Archives for posts with tag: Google Fusion Tables

Much wailing and gnashing of teeth have greeted this week’s Associated Press style change on when to abbreviate and when to spell out state names. Read on for a mnemonic inspired by the Beastie Boys.

The AP memo reads: “Effective May 1, the AP will spell out state names in the body of stories. Datelines will continue to use abbreviations.”

Here’s my mnemonic: Spell out “Illinois” in the body of a story. But in datelines, captions and party affiliations, you’re still licensed to “Ill.”

Now if only there weren’t two kinds of state abbreviations to choose from. As long as we’re simplifying style rules, why not choose one kind of state abbreviation? We have “Ill.” unless you have a mailing address, in which case “IL” is required.

I’m not writing this to complain; I think AP’s rationale of seeking efficiency is laudable. AP style is always evolving. Maybe the next step will move further toward simplicity and uniformity of rules.

If we move to using just one set of state abbreviations (and I’m sure a lot of people would hate this because it just doesn’t look right), I’d opt for the postal abbreviation.

Why?

I confess my thinking is influenced by understanding how the Google Fusion Tables application works. It understands either spelled-out state names (Kansas, for instance) or two-letter, capitalized postal abbreviations (KS). “Kan.” does not exist to Google Fusion Tables.

If you try to import a spreadsheet with geocoded data and merge it with a KML file to create a heat map, you’ll get the geocoding equivalent of the Millennium Falcon’s hyperdrive not working again: You expect to lurch into the hyperspace of data visualization, but the result is a disappointing lack of heat map. Going postal might be ugly to look at, but what works, works.

It’s interactive data map time! This one’s a choropleth map charting each state’s combined state and local sales taxes for 2012. Data are from the Tax Foundation.*

The tutorial I developed for this data set for class today is based on the Mu Lin Multimedia Journalism Blog’s tutorial on Google Fusion Tables for beginners, in case you’re wondering where to start.

While I let Dr. Mu Lin’s post do the heavy lifting Tuesday, my contribution today was in guiding the class through some critical thinking about how to apply Fusion Tables, their understanding of how to interpret data, use color to create a clear visual order, and online usability.

How do you make sense of data by categorizing them? That question begets another: Am I trying to illustrate the range of a single set of data, such as population densities? Then using a range of greens or oranges or blues might be the way to go.

How do you select a color range that establishes a clear visual order? It’s a challenge to set aside our individual preferences and pick colors that help the data make sense. I love blues, greens and purples in art, but I have to set those preferences aside in favor of asking how to use colors to make outliers stand out or how to how to show gradual difference among related information.

How do you take advantage of interactive data mapping’s strengths vs. the limitations of static design for the print world? For instance, it might not be necessary to have the names of all states in a dynamic interactive map online because the name will appear in the popup box with data relevant to that state when the user clicks on it.
Of course, you also must ask how to make the map usable to everyone. People who have colorblindness have a right to read information online, too. I don’t know a lot about color blindness, beyond the fact that red-green is the most common form of it.

If you require guidance on such decisions, as I do, a site called Color Brewer 2: Advice for Cartography has an interactive color picker. You can select the number of categories, or “buckets,” you’re using, whether you need sequential, divergent or qualitative range of colors, and any special considerations such as publication for people with color blindness, for printing, or for photo copies.

Footnote
* I am aware of criticism of the Tax Foundation. Economist Paul Krugman has called it “not a reliable source” if you’ve done a little fact checking on its demand to lower American corporate taxes. Interpretation is one thing; which data are used is another, and the raw numbers look sound. What we’re doing here is exploring it from an angle that affects everyday Americans who make retail purchases for everyday needs: combined base state taxes when combined with the average local sales tax in each state.

Corporations get a lot of attention when it comes to taxation because their decisions affect a lot of people, and their money gives them media attention and access to power. Journalists need to watch out for the interest of those who lack these advantages. So that’s why we’re looking into sales taxes in each state.

