Maps of public transportation are my favorite visual shorthand for any major city, not only because I have to rely on mass transit where ever I go, but also because these highly stylized versions of cities contain much more than the bare minimum amount of information to get from one point to the next. I will be in Tokyo checking out the public transit system and attending the 4S conference through the end of the month.
See you back here in September.
This map of New York was created by Eric Fisher. He gathered the geotags of the photos uploaded to flickr. The colors work like this: blue photos were taken by locals (deemed to be local because they had taken pictures in the same location over an extended period of time), red indicates photos taken by tourists (people taking photos outside of their frequent-photo-taking-zone), and the yellow ones were indeterminate (taken by people who hadn’t uploaded any photos in the previous 30 days though we guess they might be tourists because they may be the kind of people who only take photos while on vacation).
I like the aesthetic and the method so that’s why I decided to share.
This is an interactive graphic situation so to get the most out of it, I recommend clicking through to forbes.com and playing around.
What works is that we can see a lot of internal migration between Los Angeles and other west coast cities as well as between LA, Florida, and the DC-NYC-Boston corridor. I know plenty of bi-coastal folks so the picture matches up with my experience. To get the proper context, I suggest you click through and pick some rural counties to see how much people tend to move from small place to small place and from big place to big place but not so much from very small to very big or vice versa.
Overall, what the interactive graphic ends up showing us is that people move quite a bit. The map gets saturated with lines.
Man, we can really make some cool graphics – and interactive ones at that – with the ridiculous capacity of desktop computers these days. This is a data-driven graphic that would have been nearly impossible not that long ago.
There is no comparison for this graphic. I can’t tell if all this migration is more than normal, trending up, trending down, or anything of that nature. Sheer volume at one time can generate lots of questions but it doesn’t answer many.
I’d also be curious to know if the movers are evenly spread across the life course. Plenty of people move to go to college and then again when they leave college, or so I think. And there has been plenty of ink spilled about retirees moving south from places like Chicago and Minneapolis. Then, about ten years ago, I remember reading stories about the plight of the managerial class having to move around within their multi-national corporations to keep progressing in their companies all through their mid-life. With all those half-baked hypotheses, I would love to see how life stage impacts internal migration.
Bruner, Joseph. (15 June 2010) Map: Where Americans Are Moving at forbes.com.
Throwing in a map is smart – most Americans do not know where all the Mexican states are. Breaking the murders down by location is also smart. A national total would obscure part of the point, which is that the drug wars in Mexico are not hitting the whole country equally. Some areas are much more important to the cartels and are getting walloped while others are relatively unscathed, at least in terms of murders and other violence. Including the map and breaking the graph down by geographical boundaries both communicate the geographical specificity of the murder problem.
This graphic illustrates just one element, one metric, of a whole constellation of economic and social problems. In journalism, getting any graphics into an article on a tight deadlines is difficult. One could argue that since the troubles in Mexico are not so new, it might be worth putting some extra time into the production of a graphic that could display murders, kidnappings (of whom and for what purpose – money, political deals, revenge), the quantity and type of drugs trafficked from which areas, trafficked/processed through which locations, for distribution where and by which cartels.
Besides a comprenhensive narrative of the trade, which is probably nearly impossible to construct because if it were easy to get information about where drugs are grown, processed and distributed I would be amazed. However, there is at least one other way to demonstrate the violence in a more comprehensive fashion, just by making some assumptions about social/economic networks with respect to the people murdered. The information on kidnappings may not be available – the article mentioned that five journalists had been kidnapped but only one was reported to the police. So we are left with murders, and that is probably why the Washington Post ran the graphic that it did.
Widespread murder creates a terror society – one in which fears and suspicions impact daily life over a sustained period of time. But it can be quite difficult to quantify terror and many graphics rely on quantifiable data. The article is well written and its narrative does a good job of conveying the magnitude of the drug violence and its encroachment on the lives of everyday folks.
