Grouping levels and exploring the resulting broader categories
The package lczexplore allows to group several Local
Climate Zone levels (LCZ) into broader categories . The
groupLCZ function allows this grouping, and the main
functions of the package allow to specify the expected levels and colors
to use.
From original data…
First, let’s load the example data of Redon City.
library(lczexplore)
dirPath <- system.file("extdata/multipleWfs/Redon", package = "lczexplore")
redonOSM <- importLCZvect(dirPath = dirPath, file = "osm_lcz.fgb", column = "LCZ_PRIMARY")
redonBDT <- importLCZvect(dirPath = dirPath, file = "bdt_lcz.fgb", column = "LCZ_PRIMARY")
#If we compare the LCZ classifications produced with GeoClimate and OpenStreetMAp and French BDTOPO data, we can see they seem to quite disagree.
comparison <- compareLCZ(sf1 = redonBDT, column1 = "LCZ_PRIMARY", wf1 = "BD TOPO v2.2",
sf2 = redonOSM, column2 = "LCZ_PRIMARY", wf2 = "Open Street Map", ref = 1,
repr = "standard", exwrite = FALSE, location = "Redon", saveG = "")
#> The column LCZ_PRIMARY of the dataset sf1 is the reference against which the LCZ_PRIMARY column of the dataset sf2 will be compared.
#> For Technical reason in this package, worfklow names can't contain spaces or underscores, they were replaced by dots
#> Workflow names are used in produced graphics and as column names, long names are not suitable,
#> the first 3 and last 3 letters were kept instead
#> For Technical reason in this package, worfklow names can't contain spaces or underscores, they were replaced by dots
#> Workflow names are used in produced graphics and as column names, long names are not suitable,
#> the first 3 and last 3 letters were kept instead
#> Both sf datasets need to live in the same crs projection (srid / epsg),
#> they will be coerced to the specified reference (sf1)
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries
… to grouped levels
But maybe the disagreement is not as strong as it seems. One may want to see, for example, if the urban envelopes differ that much. Let’s group all the urban LCZ levels in a category and all the vegetation in another one, the remaining LCZ being their own categories.
Use the groupLCZ function, set the dataset with the
argument sf and the column with column. Then
pass as many vectors as there are grouped category, each of them
containing the levels to be grouped together. The newly created
categories will be stored in the outCol column. If the user
doesn’t specify any name, by default the column is called
"grouped".
The newly created dataset can be mapped with showLCZ
function. At this point, we pass the names of the new categories present
in the “grouped” column to an LCZlevels vector and the colors associated
as a colors vector.
redonOSMgrouped <- groupLCZ(redonOSM, column = "LCZ_PRIMARY",
urban = c("1", "2", "3", "4", "5", "6", "7", "8", "9"),
industry = "10",
vegetation = c("101", "102", "103", "104"),
impervious = "105", pervious = "106", water = "107",
colors = c("red", "black", "green", "grey", "burlywood", "blue"))
#> Grouping
#> One of the specified levels to group doesn't exist in the data, if it is a mispelled level of the data,
#> this level will be kept as ungroupedsimpleWarning: Unknown levels in `f`: 1, 3, 4, 7, 10, 103, 106
#> Warning: Unknown levels in `f`: 1, 3, 4, 7, 10, 103, 106
levCol(sf = redonOSMgrouped, column = "grouped", LCZlevels = c("urban", "industry", "vegetation", "impervious", "pervious", "water"),
colors = c("red", "black", "green", "grey", "burlywood", "blue"))
map1 <- showLCZ(redonOSMgrouped, column = "grouped", repr = "alter", wf = "OSM",
LCZlevels = c("urban", "industry", "vegetation", "impervious", "pervious", "water"),
colors = c("red", "black", "green", "grey", "burlywood", "blue"))
#> typeLevels before trying to group are redtypeLevels before trying to group are blacktypeLevels before trying to group are greentypeLevels before trying to group are greytypeLevels before trying to group are burlywoodtypeLevels before trying to group are bluetypeLevels before trying to group are #3dff3a
#> One vector of levels, one vector of colors, either the same length (case 9: and 10:), or colors longer (case 12:, unused colors were dropped),
#> reduced to 6: A single vector was provided, whose names don't cover the levels in the data,
#> missing levels were assigned random colors.
This is meant as an exploratory tool, as the resulting grouped columns
are not actually kept after the analysis.