Cross-posted from Energy Justice
I recently updated the Income Layer on Justice Map to use the latest 2011-2015 American Community Survey data.
You can view the data by county or by census tract (roughly 4000 people). While census tracts provide higher resolution that helps us identify areas of environmental injustice, unfortunately the confidence interval is much larger. So there is a lot of noise in the data. If you are looking at the income layer and see a random checkerboard of blue and red - that is noisy data.
As part of this process, I added a Income Change layer that shows the change in median household income between the first period (2006-2010) and a second period (2011-2015).
It is easiest to see the trend in changing income by looking at the counties. The census tract data is even noisier than the regular income data, as the confidence interval is approximately twice as large. However where there are strong trends - like gentrification in DC and Philadelphia you can easily see them at the tract level.
A couple comments on Trump's presidential election win.
1. Clinton won the popular vote by an estimated 2.09%. This election was the results of a very recent bias in the Electoral College that favors Republicans. A very small percent of the US population are swing voters (5-10%) and a small shift can result in a large change due to our winner-take-all system. There isn't necessarily a need to do analysis of "Why did Trump Win / Clinton lose" or to change our fundamental priorities.
In the 2016 general presidential election, the Green party candidates for State Auditor and State Treasurer beat the Libertarians (including the well-known Gary Johnson). But presidential candidate Jill Stein did not fare as well due to strategic voting against Trump.
I live right next to the center of Green Party support in West Philly!
Category Breaks used in the maps: 0.25%, 0.5%, 0.75%, 1%, 1.5%, 2%, 2.5%.
Click on the image for a high resolution one.
Early findings on 2016 Presidential Election
I have a county-level model that I used to predict the outcome of the Democratic Presidential Primary ahead of the election, and also a real-time model that estimated the state-wide swing based on the county swings. I used this model to make a significant amount of money betting/predicting the outcome on PredictIt.org. I also created my own real-time model for the presidential general election and used that (and the NYT real-time model) to make more money on PredictIt.
A couple months ago I made this property level map of
Philly Housing Age
A couple areas of the city are missing data (in my data source).
I'm trying to make a higher resolution map of median household income. The Census Bureau releases income data by census tract (around 4000 people) and also by block group. Relatively few people use block groups because the confidence interval is too high. Even when you pool five years of data from the American Community Survey the confidence interval for census tracts (which have about 3 times more people than block groups) is high (and can be too high to use).
I made a new map of Philadelphia Exterior Housing Condition using the Office of Property Assessment data (2015).
It is part of a series of high resolution maps I've made for Philadelphia using property level data.
It is interesting to see what parts of the city have new construction, rehabbing, and vacant and/or uninhabitable properties.
I've been following Bitcoin since June 2011. You might be interested in my Bitcoin trading thread. Recently the Bitcoin price has soared to $700 which is a two year high. Most notably in the past three weeks it went from $450 to $700.
My model has this upcoming DC Democratic Primary at around Clinton 88 / Sanders 12.
Here are my predictions for the June 8 Democratic presidential primaries.
This is from my county-level model which includes past election results, age groups, sex, education, FB likes, Google searches, density, income, caucus/primary, whether 17 year olds can vote, and more.