1. Advanced Spatial Methods in R
Michael Mann
George Washington University
Department of Geography

2. Overview Review Basics Setting up Space-time data Space-time plots
library(RasterVis)
library(plotKML)
Vector & Other Data Visualization
library(ggplot2)
Mapping ggplot2 visualizations
library(ggmap)

3. Before we begin Best places for help http://stackoverflow.com/
Question & Answer form. Quick & high quality responses
?function_name
Look up help files for a function from any library
Similar to stackoverflow, but targeted to spatial community
The fridge – grab a beer and spend some time

4. Interpreting My Slides!
Inputs into command line
Outputs Notes
Data is in the MMSpatialData folder
s = c("aa", "bb", "cc", "dd", "ee") s
[1] "aa” "bb” "cc” "dd” "ee"
An important note

5. Your location Quick survey
Please raise you hand if (before today) you have never used R or a similar language.

6. Let’s even the playing field
Beginners
Please look around you. Move if there is a beginner cluster!
Experts
Put on your teaching hats! Remember how difficult this material is. Make sure to help your teammates!

7. Overview Indexing a vector Indexing a data.frame Helpful Functions
s = c("aa", "bb", "cc", "dd", "ee")
s = data.frame(col1=c(1,2,3),
col2=c(5,6,7))
s[1]
s[c(2,5)]
col1 col2
[1] ‘aa’
[1] ‘bb’ ‘ee’
s[2,1]
s[,’col2’]
s[c(2:5)]
[1] 2
[1]
[1] ‘bb’ ‘cc’ ‘dd’ ‘ee’
vector_name[ position_# ]
vector_name[ row#, col# ]

8. Objective 1 – Raster space-time plots
library(raster)
Raster Stacks & Bricks
Multidimensional raster objects
Multi-layer (red,green,blue)
Multi-dim (time series, multi variable)
Indexing Your Data
stack[row# ,col#, Z#] Y Z X

9. Objective 1 – Raster space-time plots
Multi-layer Raster ‘Brick’
b <- brick(system.file("external/rlogo.grd", package="raster)
plot(b)
Brick[X,Y,Z]

10. Objective 1 – Raster space-time plots
Multi-layer Raster Brick
plotRGB(b, r=1, g=2, b=3)

11. Objective 1 – Raster space-time plots
Multi-dimentional Raster Stack
Good for time-series of rasters, or multivariate analysis
Time Series
Multivariate
stack[x,y, c(cwd ) ]
stack[x,y, c(crime,pop) ]

12. Objective 1 – Raster space-time plots
Multi-dimentional Raster Stack
Good for time-series of rasters, or multivariate analysis
Time Series
Multivariate
stack[x,y, c(cwd ) ]
stack[x,y, c(crime,pop) ]
Use: Data Visualization
Use: Regression Analysis, modeling

13. Task 1.1 – Setup Raster Stack Data
Helpful Functions
dir()
grep()
dir(‘C://SESYNC//data’)
grep(‘a’, c( ‘a’, ‘b’, ‘c’, ‘a’ ) )
[1]
grep(‘c’, c( ‘tab’, ‘car’, ‘bat’ ) )
[1] 2
[1] ‘data.zip’ ‘ggmap vinette.pdf’….

14. Task 1.1 – Setup Raster Stack Data
Helpful Functions – Using grep to index a vector Find the location of a element with ‘c’ in it Query vector s with the grep
s = c("aa", "bb", "cc", "dd", "ee")
grep(‘c’, s )
[1] 3
s[ grep(‘c’, s ) ]
[1] ‘cc’

15. Task 1.1 – Setup Raster Stack Data
Helpful Functions – Using grep to index a vector Find the location of a element with ‘c’ in it Query vector s with the grep
s = c("aa", "bb", "cc", "dd", "ee")
grep(‘c’, s )
[1] 3
TRY THIS!
s[ grep(‘c’, s ) ]
[1] ‘cc’

16. Task 1.1 – Let’s get started!

17. Task 1.2 – Create Raster Stacks and Assign Name Labels
Helpful Functions
paste()
names()
paste(‘Hi',c('Bill','Bob', 'Sam'), sep=' ')
names(rstack) = c(‘test2001’,’test2002’)
[1] “Hi Bill" “Hi Bob" “Hi Sam"
paste(’aet',c(’2001',’2002’), sep=’_')
[1] "aet_2001" "aet_2002"
test2002
test2001

18. Task 1.2 – Create Raster Stacks and Assign Name Labels
Helpful Functions
paste()
names()
paste(‘Hi',c('Bill','Bob', 'Sam'), sep=' ')
names(rstack) = c(‘test2001’,’test2002’)
[1] “Hi Bill" “Hi Bob" “Hi Sam"
TRY PASTE()!
paste(’aet',c(’2001',’2002’), sep=’_')
[1] "aet_2001" "aet_2002"
test2002
test2001

19. Task 1.2 – Create Raster Stacks and Assign Time Labels
Helpful Functions
seq() & as.Date()
setz()
seq(1,15, by=3)
raster_stack = setZ(raster_stack, years)
[1]
Years = seq(as.Date(’ '),
as.Date(' '), by=’1 year')
[1] " " " " …
[10] " " " "

20. Task 1.2 – Create Raster Stacks and Assign Time Labels
Helpful Functions
seq() & as.Date()
setz()
seq(1,15, by=3)
raster_stack = setZ(raster_stack, years)
[1]
Important: setZ must be passed a series of ‘Dates’ (created with as.Date function)
Years = seq(as.Date(’ '),
as.Date(' '), by=’1 year')
[1] " " " " …
[10] " " " "

