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Working with Spatial Data |
The R Script associated with this page is available here. Download this file and open it (or copy-paste into a new script) with RStudio so you can follow along.
library(sp)
library(rgdal)
library(ggplot2)
library(dplyr)
library(tidyr)
library(maptools)coords = data.frame(
x=rnorm(100),
y=rnorm(100)
)
str(coords)## 'data.frame': 100 obs. of 2 variables:
## $ x: num -1.569 0.247 0.839 0.703 -1.147 ...
## $ y: num 0.1952 0.0851 -2.2198 -0.3312 -0.7096 ...
plot(coords)
sp = SpatialPoints(coords)
str(sp)## Formal class 'SpatialPoints' [package "sp"] with 3 slots
## ..@ coords : num [1:100, 1:2] -1.569 0.247 0.839 0.703 -1.147 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:2] "x" "y"
## ..@ bbox : num [1:2, 1:2] -2.29 -2.69 3.41 2.68
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "x" "y"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr NA
First generate a dataframe (analagous to the attribute table in a shapefile)
data=data.frame(ID=1:100,group=letters[1:20])
head(data)## ID group
## 1 1 a
## 2 2 b
## 3 3 c
## 4 4 d
## 5 5 e
## 6 6 f
Combine the coordinates with the data
spdf = SpatialPointsDataFrame(coords, data)
spdf = SpatialPointsDataFrame(sp, data)
str(spdf)## Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 100 obs. of 2 variables:
## .. ..$ ID : int [1:100] 1 2 3 4 5 6 7 8 9 10 ...
## .. ..$ group: Factor w/ 20 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..@ coords.nrs : num(0)
## ..@ coords : num [1:100, 1:2] -1.569 0.247 0.839 0.703 -1.147 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:2] "x" "y"
## ..@ bbox : num [1:2, 1:2] -2.29 -2.69 3.41 2.68
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "x" "y"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr NA
Note the use of slots designated with a @. See ?slot for more.
coordinates(data) = cbind(coords$x, coords$y) str(spdf)## Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 100 obs. of 2 variables:
## .. ..$ ID : int [1:100] 1 2 3 4 5 6 7 8 9 10 ...
## .. ..$ group: Factor w/ 20 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..@ coords.nrs : num(0)
## ..@ coords : num [1:100, 1:2] -1.569 0.247 0.839 0.703 -1.147 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:2] "x" "y"
## ..@ bbox : num [1:2, 1:2] -2.29 -2.69 3.41 2.68
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "x" "y"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr NA
subset(spdf, group=="a")## class : SpatialPointsDataFrame
## features : 5
## extent : -1.56881, 1.961121, -1.717863, 0.2500004 (xmin, xmax, ymin, ymax)
## coord. ref. : NA
## variables : 2
## names : ID, group
## min values : 1, a
## max values : 81, a
Or using []
spdf[spdf$group=="a",]## class : SpatialPointsDataFrame
## features : 5
## extent : -1.56881, 1.961121, -1.717863, 0.2500004 (xmin, xmax, ymin, ymax)
## coord. ref. : NA
## variables : 2
## names : ID, group
## min values : 1, a
## max values : 81, a
Unfortunately, dplyr functions do not directly filter spatial objects.
Convert the following data.frame into a SpatialPointsDataFrame using the coordinates() method and then plot the points with plot().
df=data.frame(
lat=c(12,15,17,12),
lon=c(-35,-35,-32,-32),
id=c(1,2,3,4))lat lon id
12 -35 1 15 -35 2 17 -32 3 12 -32 4
Show Solution
coordinates(df)=c("lon","lat")
plot(df)
## Load the data
data(meuse)
str(meuse)## 'data.frame': 155 obs. of 14 variables:
## $ x : num 181072 181025 181165 181298 181307 ...
## $ y : num 333611 333558 333537 333484 333330 ...
## $ cadmium: num 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ...
## $ copper : num 85 81 68 81 48 61 31 29 37 24 ...
## $ lead : num 299 277 199 116 117 137 132 150 133 80 ...
## $ zinc : num 1022 1141 640 257 269 ...
## $ elev : num 7.91 6.98 7.8 7.66 7.48 ...
## $ dist : num 0.00136 0.01222 0.10303 0.19009 0.27709 ...
## $ om : num 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ...
## $ ffreq : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ soil : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ...
