PS_08_sf.md

title	Spatial data

Course Logistics Reminder

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Link to this script

If interested, you can download the R script associated with this presentation here.

{data-background-iframe="https://cran.r-project.org/web/views/"}

Code Reading Challenge

Write out in a sentence what this code is doing. Make sure to catch the key points in your sentence

library(downloader)
library(sf)
library(fs)
dams_path <- "https://research.idwr.idaho.gov/gis/Spatial/DamSafety/dam.zip"
df <- tempfile(); uf <- tempfile()
download(dams_path, df, mode = "wb")
unzip(df, exdir = uf)
dams <- read_sf(uf)
file_delete(df); dir_delete(uf)

Working with Spatial Data in R

Available Packages

sp First major spatial data package/format
rgdal reading and writing spatial data
rgeos Interface to open-source geometry engine (GEOS)
sf Spatial Features in the 'tidyverse'
raster gridded data (like satellite imagery)
and a few others...

What is a Spatial Feature (sf)?

Typically an object in the real world, such as a building or a tree.

Features could include:

a forest (polygon)
a tree in the forest (point or polygon)
a branch on the tree (line?)
a complete image (multipoint, polygon, raster)
a satellite image pixel of that forest (point or polgyon or raster)

Spatial Features

What information do we need to store in order to define points, lines, polygons in geographic space?

lat/lon coordinates
projection
what type (point/line/poly)
if polygon, is it a hole or not
attribute data
... ?

Geometry

Features have a geometry describing where on Earth the feature is located, and they have attributes, which describe other properties.

A Tree:

delineation of its crown
its stem
point indicating its centre

Attributes:

species
height
diameter
date of observation
...

Spatial Feature Standard

"A simple feature is defined by the OpenGIS Abstract specification to have both spatial and non-spatial attributes. Spatial attributes are geometry valued, and simple features are based on 2D geometry with linear interpolation between vertices."

Dimensions

All geometries composed of points: coordinates in 2-, 3- or 4-dimensional space.

X coordinate in X direction (typically longitude or similar)
Y coordinate in Y direction (typically latitude or similar)
Z coordinate denoting altitude
M coordinate (rarely used), denoting some measure that is associated with the point, rather than with the feature as a whole (in which case it would be a feature attribute); examples could be time of measurement, or measurement error.

Dimensions

The four possible cases then are:

2D (XY): x and y, easting and northing, or longitude and latitude
3D (XYZ): three-dimensional points
3D (XYM): three-dimensional points where 3rd is some attribute space
4D (XYZM): four-dimensional points as XYZM (the third axis is Z, fourth M)

Common Simple Feature (SF) types

Seven common Simple Feature (SF) geometry types

type	description
`POINT`	a single point
`LINESTRING`	sequence of points connected by lines
`POLYGON`	sequence of points form a closed ring
`MULTIPOINT`	set of points
`MULTILINESTRING`	set of linestrings
`MULTIPOLYGON`	set of polygons
`GEOMETRYCOLLECTION`	set of geometries

Some formats only include these (e.g. GeoJSON)

Uncommon Geometry Types

10 more geometries 10 are rare:

CIRCULARSTRING
COMPOUNDCURVE
CURVEPOLYGON
MULTICURVE
MULTISURFACE
CURVE
SURFACE
POLYHEDRALSURFACE
TIN
TRIANGLE

Coordinate reference system

SFs can only be placed on the Earth's surface when their coordinate reference system (CRS) is known; this may be an elipsoidal CRS such as WGS84, a projected, two-dimensional (Cartesian) CRS such as a UTM zone or Web Mercator, or a CRS in three-dimensions or including time. Similarly, M-coordinates need an attribute reference system, e.g. a measurement unit.

There are currently two main approaches in R to handle geographic vector data.

`sp` package

First package for spatial data: sp. Provides classes and methods to create points, lines, polygons, and grids and to operate on them.

