go to the interactive map)

Choose a type or brand of beer and see where people tweet about it or compare the attention to two different kinds of beer. Special thanks to Rich Donohue who built this slick interactive user interface with the Leaflet library. If you're curious about how this map was built and designed (or are interested in doing something like this yourself) check out the New Maps Plus program.

More saturated (darker) colors indicate a higher probability of tweets containing a textual reference to the selected beer type. You can visually explore a variety of beers by selecting a new beer from the drop-down menu at the top right. By default, a given beer is normalized by a random sample of the overall Twitter population, though you can also compare two different beers by selecting another beer from the second drop-down menu.  Hexagons without a significant number of observations/tweets do not show up. That's why some beers have more coverage than others.

Feel free to start playing right away, but in order to whet your appetite, here are some examples what you'll find. Starting with arguably the most locally-specific beer on our list, one can clearly see how Grain Belt beer is thoroughly grounded in the culture of the upper midwest, especially in Minnesota, and to a lesser degree Iowa and Wisconsin. It's interesting to note, however, that despite this very particular concentration, Grain Belt barely cracks the top 10 list for absolute beer references throughout the area. This obviously raises the important issue of recognizing that (nearly) all Grain Belt drinkers are Minnesotans, but not all Minnesotans are Grain Belt drinkers! We must admit we've not had the pleasure of trying Grain Belt ourselves, and we're not quite sure if that is a good thing or a bad thing.

Grain Belt

Although the Boston Brewing Company will be quick to tell you that it is still a craft brewery, Sam Adams is remarkably more diffuse throughout the US. However, one can also see that Sam Adams' home is very clearly in Massachusetts and extending into Maine, New Hampshire and Vermont, though the beer remains less talked about in these locations than a number of other non-local varieties.

Sam Adams

Tweeting about Yuengling, however, represents a few interesting deviations from the patterns seen with Grain Belt and Sam Adams. For one, Yuengling has a much more prominent role within the Pennsylvania area, asserting itself as a top-5 beer-of-choice throughout the state, and even coming in as the #1 beer referenced in the area around Bethlehem, PA, not far from the Yuengling brewery in Pottsville. Second, while Yuengling is similar to Sam Adams in its wider distribution throughout the US, the number of references to the beer drop off significantly to the west of the Alleghenies, and are practically non-existent to the west of the Mississippi River. Finally it's interesting to note that Yuengling also represents the unique case of a regionally-specific beer that is actually multi-polar, as the beer is also prominent in Florida due to its secondary brewery being located in Tampa.

Yuengling

A reverse of this spatial distribution can be seen in the case of Shiner Bock, whose references are dominant in much of Texas, especially around the Spoetzl Brewery in Shiner. Though concentrations extend beyond the Lone Star state into Louisiana, Arkansas, Oklahoma and Kansas, don't even try to get midwestern or New England states on board with this Texas brew.

Shiner Bock

There's nothing like the simulated authenticity of drinking a cerveza when trying to cool down on a hot day. But, as a comparison of Corona and Dos Equis shows, which Mexican beer you choose is likely (at least a bit of) a function of where you are. While Corona tends to be more concentrated in California, Florida and parts of the northeast, Dos Equis tends to be concentrated in the middle part of the country, especially centered on Texas.

Corona vs. Dos Equis

Last but not least, we thought it important to take a closer look at the geography of the country's two most popular beers, Bud Light and Coors Light. And while Bud Light sales were well over double those of Coors Light in 2014, tweeting activity around these two popular watery substances (sorry, we're solidly in the craft beer camp) reveals some interesting caveats to this seemingly one-sided competition. Indeed, just to the west of the Mississippi River appears a fairly clear dividing line at which the bevy of Bud Light in the eastern United States gives way to a western preference for Coors Light. 

Bud Light vs. Coors Light

And while the eastern seaboard between New Jersey and Rhode Island seems to be the one eastern outpost of Coors Light, Bud Light actually remains the most popular beer being tweeted about in these areas. But because the statistical comparison looks not at absolute numbers, but the prevalence compared to the expectation at the national-level, the seeming competition here is a bit deceiving. Indeed, references to "Coors Light" itself are incredibly sparse throughout the US, and the term rarely cracks the top 10 for any given locale, although the more generalized "Coors" in these areas makes clear the regional preference.

