basic gis functions and spatial stats in. packages needed today require command will install...

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

• Other Useful Vector Data Packages• > library(maptools)

• > library(shapefiles)

• > library(spatstat)

• > library(splancs)

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

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)

>View(prong@data)

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/cheatsheet.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/