The spasm of killing, kidnapping and extortion in the northeastern states of Tamaulipas and Nuevo Leon — vital trade, energy and manufacturing centers on the Texas border — marks a serious escalation in the U.S.-backed drug war and comes with a 21st-century twist: Mexican officials struggle to calm what they call a mass psychosis of fear, stoked by social-media chatter and grisly YouTube videos, by using Twitter to post warnings about “situations of risk.” — William Booth
There is clearly good reason for graphics to accompany text – each play unique roles. That being said, I still think there may be a way to create a network graph that demonstrates the social span of the violence. It could look at one of the hard hit communities in which all people are assumed to be alive and unrelated to anyone who was murdered. Let’s imagine one dot for each person, colored green to show they are alive. Now, a person who is murdered will become a black X and all of their family members and close friends will become black dots (still alive, but severely impacted by violence since they lost friends or family). In a perfect world, more distant friends would become brown dots. But it would be difficult to make a map like that because it is difficult to identify looser friendships. Use guest books at funerals to gather data? Facebook? Hard to say. Kidnappings, where known, could be similarly mapped. After constructing a network map like this, it would be much easier to see that one murder impacts a much wider swath of a community. Once there are many murders, there will be very few people left as green dots. Widespread murder is like necrotic tissue.
Where the persons murdered and kidnapped were wage earners, it would also be possible to demonstrate the income lost by the community as compared to the estimated value of the trafficking activity over a given time period. Of course the value of human life should not be measured by the money they failed to earn because they were dead. It’s just as silly as reducing the value of a human life to the market value of the chemical components of the human body. The point of such a comparison would be to demonstrate the economic impact on the community rather than some sort of death calculus.
Booth, William. (21 April 2010) “Drug war violence appears in Mexico’s Northeast, near Texas border”. In The Washington Post, World, North America section.
I cropped what you see above from an infographic that is part of GOOD magazine’s infographic section called Transparency. If you haven’t checked it out, I highly recommend it.
This was the strongest part of the graphic. It does a masterful job of elegantly illustrating a relationship both in space and time. We see that in 1998 hardly any states cared enough about gay marriage to have banned it or legalized it or had any kind of vote whatsoever. Except Alaska. Hello, Palin family. By 2004 the issue had hit the big time and gay marriage bans blanketed about half the country. The east coast showed signs of tolerance. Finally, in 2009, the east coast is holding out against a national tendency towards homophobia. Iowa surprises many by legalizing gay marriage.
Please click through to the larger graphic. I feel that the map time series is by far the strongest part of the graphic. Perhaps because it is so elegantly simple, it was shrunk and deposited in the lower right corner.
Porostocky, Thomas. (2009) A History of Gay Marriage in Transparency, a section of GOOD magazine published out of New York and Los Angeles.
![New York City - Upper East Side [from envisioning development]](https://thesocietypages.org/graphicsociology/files/2009/12/upper_east_side_sm.png "New York City - Upper East Side {from the Center for Urban Pedagogy, envisioning development project}")
![New York City - East Harlem [from envisioning development]](https://thesocietypages.org/graphicsociology/files/2009/12/east_harlem_sm.png "New York City - East Harlem {from the Center for Urban Pedagogy, envisioning development project}")
Please click through to see the full affordable housing map of the New York City metropolitan area. The images above were generated using their pdf generator, but the full flash version is simply better.
Maps are hot. They’re everywhere. I was at a final presentation last night and one of the students said, “I’m kind of a map geek”. I didn’t realize it was possible to be a map geek, but I’m starting to understand. It’s quite easy to present a map – maps have been in use for centuries and some of the oldies are still goodies. It’s not so easy to combine a map with social science data in a smart, legible way. Folks try all the time. These folks at the Center for Urban Pedagogy got it right. Their affordable housing map tool is a solid example of the capability and execution of interactive data.
Why?
The map itself has been stylized. All they want to show you is neighborhood boundaries and neighborhood names. Gone are street markings, terrain, unnecessary color, landmarks, subway stops, and so on. They’re going to add some layers and your eyes are going to be better off without excess detail at the level of the map. Plus, they’re helping you to understand that it isn’t possible to get more granular than neighborhood masses. You can’t use this map to look at property values by block or by proximity to a subway stop so there’s no reason to include the subway map or street markings.
This grey massing approach helps focus my attention (and hopefully yours) on the layer of information about income by neighborhood. This information IS in color. It is added as a layer on top of the map without obscuring the map. They’ve used a modified bar graph layout in which information is embedded in the x-axis itself. The y-axis is implied – that’s just fine here.
And it’s interactive. In a good way.