~350 of the spatial analysis packages use sp data types, so it is important to know how to convert sp to and from sf objects.

`sf` package

sf implements a formal standard called "Simple Features" that specifies a storage and access model of spatial geometries (point, line, polygon).

A feature geometry is called simple when it consists of points connected by straight line pieces, and does not intersect itself.

This standard has been adopted widely, not only by spatial databases such as PostGIS, but also more recent standards such as GeoJSON.

How simple features in R are organized

All spatial functions and methods in sf prefixed by st_ (refering to spatial and temporal)

Simple features are implemented as R native data, using simple data structures (S3 classes, lists, matrix, vector).

Stored as data.frame objects (or very similar tbl_df) with feature geometries in a data.frame column.

Since geometries are not single-valued, they are put in a list-column, a list of length equal to the number of records in the data.frame, with each list element holding the simple feature geometry of that feature.

Components

sf, the table (data.frame) with feature attributes and feature geometries, which contains
sfc, the list-column with the geometries for each feature (record), which is composed of
sfg, the feature geometry of an individual simple feature.

If you work with PostGis or GeoJSON you may have come across the WKT (well-known text) format, for example like these:

POINT (30 10)
LINESTRING (30 10, 10 30, 40 40)
POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))

sf implements this standard natively in R. Data are structured and conceptualized very differently from the sp approach.

I. Create geometric objects (topology)

Geometric objects (simple features) can be created from a numeric vector, matrix or a list with the coordinates. They are called sfg objects for Simple Feature Geometry.

Create a LINESTRING sfg object:

xy=matrix(runif(6), ncol=2)
xy

##           [,1]      [,2]
## [1,] 0.3046195 0.5242276
## [2,] 0.2268402 0.9591672
## [3,] 0.6675886 0.9305813

lnstr_sfg <- st_linestring(xy) 
class(lnstr_sfg)

## [1] "XY"         "LINESTRING" "sfg"

lnstr_sfg

## LINESTRING (0.3046195 0.5242276, 0.2268402 0.9591672, 0.6675886 0.9305813)

II. Combine all individual single feature objects for the special column.

Create a sfc (Simple Feature Collection) object of individual features:

lnstr_sfc <- st_sfc(lnstr_sfg) # just one feature here
class(lnstr_sfc)

## [1] "sfc_LINESTRING" "sfc"

lnstr_sfc

## Geometry set for 1 feature 
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: 0.2268402 ymin: 0.5242276 xmax: 0.6675886 ymax: 0.9591672
## epsg (SRID):    NA
## proj4string:    NA

## LINESTRING (0.3046195 0.5242276, 0.2268402 0.95...

The sfc object also holds the bounding box and the projection information.

III. Add attributes.

Add attributes (in a data.frame) to the sfc object to make a sf (Simple Features) object:

dfr=data.frame(type="random")
lnstr_sf <- st_sf(dfr , lnstr_sfc)
class(lnstr_sf)

## [1] "sf"         "data.frame"

lnstr_sf

## Simple feature collection with 1 feature and 1 field
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: 0.2268402 ymin: 0.5242276 xmax: 0.6675886 ymax: 0.9591672
## epsg (SRID):    NA
## proj4string:    NA
##     type                      lnstr_sfc
## 1 random LINESTRING (0.3046195 0.524...

type	lnstr_sfc
random	c(0.304619457572699, 0.226840248797089, 0.667588643264025, 0.524227628484368, 0.959167186170816, 0.930581306573004)

sf Highlights

provides fast I/O, particularly relevant for large files
directly reads from and writes to spatial databases such as PostGIS
compatibile with the tidyverse
recent ggplot release can read and plot the sf format without conversion

sp and sf are not only formats for spatial objects. Other spatial packages may use their own class definitions for spatial data (for example spatstat). Usually you can find functions that convert sp and increasingly sf objects to and from these formats.