One limitation of this data set is that it comes from 2012. Lots of cities in Alabama raised local sales taxes in 2013. That’s not reflected in this exercise. Anybody want to take on the task of creating an updated one?

Google Maps Engine Lite is a great tool for building uncluttered, functional, interactive online maps if you don’t have a lot of artistic talent or technical skills. Here are a few things you can do with it:

  • Locator maps: Students in Multimedia Journalism this week will follow a step-by-step visual guide in class on how to create a geospatial data map of the top vote-getters in a poll by the Kansas City Star. Here’s what they’re aiming to create to prepare to make their own maps to post on their blogs; it might look familiar since I blogged about the Kansas City barbecue map yesterday:

    Made with Google Map Engine Pro

    Made with Google Map Engine Pro

  • Map routes: Robb Montgomery used Google Maps Engine to show how to get to an outdoor cinema in Berlin over a couple of different routes by searching for directions and drawing routes.
  • Layered geolocated data charts: You can import spreadsheets of data organized by location (e.g., state, county, other geographic boundary) in one column and data for each location in the second column. The result is a map with pins the reader can click on to see the data. The New Haven Register used this to map crimes in the city of New Haven, Conn.

That last option is not the easiest way for readers to visualize data if the info in question involves rankable rates or numbers by geographic region. If that’s what you have to work with, what you really want to make is a choropleth.

Don’t be afraid; that’s just a fancy name for a heat map, a way of presenting data that is color coded from most to least, best to worst, etc. Doing this can help readers visually recognize the rank order of each region.

To make a chloropleth, you need to use Google Fusion Tables, which I’ll blog about later this week. Google Fusion Tables can be used to create heat maps by merging a spreadsheet containing state-by-state (or county-by-county, or country-by-country, you get the idea) data with the geographic outlines of their corresponding geographic areas.

I’ve settled back into the routine in Auburn now after spending three days conventioneering at the Online News Association conference in Atlanta. This will be a brief post since I have a ton of grading to return to, but these are my main takeaways:

  • The technological wonders never cease for info gatherers: From the fledgling journalism drone programs at the University of Nebraska-Lincoln and the Missouri School of Journalism to wearable sensors, innovative means of gathering information are popping up at every turn. The next challenge is figuring out how news organizations can put them to use (as well as figuring out how to fight government efforts to curb our adoption of these technologies, which Matt Waite of Nebraska explained in detail at the Knight Village on the convention’s Midway).
  • Nor do the possibilities for sharing data visually: I came out of ONA13 with a renewed enthusiasm for the integration of visuals with data and in a fit of irrational exuberance, I signed up for Alberto Cairo’s current MOOC on infographics and data visualization out of the Knight Center for Journalism in the Americas. I’m two weeks late into a four-week course, but he kindly assured me I didn’t need to worry about doing the homework since a lot of folks just sign up to see the course materials. I deeply appreciate his willingness to share since I’ll incorporate some of it into the multimedia journalism course I teach in the spring. This will give me a sense of best practices to apply to the stuff I learned last week about using TileMill and Google Fusion Tables for mapping data.
  • Collaboration is king: Journalists don’t have to be coders, and coders don’t have to be journalists. But it sure does help if we know each other’s language, values and guiding principles. I’ll be collaborating with a team of Auburn University coders and reporting students on a hackathon next month. Do I know code? Only in the most rudimentary way, though I’m learning more all the time. But I have done research about journalism and migration, which happens to be where the team needs expertise since that’s the subject of the hackathon. Right place, right time, right connections.
  • Journalists must master data or data will master them: The highlight of the convention for me was the Friday keynote address by Nate Silver of FiveThirtyEight. His topic: Eight Cool Things Journalists Need to Know about Statistics. So many people live-tweeted about it at the event, myself included, that it made sense to make a Storify story about it. The link is below. I hope you enjoy it!
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