| Title: | Hydrometeorological Interpolation |
|---|---|
| Description: | Hydrometeorological interpolation using the AURELHY method. |
| Authors: | Philippe Grosjean [aut, cre] |
| Maintainer: | Philippe Grosjean <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.9 |
| Built: | 2024-12-09 04:14:02 UTC |
| Source: | https://github.com/SciViews/aurelhy |
Hydrometeorological interpolation using the AURELHY method.
| Package: | aurelhy |
| Title: | Hydrometeorological Interpolation |
| Type: | Package |
| Version: | 1.0-7 |
| Date: | 2015-07-16 |
| Authors@R: | c(person("Philippe", "Grosjean", role = c("aut", "cre"), email = "[email protected]")) |
| URL: | https://github.com/phgrosjean/aurelhy |
| Depends: | sp, gstat |
| Suggests: | shapefiles |
| License: | GPL-2 |
| LazyLoad: | yes |
| Encoding: | UTF-8 |
| Author: Philippe Grosjean [aut, cre] | |
| Maintainer: | Philippe Grosjean <[email protected]> |
Philippe Grosjean <[email protected]>
An 'aurelhy' object contains principal components calculated after the various variables describing the landscape,
as well as other useful descriptors. Use the predict() method to interpolate some data with the AURELHY method.
aurelhy(geotm, geomask, landmask = auremask(), x0 = 30, y0 = 30, step = 12, nbr.pc = 10, scale = FALSE, model = "data ~ .", vgmodel = gstat::vgm(1, "Sph", 10, 1), add.vars = NULL, var.name = NULL, resample.geomask = TRUE) ## S3 method for class 'aurelhy' print(x, ...) ## S3 method for class 'aurelhy' plot(x, y, main = "PCA on land descriptors", ...) ## S3 method for class 'aurelhy' points(x, pch = ".", ...) ## S3 method for class 'aurelhy' summary(object, ...) ## S3 method for class 'aurelhy' update(object, nbr.pc, scale, model, vgmodel, ...) ## S3 method for class 'aurelhy' predict(object, geopoints, variable, v.fit = NULL, ...) ## S3 method for class 'predict.aurelhy' print(x, ...) ## S3 method for class 'predict.aurelhy' summary(object, ...) ## S3 method for class 'predict.aurelhy' plot(x, y, which = 1, ...) ## S3 method for class 'aurelhy' as.geomat(x, what = "PC1", nodata = NA, ...) ## S3 method for class 'predict.aurelhy' as.geomat(x, what = c("Interpolated", "Predicted", "KrigedResiduals", "KrigeVariance"), nodata = NA,...)aurelhy(geotm, geomask, landmask = auremask(), x0 = 30, y0 = 30, step = 12, nbr.pc = 10, scale = FALSE, model = "data ~ .", vgmodel = gstat::vgm(1, "Sph", 10, 1), add.vars = NULL, var.name = NULL, resample.geomask = TRUE) ## S3 method for class 'aurelhy' print(x, ...) ## S3 method for class 'aurelhy' plot(x, y, main = "PCA on land descriptors", ...) ## S3 method for class 'aurelhy' points(x, pch = ".", ...) ## S3 method for class 'aurelhy' summary(object, ...) ## S3 method for class 'aurelhy' update(object, nbr.pc, scale, model, vgmodel, ...) ## S3 method for class 'aurelhy' predict(object, geopoints, variable, v.fit = NULL, ...) ## S3 method for class 'predict.aurelhy' print(x, ...) ## S3 method for class 'predict.aurelhy' summary(object, ...) ## S3 method for class 'predict.aurelhy' plot(x, y, which = 1, ...) ## S3 method for class 'aurelhy' as.geomat(x, what = "PC1", nodata = NA, ...) ## S3 method for class 'predict.aurelhy' as.geomat(x, what = c("Interpolated", "Predicted", "KrigedResiduals", "KrigeVariance"), nodata = NA,...)