## $ lime : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
## $ landuse: Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ...
## $ dist.m : num 50 30 150 270 380 470 240 120 240 420 ...
Show Solution
coordinates(meuse) <- ~x+y
# OR coordinates(meuse)=cbind(meuse$x,meuse$y)
# OR coordinates(meuse))=c("x","y")
str(meuse)## Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 155 obs. of 12 variables:
## .. ..$ cadmium: num [1:155] 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ...
## .. ..$ copper : num [1:155] 85 81 68 81 48 61 31 29 37 24 ...
## .. ..$ lead : num [1:155] 299 277 199 116 117 137 132 150 133 80 ...
## .. ..$ zinc : num [1:155] 1022 1141 640 257 269 ...
## .. ..$ elev : num [1:155] 7.91 6.98 7.8 7.66 7.48 ...
## .. ..$ dist : num [1:155] 0.00136 0.01222 0.10303 0.19009 0.27709 ...
## .. ..$ om : num [1:155] 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ...
## .. ..$ ffreq : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ soil : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ...
## .. ..$ lime : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
## .. ..$ landuse: Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ...
## .. ..$ dist.m : num [1:155] 50 30 150 270 380 470 240 120 240 420 ...
## ..@ coords.nrs : int [1:2] 1 2
## ..@ coords : num [1:155, 1:2] 181072 181025 181165 181298 181307 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:155] "1" "2" "3" "4" ...
## .. .. ..$ : chr [1:2] "x" "y"
## ..@ bbox : num [1:2, 1:2] 178605 329714 181390 333611
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "x" "y"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr NA
Plot it with ggplot:
ggplot(as.data.frame(meuse),aes(x=x,y=y))+
geom_point(col="red")+
coord_equal()
Note that ggplot works only with data.frames. Convert with as.data.frame() or fortify().
L1 = Line(cbind(rnorm(5),rnorm(5)))
L2 = Line(cbind(rnorm(5),rnorm(5)))
L3 = Line(cbind(rnorm(5),rnorm(5)))
L1## An object of class "Line"
## Slot "coords":
## [,1] [,2]
## [1,] -0.2197486 1.7196384
## [2,] 0.9908976 -2.1289599
## [3,] 0.6689178 -2.1552641
## [4,] -1.7243601 0.9982039
## [5,] 0.4675741 0.3428374
plot(coordinates(L1),type="l")
Ls1 = Lines(list(L1),ID="a")
Ls2 = Lines(list(L2,L3),ID="b")
Ls2## An object of class "Lines"
## Slot "Lines":
## [[1]]
## An object of class "Line"
## Slot "coords":
## [,1] [,2]
## [1,] 2.29433830 -0.6913286
## [2,] 0.29582911 -0.7264487
## [3,] -0.90527572 -1.4385630
## [4,] 0.09601122 0.5968491
## [5,] -0.60586321 -1.4422025
##
##
## [[2]]
## An object of class "Line"
## Slot "coords":
## [,1] [,2]
## [1,] -1.0356221 -1.1887470
## [2,] 0.4151567 0.9467575
## [3,] 0.1454527 -0.2905798
## [4,] 1.0201153 1.0813600
## [5,] -1.2874176 -0.5383682
##
##
##
## Slot "ID":
## [1] "b"
SL12 = SpatialLines(list(Ls1,Ls2))
plot(SL12)
SLDF = SpatialLinesDataFrame(
SL12,
data.frame(
Z=c("road","river"),
row.names=c("a","b")
))