From a technical perspective, this site makes good use of Flash. It loads quickly and is responsive. Once a neighborhood is selected the bar graphs realign themselves with colored blocks flying in from cyberspace to construct the balance of income for that neighborhood. Note that this is enough movement to make the whole experience a little exciting, a little sparkly but it doesn’t take so long to load or run that you’ve lost interest before you’ve gotten through it.
Layers.
It is my pedagogical opinion that the best graphics encourage the viewer to formulate a question which is then answered. In this case, what we see first is a neighborhood map. The viewer has to pick a neighborhood before any of the juicy data is revealed. This is great. Now, say, we’ve picked the Upper East Side and we see a towering skyscraper-like bar graph way over in the “High Income” department. Our next step can either be to compare to nearby neighborhoods, by clicking on East Harlem to the north, or to add the information about housing prices. The title is “What is affordable housing?” so clearly this is what the designers hope you’ll do. But they aren’t so impatient about it that they try to incorporate it into the first splash page.
Center for Urban Pedagogy. (fall 2009) Envisioning Development project. New York.
Stanley Lieberson’s “A Matter of Taste” looked at the way trends spread by examining baby names. He wanted to avoid the impact of marketing and advertising – the point was not to figure out how to create, perpetuate, or stop a trend, but to see if there is such a thing as a trend in the first place. Nobody is in the business of promoting baby names, and yet there are patterns. Lieberson looked for these patterns in the US. French sociologist Baptiste Coulmont has also looked at the way baby naming trends move across space and change in popularity over time.
The graph below shows how the final syllable of female names has changed over time. The -ette ending waned in popularity while the -ine and -a or -ah endings have increased in popularity. Graphically, I love that this diagram looks like sound intensity diagrams.
More interesting yet, Coulmont also animated a map to show how the name Loic spread from Brittany across the entire country over the course of about 60 years. I like this because it takes a static map and makes it dynamic. Sure, you could have lined up maps to march across a page at five or ten year intervals and cognitively filled in the blank spots. But here, his animations do the cognitive heavy lifting for you, revealing the pattern instantly.
Here’s what Coulmont had to say about the map graphic:
“As to my animation : there is no yet an accompanying sociological argument. I was struck by the spatial mobility of “Loic” from 1945 until 2005 : it seems to be a steady eastward shift [nowadays, Loic is one of the 20 top names for boys in francophone Switzerland : the eastward movement jumped the frontier!
How to explain this movement ? It seems that “Loic” moved from one district to another by means of personal interactions : some people knew some “Loic” living in the west, chose this name for their baby boy, and the movement continued eastward. “Loic” is not alone : especially during the nineties and now, names from Brittany are somewhat fashionable (“Celtic names”) : it could be the unforseen consequence of a strong nationalist movement in Brittany during the seventies. Those independantists fought for the right to name their children with “real” Celtic names… and the names spread in other regions.”
Coulmont, Baptiste. (2009) Prénoms typiques.
Lieberson, Stanley. (2000) A Matter of Taste: How Names, Fashions, and Culture Change New Haven: Yale University Press.
Your sense of who’s single and when they’re single will grow immensely in three or four minutes of playing around with this interactive map of single-ness in the United States, by age and gender. Men get married later and die younger. This means that at young ages, there are more single men than single women because some men who will eventually get married won’t marry until later, on average, than the women they end up marrying. This is just a complicated way of saying that men often marry younger women. In old age, there are more single women than men (the imbalance is because the men start dying younger). During the decade of the twenties and then after about age 65 you’ll find the largest proportions of single-ness. People in the middle decades, from 30-60 or so, are more likely to be coupled. But don’t take my word for it, click through and play around. This data actually understates the number of people who are functionally single because single is measured here as never married. So the folks who have been divorced or widowed and haven’t remarried do not count as single for the purposes of this graphic.
The writer of the text accompanying the graphic is interested in the geographical distribution of single women and single men so there’s more on that if you click through.
I like this one a whole lot so I don’t have much to say except that I wish the designer wouldn’t have gone with the red/blue, female/male color scheme. How about purple and green? Or orange and teal?
I also think I would have counted people who are divorced/widowed and NOT remarried as single.
The graphic designer is careful to note that since homosexual couples cannot get married, they will erroneously be counted as single, even if they are partnered. That’s a problem with the underlying data collected by the census, not the graphic design.