Whew! That was a lot of work. We're off to kick-back and enjoy a cold one. Have fun with the map and be sure to tell us which beers we didn't include...We suspect there will be many future iterations!

------------------------------------------------------------------------------------------------------------------------

If you want to learn how to make maps like this, check out the new online graduate certificate and master's degree in digital mapping from New Maps Plus! The first batch of classes start October 4!

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!

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

  

  

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).

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.

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.

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).

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.

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.

Are we more interested in XXX or Xmas?

This holiday season we decide to ask the questions that really matter. As people celebrate Christmas, we wanted to know how people around the world are mentioning the holiday. And, perhaps more importantly and interestingly, how mentions of Christmas stack up against mentions of a more sexual and consumption-oriented nature. 

So, we decided to compare mentions of 'Xmas', 'XXX', and 'Xbox. 

The formula that we used (for XXX tweets for example):  

(Sum of XXX tweets in square / Sum of XXX tweets globally)

---------------------------------------------------------------------------------

( sum XXX+Xbox+xmas in square /  sum XXX+Xbox+xmas globally)

We see some important global differences. Americans (as well as the French and Spanish) are most interested in Xboxes. Strangely, the Japanese and Nigerians seem to be most fixated on Christmas. And the British, Dutch, and Italians more interested in X-rated content: giving a whole different meaning to reflections on who has been naughty and who has been nice. 

Deconstructing the (most detailed tweet) map (ever)

If you’re the kind of person who visits our blog with any regularity, you’re almost certainly also the kind of person who would have seen some version of the map below in the last couple of weeks. Created by Eric Fischer of Mapbox, this map was released along with a blogpost entitled “Making the most detailed tweet map ever”, discussing some of the data cleaning and visualization methods necessary to produce such a striking map. The map is undoubtedly interesting and has sparked a great deal of interest from all corners of the internet, but there’s just something about the framing that rubs us the wrong way. While Eric’s post emphasizes the making part of the equation, the internet hype cycle around it has caused us to read the title a bit more along the lines of:

"Making THE MOST DETAILED tweet map EVARRRR!!!!"

That is to say, for all of the admittedly really great detail about what went into making this map, the framing of this map as not only a detailed map of six billion or so geotagged tweets, but as the most detailed tweet map ever, raises more questions than it answers. For example, what constitutes ‘detail’ in tweet maps? What do competing definitions of ‘detail’ reveal about what we value in this kind of analysis? What do these particular ideas of ‘detail’ foreclose in terms of other possibilities for analysis?

These are important questions, regardless of whether they’re applied to this particular map or any other one. The issue in this case, however, seems to be that the answers to some of these questions conflict with one another, or with the ways the project is itself described. The detail that seems to be valued here is of the “every tweet ever” variety, or, put simply “more = better”, the fetish for bigger data at the expense of all else.

But more data isn’t necessarily better, and it certainly doesn’t mean that there’s more detail, especially when the only bit of detail you're concerned with in each of these six billion points is the latitude and longitude coordinates. Each of these individual tweets contains a wealth of other interesting information, from information about the user and the way they describe themselves, to the time the tweet was created to the text of the tweet itself, which might contain hashtags that link up with bigger conversations, or @-mentions to other Twitter users that might be used to understand social networks and interactions. All of these bits of information represent a kind of detail that is not included in this, the most detailed tweet map ever. 

As we’ve been arguing for the past two years or so, there are a range of social and spatial processes represented in geotagged tweets that we can’t get at if all we’re concerned with is the latitude and longitude coordinates. So to say that this represents the most detailed tweet map ever serves to reify what we see as two of the most problematic assumptions of contemporary big data/social media research: (1) that more data is equivalent to better data, and (2) that the only important aspect of the data is the geographic coordinates attached to it. There's lots of interesting stuff that can be done with this kind of data, and we can do better than simply plotting points on a map and calling it a day [1].

