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Quick maps in R

Quick maps in R. Melanie Frazier, NCEAS. Presentation materials here: http://nceas.ucsb.edu/~frazier/RSpatialGuides/ggmap/. Have Lat/Long data?. And, you want to see where they are. Data from the EPA’s WestuRe project: http://www.epa.gov/wed/pages/models/WestuRe/WestuRe.htm.

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Quick maps in R

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  1. Quick maps in R Melanie Frazier, NCEAS Presentation materials here: http://nceas.ucsb.edu/~frazier/RSpatialGuides/ggmap/

  2. Have Lat/Long data? And, you want to see where they are Data from the EPA’s WestuRe project: http://www.epa.gov/wed/pages/models/WestuRe/WestuRe.htm

  3. Many options: But we’ll stick with 2 library(plotKML) library(ggmap)

  4. ggmap Part 1 Download the map raster Part 2 Overlay data onto raster Refer to Quickstart guide:

  5. ggmap: Part 1 (getting the map) Specify coordinates - geocode myLocation <- “University of Washington” - Lat/Long myLocation <- c(lon=-95.36, lat=29.76) - Bounding box (lowerleftlon, lowerleftlat, upperrightlon, upperrightlat) myLocation <- c(-130, 30, -105, 50) [NOTE: glitchy for google maps]

  6. ggmap: Part 1 (getting the map) B. Define map source, maptype, and color maptype = watercolor toner terrain source stamen terrain satellite roadmap hybrid google color can do any of the maps in “bw” osm (and, cloudmade)

  7. ggmap: Part 1 (getting the map) B. Define map source, maptype, and color Scale matters in regard to map source/type

  8. ggmap: Part 1 (getting the map) B. get_mapfunction provides a general approach for quickly getting maps myMap <- get_map(location = myLocation, source = “stamen”, maptype = “watercolor”, color = “bw”) Additional options such as “zoom” and “crop” ?ggmap

  9. ggmap: Part 1 (getting the map) Sometimes get_map doesn’t provide the control needed to get the map you want. In this case, use the specific functions designed for the different map sources: get_googlemap get_openstreetmap get_stamenmap get_cloudmademap

  10. ggmap: Part 2 (overlaying your data) A. Plot the raster ggmap(myMap) B. Get your lat/long point data: myData <- read.csv(“http://nceas.ucsb.edu/~frazier/RSpatialGuides/ggmap/EstuaryData.csv”) C. Add points (ggplot2 syntax) ggmap(myMap)+ geom_point(aes(x=estLongitude, y=estLatitude), data=myData, alpha=0.5, color=“darkred”, size=3)

  11. ggmaps: Part 2

  12. ggmaps: Part 2

  13. ggmap: Additional options

  14. plotKML

  15. plotKML Tutorial: http://gsif.isric.org/doku.php?id=wiki:tutorial_plotkml Load libraries: library(plotKML) library(sp) Convert to spatial dataframe object: coordinates(myData) <- ~estLongitude+estLatitude Provide the projection (just copy this): proj4string(myData) <- CRS("+proj=longlat +datum=WGS84") Make the plot: plotKML(myData, colour="lnEstArea", balloon=TRUE)

  16. Questions Presentation materials here: http://nceas.ucsb.edu/~frazier/RSpatialGuides/ggmap/

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