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Visualization of vector data cubes with tmap

Martijn Tennekes 9/18/2020

Installation of stars and tmap

We recommend to install the github version of stars, and the CRAN version of tmap:

if (!require(remotes))
  install.packages("remotes")
remotes::install_github("r-spatial/stars")
install.packages("starsdata", repos = "https://gis-bigdata.uni-muenster.de/pebesma", type = "source")
install.packages("tmap")

Load the packages:

library(stars)
## Loading required package: abind

## Loading required package: sf

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

library(tmap)

North Carolina example

This example is the North Carolina SIDS (sudden infant death syndrome) data set. See https://edzer.github.io/UseR2020/#Vector_data_cube:_North_Carolina_SIDS

The code to create the vector cube is the following:

nc = read_sf(system.file("gpkg/nc.gpkg", package="sf"))
nc.df = st_set_geometry(nc, NULL) # m is a regular, non-spatial data.frame
head(nc.df)
## # A tibble: 6 x 14
##    AREA PERIMETER CNTY_ CNTY_ID NAME  FIPS  FIPSNO CRESS_ID BIR74 SID74 NWBIR74
##   <dbl>     <dbl> <dbl>   <dbl> <chr> <chr>  <dbl>    <int> <dbl> <dbl>   <dbl>
## 1 0.114      1.44  1825    1825 Ashe  37009  37009        5  1091     1      10
## 2 0.061      1.23  1827    1827 Alle~ 37005  37005        3   487     0      10
## 3 0.143      1.63  1828    1828 Surry 37171  37171       86  3188     5     208
## 4 0.07       2.97  1831    1831 Curr~ 37053  37053       27   508     1     123
## 5 0.153      2.21  1832    1832 Nort~ 37131  37131       66  1421     9    1066
## 6 0.097      1.67  1833    1833 Hert~ 37091  37091       46  1452     7     954
## # ... with 3 more variables: BIR79 <dbl>, SID79 <dbl>, NWBIR79 <dbl>

mat = as.matrix(nc.df[c("BIR74", "SID74", "NWBIR74", "BIR79", "SID79", "NWBIR79")])
dim(mat) = c(county = 100, var = 3, year = 2) # make it a 3-dimensional array
# set dimension values to the array:
dimnames(mat) = list(county = nc$NAME, var = c("BIR", "SID", "NWBIR"), year = c(1974, 1979))
# convert array into a stars object
nc.st = st_as_stars(pop = mat)

# set dimension values to polygons
nc.geom <- st_set_dimensions(nc.st, 1, st_geometry(nc))
nc.sum = sapply(split(nc.geom, 2), sum)

# calculate standardise incidence rates
IR = nc.sum[2]/nc.sum[1]
(nc.SIR = st_apply(nc.geom, c(1,3), function(x) (x[2]/x[1])/IR))
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##       pop        
##  Min.   :0.0000  
##  1st Qu.:0.5996  
##  Median :0.9663  
##  Mean   :1.0223  
##  3rd Qu.:1.3012  
##  Max.   :4.7825  
## dimension(s):
##      from  to offset delta refsys point
## sfc     1 100     NA    NA  NAD27 FALSE
## year    1   2     NA    NA     NA    NA
##                                                                 values
## sfc  MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3...
## year                                                        1974, 1979

Visualization with tmap

The next version of tmap will support those vector cubes natively. The code to create two maps, one for each year, should be as simple as:

# this code will not work yet, but in the next version of tmap (3.3)
tm_shape(nc.SIR) +
  tm_polygons("pop") +
  tm_facets(by = "year")

A workaround using the current version of tmap (3.2) that produces this map is:

# extract the years
years = st_get_dimension_values(nc.SIR, 2)

# convert the stars object to an sf object
nc.SIR.sf = st_as_sf(nc.SIR)

# plot it as follows
tm_shape(nc.SIR.sf) + tm_polygons(years, title = "SIR", palette = "Purples", n = 10) + 
  tm_facets(free.scales = FALSE) +
  tm_layout(panel.labels = years)