Note: for more information on
data storage and locations, see
vignette("v020_datastorage")
. The below workflow
is an application and demonstrates current setup from R (output is
mostly not included).
If you wish to follow by running the vignette’s code chunks, you can put a copy of the vignette in your working directory:
read_soilmap()
n2khab
n2khab_data
directory with 2
subdirectoriesThis will create n2khab_data
in the working
directory:
You can also provide a custom root ‘path’ to
fileman_folders()
, but then take a location that is a
(grand-grand-…) parent of the current working directory. This is because
reading functions of n2khab
will by default use the first
n2khab_data
directory they can find, starting from the
working directory and sequentially climbing up one directory level at a
time.
If you already have an existing n2khab_data
directory in
place, instead run:
soilmap_simple
data source in the correct
placeThe soilmap_simple
data source is a GeoPackage, provided
and documented at Zenodo. It belongs to
the collection of processed data (Zenodo-link).
soilmap_simple_path <- file.path(n2khab_data_path, "20_processed/soilmap_simple")
dir.create(soilmap_simple_path, recursive = TRUE)
download_zenodo(
doi = "10.5281/zenodo.3732903",
path = soilmap_simple_path
)
At some future time, the download will be performed automatically by
read_soilmap()
(if the soilmap_simple
data
source is missing).
soilmap_simple
data source!Inspect documentation of read_soilmap()
:
Read the data source and inspect its contents:
sm_simple <- read_soilmap()
sm_simple
#> Simple feature collection with 270550 features and 10 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 22265.45 ymin: 153062.6 xmax: 258872.2 ymax: 244027.9
#> CRS: 31370
#> # A tibble: 270,550 x 11
#> bsm_poly_id bsm_region bsm_converted bsm_mo_soilunit… bsm_mo_substr
#> * <dbl> <fct> <lgl> <fct> <fct>
#> 1 165740 Kunstmati… FALSE OB <NA>
#> 2 176046 Kunstmati… FALSE OB <NA>
#> 3 185239 Zandleems… FALSE Ldc <NA>
#> 4 162400 Kunstmati… FALSE OB <NA>
#> 5 173971 Kunstmati… FALSE OB <NA>
#> 6 173087 Zandleems… FALSE Ldp <NA>
#> 7 199453 Zandleems… FALSE Lep <NA>
#> 8 176922 Zandleems… FALSE Ldc <NA>
#> 9 227861 Zandleems… FALSE Abp(c) <NA>
#> 10 185390 Zandleems… FALSE Lca <NA>
#> # … with 270,540 more rows, and 6 more variables: bsm_mo_tex <fct>,
#> # bsm_mo_drain <fct>, bsm_mo_prof <fct>, bsm_mo_parentmat <fct>,
#> # bsm_mo_profvar <fct>, geom <MULTIPOLYGON [m]>
glimpse(sm_simple)
#> Rows: 270,550
#> Columns: 11
#> $ bsm_poly_id <dbl> 165740, 176046, 185239, 162400, 173971, 173087, 19…
#> $ bsm_region <fct> Kunstmatige gronden, Kunstmatige gronden, Zandleem…
#> $ bsm_converted <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…
#> $ bsm_mo_soilunitype <fct> OB, OB, Ldc, OB, OB, Ldp, Lep, Ldc, Abp(c), Lca, O…
#> $ bsm_mo_substr <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ bsm_mo_tex <fct> NA, NA, L, NA, NA, L, L, L, A, L, NA, L, L, NA, NA…
#> $ bsm_mo_drain <fct> NA, NA, d, NA, NA, d, e, d, b, c, NA, d, c, NA, NA…