Even if one were inclined to accept the argument that more tweets equals more detail, how should we interpret the fact that this map only visualizes about 9% of all geotagged tweets, due to the design decisions necessary in order to make the map nice and pretty [2]? Due to the existence of exact or near-duplicate coordinates that would make points indistinguishable from one another, this, the most detailed tweet map ever, actually eliminates about 91% of the detail that it seems to value most (i.e., the presence or absence of points on the map). The Gizmodo headline about the map reads, “The Most Detailed Tweet Map Ever Includes 6,341,973,478 Tweets”... except that, you know, it doesn’t [3].

Of course, there’s also good bit of imprecision in the locational accuracy associated with geotagged tweeting; our iPhones don’t come with military grade GPS units installed in them. So while Mapbox CEO Eric Gunderson was marveling at the detailed micro-geographies of an airport gate seen in the map, he was ignoring both the fact that all of those folks on the jet bridge could just have well been 40 feet away, and that a number of tweets might have been eliminated from the initial dataset due to a lack of precision in the geotagging process. Take all of that together and a lot of the detail that’s being celebrated here starts to give way to fuzziness. This map is more art than science, though the striking visuals and discursive framing give the illusion of precision and absolute insight. 

To be clear, there’s no problem with fuzziness. It’s something we all live with every day, it’s something we academics may embrace from time to time through the use of overly obtuse language. But taking all of this fuzziness and then repackaging it as the most detailed tweet map ever, comes off a bit wrong to us. These initial misgivings were only amplified when brought down to a more local level, when we saw a post from a local urbanist blog in Louisville wondering “What we can learn from where people in Louisville are using Twitter”. While relatively mundane, and certainly not nearly as celebratory, the blog’s ultimate conclusion was that "These locations [with the highest concentrations of tweets] make sense as they are places where people gather and are often held captive by events.”

This, in general, is true, but also a bit… how do we put it? Meh. More fundamentally, people tweet where people are. It comes as no surprise to anyone with even the vaguest familiarity with Louisville that people tweet in larger numbers from downtown (including 4th Street Live!), the University of Louisville campus, Bardstown Road and the St. Matthews / Oxmoor Mall area than anywhere else in the city. These are (some of) the primary gathering points on a day-to-day basis within the city.

But just identifying these locations doesn’t really help us to ‘learn’ anything beyond the fact that those are, indeed, the places with the highest concentrations of geotagged tweets in Louisville [4]. In fact, the map doesn’t even really show us actual concentrations of tweeting activity, but rather concentrations of unique tweeting locations. Take, say, two hypothetical city squares, one of just 50 x 50 meters, and another much larger one of 500 x 500 meters, both the originating point of one million geotagged tweets spread randomly over the squares. In Fischer’s method, these two squares would not 'glow' in equal amounts, but rather the larger square would show up as much more visually prominent because it has many more unique tweeting locations while many of the tweets from the smaller square would be filtered out due to a duplication of coordinates.

Further, from a data collection standpoint, all of these tweets in Louisville reveal little that isn't revealed by mapping a random sample of tweets (say 1% of tweets from 2013, see map below). If all we’re really concerned about is the question of where people are tweeting from, there isn’t much that looking at all the tweets reveals that couldn't also be found from a smaller subset, and it’s much easier to collect or analyze a few hundred thousand tweets than it is to collect 6,341,973,478 of them. But even still, all we can ‘learn’ from these kinds of maps is where people have created geotagged tweets and, to some extent, where they have not [5].

But if that’s all we can learn from this map, again, why call it the most detailed tweet map ever? Again, there are any number of details that are excluded from analysis by only looking at the locations of geotagged tweets. What if we instead took a different approach to this data, such as examining at the use history of individual Twitter users, or even collectives of Twitter users based on some kind of shared experience or identity, such as association with particular neighborhoods or other places?