geotm |
a terrain model ('geotm' object) with enough resolution to be
able to calculate all landscape descriptors (use |
geomask |
a 'geomask' object with same resolution and coverage of the
'geotm' object or the final interpolation grid (with |
landmask |
an 'auremask' object that defines the window of analysis used around each point ot calculate its landscape descriptors. |
x0 |
shift in X direction (longitude) where to consider the first point of the interpolation grid (note that interpolation grid must be less dense or equal to the terrain model grid, depending on the mask used). |
y0 |
shift in Y direction (latitude) for the first point of the interpolation grid. |
step |
resolution of the interpolation grid, i.e., we keep one point
every |
nbr.pc |
number of PCA's principal components to keep in the interpolation. This is the initial value; the example show you how you can change this after the 'aurelhy' object is calculated. |
scale |
should we scale the landscape descriptors (variance = 1) before
performing the PCA? If |
model |
a formula describing the model used to predict the data. The
left-hand side of the formula must always be 'data' and the right-hand
considers all predictors spearated by a plus sign. To use all predictors,
specify |
vgmodel |
the variogram model to fit, as defined by the |
add.vars |
additional variable(s) measured at the same points as the
geotm object, or the final interpolation grid. They will be used as
additional predictors. The example show you how you can add or remove such
variables after the 'aurelhy' object is calculated. If |
var.name |
if |
resample.geomask |
do we resample the geomask using |
x |
an 'aurelhy' or 'predict.aurelhy' object, depending on the method invoked |
y |
a 'geopoints' object to create a plot best depicting the interpolation process, or nothing to just plot the interpolation grid. |
main |
the main title of the graph |
pch |
the symbol to use for plotting points. The default value,
|
object |
an 'aurelhy' object |
geopoints |
a 'geopoints' object with data to be interpolated. |
variable |
the name of the variable in the 'geopoints' object to interpolate |
v.fit |
the fitted variogram model used to krige residuals. If |
which |
which graph to plot |
what |
what is extracted as a 'geomat' object |
nodata |
the code used to represent missing data in the 'geomat' object |
... |
further arguments passed to the function |
aurelhy() creates a new 'aurelhy' object. The object has print()
and plot() methods for further diagnostics. You should use the
predict() method to perform the AURELHY interpolation on some data. The
'aurelhy' object is also easy to save for further reuse (it is designed so
that the most time-consumming operations are done during its creation; so, it
is supposed to be generated only once and reused for different interpolations
on the same terrain model).
An 'aurelhy' object with all information required to perform an AURELHY interpolation with any 'geopoints' data.
Philippe Grosjean <[email protected]>
Benichou P, Le Breton O (1987). Prise en compte de la topographie pour la cartographie des champs pluviometriques statistiques. La Meteorologie, 7:23-34.
