Mapping the March 2014 California Earthquake with ggmap

April 1, 2014
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(This article was first published on rud.is » R, and kindly contributed to R-bloggers)

I had no intention to blog this, but @jayjacobs convinced me otherwise. I was curious about the recent (end of March, 2014) California earthquake “storm” and did a quick plot for “fun” and personal use using ggmap/ggplot.

I used data from the Southern California Earthquake Center (that I cleaned up a bit and that you can find here) but would have used the USGS quake data if the site hadn’t been down when I tried to get it from there.

The code/process isn’t exactly rocket-science, but if you’re looking for a simple way to layer some data on a “real” map (vs handling shapefiles on your own) then this is a really compact/self-contained tutorial/example.

You can find the code & data over at github as well.

There’s lots of ‘splainin in the comments (which are prbly easier to read on the github site) but drop a note in the comments or on Twitter if it needs any further explanation. The graphic is SVG, so use a proper browser :-) or run the code in R if you can’t see it here.


(click for larger version)

library(ggplot2)
library(ggmap)
library(plyr)
library(grid)
library(gridExtra)
 
# read in cleaned up data
dat <- read.table("quakes.dat", header=TRUE, stringsAsFactors=FALSE)
 
# map decimal magnitudes into an integer range
dat$m <- cut(dat$MAG, c(0:10))
 
# convert to dates
dat$DATE <- as.Date(dat$DATE)
 
# so we can re-order the data frame
dat <- dat[order(dat$DATE),]
 
# not 100% necessary, but get just the numeric portion of the cut factor
dat$Magnitude <- factor(as.numeric(dat$m))
 
# sum up by date for the barplot
dat.sum <- count(dat, .(DATE, Magnitude))
 
# start the ggmap bit
# It's super-handy that it understands things like "Los Angeles" #spoffy
# I like the 'toner' version. Would also use a stamen map but I can't get 
# to it consistently from behind a proxy server
la <- get_map(location="Los Angeles", zoom=10, color="bw", maptype="toner")
 
# get base map layer
gg <- ggmap(la) 
 
# add points. Note that the plot will produce warnings for all points not in the
# lat/lon range of the base map layer. Also note that i'm encoding magnitude by
# size and color and using alpha for depth. because of the way the data is sorted
# the most recent quakes in the set should be on top
gg <- gg + geom_point(data=dat,
                      mapping=aes(x=LON, y=LAT, 
                                  size=MAG, fill=m, alpha=DEPTH), shape=21, color="black")
 
# this takes the magnitude domain and maps it to a better range of values (IMO)
gg <- gg + scale_size_continuous(range=c(1,15))
 
# this bit makes the right size color ramp. i like the reversed view better for this map
gg <- gg + scale_fill_manual(values=rev(terrain.colors(length(levels(dat$Magnitude)))))
gg <- gg + ggtitle("Recent Earthquakes in CA & NV")
 
# no need for a legend as the bars are pretty much the legend
gg <- gg + theme(legend.position="none")
 
 
# now for the bars. we work with the summarized data frame
gg.1 <- ggplot(dat.sum, aes(x=DATE, y=freq, group=Magnitude))
 
# normally, i dislike stacked bar charts, but this is one time i think they work well
gg.1 <- gg.1 + geom_bar(aes(fill=Magnitude), position="stack", stat="identity")
 
# fancy, schmanzy color mapping again
gg.1 <- gg.1 + scale_fill_manual(values=rev(terrain.colors(length(levels(dat$Magnitude)))))
 
# show the data source!
gg.1 <- gg.1 + labs(x="Data from: http://www.data.scec.org/recent/recenteqs/Maps/Los_Angeles.html", y="Quake Count")
gg.1 <- gg.1 + theme_bw() #stopthegray
 
# use grid.arrange to make the sizes work well
grid.arrange(gg, gg.1, nrow=2, ncol=1, heights=c(3,1))

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