ADVANCED SPATIAL METHODS IN RMichael 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

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

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!

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)]

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

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)

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) ]

Multivariate

stack[x,y, c(crime,pop) ]

Use: Data Visualization

Use: Regression Analysis, modeling

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

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’

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!

Task 1.1 – Let’s get started!

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

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()!

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"

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)

Task 1.2 – Let’s get started!

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

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 z-dim 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

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”

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( rating ~ year ,data= movies, FUN='mean')

aggregate()

Summarizes data of interest by factors (categorical data)

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

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

jepson.points = fortify(jepson, region="id")

fortify()

Converts spatial polygons, lines, points to data.frame

usable in ggplot2

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

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

Task 3.1: Learn Basic ggmap Functions

Task 3.2: Crime Mapping In Houston TX

Task 3.3: Challenge Questions

WHAT SHOULD WE REVIEW?