Basic GIS Functions and Spatial Stats in

Packages Needed Today Require command will install packages and any packages it depends on What we need require(sp) require(rgdal) require(rgeos) require(raster) require(spatstat) require(spdep) require(RANN) require(gstat) require(adehabitatHR) require(ROCR) require(SDMTools)

Getting Started with Vector Data Packages: >library (sp) Provides classes and methods for spatial data >library (rgdal) Provides bindings to Frank Warmerdam's Geospatial Data Abstraction Library >library (rgeos) Provides geostatistical functions such as regression and basic shapefile operations Other Useful Vector Data Packages > library(maptools) > library(shapefiles) > library(spatstat) > library(splancs)

Getting Started with Vector Data Loading Sample Data OGR -> OpenGIS Simple Features Reference Implementation Dsn -> “data set name” Cactus Point file >cactus<- readOGR(dsn=getwd(), layer="Cactus") Streams Line File >strm<- readOGR(dsn=getwd(), layer="NHD_Streams") Study Area Polygon >stdy<- readOGR(dsn=getwd(), layer="StudyArea")

Plotting Vector Data >plot ( ) is basic R command Hundreds of ways to customize plots Plotting Our Shapefiles >plot(strm, col="blue") >plot(stdy, add=TRUE) >plot(cactus, add=TRUE, col="red")

Examining Attribute Tables View Command >View(cactus) >View(strm) >View(stdy)

Looking Under the Hood >str(stdy) 'slots' (data, polygons(or lines or coords), plotOrder, bbox, proj4string) These mimic the file structure of a shapefile @data = the *.dbf = the attribute table @polygons/lines/coords = *.shp = the geometries that define the spatial components @bbox = the extent of the object(s) in rectangular form @proj4string = *.prj = the projection file describing how to draw the coordinates in space

Examining Attribute Tables In this example we are retrieving the 5th line from the Stream Shapefile Attribute Table >strm[5,] We can plot this as well >plot(strm[5,]) This logic works for retrieving only the attribute table information. Thus @data is simply a data.frame() associated with spatial features >strm@data[5,]

Basic Spatial Analysis – Intersecting Geometries Intersect geometries Rgeos Intersect the study area streams cropped by the boundary >istrm<- gIntersection(strm, stdy, byid=TRUE) This is a set of streams that do not have attributes associated with them The byid=TRUE maintains each stream as unique feature instead of dissolving. Matching the attributes can be troublesome

Basic Spatial Analysis – Intersecting Geometries The Fix Strip off the 0 >row.names(istrm)<- as.character(sapply(row.names(istrm), FUN=function(x){strsplit(x, " ")[[1]][1]})) Match the attributes from the full set of streams with the intersected streams >istrm<- SpatialLinesDataFrame(istrm, data=strm@data[which(!is.na(match(row.names(strm), row.names(istrm)))),]) >View(istrm)

Basic Spatial Analysis – Intersecting Geometries Plot our new dataset >plot(istrm, col="blue") >plot(stdy, add=TRUE) >plot(cactus, add=TRUE, col="red")

Basic Spatial Analysis – Merge Now that we have practiced adding attributes back to an 'rgeos' derived vector layer We need to actually dissolve these streams into one feature for easier processing in future steps >istrm<- gLineMerge(istrm) The cool thing is that the clipped streams are in-memory. So, if you want to perform other stuff on the results, you can without having to store temporary shapefiles, etc...

Basic Spatial Analysis – Buffering Buffering points >pbuff<- gBuffer(cactus, byid=TRUE, width=1000) Now we have to add the attributes back to the buffers. This is a 1 to 1, so the attributes don't need manipulation >pbuff<- SpatialPolygonsDataFrame(pbuff, cactus@data, match.ID=TRUE) >plot(pbuff, add=TRUE)

Basic Spatial Analysis – Clipping Clip the areas from the study area that fall within the point buffer Rgeos Dissolve the buffered points first >pdis<- gUnaryUnion(pbuff) Get the geometric difference >pdiff<- gDifference(stdy, pdis) #; rgeos >plot(pdiff, col="red")

Basic Spatial Analysis – Building Polygons We are going to expand the study extent to match a buffer size and reduce the edge effect of zonal stats (for later) Let's build a polygon from scratch. To create a true polygon feature class, we need to deal with the spatial hierarchy >xs<- c((bbox(stdy)[1,1]-1000), (bbox(stdy)[1,2]+1000), (bbox(stdy)[1,2]+1000), (bbox(stdy)[1,1]-1000), (bbox(stdy)[1,1]- 1000)) >ys<- c((bbox(stdy)[2,1]-1000), (bbox(stdy)[2,1]-1000), (bbox(stdy)[2,2]+1000), (bbox(stdy)[2,2]+1000), (bbox(stdy)[2,1]- 1000))

Basic Spatial Analysis – Building Polygons Create a single polygon >rstdy<- Polygon(matrix(c(xs, ys), ncol=2)) Create a list of polygons (yes we only have one, but...) >rstdy<- Polygons(list(rstdy), ID="1") Now make it spatially aware >rstdy<- SpatialPolygons(list(rstdy), >proj4string=CRS(proj4string(stdy)))

Basic Spatial Analysis – Building Points Create random points and append them to our cactus points. Use the reduced extent >rp<- spsample(stdy, n=length(cactus[,1]), type="random") Add attribute table to random points >rp<- SpatialPointsDataFrame(rp, data=data.frame(PointID=1:nrow(cactus), Present=0)) Append the random points to the cactus points >cactus<- rbind(cactus, rp)

Basic Spatial Analysis – Building Points View the attributes >View(cactus@data) Map the points >plot(stdy) >plot(cactus, col=ifelse(cactus@data$Present==1, "black", "red"), add=TRUE)

