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Transcript of ADVANCED SPATIAL METHODS IN R Michael Mann George Washington University Department of Geography...
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ADVANCED SPATIAL METHODS IN RMichael Mann George Washington University
Department of Geography
http://michaelmann.i234.me/
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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)
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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
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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
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Your location
Quick survey
• Please raise you hand if (before today) you have never used R or a similar language.
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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!
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OverviewHelpful Functions
Indexing a vector
s = c("aa", "bb", "cc", "dd", "ee")
[1] ‘aa’
s[1]
[1] ‘bb’ ‘ee’
s[c(2,5)]
Indexing a data.frame
s = data.frame(col1=c(1,2,3),col2=c(5,6,7))
vector_name[ position_# ]
[1] 2
s[2,1]
[1] 5 6 7
s[,’col2’]
col1 col21 1 52 2 63 3 7
vector_name[ row#, col# ]
[1] ‘bb’ ‘cc’ ‘dd’ ‘ee’
s[c(2:5)]
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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)
X
Y
Z
stack[row# ,col#, Z#]
Indexing Your Data
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Objective 1 – Raster space-time plots• Multi-layer Raster ‘Brick’
Brick[X,Y,Z]
b <- brick(system.file("external/rlogo.grd", package="raster)plot(b)
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Objective 1 – Raster space-time plots• Multi-layer Raster Brick
plotRGB(b, r=1, g=2, b=3)
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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) ]
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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) ]
Use: Data Visualization
Use: Regression Analysis, modeling
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Task 1.1 – Setup Raster Stack Data Helpful Functions
dir(‘C://SESYNC//data’)
grep()
grep(‘a’, c( ‘a’, ‘b’, ‘c’, ‘a’ ) )
[1] ‘data.zip’ ‘ggmap vinette.pdf’….
dir()
[1] 1 4
grep(‘c’, c( ‘tab’, ‘car’, ‘bat’ ) )
[1] 2
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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
[1] 3
s = c("aa", "bb", "cc", "dd", "ee")
grep(‘c’, s )
s[ grep(‘c’, s ) ]
[1] ‘cc’
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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
[1] 3
s = c("aa", "bb", "cc", "dd", "ee")
grep(‘c’, s )
s[ grep(‘c’, s ) ]
[1] ‘cc’
TRY THIS!
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Task 1.1 – Let’s get started!
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Task 1.2 – Create Raster Stacks and Assign Name LabelsHelpful Functions
paste(‘Hi',c('Bill','Bob', 'Sam'), sep=' ')
paste()
[1] “Hi Bill" “Hi Bob" “Hi Sam"
paste(’aet',c(’2001',’2002’), sep=’_')
[1] "aet_2001" "aet_2002"
names()
names(rstack) = c(‘test2001’,’test2002’)
test2001
test2002
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Task 1.2 – Create Raster Stacks and Assign Name LabelsHelpful Functions
paste(‘Hi',c('Bill','Bob', 'Sam'), sep=' ')
paste()
[1] “Hi Bill" “Hi Bob" “Hi Sam"
paste(’aet',c(’2001',’2002’), sep=’_')
[1] "aet_2001" "aet_2002"
names()
names(rstack) = c(‘test2001’,’test2002’)
test2001
test2002
TRY PASTE()!
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Task 1.2 – Create Raster Stacks and Assign Time LabelsHelpful Functions
setz()
raster_stack = setZ(raster_stack, years)seq(1,15, by=3)
seq() & as.Date()
[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"
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Task 1.2 – Create Raster Stacks and Assign Time LabelsHelpful Functions
setz()
raster_stack = setZ(raster_stack, years)seq(1,15, by=3)
seq() & as.Date()
[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"
Important: setZ must be passed a series of ‘Dates’ (created with as.Date function)
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Task 1.2 – Let’s get started!
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Task 1.3 – Visualize Stack Data
Indexing Your Data
plot(raster_stack[[1]]) # plot first rasterplot(raster_stack[[2]]) # plot second raster
plot( raster(raster_stack,'HDen_1989') )
Method 1
Method 2
NOTE: [[ ]] is used b/c stack is a list object
NOTE: raster() is used b/c… well that is just how it works
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Task 1.3 – Let’s get started!
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Task 1.4 – Challenge Questions
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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
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Task 1.5 Visualize Space-Time Data rasterVis & plotKML packages
rasterVis• Data Format 2
• Raster stack OR Spatial points or polygons data.frame with z-dim set to slope, or direction
Vectorplot Stream Plot
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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.
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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.
All data must be unprojected Lat Lon "+proj=longlat +datum=WGS84”
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Task 1.5 – Let’s get started!
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Task 1.6 – Let’s get started!
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Objective 2: Intro to ggplot2• One of the best data visualization tools in R• Documentation available here: http://ggplot2.org/
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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.
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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)
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Task 2.1 Setting up your data
ggplot2 uses data.frames!
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Task 2.1 Setting up your data
Helpful Functions
aggregate( rating ~ year ,data= movies, FUN='mean')
aggregate()
Summarizes data of interest by factors (categorical data)
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Task 2.1 Setting up your data
Helpful Functions
jepson.points = fortify(jepson, region="id")
fortify()
Converts spatial polygons, lines, points to data.frame
usable in ggplot2
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Task 2.1 Let’s get going!
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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)
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Task 2.2 Let’s get going!
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Task 2.3 Let’s get going!
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Task 2.4 Challenge questions
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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
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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
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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 )
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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”)
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Task 3.1 Setting up your data
Helpful Functions
jepson.points = fortify(jepson, region="id")
fortify()
Converts spatial polygons, lines, points to data.frame
usable in ggplot2
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Task 3.1 Setting up your data
Helpful Functions
geocode("the white house")
geocode() revgeocode()
Converts text addresses or location names to Lat Lon coordindates
revgeocode(c(-77.03650, 38.89768), output = c("address"))
Converts Lat Lon to text addresses
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Task 3.1 Setting up your data
Helpful Functions
data = data.frame(name=c(‘mike’,’john’,’jim’), age=c(4,3,6))
subset()
name age1 mike 42 john 33 jim 6
subset(data, name != ‘mike’ )
name age2 john 33 jim 6
subset(data, age > 3 ) name age1 mike 43 jim 6
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Task 3.1: Learn Basic ggmap Functions
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Task 3.2: Crime Mapping In Houston TX
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Task 3.3: Challenge Questions
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WHAT SHOULD WE REVIEW?