Zev Ross President, ZevRoss Spatial Analysis

Transcription

1 SPATIAL ANALYSIS IN R WITH SF AND RASTER A quick refresher on the coordinate Zev Ross President, ZevRoss Spatial Analysis reference system

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3 Coordinate reference system A place on the earth is specified by a latitude and longitude or X/Y coordinates The coordinates are based on a mathematical model of the shape of the earth Some mathematical formulas involve a transformation from the 3-D globle to a 2-dimensional map

4 Projected vs unprojected CRS An unprojected CRS uses latitude and longitude coordinates and references the earth as a three-dimensional object A projected CRS uses X and Y coordinates as a two-dimensional representation of the earth

5 Your geographic files have a CRS - but it's not always defined Vector and raster spatial data was created based on a specific CRS Usually the spatial file has metadata that tells you the CRS Sometimes there is no metadata defining the CRS

6 Both sf and raster will read the CRS if it exists in the metadata st_crs() prints out a vector object's CRS crs() prints out a raster object's CRS

7 An example: This shapefile has a defined CRS > shape1 <- st_read("shape1.shp") > st_crs(shape1) $epsg [1] 4326 $proj4string [1] "+proj=longlat +ellps=wgs84 +no_defs" attr(,"class") [1] "crs" You can tell that this is an unprojected CRS because the definition (called proj4string) starts with "+proj=longlat" referring to longitude and latitude.

8 Define CRS with EPSG or proj4string You can use either an EPSG or proj4string to define the CRS. A CRS might have both but might only need one The EPSG code is a numeric representation of a CRS (e.g, 4326) The proj4string is a full set of parameters spelled out in a string (e.g., "+proj=longlat +ellps=wgs84 +no_defs")

9 An example: This shapefile does not have a defined CRS > shape2 <- st_read("shape2.shp") > st_crs(shape2) $epsg [1] NA $proj4string [1] NA attr(,"class") [1] "crs"

10 If the CRS is not defined Do background research to find out the CRS Then tell R what the CRS with st_crs()

11 Defining the crs with st_crs() Define with the proj4string > st_crs(shape2) <- "+proj=longlat +ellps=wgs84 +no_defs" Define with the EPSG code > st_crs(shape2) <- 4236

12 For a raster define the CRS with crs() An example where the CRS is defined > crs(singleband) # CRS arguments: # +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 # +lon_0=-96 +x_0=0 +y_0=0 +ellps=grs80 # +towgs84=0,0,0,0,0,0,0 +units=m +no_defs An example where the CRS is not defined > crs(multiband) # CRS arguments: NA

13 For a raster define the CRS with crs() Here we define it > crs(multiband) <- "+proj=utm +zone=18+datum=wgs84 +units=m +no_defs+ellps=wgs84 +towgs84=0,0,0"

14 Change the CRS with st_transform() or projectraster() Use st_transform() to change the CRS for vectors Use projectraster() to change the CRS for rasters

15 Transform the CRS for vector shapes > shape1_prj <- st_transform(shape1, crs = 32618) > shape1_prj <- st_transform(shape1, crs ="+proj=utm +zone=18 +ellps=wgs84 +datum=wgs84 +units=m +no_defs")

16 Transform CRS of one layer to match another layer > shape1_prj <- st_transform(shape1, crs = crs(singleband, astext = TRUE)) Note that you need astext = TRUE to force the crs() function from raster to output the CRS as a string

17 Transform the CRS for a raster > singleband_prj <- projectraster(singleband, + crs = "+proj=utm +zone=18 +ellps=wgs84 +datum=wgs84 +units=m +no_defs") > singleband_prj <- projectraster(singleband, crs = "+init=epsg:32618") Note that to use an EPSG with projectraster the syntax is "+init=epsg:32618"

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

19 SPATIAL ANALYSIS IN R WITH SF AND RASTER Slicing, dicing and simplifying your Zev Ross President, ZevRoss Spatial Analysis vectors