str(SLDF)## Formal class 'SpatialLinesDataFrame' [package "sp"] with 4 slots
## ..@ data :'data.frame': 2 obs. of 1 variable:
## .. ..$ Z: Factor w/ 2 levels "river","road": 2 1
## ..@ lines :List of 2
## .. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
## .. .. .. ..@ Lines:List of 1
## .. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
## .. .. .. .. .. .. ..@ coords: num [1:5, 1:2] -0.22 0.991 0.669 -1.724 0.468 ...
## .. .. .. ..@ ID : chr "a"
## .. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
## .. .. .. ..@ Lines:List of 2
## .. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
## .. .. .. .. .. .. ..@ coords: num [1:5, 1:2] 2.294 0.296 -0.905 0.096 -0.606 ...
## .. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
## .. .. .. .. .. .. ..@ coords: num [1:5, 1:2] -1.036 0.415 0.145 1.02 -1.287 ...
## .. .. .. ..@ ID : chr "b"
## ..@ bbox : num [1:2, 1:2] -1.72 -2.16 2.29 1.72
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "x" "y"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr NA
- Multipart Polygons
- Holes
Rarely construct by hand...
But, you rarely construct data from scratch like we did above. Usually you will import datasets created elsewhere.
Geospatial Data Abstraction Library (GDAL)
rgdal package for importing/exporting/manipulating spatial data:
readOGR()andwriteOGR(): Vector datareadGDAL()andwriteGDAL(): Raster data
Also the gdalUtils package for reprojecting, transforming, reclassifying, etc.
List the file formats that your installation of rgdal can read/write with ogrDrivers():
name long_name write copy isVector
AeronavFAA Aeronav FAA FALSE FALSE TRUE
AmigoCloud AmigoCloud TRUE FALSE TRUE
ARCGEN Arc/Info Generate FALSE FALSE TRUE
AVCBin Arc/Info Binary Coverage FALSE FALSE TRUE
AVCE00 Arc/Info E00 (ASCII) Coverage FALSE FALSE TRUE
BNA Atlas BNA TRUE FALSE TRUE
Carto Carto TRUE FALSE TRUE
Cloudant Cloudant / CouchDB TRUE FALSE TRUE
CouchDB CouchDB / GeoCouch TRUE FALSE TRUE
CSV Comma Separated Value (.csv) TRUE FALSE TRUE
CSW OGC CSW (Catalog Service for the Web) FALSE FALSE TRUE
DGN Microstation DGN TRUE FALSE TRUE
DXF AutoCAD DXF TRUE FALSE TRUE
EDIGEO French EDIGEO exchange format FALSE FALSE TRUE
ElasticSearch Elastic Search TRUE FALSE TRUE
ESRI Shapefile ESRI Shapefile TRUE FALSE TRUE
Geoconcept Geoconcept TRUE FALSE TRUE
GeoJSON GeoJSON TRUE FALSE TRUE
GeoRSS GeoRSS TRUE FALSE TRUE
GFT Google Fusion Tables TRUE FALSE TRUE
GML Geography Markup Language (GML) TRUE FALSE TRUE
GPKG GeoPackage TRUE TRUE TRUE
GPSBabel GPSBabel TRUE FALSE TRUE
GPSTrackMaker GPSTrackMaker TRUE FALSE TRUE
GPX GPX TRUE FALSE TRUE
HTF Hydrographic Transfer Vector FALSE FALSE TRUE
HTTP HTTP Fetching Wrapper FALSE FALSE TRUE
Idrisi Idrisi Vector (.vct) FALSE FALSE TRUE
JML OpenJUMP JML TRUE FALSE TRUE
KML Keyhole Markup Language (KML) TRUE FALSE TRUE
MapInfo File MapInfo File TRUE FALSE TRUE
Memory Memory TRUE FALSE TRUE
netCDF Network Common Data Format TRUE TRUE TRUE
ODS Open Document/ LibreOffice / OpenOffice Spreadsheet TRUE FALSE TRUE
OGR_GMT GMT ASCII Vectors (.gmt) TRUE FALSE TRUE
