1. Basic GIS Functions and Spatial Stats in

2. 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)

3. 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)

4. 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")

5. 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")

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

7. 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

8. 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.
is simply a data.frame() associated with spatial features

9. 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

10. 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, row.names(istrm)))),])
>View(istrm)

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

12. 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...

13. 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, match.ID=TRUE)
>plot(pbuff, add=TRUE)

14. 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")

15. 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))

16. 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)))

17. 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)

18. Basic Spatial Analysis – Building Points
View the attributes
Map the points
>plot(stdy)
>plot(cactus, "black", "red"), add=TRUE)

19. 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
StrmDist= rownames(pdist)),])

20. 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")

21. 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, "black", "red"))

22. 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")

23. 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)

24. 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)

25. 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
rownames(e)),])

26. Basic Spatial Analysis – Raster Data
Exploratory Statistics
Graphing the relationships between point data and terrain data
Examining correlations between Raster variables
use="complete.obs")
Summary Statistics

27. 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)

28. 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.
Date and time in funky format, we can fix this
Create a vector combining the date, hour, min and second
>dt<- " ", ":", ":", 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
TelemDate=dt)

29. 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 0
>for(i in 2:nrow(prong)){
#start at 2nd row
units="mins"))) }

30. Basic Spatial Analysis –Telemetry
Now value for 2nd row to the first row

31. Basic Spatial Analysis –Telemetry
Create a trajectory
>l<- as.ltraj(xy=coordinates(prong),
>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")

32. 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")

33. Other Useful Sites Cheat Sheet
atsheet.html
Spatial data with R
CRAN: Analysis of Spatial Data
Maps with R