1 Objet

Points pb sur 2025

Pour l’étude complète voir pluiM3.Rmd

library(sf)
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(mapsf)
library(mapview)
st_layers("../data/pluiM3.gpkg")
## Driver: GPKG 
## Available layers:
##                                  layer_name geometry_type features fields
## 1                         M3_HABILLAGE_SURF       Polygon       24     12
## 2                               M3_INFO_LIN   Line String        1     17
## 3                              M3_INFO_SURF                     89     17
## 4                       M3_PRESCRIPTION_LIN                    480     19
## 5                       M3_PRESCRIPTION_PCT         Point       25     17
## 6                      M3_PRESCRIPTION_SURF                    799     22
## 7     200057875_HABILLAGE_SURF_20250624.shp       Polygon       24     12
## 8           200057875_INFO_LIN_20250624.shp   Line String        1     17
## 9          200057875_INFO_SURF_20250624.shp                     96     17
## 10  200057875_PRESCRIPTION_LIN_20250624.shp                    511     17
## 11  200057875_PRESCRIPTION_PCT_20250624.shp         Point       25     17
## 12 200057875_PRESCRIPTION_SURF_20250624.shp                    799     17
## 13         200057875_ZONE_URBA_20250624.shp                    160     16
## 14                             M3_ZONE_URBA                    129     26
##                 crs_name
## 1       RGF93 Lambert 93
## 2       RGF93 Lambert 93
## 3       RGF93 Lambert 93
## 4       RGF93 Lambert 93
## 5       RGF93 Lambert 93
## 6       RGF93 Lambert 93
## 7       RGF93 Lambert 93
## 8       RGF93 Lambert 93
## 9       RGF93 Lambert 93
## 10      RGF93 Lambert 93
## 11      RGF93 Lambert 93
## 12      RGF93 Lambert 93
## 13 RGF93 v1 / Lambert-93
## 14 RGF93 v1 / Lambert-93
cadastre <- st_read("../data/cadastre2024.gpkg", "parcelle_info")
## Reading layer `parcelle_info' from data source 
##   `C:\Users\tachasa\02_cartos\data\cadastre2024.gpkg' using driver `GPKG'
## Simple feature collection with 7124 features and 18 fields (with 1 geometry empty)
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 661077.2 ymin: 6865338 xmax: 663343.6 ymax: 6869045
## Projected CRS: RGF93 v1 / Lambert-93
bondy <- st_read("../data/cadastre2024.gpkg", "bondy")
## Reading layer `bondy' from data source 
##   `C:\Users\tachasa\02_cartos\data\cadastre2024.gpkg' using driver `GPKG'
## Simple feature collection with 1 feature and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 661077.2 ymin: 6865338 xmax: 663352 ymax: 6869045
## Projected CRS: RGF93 v1 / Lambert-93

2 Zonage : anomalie des espaces de loisirs et zone de cimetière

zone <- st_read("../data/pluiM3.gpkg", "M3_ZONE_URBA")
## Reading layer `M3_ZONE_URBA' from data source 
##   `C:\Users\tachasa\02_cartos\data\pluiM3.gpkg' using driver `GPKG'
## Simple feature collection with 129 features and 26 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 661077.7 ymin: 6865338 xmax: 663352 ymax: 6869045
## Projected CRS: RGF93 v1 / Lambert-93
png("../img/M3_NC.png", width = 1000, height = 1200, res = 200)
mf_map(cadastre, col="wheat", border = NA)
mf_map(type = "typo", zone [zone$code == 'Nc',], border= NA, var = "LIBELONG", add = T, leg_pos = "right")
mf_label(zone [zone$LIBELLE == 'Nc',], var = "LIBELLE")
mf_layout("Libellé long pour les 2 cimetières", credits = "M3 PLUI\njuillet 2025")
dev.off()
## png 
##   2

3 INFO_SURF

info_surf <-  st_read("../data/pluiM3.gpkg", "M3_INFO_SURF")
## Reading layer `M3_INFO_SURF' from data source 
##   `C:\Users\tachasa\02_cartos\data\pluiM3.gpkg' using driver `GPKG'
## Simple feature collection with 89 features and 17 fields
## Geometry type: GEOMETRY
## Dimension:     XY
## Bounding box:  xmin: 661077.2 ymin: 6865338 xmax: 663352 ymax: 6869045
## Projected CRS: RGF93 Lambert 93
table(info_surf$geo_commune)
## 
## 930010 
##     89

Tout appartient bien à Bondy ?