American Community Survey (2006)
Soma, Jonathan. (2008) The Interactive Singles Map
Contextualizing the story about diabetes in New York by including data at the national and global level is quite smart. Sticking with maps to tell the whole story lends consistency.
Comparison maps like this are clearest when their scales are the same. I see no reason that they should be different or why the colors need to be different. In the sense that the scales, in fact, are different, I appreciate the choice to use different colors. At least there’s some visual indication that direct comparison between the maps is not a good idea.
With respect to the graphs, it appears that they are all the same, just different populations, but that is not the case. The city and national data shows prevalence rates but the global data shows mortality, not morbidity. Close readers can figure it out.
Kleinfield, N.R. (2006, 9 January) Diabetes and Its Awful Toll Quietly Emerge as a Crisis The New York Times.
I want to thank Mike Bader, PhD candidate in Sociology at the University of Michigan, for pointing me to these graphics. Please follow his example and send me what you come across.
In another note, for this post you really have to click through and play around with the interactive graphics at Pew or you are going to miss out on the best part.
These graphics are high quality and thoughtful, they offer more the more you look at them and play around with them. I especially like the “net movers” diagram because it works as a static graphic and as an interactive graphic. They are high quality and represent a clear effort on the part of the people at Pew to develop information graphics as a tool of information dissemination. The immigration data they use is all available from the Census Bureau (either from the decennial census or from the American Community Survey) and here that data has been masterfully presented.
The interactive graphic based on the map works the best. Because it offers just a bare minimum of information at first glance – either positive or negative net migration – users are compelled to actively engage. They have to actually move the mouse over the graphic in order to unlock the richness. It’s not a huge hurdle, there aren’t too many people who will be deterred from the interaction because it’s just too much for them, but it does mean that they have to have some kind of motivating question. And asking a question, even it the question is so simple its almost sub-conscious, “what does this thing do?” means that the user is actively looking for an answer. Pedagogically, when learners generate their own questions, they are more likely to retain the information presented. For this reason, most interactive graphics are likely to be better learning tools than would, say, a list of bulleted points about immigration patterns.
I also like the detail available in the pop-out tables. Tables are tricky beasts – they have the ability to offer a great deal of precise data which is generally tantalizing to empirical researchers. It’s tempting to build row on row and column on column of exacting detail in a grid that allows for efficient reference. But tables are much better for answering questions for posing them. If you know what you want to look up, you’re happy to have a table. But if you don’t come to a table full of data with a question, it’s highly unlikely that seeing table data is going to help you generate a question. Think: how often do you pour over the train schedules for distant cities? Probably only when you’re about to travel to those cities. A table as a graphic is about as interesting to readers as a bus schedule for nowheresville. On the other hand, if you happen to live in nowheresville, you are extremely motivated to check the train schedule because it’s imperative that you get on a bus to somewheresville.
This graphic wouldn’t have worked if it were simply a table by state with all the same data that is in the mouseovers. And yet, that same data presented in bite size table format in a rollover is suddenly interesting because the user was internally motivated to find out. Even if the motivation was no more than, “hey, what happens on mouseover?” that is enough to hook viewers into the project going on here. It’s kind of like a modern version of jack-in-the-box. An empty box is so boring one might not even see it. But a box that something is going to pop out of is irresistible. It’s not like the act of mousing over or winding a spring is inherently interesting. Both are boring in themselves. But even a little element of surprise can not only motivate people to act, but can spark a spot of enthusiasm.
The static graphic with the giant arrows floating around the country wastes its 3D quality Does anyone else see something like a giant game of chutes and ladders where emigrants climb up out of one area and then turn into immigrants as they slide down into a new area? Maybe it would be more engaging if little animated people were actually sliding around the country. The interactive graphic offers so much more information than what is contained in the static graphic – let’s face it, all we’ve got is a loose sense that there is internal movement – that I can’t even stand to look at this for more than about ten seconds before I move on to the interactive part. However, static graphics CAN be just as rich, offering just as much deep information, as interactive graphics. This one fails, but there have been many successful static graphics featured. (list of static graphics here – Jaegerman, death penalty, tomorrow’s post)
Cohn and Morin. (2008, December) Who Moves? Who Stays Put? Where’s Home?. Pew Research Center.
Keegan, M. and Rountree, S. (2008) Comings and Goings: Migration Flows in the US Pew Center for Social and Demographic Trends.
