d6geodata

The d6geodata package aims to provide spatial data for the ‘Ecological Dynamics’ Department of the IZW. The data sets are seperated in raw and processed data from different geographical regions like Germany and Berlin. Two main functions for accessing the data from the PopDynCloud and three plotting functions to visualize the raster data. Several additional functions are included but meant for the Geodatamanager to provide the datasets.

Installation

You can install the d6geodata package from GitHub:

install.packages("devtools")
devtools::install_github("EcoDynIZW/d6geodata")

(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(d6geodata)

Accessing Geodata

If you want to get geodata we already have in our Geodata archive you have two options: go on the EcoDyn Website, click on wikis and select Geodata. There you’ll find several raster layers and vector data with plots and metadata. In the metadata section, you’ll find the folder_name. You can copy this and use this together with get_geodata() function to get the data from our PopDynCloud. Another option is the function called geo_overview(). There you can select which data and from which location you want to have a list of data.

If you run the function geo_overview you have to decide if you want to see the raw or processed data by typing 1 for raw and 2 for processed data. Afterwards, you have to decide if you want to see the main (type 1) folders (the regions or sub-regions we have data from) or the sub (type 2) folders (the actually data we have in each region).

Example 1: main folder

d6geodata::geo_overview(path_to_cloud = "E:/PopDynCloud")
Raw or processed data: 

1: raw
2: processed

Auswahl: 2
choose folder type: 

1: main
2: sub

Auswahl: 1
[1] "atlas" "BB_MV_B" "berlin" "europe" "germany" "world"

Example 2: sub folder

d6geodata::geo_overview(path_to_cloud = "E:/PopDynCloud")
Raw or processed data: 

1: raw
2: processed

Auswahl: 2
choose folder type: 

1: main
2: sub

Auswahl: 2
$atlas
[1] "distance-to-human-settlements_atlas_2009_1m_03035_tif"
[2] "distance-to-kettleholes_atlas_2022_1m_03035_tif"      
[3] "distance-to-rivers_atlas_2009_1m_03035_tif"           
[4] "distance-to-streets_atlas_2022_1m_03035_tif"          
[5] "landuse_atlas_2009_1m_03035_tif"                      

$BB_MV_B
[1] "_archive" "_old_not_verified" "dist_path_bb_agroscapelabs"
[4] "scripts"                   

$berlin
 [1] "_old_not_verified"                            
 [2] "corine_berlin_2015_20m_03035_tif"            
 [3] "distance-to-paths_berlin_2022_100m_03035_tif" 
 [4]  "green-capacity_berlin_2020_10m_03035_tif"    
 [5] "imperviousness_berlin_2018_10m_03035_tif"     
 [6]  "light-pollution_berlin_2021_100m_03035_tif"  
 [7] "light-pollution_berlin_2021_10m_03035_tif"    
 [8]  "motorways_berlin_2022_100m_03035_tif"        
 [9] "noise-day-night_berlin_2017_10m_03035_tif"    
[10]  "population-density_berlin_2019_10m_03035_tif"
[11] "template-raster_berlin_2018_10m_03035_tif"    
[12] "tree-cover-density_berlin_2018_10m_03035_tif"

$europe
[1] "imperviousness_europe_2018_10m_03035_tif"

$germany
 [1] "_old_not_verified"                                          
 [2] "distance-to-motorway-rural-road_germany_2022_100m_03035_tif"
 [3] "distance-to-motorways_germany_2022_100m_03035_tif"          
 [4] "distance-to-paths_germany_2022_100m_03035_tif"              
 [5] "distance-to-roads-paths_germany_2022_100m_03035_tif"        
 [6] "distance-to-roads_germany_2022_100m_03035_tif"              
 [7] "distance_to_paths_germany_2022_100m_03035_tif"              
 [8] "motoroways_germany_2022_03035_osm_tif"                      
 [9] "motorway-rural-road_germany_2022_100m_03035_tif"            
[10] "motorways_germany_2022_100m_03035_tif"                      
[11] "paths_germany_2022_100m_03035_tif"                          
[12] "Roads-germany_2022_100m_03035_tif"                          
[13] "roads_germany_2022_100m_03035_tif"                          
[14] "tree-cover-density_germany_2015_100m_03035_tif"             

$world
character(0)

Now you can copy the name of one of the layers and paste it into the get_geodata function

corine <-
  d6geodata::get_geodata(
    data_name = "corine_berlin_2015_20m_03035_tif",
    path_to_cloud = "E:/PopDynCloud",
    download_data = FALSE
  )

If you set download_data = TRUE the data will be download and copied to your data-raw folder. If the data-raw folder doesn’t exist, it will create one.

Plotting functions

The three functions plot_binary_map(), plot_qualitative_map() and plot plot_quantitative_map() can be used to plot raster data with the respective color sceams we used for the Geodata wiki page, but for raster data only!

plot_binary_map(tif = tif)
plot_qualitative_map(tif = tif)
plot_quantitative_map(tif = tif)

Example plot

library(d6geodata)
plot_qualitative_map(tif = corine)

Session Info

Sys.time()
#> [1] "2023-03-02 11:55:26 CET"
git2r::repository()
#> Local:    main C:/Users/wenzler/Documents/GitHub/d6geodata
#> Remote:   main @ origin (https://github.com/EcoDynIZW/d6geodata.git)
#> Head:     [bc52b78] 2023-02-16: small change for labels
sessionInfo()
#> R version 4.2.2 (2022-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 17763)
#> 
#> 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    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] d6geodata_0.0.0.9000
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.0   terra_1.7-3        xfun_0.36          sf_1.0-9          
#>  [5] colorspace_2.1-0   vctrs_0.5.2        generics_0.1.3     htmltools_0.5.4   
#>  [9] stars_0.6-0        yaml_2.3.6         utf8_1.2.3         rlang_1.0.6       
#> [13] e1071_1.7-13       pillar_1.8.1       glue_1.6.2         withr_2.5.0       
#> [17] DBI_1.1.3          lifecycle_1.0.3    stringr_1.5.0      munsell_0.5.0     
#> [21] gtable_0.3.1       ragg_1.2.5         codetools_0.2-18   evaluate_0.20     
#> [25] knitr_1.42         fastmap_1.1.0      parallel_4.2.2     class_7.3-20      
#> [29] fansi_1.0.4        highr_0.10         Rcpp_1.0.10        KernSmooth_2.23-20
#> [33] scales_1.2.1       classInt_0.4-8     lwgeom_0.2-11      abind_1.4-5       
#> [37] farver_2.1.1       systemfonts_1.0.4  textshaping_0.3.6  ggplot2_3.4.1     
#> [41] digest_0.6.31      stringi_1.7.12     dplyr_1.1.0        grid_4.2.2        
#> [45] cli_3.6.0          tools_4.2.2        magrittr_2.0.3     proxy_0.4-27      
#> [49] tibble_3.1.8       rcartocolor_2.0.0  pkgconfig_2.0.3    rmarkdown_2.20    
#> [53] rstudioapi_0.14    R6_2.5.1           units_0.8-1        compiler_4.2.2    
#> [57] git2r_0.31.0

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