Hi Enrico, Start with county-to-csv.r - that converts the shape files to normal csv files. Then look at thin-all.r, which actually does the thinning - although I see that I've forgotten to source thin-better.csv. I don't have any code to convert it back to into shapefiles because I don't use them myself.
Hadley On Sun, Apr 26, 2009 at 7:54 PM, Enrico Rossi <[email protected]> wrote: > Hi all, > > Thanks for your responses! Dylan, unfortunately I can't install GRASS > (although I'm working on getting it approved). > Hadley, your code sounds very promising. Which bit of code should I > look at? thin? thin-better? Will these methods work with SpatialPoly > objects? > > Many thanks, > Enrico > > On Sun, Apr 26, 2009 at 7:13 PM, Dylan Beaudette > <[email protected]> wrote: >> Hi, >> >> You can use the v.generalize command in GRASS to reduce the complexity >> of vectors like these. >> >> Cheers, >> Dylan >> >> On Sun, Apr 26, 2009 at 3:53 PM, hadley wickham <[email protected]> wrote: >>> Hi Enrico, >>> >>> I have some code to do map generalisation (reducing map resolution >>> without visible loss in detail) at >>> http://github.com/hadley/data-counties/tree/master. It's applied to >>> counties data, but would be trivial (if slow) to modify to work with >>> zip codes instead >>> >>> Hadley >>> >>> On Sun, Apr 26, 2009 at 3:06 PM, Enrico Rossi <[email protected]> >>> wrote: >>>> Hello, >>>> >>>> I have some data at the zip code level, and I'm using the shapefiles >>>> downloaded from the Census TigerLine website >>>> (http://www2.census.gov/geo/tiger/TIGER2008/tl_2008_us_zcta5.zip) to >>>> plot a shaded map of the US. However, the files generated in this way >>>> are enormous, and take a long time to process, even on a fast machine >>>> with lots of memory. I'm wondering if there's a more efficient way to >>>> do this. Maybe rasterize before plotting somehow? >>>> >>>> If anyone on this list has experience working with zip-level data, I'd >>>> appreciate any advice. >>>> >>>> Here's some example code like what I'm doing: >>>> >>>> # This works, and produces a 1.2GB PDF file! After it's done, I can >>>> rasterize it using gs to reduce file size, but it takes almost an hour >>>> library(maptools) >>>> zip<-readShapePoly("tl_2008_us_zcta5") # This takes a while! >>>> val<-runif(length(zip[[1]])) # there are about 32000 zip codes >>>> pdf("zipplot.pdf") >>>> plot(zip,xlim=c(-130,-65),ylim=c(20,50),col=grey(val),lty=0) >>>> dev.off() >>>> system("gs -dSAFER -dBATCH -dNOPAUSE -sDEVICE=png16m -r300 >>>> -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -dMaxStripSize=8192 >>>> -sOutputFile=zipplot.png zipplot.pdf") >>>> >>>> # I've tried plotting directly to png, but it just seems to hang, my >>>> patience ran out after two hours >>>> png("zipplot.png") >>>> plot(zip,xlim=c(-130,-65),ylim=c(20,50),col=grey(val),lty=0) >>>> dev.off() >>>> >>>> # This also takes too long, I never got any output out of it >>>> library(lattice) >>>> zip$val<-val >>>> pdf("zipplot2.pdf") >>>> spplot(zip,"val",xlim=c(-130,-65),ylim=c(20,50),lty=0) >>>> dev.off() >>>> >>>> Many thanks! >>>> Enrico Rossi >>>> >>>> _______________________________________________ >>>> R-sig-Geo mailing list >>>> [email protected] >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >>>> >>> >>> >>> >>> -- >>> http://had.co.nz/ >>> >>> _______________________________________________ >>> R-sig-Geo mailing list >>> [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >>> >> > -- http://had.co.nz/ _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
