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Slides_geor_v1.0.R
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Slides_geor_v1.0.R
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## ----setup, include = FALSE----------------------------------------------------------------------------------
# opciones predeterminadas
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(
fig.width=7, fig.height=6,
fig.retina=3,
# out.width = "30%",
# out.height="30%",
echo = TRUE,
message = FALSE,
warning = FALSE,
fig.show = TRUE,
hiline = TRUE,
cache = FALSE,
fig.align = "center"
)
## ----cod_datos, eval=FALSE-----------------------------------------------------------------------------------
## library(geoR)
## # `mygeodata` es un objeto espacial: $coords y $data
## mygeodata <- as.geodata(mydata, coords.col = 1:2, data.col = 3)
## ----cod_eda, eval=FALSE-------------------------------------------------------------------------------------
## summary(mygeodata)
## plot(mygeodata)
## ----cod_sem_emp, eval=FALSE---------------------------------------------------------------------------------
## semivar_emp <- variog(mygeodata, max.dist = 2/3*distancia_maxima_coordenadas)
## plot(semivar_emp)
## ----cod_sem_teo, eval=FALSE---------------------------------------------------------------------------------
## semivar_teo <- eyefit(semivar_emp) # función interactiva
## semivar_teo # contiene los parámetros del semivariograma teórico
## cov.model sigmasq phi tausq kappa kappa2 practicalRange
## ----cod_inter, eval=FALSE-----------------------------------------------------------------------------------
## * `cov.model` = modelo de covarianza
## * `sigmasq` = varianza parcial
## * `phi` = rango o alcance
## * `tausq` = nugget
## * `practicalRange`= distancia en la que se estabiliza el semivariograma
## ----cod_krig, eval=FALSE------------------------------------------------------------------------------------
## xx <- seq(min, max, l = 51) #min y #máx para el eje x
## yy <- seq(min, max, l = 51) #min y #máx para el eje y
## grid_prediccion <- expand.grid(x = xx, y = yy)
##
## # help(krige.conv)
## krig_ord <- krige.conv(mygeodata, #datos
## loc = grid_prediccion, #grid de predicción
## krige = krige.control(obj.m = semivar_teo) #semivariograma teórico
## )
## names(krig_ord)
## ----cod_plots, eval=FALSE-----------------------------------------------------------------------------------
## #varias funciones de mapeado
## contour(krig_ord, filled = TRUE)
## image(krig_ord, val = krig_ord$krige.var) #superficie de varianzas
##
## # validación cruzada
## mygeodata_xv <- xvalid(mygeodata, model = semivar_teo)
## ----cod_3dplot, eval=FALSE----------------------------------------------------------------------------------
## library(plot3D)
## # install.packages('plot3D')
## persp3D(xx, yy, matrix(krig_ord$predict, nrow = length(xx)), theta=-60, phi=40)
## ----ms_madrid, echo=FALSE-----------------------------------------------------------------------------------
MuniMadrid <- matrix(scan("data/MuniMadrid.txt"), ncol = 2, byrow = T)
data.co=read.table("data/Madrid_LOG_co50s10h.txt", header=TRUE)
library(dplyr); library(sf); library(leaflet)
MuniMadrid_sf <- st_as_sf(as.data.frame(MuniMadrid), coords = c("V1","V2"),
crs=st_crs(25830)) %>% st_transform(4326)
ha <- st_as_sf(data.co, coords = c("x","y"), crs=st_crs(25830)) %>% st_transform(4326)
dato= data.co$co
dato <- sprintf("<strong>Level of CO: %s</strong>", round(data.co$co,2)) %>% lapply(htmltools::HTML)
leaflet(ha) %>% addTiles() %>% addMarkers(popup = dato, clusterOptions = markerClusterOptions()) %>%
addCircleMarkers(radius = 7, color = "red", popup = dato)
## ----leo_datos, echo=TRUE------------------------------------------------------------------------------------
# lectura de geoR. Instalar si no lo tengo
library(geoR)
# lectura de los datos y coordenadas
data.co=read.table("data/Madrid_LOG_co50s10h.txt", header=TRUE)
MuniMadrid <- matrix(scan("data/MuniMadrid.txt"), ncol = 2, byrow = T)
estaciones <- read.table("data/coordata.txt", header = FALSE) # lo utilizo luego
# creación del objeto as.geodata()
head(data.co)
co.50s.10h<-as.geodata(obj=data.co, coords.col = 1:2, data.col = 3)
## ----sum_co--------------------------------------------------------------------------------------------------
# descriptivos
summary(co.50s.10h)
# análisis gráfico
plot(co.50s.10h)
# Mapa de quintiles
points(co.50s.10h, pch=21, bg=8, lwd=4, cex.max=3, col=cm.colors(12) )
## ----co_sem_empirico-----------------------------------------------------------------------------------------
bin1.co.50s.10h <- variog(co.50s.10h, uvec = seq(800, 7000, l = 10), tolerance = pi/8)
cloud.co.50s.10h <- variog(co.50s.10h, option = "cloud")
