Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Beyond the Border - Kernel Density Estimation for Urban Geography

Description

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.

Link points to their centroids

Description

• Link some points to their centroids in a grid segmentation

• Relie des points aux centroides des carreaux auxquels ces points appartiennent (dans un découpage de l'espace en grille carroyée)

Usage

btb_add_centroids(
  pts,
  iCellSize,
  offset = c(0L, 0L),
  names_coords = c("x", "y"),
  names_centro = c("x_centro", "y_centro"),
  add = TRUE
)

Arguments

Details

Works with sf points but only with coordinates in meters (and not degrees !). Do not use sf points with GPS coordinates for example.

Value

• pts table with additional centroids coordinates x_centro and y_centro (df of sf object)

• Table pts avec les coordonnées des centroïdes x_centro and y_centro (objet df of sf )

Examples

pts <- data.frame(
x = c(656913.1 , 348296.3 , 842276.3 , 716750.0 , 667418.2),
y = c(6855995 , 6788073 , 6385680 , 7003984 , 6585793),
val = 1:5)
btb_add_centroids(pts, 100, names_centro = c("centroX", "centroY"))
btb_add_centroids(pts, 100, offset = c(50, 50), names_centro = c("centroX", "centroY"))
pts2 <- sf::st_as_sf(pts, coords = c("x","y"), crs = 2154)
btb_add_centroids(pts2, 50)

Creates Inpire names for a grid

Description

Creates Inpire names for a grid defined with :

• centroids coordinates

• Squares size(s)

• Projection system

Ajoute les noms des identifiants en norme Inspire des carreaux définis à partir :

• des coordonnées de leurs centroides

• de la taille de leur côté en mètres

• d'un système de projection)

Usage

btb_add_inspire(
  pts_centro,
  sEPSG,
  iCellSize,
  names_centro = c("x_centro", "y_centro")
)

Arguments

Value

(df) pts_centro table with additional idInspire variable

Examples

pts_centro <- data.frame(x_centro = c(100, 100, 300, 300, 500), 
y_centro = c(100, 300, 100, 300, 100))
btb_add_inspire(pts_centro, sEPSG = 2154, iCellSize = 200)

Compute a grid from centroid points

Description

Function to compute a grid (regular or not) from centroid points.

(Fonction permettant de générer une grille (régulière ou non) à partir de centroïdes)

Usage

btb_ptsToGrid(
  pts,
  sEPSG = NA,
  iCellSize = NULL,
  names_centro = c("x_centro", "y_centro"),
  inspire = FALSE
)

Arguments

Value

Returns an object of class sf and data.frame.

(Retourne un objet de classe sf et data.frame.)

Examples

 
# example 1 - regular grid
pts <- data.frame(x_centro = c(100, 100, 300, 300, 500), 
y_centro = c(100, 300, 100, 300, 100))
carResult <- btb_ptsToGrid(pts = pts, sEPSG = "2154", iCellSize = 200)
# write_sf(obj = carResult, dsn = "regularGrid.shp", delete_layer = TRUE)

# example 2 - irregular grid
pts <- data.frame(x = c(50, 50, 150, 150, 300)
                 , y = c(50, 150, 50, 150, 100)
                 , iCellSize = c(50, 50, 50, 50, 100))
carResult <- btb_ptsToGrid(pts = pts, sEPSG = "2154",names_centro=c("x","y"))
# write_sf(obj = carResult, dsn = "irregularGrid.shp", delete_layer = TRUE)
# Exemple 3 : sf points (no epsg)
pts <- data.frame(x = c(100, 100, 300, 300, 500), y = c(100, 300, 100, 300, 100))
pts <- sf::st_as_sf(pts,coords=c("x","y"))
carResult <- btb_ptsToGrid(pts = pts, sEPSG = "2154", iCellSize = 200)
# Exemple 3 : sf points (no epsg)
pts <- data.frame(x = c(100, 100, 300, 300, 500), 
y = c(100, 300, 100, 300, 100))
pts <- sf::st_as_sf(pts,coords=c("x","y"),crs=2154)
carResult <- btb_ptsToGrid(pts = pts, sEPSG = "2154", iCellSize = 200)

Smoothing with a bisquare kernel or median

Description

Smoothing function with a bisquare kernel or median.