Converting formats

data(world) #load 'world' data from spData package
world_sp = as(world, "Spatial") # convert from sf to sp
world_sf = st_as_sf(world_sp) #convert from sp to sf
str(world_sp)

## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
##   ..@ data       :'data.frame':	177 obs. of  10 variables:
##   .. ..$ iso_a2   : chr [1:177] "FJ" "TZ" "EH" "CA" ...
##   .. ..$ name_long: chr [1:177] "Fiji" "Tanzania" "Western Sahara" "Canada" ...
##   .. ..$ continent: chr [1:177] "Oceania" "Africa" "Africa" "North America" ...
##   .. ..$ region_un: chr [1:177] "Oceania" "Africa" "Africa" "Americas" ...
##   .. ..$ subregion: chr [1:177] "Melanesia" "Eastern Africa" "Northern Africa" "Northern America" ...
##   .. ..$ type     : chr [1:177] "Sovereign country" "Sovereign country" "Indeterminate" "Sovereign country" ...
##   .. ..$ area_km2 : num [1:177] 19290 932746 96271 10036043 9510744 ...
##   .. ..$ pop      : num [1:177] 8.86e+05 5.22e+07 NA 3.55e+07 3.19e+08 ...
##   .. ..$ lifeExp  : num [1:177] 70 64.2 NA 82 78.8 ...
##   .. ..$ gdpPercap: num [1:177] 8222 2402 NA 43079 51922 ...
##   ..@ polygons   :List of 177
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 3
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] 179.3 -16.6
##   .. .. .. .. .. .. ..@ area   : num 0.589
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
##   .. .. .. .. .. .. ..@ ringDir: int 1
##   .. .. .. .. .. .. ..@ coords : num [1:8, 1:2] 180 180 179 179 179 ...
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] 178 -17.8
##   .. .. .. .. .. .. ..@ area   : num 0.984
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
##   .. .. .. .. .. .. ..@ ringDir: int 1
##   .. .. .. .. .. .. ..@ coords : num [1:9, 1:2] 178 178 179 179 178 ...
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -179.9 -16.3
##   .. .. .. .. .. .. ..@ area   : num 0.067
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
##   .. .. .. .. .. .. ..@ ringDir: int 1
##   .. .. .. .. .. .. ..@ coords : num [1:5, 1:2] -180 -180 -180 -180 -180 ...
##   .. .. .. ..@ plotOrder: int [1:3] 2 1 3
##   .. .. .. ..@ labpt    : num [1:2] 178 -17.8
##   .. .. .. ..@ ID       : chr "1"
##   .. .. .. ..@ area     : num 1.64
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] 34.75 -6.26
##   .. .. .. .. .. .. ..@ area   : num 76.3
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ plotOrder: int 1
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##   .. .. .. ..@ ID       : chr "2"
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -12.1 24.3
##   .. .. .. .. .. .. ..@ area   : num 8.6
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##   .. .. .. ..@ plotOrder: int 1
##   .. .. .. ..@ labpt    : num [1:2] -12.1 24.3
##   .. .. .. ..@ ID       : chr "3"
##   .. .. .. ..@ area     : num 8.6
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 30
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -101.6 57.7
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##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ area   : num 0.532
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##   .. .. .. .. .. .. ..@ hole   : logi FALSE
##   .. .. .. .. .. .. ..@ ringDir: int 1
##   .. .. .. .. .. .. ..@ coords : num [1:136, 1:2] 97.3 97.4 97.1 97.1 96.4 ...
##   .. .. .. ..@ plotOrder: int 1
##   .. .. .. ..@ labpt    : num [1:2] 79.6 22.9
##   .. .. .. ..@ ID       : chr "99"
##   .. .. .. ..@ area     : num 278
##   .. .. [list output truncated]
##   ..@ plotOrder  : int [1:177] 160 19 4 5 140 30 138 23 6 10 ...
##   ..@ bbox       : num [1:2, 1:2] -180 -90 180 83.6
##   .. ..- 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 "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

str(world)

