Given the warnings for today’s winter storm, or lack of panic, I thought it would be a good time to plot the NYC evacuation maps using R. Of course these are already available online, provided by the city, but why not build them in R as well?
I obtained the shapefiles from NYC Open Data on February 28th, so it’s possible they are the new shapefiles redrawn after Hurricane Sandy, but I am not certain.
First we need the appropriate packages which are mostly included in maptools, rgeos and ggplot2.
require(maptools)
## Loading required package: maptools
## Loading required package: foreign
## Loading required package: sp
## Loading required package: lattice
## Checking rgeos availability: TRUE
require(rgeos)
## Loading required package: rgeos
## Loading required package: stringr
## Loading required package: plyr
## rgeos: (SVN revision 348) GEOS runtime version: 3.3.5-CAPI-1.7.5 Polygon ## checking: TRUE
require(ggplot2)
## Loading required package: ggplot2
require(plyr) require(grid)
## Loading required package: grid
Then we read in the shape files, fortify them to turn them into a data.frame for easy plotting then join that back into the original data to get zone information.
# read the shape file evac <- readShapeSpatial("../data/Evac_Zones_with_Additions_20121026/Evac_Zones_with_Additions_20121026.shp") # necessary for some of our work gpclibPermit()
## [1] TRUE
# create ID variable evac@data$id <- rownames(evac@data) # fortify the shape file evac.points <- fortify(evac, region = "id") # join in info from data evac.df <- join(evac.points, evac@data, by = "id") # modified data head(evac.df)
## long lat order hole piece group id Neighbrhd CAT1NNE Shape_Leng ## 1 1003293 239790 1 FALSE 1 0.1 0 <NA> A 9121 ## 2 1003313 239782 2 FALSE 1 0.1 0 <NA> A 9121 ## 3 1003312 239797 3 FALSE 1 0.1 0 <NA> A 9121 ## 4 1003301 240165 4 FALSE 1 0.1 0 <NA> A 9121 ## 5 1003337 240528 5 FALSE 1 0.1 0 <NA> A 9121 ## 6 1003340 240550 6 FALSE 1 0.1 0 <NA> A 9121 ## Shape_Area ## 1 2019091 ## 2 2019091 ## 3 2019091 ## 4 2019091 ## 5 2019091 ## 6 2019091
# as opposed to the original data head(evac@data)
## Neighbrhd CAT1NNE Shape_Leng Shape_Area id ## 0 <NA> A 9121 2019091 0 ## 1 <NA> A 12250 54770 1 ## 2 <NA> A 10013 1041886 2 ## 3 <NA> B 11985 3462377 3 ## 4 <NA> B 5816 1515518 4 ## 5 <NA> B 5286 986675 5
Now, I’ve begun working on a package to make this step, and later ones easier, but it’s far from being close to ready for production. For those who want to see it (and contribute) it is available at https://github.com/jaredlander/mapping. The idea is to make mapping (including faceting!) doable with one or two lines of code.
Now it is time for the plot.
ggplot(evac.df, aes(x = long, y = lat)) + geom_path(aes(group = group)) + geom_polygon(aes(group = group, fill = CAT1NNE)) + list(theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), panel.background = element_blank())) + coord_equal() + labs(x = NULL, y = NULL) + theme(plot.margin = unit(c(1, 1, 1, 1), "mm")) + scale_fill_discrete("Zone")
There are clearly a number of things I would change about this plot including filling in the non-evacuation regions, connecting borders and smaller margins. Perhaps some of this can be accomplished by combining this information with another shapefile of the city, but that is beyond today’s code.
Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.
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