OGR_PDS Planetary Data Systems TABLE FALSE FALSE TRUE
OGR_SDTS SDTS FALSE FALSE TRUE
OGR_VRT VRT - Virtual Datasource FALSE FALSE TRUE
OpenAir OpenAir FALSE FALSE TRUE
OpenFileGDB ESRI FileGDB FALSE FALSE TRUE
OSM OpenStreetMap XML and PBF FALSE FALSE TRUE
PCIDSK PCIDSK Database File TRUE FALSE TRUE
PDF Geospatial PDF TRUE TRUE TRUE
PGDUMP PostgreSQL SQL dump TRUE FALSE TRUE
PLSCENES Planet Labs Scenes API FALSE FALSE TRUE
REC EPIInfo .REC FALSE FALSE TRUE
S57 IHO S-57 (ENC) TRUE FALSE TRUE
SEGUKOOA SEG-P1 / UKOOA P1/90 FALSE FALSE TRUE
SEGY SEG-Y FALSE FALSE TRUE
Selafin Selafin TRUE FALSE TRUE
SQLite SQLite / Spatialite TRUE FALSE TRUE
SUA Tim Newport-Peace's Special Use Airspace Format FALSE FALSE TRUE
SVG Scalable Vector Graphics FALSE FALSE TRUE
SXF Storage and eXchange Format FALSE FALSE TRUE
TIGER U.S. Census TIGER/Line TRUE FALSE TRUE
UK .NTF UK .NTF FALSE FALSE TRUE
VDV VDV-451/VDV-452/INTREST Data Format TRUE FALSE TRUE
VFK Czech Cadastral Exchange Data Format FALSE FALSE TRUE
WAsP WAsP .map format TRUE FALSE TRUE
WFS OGC WFS (Web Feature Service) FALSE FALSE TRUE
XLSX MS Office Open XML spreadsheet TRUE FALSE TRUE
XPlane X-Plane/Flightgear aeronautical data FALSE FALSE TRUE
Now as an example, let's read in a shapefile that's included in the maptools package. You can try
## get the file path to the files
file=system.file("shapes/sids.shp", package="maptools")
## get information before importing the data
ogrInfo(dsn=file, layer="sids")## Source: "/Library/Frameworks/R.framework/Versions/3.4/Resources/library/maptools/shapes/sids.shp", layer: "sids"
## Driver: ESRI Shapefile; number of rows: 100
## Feature type: wkbPolygon with 2 dimensions
## Extent: (-84.32385 33.88199) - (-75.45698 36.58965)
## LDID: 87
## Number of fields: 14
## name type length typeName
## 1 AREA 2 12 Real
## 2 PERIMETER 2 12 Real
## 3 CNTY_ 12 11 Integer64
## 4 CNTY_ID 12 11 Integer64
## 5 NAME 4 32 String
## 6 FIPS 4 5 String
## 7 FIPSNO 12 16 Integer64
## 8 CRESS_ID 0 3 Integer
## 9 BIR74 2 12 Real
## 10 SID74 2 9 Real
## 11 NWBIR74 2 11 Real
## 12 BIR79 2 12 Real
## 13 SID79 2 9 Real
## 14 NWBIR79 2 12 Real
## Import the data
sids <- readOGR(dsn=file, layer="sids")## OGR data source with driver: ESRI Shapefile
## Source: "/Library/Frameworks/R.framework/Versions/3.4/Resources/library/maptools/shapes/sids.shp", layer: "sids"
## with 100 features
## It has 14 fields
## Integer64 fields read as strings: CNTY_ CNTY_ID FIPSNO
summary(sids)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x -84.32385 -75.45698
## y 33.88199 36.58965
## Is projected: NA
## proj4string : [NA]
## Data attributes:
## AREA PERIMETER CNTY_ CNTY_ID NAME
## Min. :0.0420 Min. :0.999 1825 : 1 1825 : 1 Alamance : 1
## 1st Qu.:0.0910 1st Qu.:1.324 1827 : 1 1827 : 1 Alexander: 1
## Median :0.1205 Median :1.609 1828 : 1 1828 : 1 Alleghany: 1
## Mean :0.1263 Mean :1.673 1831 : 1 1831 : 1 Anson : 1
## 3rd Qu.:0.1542 3rd Qu.:1.859 1832 : 1 1832 : 1 Ashe : 1
## Max. :0.2410 Max. :3.640 1833 : 1 1833 : 1 Avery : 1
## (Other):94 (Other):94 (Other) :94