## Classes 'sf' and 'data.frame':	177 obs. of  11 variables:
##  $ iso_a2   : chr  "FJ" "TZ" "EH" "CA" ...
##  $ name_long: chr  "Fiji" "Tanzania" "Western Sahara" "Canada" ...
##  $ continent: chr  "Oceania" "Africa" "Africa" "North America" ...
##  $ region_un: chr  "Oceania" "Africa" "Africa" "Americas" ...
##  $ subregion: chr  "Melanesia" "Eastern Africa" "Northern Africa" "Northern America" ...
##  $ type     : chr  "Sovereign country" "Sovereign country" "Indeterminate" "Sovereign country" ...
##  $ area_km2 : num  19290 932746 96271 10036043 9510744 ...
##  $ pop      : num  8.86e+05 5.22e+07 NA 3.55e+07 3.19e+08 ...
##  $ lifeExp  : num  70 64.2 NA 82 78.8 ...
##  $ gdpPercap: num  8222 2402 NA 43079 51922 ...
##  $ geom     :sfc_MULTIPOLYGON of length 177; first list element: List of 3
##   ..$ :List of 1
##   .. ..$ : num [1:8, 1:2] 180 180 179 179 179 ...
##   ..$ :List of 1
##   .. ..$ : num [1:9, 1:2] 178 178 179 179 178 ...
##   ..$ :List of 1
##   .. ..$ : num [1:5, 1:2] -180 -180 -180 -180 -180 ...
##   ..- attr(*, "class")= chr  "XY" "MULTIPOLYGON" "sfg"
##  - attr(*, "sf_column")= chr "geom"
##  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA
##   ..- attr(*, "names")= chr  "iso_a2" "name_long" "continent" "region_un" ...

The structures in the sp packages are more complicated - str(world_sf) vs str(world_sp)
Moreover, many of the sp functions are not "pipeable" (it's hard to combine them with the tidyverse)

world_sp %>% 
  filter(name_long == "Papua New Guinea")

Error in UseMethod("filter_") : no applicable method for 'filter_' applied to an object of class "c('SpatialPolygonsDataFrame', 'SpatialPolygons', 'Spatial', 'SpatialPolygonsNULL', 'SpatialVector')"

world %>% 
  filter(name_long == "Papua New Guinea")

## Simple feature collection with 1 feature and 10 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 141.0002 ymin: -10.65248 xmax: 156.02 ymax: -2.500002
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##   iso_a2        name_long continent region_un subregion              type
## 1     PG Papua New Guinea   Oceania   Oceania Melanesia Sovereign country
##   area_km2     pop lifeExp gdpPercap                           geom
## 1 464520.1 7755785   65.23  3709.082 MULTIPOLYGON (((141.0002 -2...

Reading and writing spatial data

vector_filepath = system.file("shapes/world.gpkg", package = "spData")
vector_filepath

## [1] "/Library/Frameworks/R.framework/Versions/3.5/Resources/library/spData/shapes/world.gpkg"

world = st_read(vector_filepath)

## Reading layer `world' from data source `/Library/Frameworks/R.framework/Versions/3.5/Resources/library/spData/shapes/world.gpkg' using driver `GPKG'
## Simple feature collection with 177 features and 10 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

Counterpart to st_read() is the st_write function, e.g. st_write(world, 'data/new_world.gpkg'). A full list of supported formats could be found using sf::st_drivers().

