After wrangling, mangling and otherwise massaging the data in our SF Beats dataset, here’s the first offering via Palladio. The map shows the connections between the birthplaces of Beat figures and San Francisco. This is a pretty cool map. But, what I discovered: locations have to be geocoded to be plotted by Palladio. This means: first, geocoding the locations (via a script I found for Google sheets), downloading the Google sheet into Excel, merging the longitude and latitude columns, adding in a column with the geocoded location for San Francisco (37.7749295, -122.4194155), cleaning up data to make sure that all entries are in the proper (lat, long) format, etc. In other words, you may have the data and it may be very good, but you might have to translate the data into terms that will work for your visualization app.
Here’s a Google Fusion-ized map of our SF Beats data. The map shows the places of education for 37 of our SF Beat figures. Google Fusion will geocode locations (e.g. Cambridge, Mass, etc.) automatically. Also, notice the little card that pops up for Catherine Cassady. That’s nice. However, Google Fusion doesn’t seem to allow as much sorting or customizing in your visualization – – as in Palladio’s “Connected Points” tile. Perhaps there are extensions that add these things to the Fusion maps.
Here, I’ve fed our SF Beats dataset into the Silk platform. This produces a pretty nice pie chart showing the years in which our Beats arrived in SF. Lesson learned: each platform (Palladio, Silk, Fusion, etc.) has different expectations about the organization and form of your data. You’ll have to monkey around a bit – – adjusting the format of your data in Excel or Google sheets to match the expectations of the platform or app that you use.
I’ve just started to really explore Tableau – – which looks like one of the most powerful of the visualization tools.