#> $ bsm_mo_prof <fct> NA, NA, c, NA, NA, p, p, c, p, a, NA, c, a, NA, NA…
#> $ bsm_mo_parentmat <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ bsm_mo_profvar <fct> NA, NA, NA, NA, NA, NA, NA, NA, (c), NA, NA, NA, N…
#> $ geom <MULTIPOLYGON [m]> MULTIPOLYGON (((204667.9 19..., MULTI…
You can also read the explanations for levels of the code variables
bsm_mo_substr
, bsm_mo_tex
,
bsm_mo_drain
etc. from the data source and insert them as
columns (with order of factor levels matching the corresponding code’s
factor level order). To do that, you must read the data source with
explan = TRUE
:
sm_simple <- read_soilmap(explan = TRUE)
glimpse(sm_simple)
#> Rows: 270,550
#> Columns: 17
#> $ bsm_poly_id <dbl> 165740, 176046, 185239, 162400, 173971, 17308…
#> $ bsm_region <fct> Kunstmatige gronden, Kunstmatige gronden, Zan…
#> $ bsm_converted <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ bsm_mo_soilunitype <fct> OB, OB, Ldc, OB, OB, Ldp, Lep, Ldc, Abp(c), L…
#> $ bsm_mo_substr <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_substr_explan <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_tex <fct> NA, NA, L, NA, NA, L, L, L, A, L, NA, L, L, N…
#> $ bsm_mo_tex_explan <fct> NA, NA, zandleem, NA, NA, zandleem, zandleem,…
#> $ bsm_mo_drain <fct> NA, NA, d, NA, NA, d, e, d, b, c, NA, d, c, N…
#> $ bsm_mo_drain_explan <fct> NA, NA, "matig nat, matig gleyig", NA, NA, "m…
#> $ bsm_mo_prof <fct> NA, NA, c, NA, NA, p, p, c, p, a, NA, c, a, N…
#> $ bsm_mo_prof_explan <fct> NA, NA, "met sterk gevlekte textuur (bij lemi…
#> $ bsm_mo_parentmat <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_parentmat_explan <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_profvar <fct> NA, NA, NA, NA, NA, NA, NA, NA, (c), NA, NA, …
#> $ bsm_mo_profvar_explan <fct> NA, NA, NA, NA, NA, NA, NA, NA, Bedolven text…
#> $ geom <MULTIPOLYGON [m]> MULTIPOLYGON (((204667.9 19..., …
tibble(
drain_levels = levels(sm_simple$bsm_mo_drain),
drain_levels_explained = levels(sm_simple$bsm_mo_drain_explan)
)
#> # A tibble: 15 x 2
#> drain_levels drain_levels_explained
#> <chr> <chr>
#> 1 a zeer droog, niet gleyig
#> 2 a-b complex van zeer droog, niet gleyig tot droog, niet gleyig
#> 3 a-d complex van zeer droog, niet gleyig tot matig nat, matig gleyig
#> 4 b droog, niet gleyig
#> 5 c matig droog, zwak gleyig
#> 6 c-d complex van droog, zwak gleyig tot matig droog, matig gleyig
#> 7 d matig nat, matig gleyig
#> 8 e nat, sterk gleyig met reductiehorizont
#> 9 e-f complex van nat, matig gleyig tot zeer nat, zeer sterk gleyig m…
#> 10 e-i complex nat, sterk gleyig met reductiehorizont tot zeer nat met…
#> 11 f zeer nat, zeer sterk gleyig met reductiehorizont
#> 12 g uiterst nat, gereduceerd
#> 13 h nat met relatief hoge ligging, sterk gleyig
#> 14 h-i complex van nat met relatief hoge ligging, sterk gleyig tot zee…
#> 15 i zeer nat met relatief hoge ligging, zeer sterk gleyig
Note: for the following chunks, it doesn’t matter whether you
used explan = TRUE
or not.
How many polygons are available per region?
