Voting Districts Day 13: Rework and Shapely Saves the Day

I reached an impasse a while back after realizing that I needed a means of determining whether voting districts shared boundaries.  I built the algorithm to cluster the voting districts together, but the clustering fell apart when my initial points were out in the wilderness or towards the end when the process had slim pickings.  Here's an example of my starting points (green Xs) and the final central points (pink diamonds) for each clustered group.  you can see it completely fell apart at the western part of the state.

Enter the Python module Shapely, which has a great touches function where you can see if two voting districts share a boundary, which I can make a requirement before associating a voting district with other districts.

Additionally, shapely does a lot of other great things that makes this process much easier.  One of the big ones is that it allows you to create polygon objects, which has lots of helpful methods, such as finding a centroid, getting the bounds and area, etc.  

So, I'm working on reworking pretty much everything to use the Shapely module.  Stay tuned.

Random Maps of Wake County Demographics

I've been playing around a lot with mapping data recently.  Here are some maps that represent voters in Wake County, North Carolina.  Each map represents a sample of 35000 voters collected from the North Carolina State Board of Elections in October 2013.  Each dot is a single voter and their residential location.

My data was collected from the following sources:

Voter registration information from the NC Board of Elections:  ftp://www.app.sboe.state.nc.us/

Mapping shapefiles from Wake county:  http://www.wakegov.com/gis/services/pages/data.aspx

and NC DENR:  http://portal.ncdenr.org/web/lr/geodetic/official-boundaries

Geocoding the addresses was done by Texas A&M's Geoservices:  http://geoservices.tamu.edu/

A Map of Registered Republicans and Democrats in Wake County

Here's my R code.  I utilized the R rgdal package for creating the maps.

This assumes that you've already got your voter data loaded into R.

roads <- readOGR("c:\\data\\poly\\wake_streets\\streets.shp","streets")

roadmap <- spTransform(roads, CRS("+proj=longlat +datum=WGS84"))

county <- readOGR("C:\\data\\poly\\nc_counties\\NC_State_County_Boundary_NAD83HARN.shp",'NC_State_County_Boundary_NAD83HARN')

countymap <- spTransform(county, CRS("+proj=longlat +datum=WGS84"))

plot(roadmap[roadmap$CLASSNAME == 'INT',],col='black',border='black', lwd=.5,axes=F,xlim=c(-79,-78.2),ylim=c(35.5,36.1))

plot(countymap[countymap$County == 'Wake',], add=T)

plot(roadmap[roadmap$CLASSNAME == 'USHWY',],col='black',border='black', lwd=.5, add=T)

points(vtx[vtx$party == 'REP','lng'],vtx[vtx$party == 'REP','lat'],col = rgb(255,0,0,50,maxColorValue=255),cex=.2,pch=20)

points(vtx[vtx$party == 'DEM','lng'],vtx[vtx$party == 'DEM','lat'],col = rgb(0,0,255,50,maxColorValue=255),cex=.2,pch=20)

title("Registered Wake County Republican or Democrats \n (sample of 35,000) - Oct 2013")

My data was collected from the following sources:

Voter registration information from the NC Board of Elections:  ftp://www.app.sboe.state.nc.us/

Mapping shapefiles from Wake county:  http://www.wakegov.com/gis/services/pages/data.aspx

and NC DENR:  http://portal.ncdenr.org/web/lr/geodetic/official-boundaries

Geocoding the addresses was done by Texas A&M's Geoservices:  http://geoservices.tamu.edu/

Mapping Shapefiles from the State of North Carolina

I've been looking forever on how to use shapefiles originating from North Carolina for making maps.  Normally, I'll get crazy latitude and longitude coordinates if I plot the shapefiles using the default parameters through R's rgdal package.

Tonight, I stumbled upon this StackOverflow post, which shows that TWO functions are needed in order to fully utilize NC shapefiles: readOGR and spTransform.

I've always just used readOGR, which works fine for shapefile originating from other places, like the US Census bureau.  I've always had problems with files from my state, however, until I came across the aforementioned StackOverflow post.

Here's what I put into R to make a simple map of all roads in Wake County:

roads <- readOGR("c:\\data\\poly\\wake_streets\\streets.shp","streets", p4s = CRS("+proj=lcc +lat_1=34.33333333333334 +lat_2=36.16666666666666 +lat_0=33.75 +lon_0=-79 +x_0=609601.2199999997 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=us-ft +no_defs"))
roadmap <- spTransform(roads, CRS("+proj=longlat +datum=WGS84"))
plot(roadmap,axes=T)
title("Roads in Wake County, North Carolina")

This'll produce a map like so:

If you want to try this at home, you can get a bunch of shapefiles from North Carolina's State Board of Elections FTP site that seem to work with this code.

Now that this mystery has finally been uncovered, my mapping options are much improved!