Structure of the sf objects

world

## Simple feature collection with 177 features and 10 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 3 features:
##   iso_a2      name_long continent region_un       subregion
## 1     FJ           Fiji   Oceania   Oceania       Melanesia
## 2     TZ       Tanzania    Africa    Africa  Eastern Africa
## 3     EH Western Sahara    Africa    Africa Northern Africa
##                type  area_km2      pop lifeExp gdpPercap
## 1 Sovereign country  19289.97   885806  69.960  8222.254
## 2 Sovereign country 932745.79 52234869  64.163  2402.099
## 3     Indeterminate  96270.60       NA      NA        NA
##                             geom
## 1 MULTIPOLYGON (((180 -16.067...
## 2 MULTIPOLYGON (((33.90371 -0...
## 3 MULTIPOLYGON (((-8.66559 27...

class(world)

## [1] "sf"         "data.frame"

Structure of the sf objects

world$name_long

## [1] Fiji           Tanzania       Western Sahara
## 177 Levels: Afghanistan Albania Algeria Angola Antarctica ... Zimbabwe

world$geom

## Geometry set for 177 features 
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 3 geometries:

## MULTIPOLYGON (((180 -16.06713, 180 -16.55522, 1...

## MULTIPOLYGON (((33.90371 -0.95, 34.07262 -1.059...

## MULTIPOLYGON (((-8.66559 27.65643, -8.665124 27...

Non-spatial operations on sf objects

Which countries have the highest population density?

print(world, n=3)

## Simple feature collection with 177 features and 10 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 3 features:
##   iso_a2      name_long continent region_un       subregion
## 1     FJ           Fiji   Oceania   Oceania       Melanesia
## 2     TZ       Tanzania    Africa    Africa  Eastern Africa
## 3     EH Western Sahara    Africa    Africa Northern Africa
##                type  area_km2      pop lifeExp gdpPercap
## 1 Sovereign country  19289.97   885806  69.960  8222.254
## 2 Sovereign country 932745.79 52234869  64.163  2402.099
## 3     Indeterminate  96270.60       NA      NA        NA
##                             geom
## 1 MULTIPOLYGON (((180 -16.067...
## 2 MULTIPOLYGON (((33.90371 -0...
## 3 MULTIPOLYGON (((-8.66559 27...

print(worldbank_df, n=3)

## # A tibble: 177 x 7
##   name        iso_a2    HDI urban_pop unemployment pop_growth literacy
##   <chr>       <chr>   <dbl>     <dbl>        <dbl>      <dbl>    <dbl>
## 1 Afghanistan AF     NA       8609463         NA        3.18      NA  
## 2 Angola      AO      0.504  11649562         NA        3.49      66.0
## 3 Albania     AL     NA       1629715         17.5     -0.207     NA  
## # ... with 174 more rows

pop_den <-
  world %>% 
  left_join(worldbank_df, by = "iso_a2") %>%
  mutate(pop_density = pop/area_km2) %>%
  dplyr::select(name_long, pop_density) %>%
  arrange(desc(pop_density))

name_long	pop_density
Bangladesh	1191.5288
Palestine	852.6094
Lebanon	554.8349
Republic of Korea	512.3629
Rwanda	485.5621

ggplot(pop_den)+
  geom_sf(aes(fill=pop_density,geometry=geom))+
  scale_fill_viridis_c()

Non-spatial operations

world_cont = world %>% 
        group_by(continent) %>% 
        summarize(pop_sum = sum(pop, na.rm = TRUE))%>%
  arrange(desc(pop_sum))

## Simple feature collection with 8 features and 2 fields
## geometry type:  GEOMETRY
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 8 x 3
##   continent   pop_sum                                                 geom
##   <fct>         <dbl>                                   <MULTIPOLYGON [°]>
## 1 Asia         4.31e9 (((120.295 -10.25865, 118.9678 -9.557969, 119.9003 …
## 2 Africa       1.15e9 (((49.54352 -12.46983, 49.80898 -12.89528, 50.05651…
## 3 Europe       6.69e8 (((-51.6578 4.156232, -52.24934 3.241094, -52.55642…
## # ... with 5 more rows

The st_set_geometry function can be used to remove the geometry column:

world_df =st_set_geometry(world_cont, NULL)
class(world_df)