## ------------------------------------------------------------------------------------------------------------
par(mfrow = c(1, 2))
plot(bin1.co.50s.10h, ylab = "Semivariograma", main = "", col = 1, pch = 21, bg = "darkgray", lwd = 2)
plot(cloud.co.50s.10h, xlim = c(0, 7000), col = "darkgray", main = " ", pch = 16, ylab = " ")
lines(bin1.co.50s.10h, type = "b", pch = 22, bg = 8, lwd = 2, cex = 1.2, ylab = " ")
## ----eyefit--------------------------------------------------------------------------------------------------
# cuadro interactivo
plot(bin1.co.50s.10h, ylab = "Semivariograma", main = "", col = 2, pch = 21, bg = "darkgray", lwd = 2)
#semivar_teo <- eyefit(bin1.co.50s.10h)
## ----co_ajuste_estad, message=FALSE--------------------------------------------------------------------------
ols <- variofit(bin1.co.50s.10h, ini = c(0.134, 1800), cov.model = "spherical",
fix.nugget = FALSE, weights = "equal")
ml <- likfit(co.50s.10h, coords = co.50s.10h$coords, data = co.50s.10h$data,
cov.model = "spherical", ini = c(0.134, 1800), nugget = FALSE, fix.psiA = FALSE,
fix.psiR = FALSE, lik.method = "ML")
wls <- variofit(bin1.co.50s.10h, ini = c(0.134, 1800), cov.model = "spherical",
fix.nugget = FALSE, weights = "npairs")
reml <- likfit(co.50s.10h, coords = co.50s.10h$coords, data = co.50s.10h$data,
cov.model = "spherical", ini = c(0.134, 1800), fix.psiA = FALSE, fix.psiR = FALSE,
fix.nugget = FALSE, lik.method = "RML")
# Representa ambos semivariogramas (empírico y teórico)
plot(bin1.co.50s.10h, ylab = "Semivariogram", main = " ",
col = 1, pch = 21, bg = "yellow", lwd = 2, cex = 1.2)
lines(ols, lwd = 2, lty = 3);
lines(wls, lwd = 2, lty = 1);
lines(ml, lwd = 1, lty = 1);
lines(reml, lwd = 2, lty = 2)
legend(0.55, 0.17, legend = c("OLS", "WLS", "ML", "REML"),
lty = c(3, 1, 1, 2), lwd = c(2, 2, 1, 2), cex = 0.7)
## ----co_kriging----------------------------------------------------------------------------------------------
# creamos una malla de interopolación
xx <- seq(min(MuniMadrid[, 1]), max(MuniMadrid[, 1]), l = 51)
yy <- seq(min(MuniMadrid[, 2]), max(MuniMadrid[, 2]), l = 51)
grid_prediccion <- expand.grid(xx, yy)
# Kriging ordinario
kc.co.2s.10h <- krige.conv(co.50s.10h,
coords = co.50s.10h$coords,
data = co.50s.10h$data,
loc = grid_prediccion,
#Opición 1: especificamos todos los parámetros del semivariorama
krige = krige.control(cov.model = "spherical",
cov.pars = c(0.1403, 6096.4841),
nugget = 0)
# Opición 2: especificamos el semivariograma
#krige = krige.control(obj.m = semivar_teo) #semivariograma teórico
)
str(kc.co.2s.10h)
## ----co_krig_sd----------------------------------------------------------------------------------------------
# prediction map
contour(kc.co.2s.10h, borders = MuniMadrid, filled=TRUE, cex=1, col=terrain.colors(20))
# sd map
contour(kc.co.2s.10h, values = sqrt(kc.co.2s.10h$krige.var),
borders = MuniMadrid, filled=TRUE)
## ----co_3D, echo=FALSE, include=FALSE------------------------------------------------------------------------
par(mfrow = c(1, 1), mar = c(3.5, 3.5, 1, 0), mgp = c(1.5, 0.5, 0))
persp(kc.co.2s.10h, borders = MuniMadrid, main = "3D Prediction map", theta = 0,
phi = 40, expand = 0.5, col = "green")
## ----co_leaflet, echo=FALSE----------------------------------------------------------------------------------
# Paso la predicción a raster y lo proyecto a 4326
library(raster)
library(sp)
library(leaflet.providers)
# Paso la predicción a raster y lo proyecto a 4326
pred <- rasterFromXYZ(cbind(grid_prediccion, kc.co.2s.10h$predict))
crs(pred) <- st_crs(25830)$proj4string
# Projecto a lonlat (4326)
pred <- projectRaster(pred, crs = st_crs(4326)$proj4string)
# Recorto a Madrid
library(mapSpain)
Madrid_sf <- esp_get_munic_siane(munic = "^Madrid$", epsg = 4326)
pred <- mask(pred, Madrid_sf)
# preparo leaflet
pal <- colorNumeric(hcl.colors(10, "Inferno", rev = TRUE), values(pred),
na.color = "transparent"
)
# Uso una capa con carreteras:
# https://leaflet-extras.github.io/leaflet-providers/preview/
leaflet() %>%
# Capa fotos
addProviderEspTiles("PNOA", group = "Terreno") %>%
# Capa callejero
addTiles(group = "Callejero") %>%
# Capa carreteras
addProviderTiles(provider = "Stamen.TonerLines", group = "Carreteras") %>%
addRasterImage(pred,
colors = pal,
opacity = 0.7,
group = "Predicción"
) %>%
addPolygons(data = Madrid_sf, fill = FALSE) %>%
addLayersControl(
baseGroups = c("Carreteras", "Terreno", "Callejero"),
overlayGroups = c("Predicción"),
options = layersControlOptions(collapsed = TRUE)
) %>%
addLegend(
pal = pal,
values = values(pred),
title = "CO (ln)"
)
## ----saca_cod_r, eval=FALSE, include=FALSE, message=FALSE, results='hide', echo=FALSE------------------------
## library(knitr)
## knit('Slides_geor_v1.0.Rmd', tangle=TRUE)
## source('Slides_geor_v1.0.R')