Basic Spatial Analysis – Find distance between 2 geometries VECTOR method Transpose the returned matrix >pdist<- t(gDistance(cactus, istrm, byid=TRUE)) Get the average distance to the streams >mean(pdist) Get the minimum distance to the streams >min(pdist) Add the results to the in-memory point shapefile >cactus@data<- data.frame(cactus@data, StrmDist= pdist[match(rownames(cactus@data), rownames(pdist)),]) >View(cactus@data)

Basic Spatial Analysis – Raster Data First we need to read in the raster data >dem<- raster("DEM_30m.img") >plot(dem) >plot(stdy, add=TRUE, col="red")

Basic Spatial Analysis – Raster Data Now we need to clip it to our study area Crop versus Mask Crop = clip Mask will maintain full extent of original raster >cdem<- crop(dem, rstdy) >plot(cdem) >plot(rstdy, add=TRUE) >plot(cactus, add=TRUE, col=ifelse(cactus@data$Present==1, "black", "red"))

Basic Spatial Analysis – Raster Data Terrain Analysis Terrain Command has several functions Aspect, Slope, Focal, etc Elevation Data should be in Meters Uses 3x3 window Now calculate the slope using the DEM >slp<- terrain(cdem, opt="slope", unit="degrees", progress="text")

Basic Spatial Analysis – Raster Data The raster package is the ability to stack rasters in-memory and perform functions on multiple rasters Each raster must have the exact same extent and cell size. Similar to Raster Calculator in ArcGIS Let’s Stack the slope raster on top of our 30m DEM >rs<- stack(cdem, slp)

Basic Spatial Analysis – Raster Data Creating a moving window/focal raster. Give it mean value of entire raster to reduce edge effects >ms<- focal(rs[[2]], w=matrix(1, ncol=3, nrow=3), fun=mean, pad=TRUE, padValue=3.673, progress="text") >names(ms)<- "MeanSlope" Add to the existing raster stack >rs<- stack(rs, ms)

Basic Spatial Analysis – Raster Data Exacting Raster Values to Points >e<- data.frame(extract(rs, cactus)) Now we can add the results to the in-memory point shapefile >cactus@data<- data.frame(cactus@data, e[match(rownames(cactus@data), rownames(e)),]) >View(cactus@data)

Basic Spatial Analysis – Raster Data Exploratory Statistics Graphing the relationships between point data and terrain data >plot(cactus@data$DEM_30m, cactus@data$slope) Examining correlations between Raster variables >cor(cactus@data$DEM_30m, cactus@data$slope, use="complete.obs") Summary Statistics >summary(cactus@data$DEM_30m) >summary(cactus@data$slope) >summary(cactus@data$MeanSlope)

Basic Spatial Analysis –Telemetry Repeated Measurements/GPS Collar Data: Homerange Analysis and Trajectories Load in pronghorn dataset >prong<- readOGR(dsn=getwd(), layer="ph10") Use a DEM to provide spatial context >pdem<- raster("DEMph.img") Make a map >plot(pdem) >plot(prong, add=TRUE)

Basic Spatial Analysis –Telemetry To perform many analyses, we need an accurate date-stamp. Just so you know, dbf files cannot store the date and time in same field. >View(prong@data) Date and time in funky format, we can fix this Create a vector combining the date, hour, min and second >dt<- paste(prong@data$DATE, " ", prong@data$HOURS, ":", prong@data$MINUTES, ":", prong@data$SECONDS, sep="") Convert to the R date/time format >dt<- as.POSIXct(strptime(dt, format="%Y.%m.%d %H:%M:%S", tz="UTC")) We choose UTC as the time zone so as to ignore DST while performing a calculation. Add the new column to our attribute table >prong@data<- data.frame(prong@data, TelemDate=dt)

Basic Spatial Analysis –Telemetry A great thing about R is the ability to iterate through rows of data and perform calculations. Determine the number of minutes between each relocation event using a loop Start at the 2nd row of data because we don't know anything prior to the 1st row of info Create a new column to store values >prong@data$TimeLag<- 0 >for(i in 2:nrow(prong)){ #start at 2nd row prong@data$TimeLag[i]<- round(as.numeric(difftime(prong@data$TelemDate[i], prong@data$TelemDate[(i-1)], units="mins"))) }

Basic Spatial Analysis –Telemetry Now value for 2nd row to the first row >prong@data$TimeLag[1]<- prong@data$TimeLag[2] >View(prong@data) >str(prong@data)

Basic Spatial Analysis –Telemetry Create a trajectory >l<- as.ltraj(xy=coordinates(prong), date=prong@data[,"TelemDate"], id=prong@data[1,"ID__"], burst=prong@data[1,"ID__"]) >plot(l) Convert trajetory to a line >tl<- ltraj2sldf(l) Give it the same projection >proj4string(tl)<- proj4string(prong) >plot(tl) Simplify the line >sl<- gSimplify(tl, tol=6000) #rgeos package >plot(sl, add=TRUE, col="red")

Basic Spatial Analysis –Telemetry Calculate home ranges Start Minimum Convex Polygon (MCP) >cp95 <- mcp(prong, percent = 95) Plot the results >plot(pdem) >plot(prong, add=TRUE, col="green") >plot(cp95, add=TRUE, border="red")

Other Useful Sites Cheat Sheet http://www.maths.lancs.ac.uk/~rowlings/Teaching/UseR2012/che atsheet.html Spatial data with R http://spatial.ly/r/ CRAN: Analysis of Spatial Data http://cran.r-project.org/web/views/Spatial.html Maps with R http://procomun.wordpress.com/2012/02/18/maps_with_r_1/