Zev Ross President, ZevRoss Spatial Analysis

Transcription

1 SPATIAL ANALYSIS IN R WITH SF AND RASTER Welcome! Zev Ross President, ZevRoss Spatial Analysis

2 Packages we will use in this course Two key packages sf for vectors raster for grids Additional packages discussed ggplot2 tmap dplyr

3 Analyze New York City data You will compute tree density and greenspace by neighborhood in New York City.

4 Reading spatial data Read in vector data with the sf package and st_read() function Read in raster data using the raster package and either the raster() or brick() functions

5 Reading vector data with st_read() The st_read() function can be used to read a wide range of formats You feed the function your file path The function guesses the format of the file based on the input file suffix

6 An st_read() example > library(sf) > county <- st_read("folder1/county.shp")

7 st_read() supports tons of vector formats Shapefiles GeoJSON GPS netcdf Many others...

8 In this course you will be reading shapefiles New York City tree data from the Street Tree Census New York City neighborhoods New York City parks

9 Read in raster data using raster() and brick() Use raster() to read single-band rasters Use brick() to read multi-band rasters

10 Single-band rasters These have a single band/layer with a set of values, so one value per grid cell Examples might include rasters of elevation or land use

11 Use raster() for single-band rasters # elevation.tif is a single-band raster > elevation <- raster("elevation.tif")

12 Multi-band rasters Each grid cell has multiple values, one for each layer Examples include satellite images or aerial photos

13 Use brick() for multi-band rasters # aerial.tif is a multi-band raster > aerial <- brick("aerial.tif")

14 Many raster formats supported GeoTIFF ESRI grids ENVI grids ERDAS grids Others...

15 Write your data Write vector data > st_write(my_poly, "data/my_poly.shp") Write raster data > writeraster(my_raster, "data/my_raster.tif")

16 SPATIAL ANALYSIS IN R WITH SF AND RASTER Let's practice!

17 SPATIAL ANALYSIS IN R WITH SF AND RASTER Getting to know your vector data Zev Ross President, ZevRoss Spatial Analysis

18 Vector spatial objects are data frames! Spatial objects are stored as data frames One row per feature You can use standard tools like dplyr to slice and dice These data frames have some special properties...

19 sf data frames have special properties They include spatial metadata like the coordinate reference system The geometry is stored in a list column > trees <- st_read("trees.shp") > head(trees, 3) # Simple feature collection with 6 features and 2 fields # geometry type: POINT # dimension: XY # bbox: xmin: ymin: xmax: # epsg (SRID): 4326 # proj4string: +proj=longlat +datum=wgs84 +no_defs # tree_id species geometry # honeylocust POINT( # Callery pear POINT( # Chinese elm POINT(

20 The magic of the list column A non-spatial example > d <- data.frame(a = 1:3, b = 1:3) > new_list <- list(1:2, 1:5, 1:3) > new_list [[1]] [1] 1 2 [[2]] [1] [[3]] [1] 1 2 3

21 A list column is another variable in your data frame Each list element can contain as much (or as little) detail as you need. > d$c <- new_list library(dplyr) > d <- tbl_df(d) > d # A tibble: 3 x 3 a b c <int> <int> <list> <int [2]> <int [5]> <int [3]>

22 Take it one step further, geometry as list column # Read in a vector file > library(sf) > trees <- st_read("trees.shp") The geometry column is a list column. > head(trees, 3) # Simple feature collection with 6 features and 2 fields # geometry type: POINT # dimension: XY # bbox: xmin: ymin: xmax: # epsg (SRID): 4326 # proj4string: +proj=longlat +datum=wgs84 +no_defs # tree_id species geometry # honeylocust POINT( # Callery pear POINT( # Chinese elm POINT(

23 Geometry is a simple features list column -- sfc The species column is not a list > is.list(trees$species) [1] FALSE The geometry column is a list > is.list(trees$geometry) [1] TRUE Geometry is a special type of list, a simple features list column > class(trees$geometry) [1] "sfc_point" "sfc"

