Ideas for the online revolution.

WY Caucus prediction

Using my county-level model and a caucus model (excludes the primaries), which has a much smaller standard error than when I combine primaries with caucuses - I am predicting WY - Sanders: 70.5% / Clinton 29.5% (of the Clinton + Sanders vote).

I am hoping that this will be within 2-3 percent of the final result, but no guarantees. My WI forecast was off by 5.85%.

This is based on race, income, age, sex, old FB like data (could be an issue), education, density, and past election results. It also includes Google Search trends for the last 7 days (the latest 7 days possible).

Wisconsin Democratic Primary Prediction - 2016

I've been updating my county-level model to predict the outcome of the democratic primary.

I recently added a Google search trend variable that uses the last seven days before the election (but not including the actual election date) and is equal to Sanders / (Sanders + Clinton). While Sanders dominates the search engine trends (typically 2:1), there is a strong positive correlation between the percent of searches that he gets and the outcome. As of this past hour, Sanders is getting 73% of the searches in Wisconsin - which is a strong showing.

Improving my Turnout Model - Latest Predictions

Previously I was relying upon Obama presidential 2008 vote to be solely predictive of turnout. However I've now created a turnout model for primaries with additional variables.

When I apply this to my predicted Sanders vote percentages (county-level), I get some small to medium sized changes in my predictions. Old values in parentheses.

Sanders Predicted Vote Share:
CT: 45.2% (45.3)
DE: 39.6% (35.5) <--- biggest change
IN: 53.4% - no change
KY: 44.3% (44.0)
MD: 27.6% (28.1)
NY: 33.1% (33.9)
OR: 71.8% (71.7)
PA: 42.7% (42.5)

Improving my Democratic Primary Prediction Model and Mapping Sanders Support

I'm learning a lot and have made significant improvements to my model.

Notably I've added a turnout variable - and am assuming that turnout will be proportional to Obama's presidential vote in 2012. While this is likely flawed, I don't have a better idea on how to predict turnout.

I added FB likes by county. Interestingly the FB likes by state are still significant.

I created a caucus-only model which has a much smaller confidence interval for its estimates (40% of the general model's interval).


Prediction Models for the Democratic 2016 Primary

Prediction Models for the Democratic 2016 Primary

There are a lot of people creating models for the 2016 primary. I decided to focus on the Democratic primary as it should be easier to predict the outcome in what is primarily a two person race. While I do prefer Sanders over the other candidates, I am currently not planning on voting for him as I prefer to vote for left-wing candidates (Green

My model for predicting the 2016 Democratic Primary/Caucus Results

I'm developing a model for predicting the Sanders vote share in the upcoming Democratic Party primaries and caucuses (2016). Unlike others, I've chosen to create a county level model. Ultimately this can be used to do real-time analysis of votes as they come in on election night. If anyone wants to work on developing this model, I would LOVE to hear from you. I've got an early version of the model (and a real-time "vote swing" analyzer), but it needs work. Notably I need a method for estimating county level turnout so I can translate the county swings into a state wide swing.

Who would Benefit from Rent Control in Philadelphia?

As rent continues to rise at a rate faster than wages and inflation for most Americans, many people think that rent control could help.

Previously, I made a Rent Change Map for Philly that shows that most areas had rent increase faster than inflation between the 2000 Census and the 2009-2013 American Community Survey.

I decided to extend this analysis by looking at which demographic groups might benefit the most from rent control.

Poll Averaging is Science but most Sanders Supporters Don't Use It

For the past five months, my Facebook news has been full of pro-Sanders friends posting stories about how well Sanders is doing in the polls. They systematically cherry-pick the most outlying polls, while ignoring the outliers that go in favor of Clinton.

For instance, they'll pick the recent NH poll that had Sanders 60% to 33% for Clinton, but ignore the two polls that have the race at 46% to 43% and 49% to 43%.

Sanders 2016 Events - A Spatial Justice Test

I used my Spatial Justice Test to do an analysis of the demographics (race and income) of who lives near where Bernie Sanders 2016 events are being organized.

The surprising result is that white people are the least likely of any race group, relative to their percent of the general US population, to be near Bernie 2016 events

Here is the analysis

2015 Philly Election and Increasing Third Party Potential

I wrote a lengthy analysis of the 2015 Philly election and the potential for third party candidates featuring maps, charts, and data analysis!


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