## [1] "tbl_df"     "tbl"        "data.frame"

Spatial operations

It's a big topic which includes:

Spatial subsetting
Spatial joining/aggregation
Topological relations
Distances
Spatial geometry modification
Raster operations (map algebra)

See Chapter 4 of Geocomputation with R

CRS

Transform (warp) to a different projection:

na_2163 = world %>%
  filter(continent == "North America") %>% 
  st_transform(2163) #US National Atlas Equal Area
st_crs(na_2163)

## Coordinate Reference System:
##   EPSG: 2163 
##   proj4string: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"

Compare projections

library(gridExtra)  # for combining ggplots
na = world %>% filter(continent == "North America")
p1=na_2163 %>% ggplot()+geom_sf(aes(geometry=geom))
p2=na %>% ggplot()+geom_sf(aes(geometry=geom))
grid.arrange(p1,p2,nrow=1)

For more on grid.arrange, see here.

Spatial operations

canada = na_2163 %>% 
  filter(name_long=="Canada")

canada_buffer=canada%>%
  st_buffer(500000)

ggplot()+
  geom_sf(data=canada,aes(geometry=geom))+
  geom_sf(data=canada_buffer,col="red",fill=NA)

Visualization

Basic maps

Basic maps of sf objects can be quickly created using the plot() function:

plot(world[0])

plot(world["pop"])

ggplot and geom_sf()

ggplot(world)+
  geom_sf(aes(geometry=geom,fill=lifeExp))+
  scale_fill_viridis_c()

All the nice ggplot features are available

leaflet: javascript library for interactive maps

library(leaflet)
library(widgetframe)

Leaflet leading open-source JavaScript library for mobile-friendly interactive maps. Weighing just about 38 KB of JS, it has all the mapping features most developers ever need.

Construct the leaflet map

l=leaflet(world) %>%
        addTiles() %>%
        addPolygons(color = "#444444", weight = 1, fillOpacity = 0.5,
                    fillColor = ~colorQuantile("YlOrRd", lifeExp)(lifeExp),
                    popup = paste("Life Expectancy =", round(world$lifeExp, 2)))

Page hosted on Github for free (except the domain name)...

Raster data in the tidyverse

Raster data is not yet closely connected to the tidyverse, however:

Some functions from raster work well in pipes
Convert vector data to Spatial* form using as(my_vector, "Spatial") for raster-vector interactions
Some early efforts to bring raster data into the tidyverse, including tabularaster, sfraster, fasterize, and stars (multidimensional, large datasets).

Sources

Slides adapted from:
- "Robin Lovelace and Jakub Nowosad" draft book Geocomputation with R (to be published in 2018). Source code at https://github.com/robinlovelace/geocompr.
- Claudia Engel's spatial analysis workshop

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PS_08_sf.md

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File metadata and controls

Course Logistics Reminder

Project Proposal

Link to this script

{data-background-iframe="https://cran.r-project.org/web/views/"}

Code Reading Challenge

Working with Spatial Data in R

Available Packages

What is a Spatial Feature (sf)?

Spatial Features

Geometry

Spatial Feature Standard

Dimensions

Dimensions

Common Simple Feature (SF) types

Seven common Simple Feature (SF) geometry types

Uncommon Geometry Types

Coordinate reference system

sp package

sf package

How simple features in R are organized

Components

I. Create geometric objects (topology)

II. Combine all individual single feature objects for the special column.

III. Add attributes.

sf Highlights

Converting formats

Reading and writing spatial data

Structure of the sf objects

Structure of the sf objects

Non-spatial operations on sf objects

Non-spatial operations

Spatial operations

CRS

Compare projections

Spatial operations

Visualization

Basic maps

ggplot and geom_sf()

leaflet: javascript library for interactive maps

Construct the leaflet map

Raster data in the tidyverse

Sources

`sp` package

`sf` package