Wat is the average polygon area per region?
sm_simple %>%
mutate(area = st_area(.)) %>%
st_drop_geometry() %>%
group_by(bsm_region) %>%
summarise(mean_area = mean(area))
Plot polygons of the ‘Zwin’ region:
zwin_map <-
sm_simple %>%
filter(bsm_region == "Zwin") %>%
ggplot(aes(fill = bsm_mo_tex)) +
geom_sf()
sm_simple %>%
filter(bsm_region == "Zwin") %>%
mutate(bsm_mo_tex = as.character(bsm_mo_tex)) %>%
mapview(
zcol = "bsm_mo_tex",
alpha.region = 0.2,
map.types = c("OpenStreetMap", "OpenTopoMap")
)
Clicking a feature on the above generated map reveals all attributes.
soilmap
data sourcesoilmap
data source in the correct
placeThe digital soil map of the Flemish Region is published at
DOV (Databank Ondergrond Vlaanderen). The soilmap
data
source is the (renamed) shapefile, stored (with versioning) at Zenodo in order to
support the read_soilmap()
function and to sustain
long-term workflow reproducibility. The soilmap
data source
belongs to the raw data collection (Zenodo-link).
soilmap_path <- file.path(n2khab_data_path, "10_raw/soilmap")
dir.create(soilmap_path)
download_zenodo(
doi = "10.5281/zenodo.3387008",
path = soilmap_path,
parallel = TRUE
)
At some time in future, the download will be performed automatically
by read_soilmap()
(if the soilmap
data source
is missing).
Read the data source – this takes a while (large dataset) – and inspect its contents:
sm <- read_soilmap(use_processed = FALSE)
sm
#> Simple feature collection with 270550 features and 37 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 22265.45 ymin: 153062.6 xmax: 258872.2 ymax: 244027.9
#> CRS: EPSG:31370
#> # A tibble: 270,550 x 38
#> bsm_poly_id bsm_map_id bsm_region bsm_ge_region bsm_legend bsm_legend_title
#> * <dbl> <fct> <fct> <fct> <fct> <fct>
#> 1 165740 61E Kunstmati… <NA> Antropoge… bodemserie OB
#> 2 176046 78W Kunstmati… <NA> Antropoge… bodemserie OB
#> 3 185239 95W Zandleems… <NA> Vochtig z… bodemseries Lda…
#> 4 162400 75E Kunstmati… <NA> Antropoge… bodemserie OB
#> 5 173971 63W Kunstmati… <NA> Antropoge… bodemserie OB
#> 6 173087 64W Zandleems… <NA> Vochtig z… bodemserie Ldp …
#> 7 199453 98E Zandleems… <NA> Nat zandl… bodemserie Lep …
#> 8 176922 81W Zandleems… <NA> Vochtig z… bodemseries Lda…
#> 9 227861 88W Zandleems… <NA> Droge leem bodemserie Abp …
#> 10 185390 95W Zandleems… <NA> Vochtig z… bodemseries Lca…
#> # … with 270,540 more rows, and 32 more variables: bsm_legend_explan <fct>,
#> # bsm_soiltype_id <dbl>, bsm_soiltype <fct>, bsm_ge_typology <lgl>,
#> # bsm_soiltype_region <fct>, bsm_soilseries <fct>,
#> # bsm_soilseries_explan <fct>, bsm_mo_soilunitype <fct>, bsm_mo_substr <fct>,
#> # bsm_mo_substr_explan <fct>, bsm_mo_tex <fct>, bsm_mo_tex_explan <fct>,
#> # bsm_mo_drain <fct>, bsm_mo_drain_explan <fct>, bsm_mo_prof <fct>,
#> # bsm_mo_prof_explan <fct>, bsm_mo_parentmat <fct>,
#> # bsm_mo_parentmat_explan <fct>, bsm_mo_profvar <fct>,
#> # bsm_mo_profvar_explan <fct>, bsm_mo_phase <fct>, bsm_ge_substr <fct>,
#> # bsm_ge_substr_explan <fct>, bsm_ge_series <fct>,
#> # bsm_ge_series_explan <fct>, bsm_ge_subseries <fct>,
#> # bsm_ge_subseries_explan <fct>, bsm_map_url <fct>, bsm_book_url <fct>,