## FIPS FIPSNO CRESS_ID BIR74
## 37001 : 1 37001 : 1 Min. : 1.00 Min. : 248
## 37003 : 1 37003 : 1 1st Qu.: 25.75 1st Qu.: 1077
## 37005 : 1 37005 : 1 Median : 50.50 Median : 2180
## 37007 : 1 37007 : 1 Mean : 50.50 Mean : 3300
## 37009 : 1 37009 : 1 3rd Qu.: 75.25 3rd Qu.: 3936
## 37011 : 1 37011 : 1 Max. :100.00 Max. :21588
## (Other):94 (Other):94
## SID74 NWBIR74 BIR79 SID79
## Min. : 0.00 Min. : 1.0 Min. : 319 Min. : 0.00
## 1st Qu.: 2.00 1st Qu.: 190.0 1st Qu.: 1336 1st Qu.: 2.00
## Median : 4.00 Median : 697.5 Median : 2636 Median : 5.00
## Mean : 6.67 Mean :1050.8 Mean : 4224 Mean : 8.36
## 3rd Qu.: 8.25 3rd Qu.:1168.5 3rd Qu.: 4889 3rd Qu.:10.25
## Max. :44.00 Max. :8027.0 Max. :30757 Max. :57.00
##
## NWBIR79
## Min. : 3.0
## 1st Qu.: 250.5
## Median : 874.5
## Mean : 1352.8
## 3rd Qu.: 1406.8
## Max. :11631.0
##
plot(sids)
The maptools package has an alternative function for importing shapefiles that can be a little easier to use (but has fewer options).
readShapeSpatial
sids <- readShapeSpatial(file)## Warning: use rgdal::readOGR or sf::st_read
## Warning: use rgdal::readOGR or sf::st_read
We'll deal with raster data in the next section.
- Earth isn't flat
- But small parts of it are close enough
- Many coordinate systems exist
- Anything
Spatial*(orraster*) can have one
The Proj.4 library
Library for performing conversions between cartographic projections.
See http://spatialreference.org for information on specifying projections. For example,
WGS 84:
- proj4:
+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs - .prj / ESRI WKT:
GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]] - EPSG:
4326
Note that it has no projection information assigned (since it came from a simple data frame). From the help file (?meuse) we can see that the projection is EPSG:28992.
proj4string(sids) <- CRS("+proj=longlat +ellps=clrk66")
proj4string(sids)## [1] "+proj=longlat +ellps=clrk66"
Assigning a CRS doesn't change the projection of the data, it just indicates which projection the data are currently in.
So assigning the wrong CRS really messes things up.
Transform (warp) projection from one to another with spTransform
Project the sids data to the US National Atlas Equal Area (Lambert azimuthal equal-area projection):
sids_us = spTransform(sids,CRS("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"))Compare the bounding box:
bbox(sids)## min max
## x -84.32385 -75.45698
## y 33.88199 36.58965
bbox(sids_us)## min max
## x 1422262.8 2192698.1
## y -984904.1 -629133.4
And plot them:
# Geographic
ggplot(fortify(sids),aes(x=long,y=lat,order=order,group=group))+
geom_polygon(fill="white",col="black")+
coord_equal()## Regions defined for each Polygons
# Equal Area
ggplot(fortify(sids_us),aes(x=long,y=lat,order=order,group=group))+
geom_polygon(fill="white",col="black")+
coord_equal()+
ylab("Northing")+xlab("Easting")## Regions defined for each Polygons
Interface to Geometry Engine - Open Source (GEOS) using a C API for topology operations (e.g. union, simplification) on geometries (lines and polygons).