(Fonction de lissage à partir d'un noyau bisquare ou de la médiane.)

Usage

btb_smooth(
  pts,
  sEPSG = NA,
  iCellSize = NA,
  iBandwidth,
  vQuantiles = NULL,
  dfCentroids = NULL,
  iNeighbor = NULL,
  inspire = FALSE,
  iNbObsMin = 250
)

Arguments

Details

Returns an object inheriting from the data.frame class. (Retourne un objet qui se comporte comme un data.frame, par heritage.)

• Smoothing covers a set of methods to extract pertinent and structuring information from noisy data. In the field of spatial analysis, and most widely in quantitative geography, smoothing is used to modelise density variations of a population distribution in geographical space. Kernel smoothing methods are widely used. In this method, for each location x, we count the number of events of a process within a distance h of x, and weighted by the square reciprocal of the radius h. We apply a edge-correction to deal with edge-effects. So the method is conservative..

• Le lissage recouvre un ensemble de méthodes pour extraire d'une source de données bruitées une information pertinente et structurante. Dans le champ de l'analyse spatiale et plus largement de la géographie quantitative, le lissage est principalement utilisé pour modéliser les variations de densités d'une distribution de population dans l'espace géographique. On utilise principalement des méthodes de lissage par noyau. Il s'agit ici, pour chaque point x, de comptabliser le nombre d' "évènements" d'un processus à une distance h de ce point, tout en ponderant ce nombre par l'inverse de la distance h au carré. On applique une correction à la ponderation afin de traiter les effets de bord. Cette méthode est conservative.

Value

Returns an object inheriting from the data.frame class. (Retourne un objet qui se comporte comme un data.frame, par heritage.)

References

• "Geographically weighted summary statistics : a framework for localised exploratory data analysis", C.Brunsdon & al., in Computers, Environment and Urban Systems 2002

• Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, 2003, pp. 83-86

Examples

# Example 1
data(dfPrix_SP95_2016)
dfPrix_SP95_2016$nbObs <- 1L
dfSmoothed <- btb::btb_smooth(pts = dfPrix_SP95_2016,
                              sEPSG = "2154",
                              iCellSize = 5000L,
                              iBandwidth = 30000L, 
                              inspire = TRUE)
dfSmoothed$prix95 <- dfSmoothed$SP95 / dfSmoothed$nbObs * 100
library(mapsf)
mf_map(dfSmoothed,
      type = "choro",
      var = "prix95",
      breaks = "fisher",
      nbreaks = 5,
      border = NA,
      leg_title = "prix du SP95 en centimes")
# Example 2
data(reunion)
# Call mode 1: classic smoothing - automatic grid
reunionSmoothed <- btb_smooth( pts = reunion,
                                    sEPSG = "32740",
                                    iCellSize = 200L,
                                    iBandwidth = 400L)
library(mapsf)
mf_map(reunionSmoothed,
      type = "choro",
      var = "houhold",
      breaks = "fisher",
      nbreaks = 5,
      border = NA)
# Call mode 2: median smoothing - automatic grid
reunionSmoothed <- btb_smooth( pts = reunion,
                                     sEPSG = "32740",
                                     iCellSize = 200L,
                                     iBandwidth = 400L,
                                     vQuantiles = c(0.1, 0.5, 0.9))
mf_map(reunionSmoothed,
      type = "choro",
      var = "houhold_05",
      breaks = "fisher",
      nbreaks = 5,
      border = NA)
# Call mode 3: classic smoothing - user grid
dfCentroidsUser <- merge( x = seq(from =  314400L, to =  378800L, by = 200L),
                          y = seq(from = 7634000L, to = 7691200L, by = 200L))
reunionSmoothed <- btb_smooth( pts = reunion,
                                    sEPSG = "32740",
                                    iCellSize = 200L,
                                    iBandwidth = 400L,
                                    dfCentroids = dfCentroidsUser)
reunionSmoothed <- reunionSmoothed[reunionSmoothed$houhold > 0, ]
mf_map(reunionSmoothed,
      type = "choro",
      var = "houhold",
      breaks = "fisher",
      nbreaks = 5,
      border = NA)
# Call mode 4: median smoothing - user grid
reunionSmoothed <- btb_smooth( pts = reunion,
                                    sEPSG = "32740",
                                    iCellSize = 200L,
                                    iBandwidth = 400L,
                                    vQuantiles = c(0.1, 0.5, 0.9),
                                    dfCentroids = dfCentroidsUser)
reunionSmoothed <- reunionSmoothed[reunionSmoothed$nbObs > 0, ]
mf_map(reunionSmoothed,
      type = "choro",
      var = "houhold_05",
      breaks = "fisher",
      nbreaks = 5,
      border = NA)

constituerGrappes

Description

Quadtree variant function.

