Estimating election data down to the block level

Highly granular voting data for the 2016 election was compiled a few years ago and made public online via websites operated by Harvard and MIT. For the first time, this made precinct-level data across the country relatively accessible. "Voter precincts" are fairly small, at about a third of a square mile to one square mile in a typical urban area. But the people living within a given precinct can vary widely. From one neighborhood or subdivision to the next, there are different types of housing for different income levels and family sizes. These differences contribute to different political preferences.

What if we wanted to estimate what is happening at the city block level? You may want to do this when screening blocks that you want to live in, if it is important to you to find the prefect niche for yourself to live where you feel most comfortable. Or, it is valuable for other purposes, like campaign planning. Referring to our favorite diagram, we can see just how much more detail we can get by taking it down to the block level.

Let's get detailed!

​We can get an idea of the block level patterns by using the datasets we developed for the Locate Alpha product. We use location factors and block-level demographics to create a predictive model. See some example results below:

Plano area election results: precinct level

Plano area election results: block-level estimates (single family home blocks only)

​About Spatial Laser

We are building a software-as-a-service offering to help real estate investors in single-family homes form a micro-level investment strategy and avoid making mistakes. We use machine learning to model sales prices, rents, investment returns, risk, and market conditions at the hyper-local level using ranking and scoring. We make it easy and actionable  with maps and color codes.

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