Visualizing 2012 census estimates using CartoDB and Leaflet

I’ve been tinkering around with some new mapping tools lately, and figured I’d put them to good use by displaying the 2011-2012 population estimates released last week by the U.S. Census Bureau. The inherently geographical nature of the census makes it a data set just begging to be mapped.

Rather than the de facto Google Maps JavaScript API V3, I decided to go with CartoDB and Leaflet to see what I could produce.

As I mentioned in a recent post, CartoDB offers an excellent Fusions-esque interface, although it allows for far less front-end customization and requires more beneath the hood programming. Nonetheless, CartoDB can make pretty maps right out of the box, which you can then fully customize using the CartoDB API and basic SQL statements. There’s one caveat, however: The service only allows you to upload 5 tables for free. That could be a dealbreaker for cash-strapped news organizations and freelance data journalists.

Anyhow, I downloaded a .zip shapefile package of all 159 Georgia counties from the U.S. Census Bureau, then brought the package into CartoDB using the service’s default upload interface. Using Excel, I calculated the percent change from the most recent population estimates to last year’s estimates. I then added the resulting values as a column in my CartoDB table, which you can see here.

After playing a bit with the API, I was able to format a diverging chloropleth map from my table with the following style parameters, written using 0to255 to ensure an equidistant color scheme:

#statewidepop {
   line-color:#FFFFFF;
   line-width:1;
   line-opacity:0.52;
   polygon-opacity:1;
}
#statewidepop  {
   polygon-fill:#558740
}
#statewidepop  {
   polygon-fill:#609948
}
#statewidepop  {
   polygon-fill:#6AA84F
}
#statewidepop  {
   polygon-fill:#BECFA8
}
#statewidepop  {
   polygon-fill:#D0D8BB
}
#statewidepop  {
   polygon-fill:#B8CCA1
}
#statewidepop  {
   polygon-fill:#D9DCC4
}
#statewidepop  {
   polygon-fill:#D3BAAF
}
#statewidepop  {
   polygon-fill:#E7D1C5
}
#statewidepop  {
   polygon-fill:#D8A696
}
#statewidepop  {
   polygon-fill:#C36E59
}
#statewidepop  {
   polygon-fill:#BC5942
}
#statewidepop  {
   polygon-fill:#B34027
}
#tl_2009_13_county {
   polygon-fill:#AB2B10
}

Check out the resulting map:

The map above shows the percent change in population from July 2010 to July 2011 in all 159 Georgia counties, as estimated by the U.S. Census Bureau. The darker the green, the higher the positive percent change. The darker the red, the higher the negative percent change. Click on a county to see its percent change.
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Using data-viz to make a wire story stand out from the pack

I’ve been interested lately in finding examples of online-only, collaborative, non-profit newsrooms who’ve utilized the power of data visualization techniques to give added value to stories that otherwise wouldn’t necessarily be unique, and in doing so beat out legacy news organizations who published a text narrative alone. Take, for example, this data-rich story and interactive map displaying statewide testing results published by NJSpotlight Friday. While the news that only 8 out of 10 graduating seniors had passed New Jersey’s current standardized test in 2011 was widely reported across the state last week, including by the Star-Ledger in Newark and by The Press of Atlantic City, only NJSpotlight took advantage of the story’s strong data element to produce a more concise, data-driven visual narrative.
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Overlaying a bubble chart onto a Google map

Others may hate, but I’m a big fan of using bubbles to display data. When implemented correctly (i.e. scaled in terms of area instead of diameter), bubbles can be an aesthetically appealing and concise way to represent the value of data points in an inherently visual format. Bubbles are even more useful when they include interactivity, with events like mouseover and zoom allowing users to drill down and compare similar-sized bubbles more easily than they can in static graphics. So, when I was recently working on a class project on autism diagnoses in New York City, I decided to use bubbles to represent the percentage of students with individualized education plans at all 1250 or so K-8 New York City schools. Continue reading

On Richard Boarman’s “Bubble Trees: The Visualization of Hierarchical Structure”

In his brief two-page paper “Bubble Trees: The Visualization of Hierarchical Structure,” Richard Boardman proposes a new type of interactive presentation of hierarchical data that he calls the bubble tree. To bolster his argument, Boardman points out the difficulties inherent in the traditional “tree” structure, which suffers from the “breadth versus depth” problem by leading to information overload and taking up too much screen real estate. As a solution, he proposes a clickable bubble tree that leads to child and grandchild bubbles. Because of its interactive nature and nested structure, Boardman’s bubble tree would “naturally allow the user to explore and work out relationships for themselves,” he says.

