fulmar: Fulmaris glacialis data

Description Usage Format Author(s) See Also Examples

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

1

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)

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

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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 March 19, 2021, 9:06 a.m.