20 Using dplyr with spatial data > head(trees) # Simple feature collection with 6 features and 2 fields # geometry type: POINT # dimension: XY # bbox: xmin: ymin: xmax: # epsg (SRID): 4326 # proj4string: +proj=longlat +ellps=wgs84 +no_defs # Source: local data frame [6 x 3] # Groups: hood [2] # A tibble: 6 x 3 # tree_id hood geometry # <dbl> <fctr> <simple_feature> # BK09 <POINT( > # BK09 <POINT( > # BK09 <POINT( > # BK17 <POINT( > # BK17 <POINT( > # BK17 <POINT( >

21 Count trees by neighborhood Use the count() function from dplyr and arrange in descending order. > cnt <- count(trees, hood) %>% + arrange(desc(n)) > cnt # Simple feature collection with 188 features and 2 fields # geometry type: MULTIPOINT # dimension: XY # bbox: xmin: ymin: xmax: # epsg (SRID): 4326 # proj4string: +proj=longlat +ellps=wgs84 +no_defs # A tibble: 188 x 3 # hood n geometry # <fctr> <int> <simple_feature> # 1 SI <MULTIPOINT(-...> # 2 BK <MULTIPOINT(-...> # 3 SI <MULTIPOINT(-...> # 4 QN <MULTIPOINT(-...> #... with 184 more rows

22 Drop geometry by setting it to NULL # Without a pipe > tree_cnt <- count(trees, hood) > tree_cnt <- st_set_geometry(tree_cnt, NULL) # With a pipe > tree_cnt <- count(trees, hood) %>% + st_set_geometry(null) > head(tree_cnt) # A tibble: 3 x 2 # hood n # <fctr> <int> # 1 BK09 3 # 2 BK17 3 # 3 BK19 3

23 Join spatial and non-spatial data - setup > tree_sm <- tree[1:3,] > head(tree_sm) # tree_id hood geometry # QN76 POINT( # MN32 POINT( # QN70 POINT( Fake data for our example. Goal is to get the val variable into the tree data. > dat <- data.frame(hood = c("qn76", "MN32", "QN70"), + val = c(1, 2, 3))

24 Join spatial and non-spatial data with inner_join() # tree_sm is spatial, dat is non-spatial > res <- inner_join(tree_sm, dat, by = "hood") # The dataset now how the "val" variable > head(res) # tree_id hood val geometry # QN76 1 POINT( # MN32 2 POINT( # QN70 3 POINT(

25 Simplify your vectors

26 Vector data can be more detailed than needed Administrative boundaries and rivers, for example, can have far more detail than required Simplifying will speed up computations

27 The size of our example data before simplification Size in memory > library(pryr) > object_size(county) 489 kb Number of vertices # "cast" our polygons to bundles of points -- "MULTIPOINT" then count > pts <- st_cast(county$geometry, "MULTIPOINT") > sum(sapply(pts, length)) [1] 57886

28 Simplify with st_simplify() Tolerance controls simplification. Bigger numbers mean more simplification. Units are the units of the CRS. > boro_simp <- st_simplify(boro, dtolerance = 500)

29 Visually there is barely a difference Non-Simplified Simplified

30 16x smaller and 33x fewer vertices Original object size Simplified object size > library(pryr) > object_size(boro) 489 kb # Cast code left off here > sum(sapply(pts, length)) [1] > library(pryr) > object_size(boro_simp) 29.4 kb # Cast code left off here > sum(sapply(pts, length)) [1] 1770

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

32 SPATIAL ANALYSIS IN R WITH SF AND RASTER Converting sf objects between sp and raw Zev Ross President, ZevRoss Spatial Analysis coordinates

33 sp has had a long and useful live sp was created more than a decade ago Many package make use of sp objects

34 Points often exist in non-spatial data frames of coordinates To use spatial functionality, you need to convert a data frame of coordinates to sf objects