21. Task 1.2 – Let’s get started!

22. Task 1.3 – Visualize Stack Data
Indexing Your Data
Method 1
plot(raster_stack[[1]]) # plot first raster
plot(raster_stack[[2]]) # plot second raster
NOTE: [[ ]] is used b/c stack is a list object
Method 2
plot( raster(raster_stack,'HDen_1989') )
NOTE: raster() is used b/c… well that is just how it works

23. Task 1.3 – Let’s get started!

24. Task 1.4 – Challenge Questions

25. Task 1.5 Visualize Space-Time Data rasterVis & plotKML packages
Excellent tutorial available at
Data Format
Raster stack with z-dim set to dates using: as.Date()
Spatial points or polygons data.frame with z-dim set to dates
Hovmoller Plot
Horizon Plot

26. Task 1.5 Visualize Space-Time Data rasterVis & plotKML packages
Data Format 2
Raster stack OR Spatial points or polygons data.frame with z-dim set to slope, or direction
Vectorplot
Stream Plot

27. Task 1.5 Visualize Space-Time Data rasterVis & plotKML packages
Excellent tutorial:
Data Format
Exports many formats including raster stacks
Note: This code outputs both a Housing.kml file and a series of other image files (.png files). In order for this to work, the Housing.kml file needs to be in the same directory as all the image files.

28. Task 1.5 Visualize Space-Time Data rasterVis & plotKML packages
Excellent tutorial:
Data Format
Exports many formats including raster stacks
All data must be unprojected Lat Lon
"+proj=longlat +datum=WGS84”
Note: This code outputs both a Housing.kml file and a series of other image files (.png files). In order for this to work, the Housing.kml file needs to be in the same directory as all the image files.

29. Task 1.5 – Let’s get started!

30. Task 1.6 – Let’s get started!

31. Objective 2: Intro to ggplot2
One of the best data visualization tools in R
Documentation available here:

32. Objective 2: Intro to ggplot2
A plot is made up of multiple layers.
A layer consists of data, a set of mappings between variables and aesthetics, a geometric object and a statistical transformation
Scales control the details of the mapping.
All components are independent and reusable.

33. Objective 2: Intro to ggplot2
aesthetics
geometric object
scales control
‘+’ add mappings
Typical Command
a = ggplot(movies, aes(y = budget, x = year, group = round_any(year, 10) ))
+ geom_boxplot() + scale_y_log10()
plot(a)

34. Task 2.1 Setting up your data
ggplot2 uses data.frames!

35. Task 2.1 Setting up your data
Helpful Functions
aggregate()
Summarizes data of interest by factors (categorical data)
aggregate( rating ~ year ,data= movies, FUN='mean')

36. Task 2.1 Setting up your data
Helpful Functions
fortify()
Converts spatial polygons, lines, points to data.frame
usable in ggplot2
jepson.points = fortify(jepson, region="id")

37. Task 2.1 Let’s get going!

38. Objective 2: Intro to ggplot2
plot reminder
aesthetics
geometric object
scales control
‘+’ add mappings
Typical Command
a = ggplot(jepson.df) + aes(long,lat,group=group,fill=ECOREGION) + geom_polygon()
plot(a)

39. Task 2.2 Let’s get going!

40. Task 2.3 Let’s get going!

41. Task 2.4 Challenge questions

42. Objective 3: Intro to ggmap
Ggmap enables visualization of layered graphics using implementation similar to ggplot2
Combines the functionality of ggplot2 and spatial information of static maps from Google Maps, OpenStreetMap, Stamen Maps or CloudMade

43. Objective 3: Intro to ggmap
Ggmap enables visualization of layered graphics using implementation similar to ggplot2
qmap() Downloads static maps from google, or OSM
Defined by a central Lat and Lon and a ‘Zoom’ level
Takes additional ‘+’ commands to overlay ggplot2 graphics
Zoom = 5
Zoom = 14

44. Objective 3: Intro to ggmap
plot
aesthetics
geometric object
scales control
coordinate system
Done in background through qmap
‘+’ add mappings
Typical Command
HoustonMap <- qmap("houston", zoom = 13, color = "bw")
HoustonMap + geom_point(data=violent_crimes,aes(x = lon, y = lat, colour = offense ) )
plot( HoustonMap )

45. Task 3.1 Setting up your data
Helpful Functions
projectRaster() & project()
Converts spatial -polygons, -lines, -points to data.frame
usable in ggplot2 & ggmap.
Ggmap data must be in unprojected lat lon (defined below)
# Project a raster to unprojected Lat Lon using nearest neighbor algorithm
stack_proj = projectRaster(raster_stack, crs="+proj=longlat +datum=WGS84”
, method='ngb')
# Project a polygon to unprojected Lat Lon using nearest neighbor algorithm
jepson_proj = project(jepson, crs="+proj=longlat +datum=WGS84”)

46. Task 3.1 Setting up your data
Helpful Functions
fortify()
Converts spatial polygons, lines, points to data.frame
usable in ggplot2
jepson.points = fortify(jepson, region="id")

47. Task 3.1 Setting up your data
Helpful Functions
geocode() revgeocode()
Converts text addresses or location names to Lat Lon coordindates
Converts Lat Lon to text addresses
geocode("the white house")
revgeocode(c( , ), output = c("address"))

48. Task 3.1 Setting up your data
Helpful Functions
subset()
data = data.frame(name=c(‘mike’,’john’,’jim’), age=c(4,3,6))
name age
mike 4
john
jim
name age
2 john
3 jim
subset(data, name != ‘mike’ )
name age
1 mike 4
3 jim
subset(data, age > 3 )

49. Task 3.1: Learn Basic ggmap Functions

50. Task 3.2: Crime Mapping In Houston TX

51. Task 3.3: Challenge Questions

52. What should we review?