OK, you're right. This particular question is a bit self-serving, as this is precisely the kind of thing we've been working on for some time now. And so rather than just offering a critique of someone else's work, we really want to see if we can push this kind of analysis in more productive directions. So we offer up the map below, which comes from a paper we currently have under review, that attempts to demonstrate how geotagged tweets can help us to better understand urban socio-spatial inequality beyond simply identifying the presence or absence of tweets in a given area, as is so often done.

Using Louisville and the now-common ‘9th Street Divide’ trope as a starting point, we sought out to understand how people from different parts of the city used and moved around the city in different ways. So in a manner not uncommon to some other things Eric Fischer has done previously, we identified a number of Twitter users as belonging to one of two groups, those with close ties to either the West End (traditionally a poorer and predominantly African-American part of the city) or the East End (a more affluent and largely white part of the city), and collected all of the geotagged tweets from those users [6]. We then compared the spatial footprint of these groups' tweeting activity via an odds-ratio measure. On the map areas in purple represent places with greater-than-usual levels of West End user tweeting activity, while orange hexagons represent places where East End users were relatively more dominant than expected. Those places which demonstrate roughly equivalent or expected levels of tweeting are signified by those hexagons with hashes.

This map, in short, represents those places in the city of Louisville which are more socially heterogeneous and homogeneous, dominated either by West End or East End residents, or characterized by a relative mix of people from parts of the city. Though it’s evident that there is indeed a kind of divide between the West End and the rest of the city, this map also shows that West End residents are incredibly spatially mobile within the city, while East End residents tend to be much more spatially constrained, sticking to their own parts of town.

While there are certainly a lot of underlying factors driving this process, suffice it to say that this map provides an alternative way of understanding socio-spatial inequality than simply identifying those places that do or do not have significant concentrations of geotagged tweets [7]. Through our analysis, we also learned that contrary to the kind of assumptions often made about this kind of informational inequality, West End users actually produce a significantly greater number of geotagged tweets than their East End counterparts, it's just that many of these tweets are created in other parts of the city. This is, of course, an important kind of detail that we can draw from the mapping and analysis of geotagged tweets and one that, in many ways, is more detailed than the most detailed tweet map ever.

There is, of course, a whole lot more detail in the paper that this one map and blog post can’t capture, just as is the case with Eric Fischer’s map. Just to be clear, we think Eric Fischer does some fantastic and beautiful work with geotagged social media data, and commend him for openly discussing and sharing his methods. And yet, we can’t help but feel like the characterization of his map as being the most detailed tweet map ever is at best a half-truth, and helps to reproduce some of the most common problems with the analysis of geotagged social media data. But the more we think about it, we’re not so sure that a single most detailed tweet map could exist, or that it’s even desirable to have such a thing. Instead, we should be striving to create any number of highly-detailed, geographically-situated tweet maps, that collectively contribute to better understandings of the complex social and spatial processes that are represented and reproduced through this kind of data. 

----------------

[1] That’s the royal we. 

[2] Which it most certainly is.

[3] As Fischer notes, there are actually no more than about 590 million dots on the map due to his filtering process. When one zooms all the way out on the map so that the entire globe is represented in a single map tile, there are only 1,586 visible tweets, a far cry from the 6 billion number that seems so, well… big.

[4] #tautology

[5] This is qualified in this way because, as Kenneth Field pointed out in a Twitter exchange with Eric Fischer about these maps, geotagged tweets that he has consciously created from his house do not appear on the map. So while we know that all of the tweets on the map were created in that place, we can't say definitively that tweets were not also created in places where they do not appear on the map.
[6] In order to do this classification, we collected all geotagged tweets created within the defined boundaries of these two areas, and then identified those users with more than 40 tweets within either area, where those 40+ tweets represented greater than 50% of their overall geotagged tweeting activity. This concentration of activity indicates that users had a strong association with, and presence within, either area, while also making sure that no users were identified as belonging to both areas.
[7] We also see this map as complicating the conventional narrative in Louisville of 9th Street as representing a kind of impenetrable barrier within the city. But since this is less directly relevant to our argument here, we'll make you wait to hear more about that particular line of reasoning.