#> # bsm_detailmap_url <fct>, bsm_profloc_url <fct>, geometry <MULTIPOLYGON [m]>
Extract features that belong to the ‘Middellandpolders’ region:
sm_mp %>%
mutate(bsm_ge_series = as.character(bsm_ge_series)) %>%
mapview(
zcol = "bsm_ge_series",
alpha.region = 0.2,
map.types = c("Wikimedia", "CartoDB.Positron"),
alpha = 0
)
bsm_ge_series
:sm_mp %>%
mutate(area = st_area(.) %>% set_units("ha")) %>%
st_drop_geometry() %>%
group_by(bsm_ge_series, bsm_ge_series_explan) %>%
summarise(
area = sum(area) %>% round(2),
nr_polygons = n()
) %>%
arrange(desc(area)) %>%
kable()
bsm_ge_series | bsm_ge_series_explan | area | nr_polygons |
---|---|---|---|
D | overdekte kreekruggronden - Middelland materiaal over Oudland kreekrugmateriaal | 9126.95 [ha] | 1387 |
E | dekkleigronden - meer dan 100 cm Duinkerken III-klei | 7160.28 [ha] | 761 |
F | overdekte poelgronden en overdekte oude kleiplaatgronden met storende laag op geringe diepte | 4848.24 [ha] | 1110 |
P | overdekte Pleistocene gronden - gebroken poldermateriaal op Pleistoceen zand | 3506.81 [ha] | 582 |
G | geulgronden - meer dan 100 cm zware Duinkerken III-klei in laaggelegen geulen | 815.64 [ha] | 72 |
A | kreekruggronden - slibhoudend zand tot klei overgaand naar lichter materiaal | 308.98 [ha] | 27 |
A | kreekruggronden - slibhoudend zand tot klei overgaand naar lichter materiaal + overdekte kreekrugronden - Middellland materiaal over Oudland kreekrugmateriaal | 230.75 [ha] | 33 |
M | gronden van de Lage Moeren | 163.11 [ha] | 19 |
bsm_ge_series == "D"
:sm_mp %>%
filter(bsm_ge_series == "D") %>%
mutate(area = st_area(.) %>% set_units("ha")) %>%
st_drop_geometry() %>%
group_by(bsm_soilseries, bsm_soilseries_explan) %>%
summarise(
area = sum(area) %>% round(2),
nr_polygons = n()
) %>%
arrange(desc(area)) %>%
kable()
bsm_soilseries | bsm_soilseries_explan | area | nr_polygons |
---|---|---|---|
m.D5 | Overdekte kreekruggronden (Middellandpolders) | 4628.42 [ha] | 642 |
m.Dk5 | Overdekte kreekruggronden - klei (Middellandpolders) | 1287.51 [ha] | 123 |
m.Dl5 | Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) | 1070.21 [ha] | 105 |
m.D4 | Overdekte kreekruggronden (Middellandpolders) | 843.91 [ha] | 259 |
m.D2 | Overdekte kreekruggronden (Middellandpolders) | 392.31 [ha] | 75 |
m.Dl6 | Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) | 170.04 [ha] | 37 |
m.D5l | Overdekte kreekruggronden (Middellandpolders) | 169.59 [ha] | 16 |
m.Dl4 | Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) | 159.95 [ha] | 29 |
m.Dl2 | Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) | 127.59 [ha] | 28 |
m.Dk6 | Overdekte kreekruggronden - klei (Middellandpolders) | 115.02 [ha] | 19 |
m.D4l | Overdekte kreekruggronden (Middellandpolders) | 85.74 [ha] | 6 |
m.D3 | Overdekte kreekruggronden (Middellandpolders) | 20.93 [ha] | 4 |
m.Df1 | Overdekte kreekruggronden - zware klei (Middellandpolders) | 20.43 [ha] | 23 |
m.Dk4 | Overdekte kreekruggronden - klei (Middellandpolders) | 15.08 [ha] | 6 |
m.D1 | Overdekte kreekruggronden (Middellandpolders) | 13.74 [ha] | 9 |
m.D5d | Overdekte kreekruggronden (Middellandpolders) | 6.46 [ha] | 6 |
bsm_soilseries == "m.D5"
(providing all mapview
’s default base maps):