Since the publication of Boardman’s paper, this style of bubbletree has become somewhat ad nouveau in the information design community, with popular JavaScript libraries such as Bubbletree.js putting the creation of complex, hierarchical bubble trees into the hands of the general web development public. As its popular use has demonstrated, Bubbletree.js can be particularly handy when it comes to displaying Open Spending data.

Critique, “French wine map shows the best vintage, from 1978 to 2011”

It’s nearing the end of the week, so what better way to relax than with a good bottle of wine and some leisure reading? Problem is, I’m not very skilled at buying wine that tastes any good. I always end up paying more for the bitter, expensive stuff. Fortunately,  there’s a pretty cool news app for that. The Telegraph UK’s recent interactive app on French wine ratings allows users to browse through the years to see which regions of the country produced the best-tasting wines in each year. With a handy HTML5 slider on the bottom, the user can locate the year of the bottle while in the store, then match that up with the region the bottle was harvested in. Then by mousing over the corresponding region labels, users can get an idea of that year and region’s quality as rated by conniseaurs.

The only thing that concerns me is this app’s use of mapping when mapping was not required. Granted, the geographical component to this topic is very important and likely justifies a map. But at the same time, the map feels bare with only the wine regions colored and the rest of France empty. What stands out more than anything, however, is the app’s use of color. The deep red and pink colors combined with the light green shading not only represents white and red wine visually, but it also gives the app an aesthetically appealing and bright color scheme against the canvas-colored backdrop.

Should data viz be a specialty or a commodity skill in the newsroom?

An interesting question came up at last Wednesday’s Doing Data Journalism (#doingdataj) panel hosted by the Tow Center for Digital Journalism here at Columbia’s J-School: Should there be data specialists in the newsroom, or can everyone be a data journalist? For New York Times interactive editor Aron Pilholfer, who participated in the panel, the question is not so much should everyone do data as will everyone do data. And for Pilholfer, the answer to that question clearly seems to be no:

I kind of naively thought that at one time you could train everybody to be at least a base level of competency with something like Excel, but I’m not of that belief anymore. I think you do need specialists.

I’ve always hated the idea of having technology or innovation ‘specialists’ in a work environment that should ideally be collaborative. So, at first I tended to disagree with Pilholfer’s argument. But what won me over was the reasoning behind his claim. For Pilholfer, it’s not that the technology, human talent or open source tools aren’t there for everyone to scrape, analyze and process data –– in fact, it’s now easier than ever to organize messy data with simple and often free desktop applications like Excel and Google Refine. The problem is that there’s a cultural lack of interest within newsrooms, often from an editorial level, to produce data-driven stories. As Pilholfer says in what appears to be an indictment of upper-level editors for disregarding the value of data,

The problem is that we continue to reward crap journalism that’s based on anecdotal evidence alone . . . But truly if it’s not a priority at the top to reward good data-driven journalism, it’s going to be impossible to get people into data because they just don’t think it’s worth it.

I totally agree, but with one lurking suspicion. As with the top-level editors, many traditional users –– or ‘readers,’ as one might call them –– still at least think they like to read pretty, anecdotal narratives, and tend not to care as much whether the hard data backs them up. In other words, it’s an audience problem just as much as it is a managerial or institutional one. Some legacy news consumers just still aren’t data literate. Because they’re not accustomed to even having such data freely available to them, they don’t even value having it. As the old saying goes, “You can’t miss what you never had.” Yet as traffic and engagement statistics continually confirm, as soon users have open data readily available to them through news apps and data visualizations, they spend more time accessing the data than they do reading the print narrative.

Aron Pilholfer at #doingdataj

Totally agree, but harbor the lurking suspicion that many traditional readers still like to read pretty narratives and don’t care as much if the facts back them up. In other words, it’s an audience problem just as much as it is an editorial one.