# map_bidev: Map Multiscalar Typology (2 deviations) In MTA: Multiscalar Territorial Analysis

 map_bidev R Documentation

## Map Multiscalar Typology (2 deviations)

### Description

Compute the multiscalar typology (2 deviations) and propose colors for mapping the results.

### Usage

``````map_bidev(x, dev1, dev2, breaks = c(25, 50, 100), xid = NULL)
``````

### Arguments

 `x` A sf object including a variable resulting from the mst function. `dev1` column name of the first relative deviation in x. `dev2` coumn name of the second relative deviation in x. `breaks` Distance to the index 100 (average of the context), in percentage. A vector of three values. Defaut c(25,50,100). 25 to indexes 80 and 125. 50 and 200. `xid` identifier field in x. Default the first column.

### Value

A list including a ordered sf object for mapping mst column (geom) and a vector of suggested colors (cols).

bidev typology values :

• ZZ: Near the average for the two selected deviation

• A1: Above the average for dev1 and dev2, distance to the avarage : +

• A2: Above the average for dev1 and dev2, distance to the avarage : ++

• A3: Above the average for dev1 and dev2, distance to the avarage : +++

• B1: Above the average for dev1 and below for dev2, distance to the avarage : +

• B2: Above the average for dev1 and below for dev2, distance to the avarage : ++

• B3: Above the average for dev1 and below for dev2, distance to the avarage : +++

• C1: Below the average for dev1 and dev2, distance to the avarage : +

• C2: Below the average for dev1 and dev2, distance to the avarage : ++

• C3: Below the average for dev1 and dev2, distance to the avarage : +++

• D1: Below the average for dev1 and above for dev2, distance to the avarage : +

• D2: Below the average for dev1 and above for dev2, distance to the avarage : ++

• D3: Below the average for dev1 and above for dev2, distance to the avarage : +++

### Examples

``````# Focus on exceptional values (50, 100 and 200 % above-under the average)
library(sf)
com <- st_read(system.file("metroparis.gpkg", package = "MTA"), layer = "com", quiet = TRUE)
ept <- st_read(system.file("metroparis.gpkg", package = "MTA"), layer = "ept", quiet = TRUE)

# Prerequisite  - Compute 2 deviations
com\$gdev <- gdev(x = com, var1 = "INC", var2 = "TH")
com\$tdev <- tdev(x = com, var1 = "INC", var2 = "TH", key = "EPT")

# Compute map_bidev
bidev <- map_bidev(x = com, dev1 = "gdev", dev2 = "tdev", breaks = c(50, 100, 200))

# Unlist resulting function
com <- bidev\$geom
cols <- bidev\$cols

#Visualization
# One side for the map, another for the plot
opar <- par(mfrow = c(1,2), mar = c(0,4,0,0))

if(require(mapsf)){
# Cartography
mf_map(x = com, var = "bidev", type = "typo", val_order = unique(com\$bidev),
border = "grey50", pal = cols, lwd = 0.2, leg_pos = NA)
mf_map(ept, col = NA, add = TRUE)

# Label territories in the C3 category
mf_label(x = com[com\$bidev == "C3",], var = "LIBCOM", halo = TRUE)

mf_layout(title = "2-Deviations synthesis : general and territorial contexts",
credits = paste0("Sources: GEOFLA® 2015 v2.1, Apur, impots.gouv.fr",
"\nMTA", packageVersion("MTA")),
arrow = FALSE)

# Add plot_bidev on the right side of the map
plot_bidev(x = com,  dev1 = "gdev",  dev2 = "tdev",
dev1.lab = "General deviation (MGP Area)",
dev2.lab = "Territorial deviation (EPT of belonging)",
breaks = c(50, 100, 200),
lib.var = "LIBCOM", lib.val = "Clichy-sous-Bois", cex.lab = 0.8)
par(opar)
}
``````

MTA documentation built on Nov. 2, 2023, 5:06 p.m.