24 Your first visual using plot() # Plots each attribute (tree_id, species) > plot(trees)

25 Extract and plot only the geometry with st_geometry() # Extract geometry > geo <- st_geometry(trees) # Plot only the geometry > plot(st_geometry(trees))

26 SPATIAL ANALYSIS IN R WITH SF AND RASTER Let's practice!

27 SPATIAL ANALYSIS IN R WITH SF AND RASTER Getting to know your raster data Zev Ross President, ZevRoss Spatial Analysis

28 Rasters will be stored as a RasterLayer or RasterBrick Single-band rasters read in with raster() will have a class of RasterLayer > singleband <- raster("data/single.tif") > class(singleband) [1] "RasterLayer" attr(,"package") [1] "raster" Multi-band rasters read in with brick() will have a class of RasterBrick > multiband <- brick("data/multi.tif") > class(multiband) [1] "RasterBrick" attr(,"package") [1] "raster"

29 A quick look at the metadata of a RasterLayer object > singleband # class : RasterLayer # dimensions : 230, 253, (nrow, ncol, ncell) # resolution : 300, 300 (x, y) # extent : , , , (xmin, xmax, ymin, ymax) # coord. ref. : +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0= # data source : /Users/johndoe/git-repos/courses-geographic-information... # names : canopy # values : 0, 255 (min, max) Note that under "names" there is just one name, meaning just one band/layer.

30 The metadata in a RasterBrick object is very similar > multiband # class : RasterBrick # dimensions : 773, 801, , 3 (nrow, ncol, ncell, nlayers) # resolution : , (x, y) # extent : , , , (xmin, xmax, ymin,... # coord. ref. : +proj=utm +zone=18... # data source : /Users/johndoe/git-repos/courses-geographic-information... # names : manhattan.1, manhattan.2, manhattan.3 # min values : 0, 0, 0 # max values : 255, 255, 255 Notice that instead of one name there are three, meaning that this is a multi-band raster.

31 Functions to extract pieces of the metadata (I) extent() gives the minimum and maximum X and Y coordinates of the raster > extent(multiband) # class : Extent # xmin : # xmax : # ymin : # ymax :

32 Functions to extract pieces of the metadata (II) ncell() and nlayers() give the total number of grid cells or layers, respectively > ncell(multiband) [1] > nlayers(multiband) [1] 3

33 Functions to extract pieces of the metadata (III) crs() gives the coordinate reference system > crs(multiband) # CRS arguments: # +proj=utm +zone=18 +datum=wgs84 +units=m +no_defs +ellps=wgs84...

34 Note when importing rasters.. raster() and brick() do not read in raster values by default Why? Rasters can be very big To conserve memory raster values are imported only when required

35 Importing rasters - example As an example, the multiband raster we're reading in is approximately 1.5 megabytes on disk but the object.size() function shows that in memory it's just megabytes. # File size on disk (nearly 2 million bytes) > file.size("data/multi.tif") [1] # File size in memory (only 12,000 bytes) > object.size(multiband) bytes

36 The inmemory() function tells you if the raster values have been read into R > inmemory(multiband) [1] FALSE

37 You can read the values with the getvalues() function > vals <- getvalues(multiband) > head(vals) manhattan.1 manhattan.2 manhattan.3 [1,] [2,] [3,] [4,] [5,] [6,]

38 Creating quick maps of your rasters with plot() and plotrgb() Use plot() for single-band rasters or to look at the individual layers in a multi-band raster Use plotrgb() to create a true color map of a multi-layered object

39 Using plot() with a single-band raster > plot(singleband)

40 Using plot() with a multi-band raster > plot(multiband)

41 Using plotrgb() to create a true color image > plotrgb(multiband)

42 SPATIAL ANALYSIS IN R WITH SF AND RASTER Let's practice!