Description Usage Arguments Details Author(s) References Examples
Smoothing function with a bisquare kernel or median.
(Fonction de lissage à partir d'un noyau bisquare ou de la médiane.)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Call mode 1: bisquare kernel smoothing - automatic grid
kernelSmoothing( dfObservations
, cellSize
, bandwidth
, vQuantiles = NULL
, dfCentroids = NULL
, fUpdateProgress = NULL
, neighbor = NULL
)
# Call mode 2: median smoothing - automatic grid
kernelSmoothing( dfObservations
, cellSize
, bandwidth
, vQuantiles
, dfCentroids = NULL
, fUpdateProgress = NULL
, neighbor = NULL
)
# Call mode 3: bisquare kernel smoothing - user grid
kernelSmoothing( dfObservations
, cellSize
, bandwidth
, vQuantiles = NULL
, dfCentroids
, fUpdateProgress = NULL
, neighbor = NULL
)
# Call mode 4: median smoothing - user grid
kernelSmoothing( dfObservations
, cellSize
, bandwidth
, vQuantiles
, dfCentroids
, fUpdateProgress = NULL
, neighbor = NULL
)
|
dfObservations |
A (Un |
cellSize |
Cell size of the grid ( (Taille des carreaux ( |
bandwidth |
Radius of the Kernel Density Estimator ( (Rayon de lissage de l'estimation d'intensité par noyau ( |
vQuantiles |
Percentile vector to calculate. For example c(0.1, 0.25, 0.5) will calculate the first decile, the first quartile and the median. (Vecteur des quantiles à calculer. Par exemple c(0.1, 0.25, 0.5) retournera le premier décile, le premier quartile et la mediane.) |
dfCentroids |
A (Un |
fUpdateProgress |
A (Une |
neighbor |
Technical parameter, leave empty. ( (Paramètre technique pour calculer l'étendue des points d'estimations, à ne pas remplir. ( |
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 quantiative geography, smoothing is used to modelise density variations of a population distribution in geographical space. Kernel smootthing 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 methodes 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 densites 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'"évenements" 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.
Psar Analyse Urbaine Insee - Thierry Cornely, Laure Genebes, Arlindo Dos Santos, Cynthia Faivre, Auriane Renaud and Francois Semecurbe
"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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ## Not run:
data(reunion)
# Smoothing all variables for Reunion (Lissage de toutes les variables pour la Reunion)
# Call mode 1: classic smoothing - automatic grid
reunionSmooth <- kernelSmoothing( dfObservations = reunion
, cellSize = 200
, bandwidth = 400)
# Call mode 2: median smoothing - automatic grid
reunionSmooth <- kernelSmoothing( dfObservations = reunion
, cellSize = 200
, bandwidth = 400
, vQuantiles = c(0.1, 0.5, 0.9))
# Call mode 3: classic smoothing - user grid
dfCentroidsUser <- merge( x = seq(from = 314400, to = 378800, by = 200)
, y = seq(from = 7634000, to = 7691200, by = 200))
reunionSmooth <- kernelSmoothing( dfObservations = reunion
, cellSize = 200
, bandwidth = 400
, dfCentroids = dfCentroidsUser)
# Call mode 4: median smoothing - user grid
reunionSmooth <- kernelSmoothing( dfObservations = reunion
, cellSize = 200
, bandwidth = 400
, vQuantiles = c(0.1, 0.5, 0.9)
, dfCentroids = dfCentroidsUser)
# Building of the associated basemap (Creation du fond de carte associe)
grid <- smoothingToGrid(reunionSmooth, "32740")
# preview (Apercu)
library(sp)
library(cartography)
cartography::choroLayer(grid, var = "houhold", nclass = 5
, method = "fisher-jenks", border = NA)
# Export of the basemap in shapefile format (Export du fond de carte au format shapefile)
library(rgdal)
rgdal::writeOGR(grid, "reunion.shp", "reunion", driver = "ESRI Shapefile")
## End(Not run)
|
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