The students, working journalists and professors, were quite impressive. Working in groups, they created several data-driven projects of their own — a few of which were publishable after just a few days’ work.
For a mapping exercise, students identified several locations by latitude and longitude.
Students demo their project related to Chinese perception of the U.S. presidential election.
I love China, and it was a real honor to be invited to work with such talented group. 谢谢
To start, I grabbed a simple test data set — five months of geocoded major crimes in D.C. from January to May this year — to check out some features. One I like is allowing users to query their data in the browser-based interface and filter for specific types of records.
Here, for example, I narrowed the map to show just thefts:
Assaults with deadly weapons:
Thefts from vehicles:
I made these maps in less than five minutes, so I’m sure there are much more useful stories to tell with the tool. There are also many, many features I didn’t explore, like the ability to style the map using Carto, the CSS-like language, rather than the UI.
Anyway, give it a shot, and let me know what you build.
Following Nathan Yau’s excellent tutorial for creating heat maps with time series data (he used vehicle accidents by day for a year), I visualized 3,559 of my tweets back to March 2009.
These maps, created with a modified R script from the tutorial, show how often I sent tweets (both personal and RT), with darker shades representing more activity. It’s fun to go back to the dark days and recall what sparked flurries of tweets: