Testing Spatial Equilibrium Concepts With tidycensus

2018-07-16

Let’s take the model to the data and reproduce figures 2.1. and 2.2 of “Cities, Agglomeration, and Spatial Equilibrium”. The focus are two cities, Chicago and Boston. These cities are chosen because both differ in how easy is to access to their city centers. Chicago is fairly easy, Boston is more complicated. Our model then implies that gradients then should reflect the differential costs to access the city centers.

So let’s begin, the first step is to get some data. To do so I’m are going to use the “tidycensus” package. This package will allow me to get data from the census website using their API. We are also going to need the help of three other packages: “sf” to handle spatial data, “dplyr” my go-to package to wrangle data, and “ggplot2” to plot my results.

``require("tidycensus", quietly=TRUE)require("sf", quietly=TRUE)require("dplyr", quietly=TRUE)require("ggplot2", quietly=TRUE)``

In order to get access to the Census API, I need to supply a key, which can be obtained from http://api.census.gov/data/key_signup.html.

Read on for theory and a test.  H/T R-bloggers

Data Science And Data Engineering In HDP 3.0

2018-10-17

Saumitra Buragohain, et al, show off some of the things added to the Hortonworks Data Platform for data scientists and data engineers: We leverage the power of HDP 3.0 from efficient storage (erasure coding), GPU pooling to containerized TensorFlow and Zeppelin to enable this use case. We will the save the details for a different […]

Using wrapr For A Consistent Pipe With ggplot2

2018-10-16

John Mount shows how you can use the wrapr pipe to perform data processing and building a ggplot2 visual: Now we can run a single pipeline that combines data processing steps and ggplot plot construction. data.frame(x = 1:20) %.>% mutate(., y = cos(3*x)) %.>% ggplot(., aes(x = x, y = y)) %.>% geom_point() %.>% geom_line() %.>% ggtitle("piped ggplot2") Check […]

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