The (Rust) Belt of Basic-ness? Mapping the Pumpkin Spice Latte

As fall gives way to winter, we're all left clinging to the best vestiges of the passing season: the changing leaves, college football, temperatures above freezing and, for many of the most basic amongst us, the pumpkin spice latte. Debuted by Starbucks in 2004, and featuring no actual pumpkin content, the pumpkin spice latte has become a staple of fall, with Ugg boots and yoga pants-wearing women practically crawling out of the woodwork to get their hands on the thing. And while Starbucks touts that over 29,000 tweets have mentioned #pumpkinspice since 2012, we suspected there was much more to the story of the pumpkin spice latte [1]. Despite the fervor, we noticed that there's been no definitive tracking of the geographical expansion of the pumpkin spice latte as it seeks to colonize the world of regular, everyday people drinking plain ol' coffee.

Searching only for the latest manifestation of the pumpkin spice phenomena, we collected all geotagged tweets in the continental United States for September and October 2014 with references to either "pumpkin spice" or "#psl", yielding a total of 19,537 tweets. But rather than simply mapping the basic distribution of these tweets, we've instead normalized this data by tweets referencing "coffee" during the same period. Using a 25% sample of all of these coffee-related tweets -- totaling 42,696 tweets -- aggregated to hexagonal cells, we calculated the odds ratio at the lower bound of the 95% confidence interval in order to provide a bit more context and account for any number of biases within the data. Using this measure, we've identified those places with greater-than-usual numbers of pumpkin spice latte tweets relative to those tweets referencing coffee (orange), and vice versa (purple), as seen in the map below.

References to Pumpkin Spice Lattes relative to References to Coffee [2]

Based on our binary classification, it's evident that the vast majority of the country has stuck with their preference for coffee, even during the PSL's peak season. But given that our interest is in mapping the prevalence of the PSL in particular, we want to pay closer attention to the smattering of orange hexagons in the map. While there are no definitive clusters of PSL-related tweeting, if you squint your eyes you can just barely visualize a belt of pro-PSL places stretching from St. Louis up to Chicago, and from Cincinnati up to Toledo and Detroit, and from Cleveland to Pittsburgh, what we've termed "the basic belt". While this belt roughly corresponds to the vernacular region of the Rust Belt, Ohio in particular sits at the center of this pumpkin spice-loving portion of the country, representing the buckle on our belt [3]. Given this clustering of PSLs, we suspect that the Buckeye State might well be on its way to becoming the Pumpkin Spice State.
----
[1] Well, actually, Renee Kaufmann had this hunch. All credit for the idea behind this post goes to her.
[2] Sorry about the Web Mercator projection, y'all.
[3] Can one still wear a belt with yoga pants?

Geographies of Grits

Throughout the history of this blog, we’ve mapped any number of geographically-specific social phenomenon. But often times, we’ve been drawn to mapping things associated with the American South, whether because it’s arguably the most distinctive cultural region in the United States or because all of us have lived on its outskirts for some time or another, we’re not quite sure. But if you had to choose just one region to map using social media data, the South is probably a good place to start.

Continuing this persistent obsession, we decided to map one of the South’s most prominent culinary traditions: grits. As such, we collected all geotagged tweets in the United States from June 2012 to September 2014 mentioning the strange (read: awesome) ground-corn porridge-like dish, totaling around 64,000 tweets. Keeping in mind that geotagged tweets still represent only around 2-3% of all tweets, this figure represents a breakfast table conversation of several thousand tweets per day, and highlights the ability of this kind of social media data to provide insight into a particular cultural phenomenon that is relatively more difficult (though certainly still possible) to measure through more conventional means.

The map below represents a normalized visualization of grits-related tweeting throughout the continental United States. Using a grid of hexagonal cells, the number of grits tweets were normalized using an odds ratio by a random sample of tweets from the same time period. In this measure, a value of 1 signifies that there are exactly as many grits tweets in a given location as one would expect according to the baseline measures of tweeting, with values greater than 1 indicating that there is a greater predominance of grits tweets than one would otherwise expect. In effect, this analysis cuts out the potential for these maps to simply reproduce maps of population density, honing in on the actual phenomena at hand.

