d6berlin

The d6berlin package aims to provide spatial data and template maps for Berlin. The data sets include green spaces, water bodies, district borders, raleways and more. A template map of imperviousness across Berlin with carefully chosen and aesthetically pleasing default is included to serve as a base map to visualize spatial data.

Installation

You can install the d6berlin package from GitHub:

install.packages("devtools")
devtools::install_github("EcoDynIZW/d6berlin")

(Note: If you are asked if you want to update other packages either press “No” (option 3) and continue or update the packages before running the install command again.)

Afterwards, load the functionality and data of the package in each session:

library(d6berlin)

Berlin Data Sets

The package contains several data sets for Berlin. All of them start with sf_, e.g. d6berlin::sf_roads. Here is a full overview of the data sets that are stored as simple feature objects and directly accessible:

sf_berlin      -- Berlin border
sf_districts   -- district borders
sf_green       -- green spaces
sf_metro       -- U- and S-train stations
sf_railways    -- railroad lines
sf_roads       -- motorways and streets
sf_water       -- water ways and bodies

More information about each data set is available in the help:

?sf_green

An sf object containing the shape of all green spaces (defined as natural areas and landuse categories “forest”, “grass”, “meadow”, “nature_reserve”, “scrub”, “heath”, “beach”, and “cliff”) in Berlin.

Furthermore, you can work with the spatial data as you usually do:

unique(sf_green$fclass)
#> [1] "scrub"          "grass"          "forest"         "meadow"        
#> [5] "nature_reserve" "beach"          "heath"          "cliff"

sf_forests <- subset(sf_green, fclass == "forest")
head(sf_forests)
#> Simple feature collection with 6 features and 7 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 13.22869 ymin: 52.54882 xmax: 13.37107 ymax: 52.61291
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 x 8
#>   osm_id    code fclass name              district_name district_id district_key
#>   <fct>    <int> <chr>  <chr>             <chr>         <chr>              <dbl>
#> 1 5643109   7201 forest  <NA>             Reinickendorf 012             11000012
#> 2 3085634   7201 forest  <NA>             Reinickendorf 012             11000012
#> 3 23353374  7201 forest "Lindwerder"      Reinickendorf 012             11000012
#> 4 24482177  7201 forest  <NA>             Reinickendorf 012             11000012
#> 5 3085413   7201 forest "NABU-Schutzgebi~ Reinickendorf 012             11000012
#> 6 27835164  7201 forest  <NA>             Reinickendorf 012             11000012
#> # ... with 1 more variable: geometry <POLYGON [°]>

A Basic Template Map of Imperviousness

The basic template map shows levels of imperviousness and green areas in Berlin. The imperviousness raster data was derived from Geoportal Berlin (FIS-Broker) with a resolution of 10m. The vector data on green spaces was collected from data provided by the OpenStreetMap Contributors. The green spaces consist of a mixture of land use and natural categories (namely “forest”, “grass”, “meadow”, “nature_reserve”, “scrub”, “heath”, “beach”, “cliff”).

The map is projected in EPSG 4326 (WGS84).

base_map_imp()

You can also customize the arguments, e.g. change the color intensity, add a globe with a locator pin, change the resolution of the raster, and move the legend to a custom position:

base_map_imp(color_intensity = 1, globe = TRUE, resolution = 500,
             legend_x = .17, legend_y = .12)

If you think the legend is absolute, there is also an option called "none". (The default is "bottom". You can also use of the predefined setting "top" as illustrated below or a custom position as shown in the previous example.)

Adding Locations to the Map

Let’s assume you have recorded some animal locations or you want to plot another information on top of our base map. For example, let’s visualize the Berlin metro stations by adding geom_sf(data = x) to the map object:

library(ggplot2)
library(sf)

map <- base_map_imp(color_intensity = .3, resolution = 250, legend = "top")

map + geom_sf(data = sf_metro) ## sf_metro is contained in the d6berlin package

Note: Since the template map contains many filled areas, we recommend to add geometries with variables mapped to color|xolour|col to the template maps.

You can, of course, style the appearance of the points as usual:

map + geom_sf(data = sf_metro, shape = 8, color = "red", size = 2)

It is also possible to filter the data inside the geom_sf function — no need to use subset:

library(dplyr) ## for filtering
library(stringr) ## for filtering based on name

map + 
  geom_sf(data = filter(sf_metro, str_detect(name, "^U")), 
          shape = 21, fill = "dodgerblue", size = 2) +
  geom_sf(data = filter(sf_metro, str_detect(name, "^S")), 
          shape = 21, fill = "forestgreen", size = 2)

You can also use the mapping functionality of ggplot2 to address variables from your data set:

map + 
  geom_sf(data = sf_metro, aes(color = type), size = 2) +
  scale_color_discrete(type = c("dodgerblue", "forestgreen"), 
                       name = NULL) +
  guides(color = guide_legend(direction = "horizontal",
                              title.position = "top", 
                              title.hjust = .5))

(It looks better if you style the legend in the same horizontal layout.)

