fulmar: Fulmaris glacialis data

In gstat: Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

Description

Airborne counts of Fulmaris glacialis during the Aug/Sept 1998 and 1999 flights on the Dutch (Netherlands) part of the North Sea (NCP, Nederlands Continentaal Plat).

Usage

data(fulmar)

Format

This data frame contains the following columns:

year

year of measurement: 1998 or 1999

x

x-coordinate in UTM zone 31

y

y-coordinate in UTM zone 31

depth

sea water depth, in m

coast

distance to coast of the Netherlands, in km.

fulmar

observed density (number of birds per square km)

Author(s)

Dutch National Institute for Coastal and Marine Management (RIKZ), http://www.rikz.nl/

See Also

ncp.grid

E.J. Pebesma, R.N.M. Duin, P.A. Burrough, 2005. Mapping Sea Bird Densities over the North Sea: Spatially Aggregated Estimates and Temporal Changes. Environmetrics 16, (6), p 573-587.

Examples

data(fulmar)
summary(fulmar)
## Not run: 
demo(fulmar)

## End(Not run)

Example output

      year            x                y               depth     
 Min.   :1998   Min.   :476210   Min.   :5694947   Min.   : 1.0  
 1st Qu.:1998   1st Qu.:535522   1st Qu.:5806777   1st Qu.:13.0  
 Median :1999   Median :568618   Median :5896021   Median :25.0  
 Mean   :1999   Mean   :576982   Mean   :5889112   Mean   :23.6  
 3rd Qu.:1999   3rd Qu.:604579   3rd Qu.:5946550   3rd Qu.:32.0  
 Max.   :1999   Max.   :739042   Max.   :6150942   Max.   :54.0  
     coast             fulmar      
 Min.   :  1.024   Min.   : 0.000  
 1st Qu.:  4.857   1st Qu.: 0.000  
 Median : 38.696   Median : 0.000  
 Mean   : 56.642   Mean   : 1.005  
 3rd Qu.: 89.929   3rd Qu.: 0.000  
 Max.   :263.205   Max.   :46.487  


	demo(fulmar)
	---- ~~~~~~

> # $Id: fulmar.R,v 1.3 2006-02-10 19:05:02 edzer Exp $
> library(sp)

> library(gstat)

> data(fulmar)

> data(ncp.grid)

> glm98 <- glm(formula = fulmar ~ depth + coast, family = quasipoisson,
+     data = fulmar[fulmar$year == 1998, ])

> glm99 <- glm(formula = fulmar ~ depth + coast, family = quasipoisson,
+     data = fulmar[fulmar$year == 1999, ])

> fulmar98 = data.frame(fulmar[fulmar$year == 1998,], 
+ 	pr98 = predict(glm98, type = "response"))

> fulmar99 <- data.frame(fulmar[fulmar$year == 1999,], 
+ 	pr99 = predict(glm99, type = "response"))

> pr98.grd <- predict(glm98, newdata = ncp.grid, type = "response", se.fit=TRUE)

> pr99.grd <- predict(glm99, newdata = ncp.grid, type = "response", se.fit=TRUE)

> pr <- data.frame(ncp.grid, pr98=pr98.grd$fit, pr99=pr99.grd$fit,
+ 	se98 = pr98.grd$se.fit, se99 = pr99.grd$se.fit)

> # B.3 create gstat object
> g <- gstat(id = "fulmar98", formula = fulmar~pr98, locations = ~x+y, 
+ 	data = fulmar98, model = vgm(1.89629, "Exp", 50000, 0.852478), 
+ 	beta = c(0,1), variance = "mu")

> g <- gstat(g, id = "fulmar99", formula = fulmar~pr99, locations = ~x+y, 
+ 	data = fulmar99, model = vgm(2.52259, "Exp", 50000, 1.76474), 
+ 	beta = c(0,1), variance = "mu")

> h <- g

> h <- gstat(h, id = c("fulmar98","fulmar99"), 
+ 	model = vgm(2.18, "Exp", 50000, 1.22))

> # predict block means for blocks in ncp.grid$area (table 2; cokriging)
> library(maptools)
Checking rgeos availability: FALSE
 	Note: when rgeos is not available, polygon geometry 	computations in maptools depend on gpclib,
 	which has a restricted licence. It is disabled by default;
 	to enable gpclib, type gpclibPermit()

> areas.r = readShapePoly(system.file("external/ncp.shp", package="gstat"))

> #areas.r <- as.SpatialRings.Shapes(areas.shp$Shapes, areas.shp$att.data$WSVGEB_)
> coordinates(pr) = ~x+y

> #pr.df = overlay(pr, areas.r, fn = mean)
> pr.df = na.omit(as(aggregate(pr, areas.r, FUN = mean), "data.frame"))

> # match non-empty (and relevant) areas:
> #areas = SpatialPolygonsDataFrame(areas.r[c(2,3,4,16),"WSVGEB_"], pr.df[c(1,2,3,5),])#,match.ID=F)
> areas = SpatialPolygonsDataFrame(areas.r[c(1,2,12,7),"WSVGEB_"], 
+ 	pr.df[c(1,2,3,5),], match.ID=F)

> # areas ID's 0 1 2 14
> sk = predict(g, areas)
[using simple kriging]

> cok = predict(h, areas)
Linear Model of Coregionalization found. Good.
[using simple cokriging]

> spplot(cok, c(3,5), names.attr = c("1998", "1999"), 
+ 	main = "Fulmaris glacialis, density estimates\n(by irregular block cokriging)")

> sk = as.data.frame(sk)

> cok = as.data.frame(cok)

> print(data.frame(area = c(1,2,3,16), 
+ 		SK98 = sk$fulmar98.pred, SE98 = sqrt(sk$fulmar98.var),
+ 		SK99 = sk$fulmar99.pred, SE99 = sqrt(sk$fulmar99.var),
+ 		CK98 = cok$fulmar98.pred, SE98 = sqrt(cok$fulmar98.var),
+ 		CK99 = cok$fulmar99.pred, SE99 = sqrt(cok$fulmar99.var)),
+ 	digits=3)
  area   SK98  SE98   SK99  SE99   CK98 SE98.1   CK99 SE99.1
1    1 2.9469 0.642 3.7043 0.740 2.9469  0.642 3.7043  0.740
2    2 0.2289 0.634 0.5242 0.731 0.2289  0.634 0.5242  0.731
3    3 0.0442 1.107 0.1003 1.276 0.0442  1.107 0.1003  1.276
4   16 0.0324 1.365 0.0517 1.574 0.0324  1.365 0.0517  1.574

> print(data.frame(area = c(1,2,3,16), 
+ 		dSK = sk$fulmar99.pred - sk$fulmar98.pred, 
+ 		SEdSK = sqrt(sk$fulmar98.var+sk$fulmar99.var),
+ 		dCOK = cok$fulmar99.pred - cok$fulmar98.pred, 
+ 		SEdCOK = sqrt(cok$fulmar98.var+cok$fulmar99.var
+ 			 - 2*cok$cov.fulmar98.fulmar99)),
+ 	digits=3)
  area    dSK SEdSK   dCOK SEdCOK
1    1 0.7574 0.979 0.7574  0.113
2    2 0.2953 0.967 0.2953  0.112
3    3 0.0561 1.689 0.0561  0.195
4   16 0.0194 2.083 0.0194  0.240
There were 13 warnings (use warnings() to see them)

gstat documentation built on May 2, 2019, 4:59 p.m.