35 Convert sf objects to sp with as() The class for sp objects is Spatial. # Our sf object > polys <- st_read("polygons.shp") > class(polys) [1] "sf" "data.frame" # Convert to Spatial object > polys_sp <- as(polys, Class = "Spatial") > class(polys_sp) [1] "SpatialPolygonsDataFrame" attr(,"package") [1] "sp"

36 Convert from sp to sf with st_as_sf() # Convert from sp to sf object > polys_sf <- st_as_sf(polys_sp) > class(polys_sf) [1] "sf" "data.frame"

37 Coordinates to an sf points object with st_as_sf() The coords argument specifies the coordinate columns and must be in longitude, latitude or X, Y order. # Simple dataframe with coordinates > pts <- data.frame(id = 1:2, lon = c(-73, -72),lat = c(41, 42)) # Convert to an sf object > pts <- st_as_sf(pts, coords = c("lon", "lat")) > pts # Simple feature collection with 2 features and 1 field # geometry type: POINT # dimension: XY # bbox: xmin: -73 ymin: 41 xmax: -72 ymax: 42 # epsg (SRID): NA # proj4string: NA # ID geometry # 1 1 POINT (-73 41) # 2 2 POINT (-72 42)

38 You can specify the CRS with the crs argument Specify the CRS with either a proj4string or a EPSG code > # WGS 84 with a proj4string > st_as_sf(pts, coords = c("lon", "lat"), + crs = "+proj=longlat +ellps=wgs84 +datum=wgs84 +no_defs") With long/lat you can probably use EPSG = 4326 (WGS 84) # WGS 84 with an EPSG code > st_as_sf(pts, coords = c("lon", "lat"), crs = 4326)

39 Write your points to a CSV with coordinates Writing to other spatial formats like shapefiles is easy with st_write() If you use st_write() with points and specify a CSV, coordinates won't be included Use a "hidden" argument, layer_options to write coordinates > st_write(pts, "pts.csv", layer_options = "GEOMETRY=AS_XY")

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

41 SPATIAL ANALYSIS IN R WITH SF AND RASTER Manipulating raster layers Zev Ross President, ZevRoss Spatial Analysis

42 Two key functions Reduce raster resolution with aggregate() Reclassify values with reclassify()

43 Example: Elevation data for area around Ithaca, NY > elevation <- raster("elevation.tif") > ncell(elevation) [1] > res(elevation) [1] > file.size("elevation.tif") [1] # ~ 25 megabytes > plot(elevation)

44 Reduce raster resolution with aggregate() Use the fact argument to specify the factor of aggregation > dem_low <- aggregate(dem, fact = 20) Use fun to specify the function to do aggregation > dem_low <- aggregate(dem, fact = 20, fun = mean)

45 Significant reduction in file size > dem_low <- aggregate(dem, fact = 20, fun = mean) > ncell(dem_low) [1] > res(dem_low) [1] > writeraster(dem_low, "elevation-small.tif") > file.size("elevation-small.tif") [1] # 0.11 megabytes

46 Aggregated raster

47 Reclassify raster values with reclassify() Here we use a 2-column matrix to change values of 5 to 100 > new_vals <- cbind(5, 100) > new_rast <- reclassify(old_raster, rcl = new_vals) Here we use a 3-column matrix to change values between 1 and 3 to NA > new_vals <- cbind(1, 3, NA) > new_rast <- reclassify(old_raster, rcl = new_vals)

48 Use a multi-row matrix to change many values at once Reclassify values into three groups (example from the help) # Values between 0 and 0.25 become 1 and so on > m <- c(0.00, 0.25, 1, 0.25, 0.50, 2, 0.50, 1.00, 3) > rclmat <- matrix(m, ncol = 3, byrow = TRUE) > rc <- reclassify(r, rclmat)

49 Example with the elevation data data Create a raster with just four categories of elevation > m <- c( 0, 300, 1, 300, 400, 2, 400, 500, 3, 500, 650, 4) > rclmat <- matrix(m, nrow = 4, byrow = TRUE) > rc <- reclassify(dem_low, rcl = rclmat)

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