Custom Styling

Since the output is a ggplot object, you can manipulate the result as you like (but don’t apply a new theme, this will mess up the legend design):

library(systemfonts) ## for title font

base_map_imp(color_intensity = 1, resolution = 250, globe = TRUE,
             legend_x = .17, legend_y = .12) + 
  geom_sf(data = sf_metro, shape = 21, fill = "white", 
          stroke = .4, size = 4) +
  ggtitle("Metro Stations in Berlin") + 
  theme(plot.title = element_text(size = 30, hjust = .5, family = "Bangers"),
        panel.grid.major = element_line(color = "white", size = .3),
        axis.text = element_text(color = "black", size = 8),
        plot.background = element_rect(fill = "#fff0de", color = NA),
        plot.margin = margin(rep(20, 4)))

Save Map

Unfortunately, the size of the text elements is fixed. The best aspect ratio to export the map is 12x9 and you can save it with ggsave() for example:

ggsave("metro_map.pdf", width = 12, height = 9, device = cairo_pdf)

Session Info

Sys.time()
#> [1] "2022-02-28 16:06:22 CET"
git2r::repository()
#> Local:    main C:/Users/DataVizard/PopDynIZW Dropbox/GeoData/d6berlin
#> Remote:   main @ origin (https://github.com/EcoDynIZW/d6berlin.git)
#> Head:     [fe860e1] 2022-02-28: update readme and data description
sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19043)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=German_Germany.1252  LC_CTYPE=German_Germany.1252   
#> [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C                   
#> [5] LC_TIME=German_Germany.1252    
#> system code page: 65001
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] systemfonts_1.0.3   stringr_1.4.0       dplyr_1.0.7        
#> [4] sf_1.0-5            ggplot2_3.3.5       d6berlin_0.0.0.9000
#> 
#> loaded via a namespace (and not attached):
#>  [1] rnaturalearth_0.1.0 assertthat_0.2.1    sp_1.4-6           
#>  [4] highr_0.9           stats4_4.1.2        yaml_2.2.1         
#>  [7] pillar_1.6.4        lattice_0.20-45     glue_1.4.2         
#> [10] digest_0.6.29       colorspace_2.0-2    htmltools_0.5.2    
#> [13] pkgconfig_2.0.3     ggspatial_1.1.5     raster_3.5-11      
#> [16] stars_0.5-5         s2_1.0.7            purrr_0.3.4        
#> [19] scales_1.1.1        webshot_0.5.2       terra_1.4-22       
#> [22] satellite_1.0.4     git2r_0.29.0        tibble_3.1.6       
#> [25] proxy_0.4-26        generics_0.1.1      farver_2.1.0       
#> [28] ellipsis_0.3.2      withr_2.4.3         cli_3.1.0          
#> [31] mapview_2.10.0      magrittr_2.0.1      crayon_1.4.2       
#> [34] evaluate_0.14       fansi_0.5.0         lwgeom_0.2-8       
#> [37] class_7.3-19        textshaping_0.3.6   tools_4.1.2        
#> [40] lifecycle_1.0.1     munsell_0.5.0       compiler_4.1.2     
#> [43] e1071_1.7-9         rlang_0.4.12        classInt_0.4-3     
#> [46] units_0.7-2         grid_4.1.2          rstudioapi_0.13    
#> [49] htmlwidgets_1.5.4   crosstalk_1.2.0     leafem_0.1.6       
#> [52] base64enc_0.1-3     labeling_0.4.2      rmarkdown_2.11     
#> [55] wk_0.6.0            gtable_0.3.0        codetools_0.2-18   
#> [58] abind_1.4-5         DBI_1.1.2           R6_2.5.1           
#> [61] knitr_1.36          rgdal_1.5-28        fastmap_1.1.0      
#> [64] utf8_1.2.2          ragg_1.1.3          KernSmooth_2.23-20 
#> [67] stringi_1.7.5       parallel_4.1.2      Rcpp_1.0.7         
#> [70] vctrs_0.3.8         png_0.1-7           leaflet_2.0.4.1    
#> [73] tidyselect_1.1.1    xfun_0.27

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