This week’s Digest features maps displaying data on global, state and local levels. But the fact that they’re maps is about all that these three great data visualizations have in common. The exact data they expose is – pardon the pun – all over the map: it ranges from global shopping obsessions to highway roadkill to county-by-county demographics. We’ll start big and zoom in.
Buy it Now: What we crave depends on where we are. One way to track that (if not prove it) is to look at Google search auto-complete results: Type “How much does * cost?” and see how Google fills in the asterisk. That’s what cost estimating website Fixr has done for many countries to create the map above. The result isn’t scientific, but it’s a lot of fun. (Click through for continent-by-continent maps and analysis by Fixr Director of Content Development Raul Amoros.) The maps group and color-code the results into broad categories like education, real estate, and electronics, but each country’s specific “cost obsession” is named. Some highlights: Russians want the fare to fly a MIG, Argentinians are curious about the price of beer, and Japanese are searching for the cost of watermelon. Thanks to Michael Singer (whose input is priceless, by the way) for suggesting this item.
On the Road: CROS, the California Roadkill Observation System (yes, there is such a thing; Maine has one, too) collects and shares data on where wildlife-vehicle collisions happen. Run by UC Davis, CROS uses the data “to inform policy, management, and financial investment in reducing road-kill.” Using information supplied by an army of roadkill reporters (you can join them using an Android application), CROS applies GIS and statistical modeling techniques to create an interactive map showing where our fenders found our furry and feathered friends. Zooming, clicking and sorting this data is morbid, but fascinating.
Demo Graphics: Frank Bowers has brought together a variety of statistics about the Bay Area in one interactive map using D3. Income, race/ethnicity, place of birth, age, and housing costs are all included. Bowers’ map (screenshot above; click through for the interactive version) draws on data from the IRS, the US Census and its American Community Survey, Zillow, and MyApartmentMap. If you want to see how the map was created, all the data sources and source code are shared on GitHub.
Wanted Skills (bonus item): “It’s no surprise that tech pros skilled in data analytics and building apps continue to be in high demand by employers,” says tech career website Dice.com. “That being said, some skills are more frequently requested than others.” Hot skills, according to Dice’s online job postings, are shown in the chart above. The static chart does a good job of displaying a lot of data in a concise, well-organized format: the length of the green bar varies depending on salary, while the length of the white bars on the right show (and sort) the various skills by the rise demand from 2013 to 2014. In all cases the underlying numbers are easy to read. Nicely done.
Like what you see? Every Friday we share favorite examples of data visualization and embedded analytics that have come onto our radar in the past week. If you have a favorite or trending example, share it: Submit ideas to blogactuate@actuate.com or add a comment below. Subscribe (at left) and we’ll email you when new entries are posted.
Recent Data Driven Digests:
April 17: Rage in the Iliad, comparing country sizes, 25,000 songs
April 10: Stick charts, water for food, UFO sightings, dataviz videos