Mapping the 'Grits Belt'

Indeed, here you can see that while the South in general demonstrates a general preference for grits over the rest of the country, it is actually a relatively small number of coastal localities in the low country that have the strongest connection to grits through social media. While New Orleans represents something of an outlier in the far corner of the South, there is also a consistent band of concentrated grits tweeting stretching from just north of Charleston, South Carolina down through Beaufort (though seemingly skipping over Hilton Head, a popular tourist destination that might be understood as relationally disconnected from much of the rest of the distinctly southern culture surrounding it) and Savannah, Georgia, all the way to Brunswick.

In general, this map demonstrates the general potential of this kind of method to locate geographically-specific cultural practices in space, as well as the notion that these kinds of maps can reinforce the persistent connectedness between virtual representations of the world and people’s everyday lives and material practices. But there is more that we can do with this data by putting it into relation with other datasets. The map below does just that, by comparing our existing dataset of tweets mentioning ‘grits’ with all geotagged tweets during the same time period that mention ‘oats’. We again employ the odds ratio measure, but rather than comparing using a baseline population of tweets, we use the oats-related tweets to normalize our values. In this analysis, values less than 1 signify a preference for oats, while values greater than 1 represent a tendency towards grits. Not only does this comparison continue to affirm the identification of a ‘grits belt’ in the South, but it also highlights other areas of the country – an ‘oats oval’ stretching from the Northeast to the Midwest – that stand in stark contrast to the southeast in terms of digital porridge discourse.

Grits vs. Oats: The Emergence of the 'Oats Oval'

Thus a key avenue for analysis of digital social datasets is examining the relationships between individual users or individual messages. It is also possible to identify relationships between places, based on visits or tweets made by the same person in these different places. While we’ve already identified the South as the key locus of grits-related tweeting in the United States, it’s important to not simply ignore all of the other data points available to us that are just not quite as spatially clustered. Indeed, given the strong connections between the cultural practice of grits preparation and consumption and the vernacular region of the South, we might hypothesize that even those people tweeting about grits outside of the South are likely to have some kind of connection to the South, perhaps as a kind of diasporic community now living in other parts of the country, or even just traveling for a short period of time.

Relational 'Gritspace'

To examine this relationship, we begin by looking for users in our original dataset that have tweeted about grits more than once – yielding a total of 8,958 users – then drawing a line from the tweet locations in chronological order. The resulting map below clearly shows that there is a strong relational connection with the South for those who tweet about grits from other places, even for cities like Los Angeles that are quite distant in absolute space, as well as in terms of cultural identity. Indeed, the gravity of grits appears quite strong, as of the users tweeting about grits from outside the South, approximately 55% of these also sent tweets from inside the cluster identified in the first map in this post. So even for those grits-obsessed tweeters outside of the South, the pull of porridge remains strong… and, we would expect, even stronger when you throw a bit of cheese and jalapenos in, too.

-----
If you’re interested in reading more about the methods used to make these maps, and about the utility of mapping geotagged social media data more generally, you can check out the following pre-publication version of a forthcoming book chapter from which this work was drawn:

Poorthuis, A., M. Zook, T. Shelton, M. Graham and M. Stephens. Forthcoming. "Using Geotagged Digital Social Data in Geographic Research". In Key Methods in Geography, eds. N. Clifford, S. French, M. Cope and T. Gillespie. London: Sage.

Abstract: This chapter outlines how one might utilize the massive amounts of web-based, geographically-referenced digital social data for geographical research. Because much of these data are user-generated and produced through social media platforms, we also focus on the pitfalls associated with such sources and the benefits of a mixed methods approach to these data. Not only can digital social data be mapped for visual analysis, it is also useful to use a range of quantitative methods to understand relationships between different subsets of the data. In addition, closer, systematic readings via qualitative methods of social data provides insights of particular people’s perceptions and experiences of the world around them. Thus, while making maps is often the starting point for geographers working with this kind of research, it is rarely the end point.