3.1 RCU = pb découpage

rcu <- info_surf [substring(info_surf$LIBELLE, 1,4) == "Zone", ]
dedans <- as.vector(st_within(cadastre, rcu, sparse = F))
dedans <- which(dedans ==TRUE)
# En 2025 pas de parcelle cadastrale
dedans <- cadastre [dedans,]
cadZa <- cadastre [dedans, ]
mf_export(x = rcu, filename = "../img/rcu2025.png", 
          expandBB = c(0,0,0,0.0),
          width = 1000, height = 1000 ,res = 150)
  mf_init(rcu, theme = "candy")
  mf_shadow(bondy, add = TRUE)
  mf_map(bondy, add = TRUE)
  mf_map(cadastre, col = "cornsilk", border = "wheat", add = T)
  mf_map(
    rcu,
    col = NA,
    type = "base",
    border = "green",
    lwd = 3,
    add = T
  )

mf_label(cadZa,, var = "code", halo = T, cex = 0.5, overlap = F)  
mf_layout(title = rcu$LIBELLE,
            credits = "Plui M2\nBondy,  juillet 2025")
dev.off()

3.2 PPR = pb découpage 2

ppr <- info_surf [substring(info_surf$LIBELLE, 1,3) == "PPR", ]
mf_init(ppr)
  mf_map(cadastre, col = "cornsilk", border = "wheat", add = T)
  mf_map(ppr, border = "green",  color = NA, lwd = 3,add = T)

# verif
dedans <- as.vector(st_within(cadastre, ppr, sparse = F))
dedans <- which(dedans ==TRUE)
# pas de parcelle cadastrale

Même pb avec la zac, conclusion tout info_surf sans parcelle cadastrale invalide même si inclue le territoire dans l’attribut ?

3.3 PPCOA (ancien sursis à statuer)

A quel endroit dans le fichier operis “périmètres” ou “servitudes” ?

ppcoa <- info_surf [substring(info_surf$LIBELLE, 1,5) == "PPCOA", ]
mf_export(x = ppcoa, filename = "../img/ppcoa_2025.png", 
          expandBB = c(0,0,0,0.0),
          width = 1000, height = 1000 ,res = 150)
  mf_init(ppcoa, theme = "candy")
  mf_shadow(bondy, add = TRUE)
  mf_map(bondy, add = TRUE)
  mf_map(cadastre, col = "cornsilk", border = "wheat", add = T)
  mf_map(
    ppcoa,
    var = "LIBELLE",
    col = NA,
    type = "typo",
    border = "green",
    lwd = 3,
    add = T
  )
## The following argument is not relevant when using type = 'typo': col.
mf_layout(title = "PPCOA",
            credits = "Plui M3\nBondy,  juin 2025")
dev.off()
## png 
##   2

3.4 Archéologie

2 questions :

  • différence périmètre de saisie et ZPPA zone de présomption de prescription

  • PPCOA n’englobe pas l’église ?

4 PRESCRIPTION_LINEAIRE = les sentes

4.1 Linéaire actif I 41

pb I41 déja relevé sur le M2 et corrigé ?

pres_lin <- st_read("../data/pluiM3.gpkg", "M3_PRESCRIPTION_LIN")
## Reading layer `M3_PRESCRIPTION_LIN' from data source 
##   `C:\Users\tachasa\02_cartos\data\pluiM3.gpkg' using driver `GPKG'
## Simple feature collection with 480 features and 19 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: 661166.9 ymin: 6865404 xmax: 663343.6 ymax: 6868901
## Projected CRS: RGF93 Lambert 93
I41   <- cadastre [cadastre$code == " I0041",]
# TODO pourquoi grep nécessaire ?
grep("I0041", cadastre$code)
## [1] 1251 3658
I41 <- cadastre [1251,]
lineaire <- pres_lin [substring(pres_lin$LIBELLE, 10, 14) == "actif", ]
arbre <- pres_lin [substring(pres_lin$LIBELLE, 14, 19)== "arbres",]
mf_init(st_buffer(I41, 10))
mf_map(cadastre, col = "cornsilk", border = "wheat", add = T)
mf_map(lineaire,  col = "red",    lwd = 5,add = TRUE)
mf_map(arbre, col = "green", lty = 2, lwd = 5, add = T)
mf_label(I41, var = "code", cex = 1.5)
mf_layout("linéaire actif à créer et parcelle I41 : demande de continuation", credits = "Bondy / DGST\nPLUi M3, juillet 2025")

4.2 Sentes

Modification du fichier

mf_map(bondy)
mf_map(cadastre, col = "cornsilk", border = "wheat", add = T)
sente <- st_read("../data/pluiM3.gpkg", "M3_prescription_lin_sente")
## Reading layer `M3_prescription_lin_sente' from data source 
##   `C:\Users\tachasa\02_cartos\data\pluiM3.gpkg' using driver `GPKG'
## Simple feature collection with 6 features and 19 fields
## Geometry type: LINESTRING
## Dimension:     XY
## Bounding box:  xmin: 661615.7 ymin: 6865687 xmax: 663268.2 ymax: 6868650
## Projected CRS: RGF93 Lambert 93
mf_map(sente, col = "green", add = T, lwd = 4)
mf_label(sente, var = "TXT")
mf_layout(title = "Sentes",
            credits = "Plui M3\nBondy,  juillet 2025")