I am working with spatial data in ggmap, generally with great success. I have a
huge data set with the coordinates in NAD 83 UTM Zone 11 (meters). To map the
data the coordinates were converted to Lat Long in GIS prior to use in R and
ggmap/ggplot. I am using hexagonal binning to aggregate the data :
#create bins and calculate stats
hb<-hexbin(DF$lon,DF$lat,xbins=80,IDs=TRUE)
hb.avg<-hexTapply(hb,DF$Res,mean,na.rm=TRUE)
hb.mx<-hexTapply(hb,DF$Res,max,na.rm=TRUE)
hb.p80<-hexTapply(hb,DF$Res,quantile,prob=0.80,na.rm=TRUE)
#create df for ggplot
hx_dat <- data.frame(hcell2xy(hb), count = hb@count,
xo = hb@xcm, yo = hb@ycm, Mean=hb.avg,Max=hb.mx,
p80=hb.p80)
#Base Map
#BBox is the bounding box
Base<-get_map(BBox,source='google')
m_hx<-ggmap(Base,legend = "bottom", base_layer=ggplot(aes(x=x,y=y),data=hx_dat))
#Map of means
a<-0.55
hc<-'grey60'
m_hx+geom_hex(aes(x = x, y = y, fill = Mean),
color = hc, ,alpha=a,stat = "identity") +
scale_fill_gradientn("Mean",colours=rev(rainbow(4)),trans='sqrt')
...and so on for other stats
I can also run statistical analyses on hx_dat.
By creating hexbins based on lat/long it seems there will be distortion due to
the differences in length of a degree at different locations on the earth's
surface. What is the most efficient way to eliminate this distortion? Should I
run hexbin in NAD83 and convert the x/y coordinates to Lat Long? Can I get
ggmap to convert the baselayer to NAD84 and just do everything in NAD(my
preferred option)?
I have tried converting Lat Long to NAD84 and back but the coordinates are
coming up in the eastern Pacific and not in California, so I am missing
something and I am not sure that is the best way to solve the problem anyway.
Thanks in advance, any help is greatly appreciated
Mike
Michael J. Bock, PhD | Senior Manager
mb...@environcorp.com
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