library(rgeos)- Area calculations (
gArea) - Centroids (
gCentroid) - Convex Hull (
gConvexHull) - Intersections (
gIntersection) - Unions (
gUnion) - Simplification (
gSimplify)
If you have trouble installing rgeos on OS X, look here
Make up some lines and polygons:
p = readWKT(paste("POLYGON((0 40,10 50,0 60,40 60,40 100,50 90,60 100,60",
"60,100 60,90 50,100 40,60 40,60 0,50 10,40 0,40 40,0 40))"))
l = readWKT("LINESTRING(0 7,1 6,2 1,3 4,4 1,5 7,6 6,7 4,8 6,9 4)")par(mfrow=c(1,4)) # this sets up a 1x4 grid for the plots
plot(l);title("Original")
plot(gSimplify(l,tol=3));title("tol: 3")
plot(gSimplify(l,tol=5));title("tol: 5")
plot(gSimplify(l,tol=7));title("tol: 7")
par(mfrow=c(1,4)) # this sets up a 1x4 grid for the plots
plot(p);title("Original")
plot(gSimplify(p,tol=10));title("tol: 10")
plot(gSimplify(p,tol=20));title("tol: 20")
plot(gSimplify(p,tol=25));title("tol: 25")
Load the sids data again
file = system.file("shapes/sids.shp", package="maptools")
sids = readOGR(dsn=file, layer="sids")## OGR data source with driver: ESRI Shapefile
## Source: "/Library/Frameworks/R.framework/Versions/3.4/Resources/library/maptools/shapes/sids.shp", layer: "sids"
## with 100 features
## It has 14 fields
## Integer64 fields read as strings: CNTY_ CNTY_ID FIPSNO
sids2=gSimplify(sids,tol = 0.2,topologyPreserve=T)fortify() in ggplot useful for converting Spatial* objects into plottable data.frames.
sids%>%
fortify()%>%
head()## Regions defined for each Polygons
## long lat order hole piece id group
## 1 -81.47276 36.23436 1 FALSE 1 0 0.1
## 2 -81.54084 36.27251 2 FALSE 1 0 0.1
## 3 -81.56198 36.27359 3 FALSE 1 0 0.1
## 4 -81.63306 36.34069 4 FALSE 1 0 0.1
## 5 -81.74107 36.39178 5 FALSE 1 0 0.1
## 6 -81.69828 36.47178 6 FALSE 1 0 0.1
To use ggplot with a fortifyed spatial object, you must specify aes(x=long,y=lat,order=order, group=group) to indicate that each polygon should be plotted separately.
ggplot(fortify(sids),aes(x=long,y=lat,order=order, group=group))+
geom_polygon(lwd=2,fill="grey",col="blue")+
coord_map()## Regions defined for each Polygons
Now let's overlay the simplified version to see how they differ.
ggplot(fortify(sids),aes(x=long,y=lat,order=order, group=group))+
geom_polygon(lwd=2,fill="grey",col="blue")+
geom_polygon(data=fortify(sids2),col="red",fill=NA)+
coord_map()## Regions defined for each Polygons
How does changing the tolerance (tol) affect the map?
sids$area=gArea(sids,byid = T)From Wikipedia:
A choropleth (from Greek χώρο ("area/region") + πλήθος ("multitude")) is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income.
By default, the rownames in the dataframe are the unique identifier (e.g. the FID) for the polygons.
## add the ID to the dataframe itself for easier indexing
sids$id=as.numeric(rownames(sids@data))
## create fortified version for plotting with ggplot()
fsids=fortify(sids,region="id")
ggplot(sids@data, aes(map_id = id)) +
expand_limits(x = fsids$long, y = fsids$lat)+
scale_fill_gradientn(colours = c("grey","goldenrod","darkgreen","green"))+
coord_map()+
geom_map(aes(fill = area), map = fsids)
Merge sub-geometries (polygons) together with gUnionCascaded()
sids_all=gUnionCascaded(sids)ggplot(fortify(sids_all),aes(x=long,y=lat,group=group,order=order))+
geom_path()+
coord_map()
See also: Raster package for working with raster data
Sources:
- UseR 2012 Spatial Data Workshop by Barry Rowlingson
Licensing: