Dear all,
I have temperature records of nearly 1200 locations in southern Brazil.
I am writing a shiny app that will show an interactive map with the locations
plotted as circles, where the user can click a location to see its temperature
time series.
However, if I show all the locations in the map, it will look really bad, too
cramped.
Therefore, in an attempt to make the map look a bit cleaner, I am trying to
think of an objective way to subset the locations. My initial approach would be
to show only the "largest" locations, i.e. the ones with a population above a
certain threshold.
The problem is: the distribution of the population is so positively skewed that
I am having a hard time determining the optimal cutoff point.
Does anybody here know any tool or method, possibly spatial, that can assist me
with this analysis?
These are the locations I am working with:
#-------------------------------
# Download and summarize
locs <-
read.csv("https://www.dropbox.com/s/ykdd8x1mlc76klt/locations.csv?raw=1";)
hist(locs$Population)
summary(locs$Population)
# Convert to spatial points and plot
require(sp)
coordinates(locs) <- cbind(locs$Lon , locs$Lat)
plot(locs)
bubble(locs,"Population")
#-------------------------------
Thanks in advance,
-- Thiago V. dos Santos
PhD student
Land and Atmospheric Science
University of Minnesota
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