Using open data to find the perfect home

Posted to Maps  |  Tags: , ,  |  Nathan Yau

Justin Palmer and his family have lived in a dense urban area of Portland, Oregon for the past seven years, but now they’re in the market for somewhere more spacious. He narrowed his search down to two main criteria — walking distance to a grocery store and walking distance to a rail stop. The search began with open data.

I defined walking distance as ~5 blocks, but ~10 blocks is still a pretty sane distance. I want to be close to a grocery store and close to a MAX or Streetcar stop. Unfortunately, none of the real estate applications I tried had a feature like this so I decided to create what I needed using open data that I had already been working with for some time now.

Code snippets and explainers follow for how Palmer found his target zones, using a combination of the data, a database, and TileMill.

Favorites

Jobs Charted by State and Salary

Jobs and pay can vary a lot depending on where you live, based on 2013 data from the Bureau of Labor Statistics. Here’s an interactive to look.

Where Bars Outnumber Grocery Stores

A closer look at the age old question of where there are more bars than grocery stores, and vice versa.

Best Data Visualization Projects of 2016

Here are my favorites for the year.

Divorce Rates for Different Groups

We know when people usually get married. We know who never marries. Finally, it’s time to look at the other side: divorce and remarriage.