ADVANCED SPATIAL
METHODS IN R
Michael Mann
George Washington University
Department of Geography
mmann1123@gwu.edu
http://michaelmann.i234.me/
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)
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
• http://gis.stackexchange.com/
• Similar to stackoverflow, but targeted to spatial
community
• The fridge – grab a beer and spend some time
Interpreting My Slides!
Inputs into command line
s = c("aa", "bb", "cc", "dd", "ee")
s
Outputs
[1] "aa” "bb” "cc” "dd” "ee"
Notes
An important
note
Data is in the MMSpatialData folder
Your location
Quick survey
• Please raise you hand if (before
today) you have never used R
or a similar language.
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!
Overview
Helpful Functions
Indexing a vector
Indexing a data.frame
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)]
[1] ‘aa’
[1] ‘bb’ ‘ee’
col1 col2
1 1 5
2 2 6
3 3 7
s[c(2:5)]
s[2,1]
s[,’col2’]
[1] ‘bb’ ‘cc’ ‘dd’ ‘ee’
[1] 2
[1] 5 6 7
vector_name[ position_# ]
vector_name[ row#, col# ]
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
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]
Objective 1 – Raster space-time plots
• Multi-layer Raster Brick
plotRGB(b, r=1, g=2, b=3)
Objective 1 – Raster space-time plots
• Multi-dimentional Raster Stack
• Good for time-series of rasters, or multivariate analysis
Time Series
stack[x,y, c(cwd2000-2010) ]
Multivariate
stack[x,y, c(crime,pop) ]
Objective 1 – Raster space-time plots
• Multi-dimentional Raster Stack
• Good for time-series of rasters, or multivariate analysis
Time Series
stack[x,y, c(cwd2000-2010) ]
Use: Data
Visualization
Multivariate
stack[x,y, c(crime,pop) ]
Use: Regression
Analysis, modeling
Task 1.1 – Setup Raster Stack Data
Helpful Functions
grep()
dir()
dir(‘C://SESYNC//data’)
grep(‘a’,
[1]
grep(‘c’,
[1]
[1] ‘data.zip’ ‘ggmap vinette.pdf’….
c( ‘a’, ‘b’, ‘c’, ‘a’ )
1
4
c( ‘tab’, ‘car’, ‘bat’ )
2
)
)
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
s = c("aa", "bb", "cc", "dd", "ee")
grep(‘c’, s )
[1]
3
Query vector s with the grep
s[ grep(‘c’, s ) ]
[1]
‘cc’
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
s = c("aa", "bb", "cc", "dd", "ee")
grep(‘c’, s )
[1]
3
Query vector s with the grep
s[ grep(‘c’, s ) ]
[1]
‘cc’
Task 1.1 – Let’s get started!
Task 1.2 – Create Raster Stacks and
Assign Name Labels
Helpful Functions
paste()
paste(‘Hi',c('Bill','Bob', 'Sam'), sep=' ')
names()
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
Task 1.2 – Create Raster Stacks and
Assign Name Labels
Helpful Functions
paste()
paste(‘Hi',c('Bill','Bob', 'Sam'), sep=' ')
names()
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
Task 1.2 – Create Raster Stacks and
Assign Time Labels
Helpful Functions
seq() & as.Date()
seq(1,15, by=3)
[1] 1 4 7 10 13
Years = seq(as.Date(’2001-01-01'),
as.Date('2010-01-01'), by=’1 year')
[1] "2000-01-01" "2001-01-01"
…
[10] "2009-01-01" "2010-01-01"
setz()
raster_stack = setZ(raster_stack, years)
Task 1.2 – Create Raster Stacks and
Assign Time Labels
Helpful Functions
seq() & as.Date()
seq(1,15, by=3)
[1] 1 4 7 10 13
Years = seq(as.Date(’2001-01-01'),
as.Date('2010-01-01'), by=’1 year')
[1] "2000-01-01" "2001-01-01"
…
[10] "2009-01-01" "2010-01-01"
setz()
raster_stack = setZ(raster_stack, years)
Important: setZ must be
passed a series of ‘Dates’
(created with as.Date
function)
Task 1.2 – Let’s get started!
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
Task 1.3 – Let’s get started!
Task 1.4 – Challenge Questions
Task 1.5 Visualize Space-Time Data
rasterVis & plotKML packages
rasterVis
• Excellent tutorial available at http://rastervis.r-forge.r-project.org/
• 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
Task 1.5 Visualize Space-Time Data
rasterVis & plotKML packages
rasterVis
• Data Format 2
• Raster stack OR Spatial points or polygons data.frame with zdim set to slope, or direction
Vectorplot
Stream Plot
Task 1.5 Visualize Space-Time Data
rasterVis & plotKML packages
plotKML
• Excellent tutorial: http://plotkml.r-forge.r-project.org/
• 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.
Task 1.5 Visualize Space-Time Data
rasterVis & plotKML packages
plotKML
• Excellent tutorial: http://plotkml.r-forge.r-project.org/
• 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.
Task 1.5 – Let’s get started!
Task 1.6 – Let’s get started!
Objective 2: Intro to ggplot2
• One of the best data visualization tools in R
• Documentation available here: http://ggplot2.org/
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.
Objective 2: Intro to ggplot2
• plot
• 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)
Task 2.1 Setting up your data
ggplot2 uses data.frames!
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')
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")
Task 2.1 Let’s get going!
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)
Task 2.2 Let’s get going!
Task 2.3 Let’s get going!
Task 2.4 Challenge questions
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
Objective 3: Intro to ggmap
• Ggmap enables visualization of layered graphics using
implementation similar to ggplot2
1. qmap() Downloads static maps from google, or OSM
• Defined by a central Lat and Lon and a ‘Zoom’ level
2. Takes additional ‘+’ commands to overlay ggplot2
graphics
Zoom = 5
Zoom = 14
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 )
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”)
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")
Task 3.1 Setting up your data
Helpful Functions
geocode()
Converts text
addresses or location
names to Lat Lon
coordindates
geocode("the white house")
revgeocode()
Converts Lat Lon to text
addresses
revgeocode(c(-77.03650, 38.89768),
output = c("address"))
Task 3.1 Setting up your data
Helpful Functions
subset()
data = data.frame(name=c(‘mike’,’john’,’jim’), age=c(4,3,6))
1
2
3
name age
mike 4
john
3
jim
6
subset(data, name != ‘mike’ )
subset(data, age > 3 )
2
3
name age
john
3
jim
6
1
3
name age
mike 4
jim
6
Task 3.1: Learn Basic ggmap Functions
Task 3.2: Crime Mapping In Houston TX
Task 3.3: Challenge Questions
WHAT SHOULD WE
REVIEW?