![]() There’s plenty of data in this dataset to splice and dice. NextĪre you a data-nerd and like pinball? Stay tuned. I tweaked the distance decay parameters a bit to get a heat map that made sense for the distribution of my points and the area of interest (i.e., the entire U.S.). I simply put the x,y coordinates of all the pinball locations with a count of pinball machines in each establishment as the “heat” value. Chris Love put together a really useful macro to do nearly all the work here. Alteryx is an excellent spatial data tool, and it’s possible to set up the heat map data in Alteryx to display in Tableau. Though, the data prep can be a little cumbersome, even in traditional GIS tools. Heat maps are fun and a good way to visualize relative geographic distribution of a phenomena. This download is real handy if you need quick access to 2010 Census tables and geometry but don’t want to mess around with Census American Factfinder. For you Alteryx users out there, you don’t need the spatial data package to access 2010 Census data with their Allocate tools. I also use Metropolitan Statistical Areas for my unit of analysis. In the viz, I calculate pinball machines per capita, which requires the inclusion of Census population data. Clip the points to the boundary of the U.S.Spatial match (or spatial join in the GIS world) the pinball locations to Census CBSA’s to bring in population data and provide a geographic unit of analysis.Cross tabulate the data to get a unique record for each pinball machine.Point to the directory with the 93 JSON files and union together with Alteryx’s Wildcard feature inside the Input tool.I needed to spatial join (or spatial match) pinball locations to Census geometry and population values.The JSON format played well the Alteryx’s JSON parser.Here’s what that looks like:īy all means, the data could be prepped in Python. Though, for a few reasons, I turned to my favorite data Swiss army knife, Alteryx. So, I wrote a quick python script to find all the regions and programmatically download all regions. ![]() I could take the time to download each region manually, but that’s way too much work. The data is divided into 93 separate regions, which can be individually downloaded. I used a combination of Python and Alteryx for the data prep – Python to get the data and Alteryx for the rest.
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