Toronto Elections data with Neo4j and Python part 3 of 3

As the title suggests, this is the home stretch for my 3-part series on Neo4j and Python. This last bit is more Neo4j focussed, with Python doing most of the heavy lifting in the first two posts. Using a 2006 elections contribution dataset, I’ve loaded into Neo4j (2.0 Community Edition) the candidates, contributions, contributor names, postal codes. Additionally, I tried to get the distance between the postal codes for some geocoding. Now to try a few simple queries to test this out. The first one, is to see the top…

Toronto Elections data with Neo4j and Python part 2 of 3

In my last post, I took some campaign contribution data and plugged it into Neo4j using a sweet Python plugin called py2neo. Now we’re going to take that same graph and give it some added value, namely flesh out the geospatial aspect of it. Getting back to the example from last time, if you take a nice close look, you’ll see that there are postal codes. What’s missing? ….that’s right: You have all these postal codes, but you can’t really do that much with them because you don’t have any…