(Variante de la fonction Quadtree.)

Usage

constituerGrappes(iNbObsMin, mEffectifs)

Arguments

Value

• Returns a matrix with the cluster number for each cell .

• Retourne une matrix avec le numéro de grappe pour chaque cellule.

Author(s)

• Psar Analyse Urbaine Insee

• Arlindo Dos Santos

• Francois Semecurbe

constituerMatriceEffectifs

Description

Function constituting a membership matrix (Fonction constituant une matrice des effectifs)

Usage

constituerMatriceEffectifs(vLigneObservation, vColonneObservation)

Arguments

Value

Returns a matrix with the number of observations for each cell.

(Retourne une matrix avec le nombre d'observations pour chaque cellule.)

Author(s)

• Psar Analyse Urbaine Insee

• Arlindo Dos Santos

• Francois Semecurbe

Unleaded 95 price in France in 2016

Description

Unleaded 95 price in France in 2016 (prix du sans-plomb 95 en France en 2016)

Usage

dfPrix_SP95_2016

Format

dfPrix_SP95_2016

A data frame with 5,573 rows and 3 columns:

x

longitude

y

latitude

SP95

Unleaded price - prix du sans plomb

Source

https://www.prix-carburants.gouv.fr/rubrique/opendata/

Parisian restaurants

Description

Parisian restaurants (Restaurants parisiens)

Usage

dfRestaurantParis

Format

dfRestaurantParis

A data frame with 13,823 rows and 17 columns:

x

longitude

y

latitude

french

French restaurant

asian

asian restaurant

caribbean

caribbean restaurant

maghreb

maghreb restaurant

african

african restaurant

european

european restaurant

south_america

south_american restaurant

indian

indian restaurant

world

world food restaurant

cafeteria

cafeteria restaurant

tea_house

tea_house

brasserie

brasserie restaurant

fastfood

fastfood restaurant

bar

bar

restaurant

restaurant

Source

source : https://opendata.paris.fr

Depreciated function of btb_ptsToGrid

Description

Function dfToGrid has been replaced by btb_ptsToGrid

Usage

dfToGrid(df, sEPSG, iCellSize = NULL)

Arguments

Value

a grid

Depreciated function of btb_smooth

Description

Function kernelSmoothing has been replaced by btb_smooth

Usage

kernelSmoothing(
  dfObservations,
  sEPSG,
  iCellSize,
  iBandwidth,
  vQuantiles = NULL,
  dfCentroids = NULL,
  fUpdateProgress = NULL,
  iNeighbor = NULL,
  iNbObsMin = 250
)

Arguments

Value

a warning message

France grid with 1km square tiles

Description

Whole France grid with 1km square tiles. Lambert 93 projection.

(Grille France entière avec des carreaux d'1km de côté. Projection Lambert 93)

Usage

pixel_france

Format

pixel_france

A data frame with 54,8495 rows and 2 columns:

x

longitude of pixel's centroids

y

latitude of pixel's centroids

Source

source : https://opendata.paris.fr

Households of Reunion

Description

Households of Reunion (Menages de La Reunion) Gridded database with a grid cell resolution of 200 meters. (Données carroyées à 200 mètres).

Usage

reunion

Format

reunion

A data frame with 14,076 rows and 4 columns:

x

longitude

y

latitude

houhold

number of households - nombre de ménages

phouhold

number of poor households - nombre de ménages pauvres

Source

Source : Insee, Localized data : Fiscal Revenue - 31/12/2010 and Housing Tax - 01/01/2011

Source : Insee, Revenus Fiscaux Localisés (RFL) au 31 decembre 2010 et Taxe d'habitation (TH) au 1er janvier 2011)