Psar Analyse Urbaine Insee - Thierry Cornely, Laure Genebes, Arlindo Dos Santos, Cynthia Faivre, Auriane Renaud and Francois Semecurbe
Kernel density estimation dedicated to urban geography.

Thierry Cornely, Laure Genebes, Arlindo Dos Santos, Cynthia Faivre,
Auriane Renaud, Farida Marouchi and Francois Semecurbe

Psar Analyse Urbaine Insee - Thierry Cornely, Laure Genebes, Arlindo Dos
Santos, Cynthia Faivre, Auriane Renaud and Francois Semecurbe

methods, Rcpp (>= 0.11.3), sp, sf, RcppParallel
Rcpp, RcppParallel, BH (>= 1.60.0-1), RcppArmadillo

Beyond the Border - Kernel Density Estimation for Urban
Geography
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, bandwith) for a classical kernel smoothing and automatic grid.
The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, quantiles) for a geographically weighted median and automatic grid.
The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, centroids) for a classical kernel smoothing and user grid.
The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, quantiles, centroids) for a geographically weighted median and user grid.
Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>,
Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
Arlindo Dos Santos [cre],
Francois Semecurbe [drt, aut],
Auriane Renaud [ctb],
Cynthia Faivre [ctb],
Thierry Cornely [ctb],
Farida Marouchi [ctb],
Farida Marouchi [ctb]

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.
Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>,
Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
methods, Rcpp (>= 1.0.9), sf, RcppParallel, magrittr
c(
person("Arlindo", "Dos Santos", role = c("aut")),
person("François", "Sémécurbe", role = c("aut")),
person("Julien", "Pramil", role = c("aut")),
person("Kim", "Antunez", role = c("cre", "ctb"), email = "antuki.kim+cran@gmail.com"),
person("Auriane", "Renaud", role = c("ctb")),
person("Farida", "Marouchi", role = c("ctb")),
person("Joachim", "Timotéo", role = c("ctb")),
person("Institut national de la statistique et des études économiques", role = c("cph"))
)
Arlindo Dos Santos [aut],
François Sémécurbe [aut],
Julien Pramil [aut],
Kim Antunez [cre, ctb],
Auriane Renaud [ctb],
Farida Marouchi [ctb],
Joachim Timotéo [ctb],
Institut national de la statistique et des études économiques [cph]