# Create an aurelhy object for the Morocco terrain data data(morocco) # The terrain model with a grid of about 924x924m data(mbord) # A shape with the area around Morocco to analyze data(mmask) # A 924x924m grid with a mask covering territory to analyze data(mseadist) # The distance to the sea for territory to analyze data(mrain) # Rain data measured at 43 stations to be interpolated # Create a map with these data image(morocco) # Plot the terrain model grid() lines(mbord, col = "red") # Add borders of territory to analyze in red # Make sure we use all the stations from mrain in the geomask mmask2 <- add.points(mmask, mrain) # Now, create an aurelhy object with landscape description, using the # first ten PCs, plus the distance to the sea (mseadist) for prediction # Use a default radial window of analysis of 26km as maximum distance # and an interpolation grid of 0.1x0.1degrees (roughly 11x11km) # The variogram model is kept simple here, see ?gstat::vgm for other choices # Be patient... this takes a little time to calculate! maurelhy <- aurelhy(morocco, mmask2, auremask(), x0 = 30, y0 = 54, step = 12, scale = TRUE, nbr.pc = 10, vgmodel = gstat::vgm(100, "Sph", 1, 1), add.vars = mseadist, var.name = "seadist") maurelhy points(maurelhy) # Add the interpolated points on the map points(mrain, col = "red") # Add location of weather stations in red # Diagnostic of the PCA on land descriptors summary(maurelhy) plot(maurelhy) # Interpolate 'rain' variable on the considered territory around Morocco # Since we do not want negative values for this variable and it is log-normally # distributed, we will interpolate log10(rain) instead mrain$logRain <- log10(mrain$rain) pmrain <- predict(maurelhy, mrain, "logRain") pmrain # Diagnostic of regression model summary(pmrain) # Significant predictors at alpha = 0.01 are x, y, PC3, PC6 and PC7 # one could simplify the model as data ~ x + y + PC3 + PC6 + PC7 # but it is faster to keep the full model for final interpolation # when we are only interested by the final interpolation or when processing # is automated... # Any of the predictors can be extracted from maurelhy as a geomat object # for further inspection. For instance, let's look at PC3, PC6 and PC7 components persp(as.geomat(maurelhy, "PC3"), expand = 50) persp(as.geomat(maurelhy, "PC6"), expand = 50) persp(as.geomat(maurelhy, "PC7"), expand = 50) plot(pmrain, which = 1) # Residuals versus fitted (how residuals spread?) plot(pmrain, which = 2) # Normal Q-Q plot of residuals (residuals distribution) plot(pmrain, which = 3) # Best graph to look at residuals homoscedasticity plot(pmrain, which = 4) # Cook's distance of residuals versus observation plot(pmrain, which = 5) # Residuals leverage: are there influencial points? # Map of predicted values filled.contour(as.geomat(pmrain, "Predicted"), asp = 1, color.palette = terrain.colors, main = "Values predicted by the linear model") # Residuals kriging diagnostic plot(pmrain, which = 6) # Semi-variogram and adjusted model filled.contour(as.geomat(pmrain, "KrigedResiduals"), asp = 1, color.palette = terrain.colors, main = "Kriged residuals") filled.contour(as.geomat(pmrain, "KrigeVariance"), asp = 1, color.palette = terrain.colors, main = "Kriged residuals variance") # As we can expect, kriging variance is larger in the south/south-west part # where density of stations is low # AURELHY interpolation diagnostic plots # Graph showing the importance of predicted versus kriged residuals for # all observations plot(pmrain, which = 7) # Model prediction and kriged residuals # Extract interpolated log(rain) and transform back into rain (mm) geomrain <- as.geomat(pmrain) geomrain <- 10^geomrain # How is interpolated rain distributed? range(geomrain, na.rm = TRUE) range(mrain$rain) # Ooops! We have some very high values! How many? sum(geomrain > 1000, na.rm = TRUE) # This is probably due to a lack of data at high altitudes # Let's truncate them to 1000 for a better graph geomrain[geomrain > 1000] <- 1000 # ... and plot the result image(geomrain, col = topo.colors(12)) contour(geomrain, add = TRUE) lines(mbord, col = "red") points(mrain, col = "red") # A better plot for these interpolated rain data filled.contour(coords(geomrain, "x"), coords(geomrain, "y"), geomrain, asp = 1, xlab = "Longitude", ylab = "Latitude", main = "AURELHY interpolated rain data", key.title = title(sub = "Rain (mm)\n\n"), color.palette = colorRampPalette(c("red", "gray", "blue"), bias = 2)) # One can experiment different interpolation parameters using update() # Suppose we (1) don't want to scale PCs, (2) to keep only first 7 PCs, # (3) we want an upgraded linear model like this: # data <- a.x + b.y + c.z + d.PC3 + e.PC6 + f.PC7 + g.seadist + h.seadist^2 + i # and (4) we want a Gaussian model for the semi-variogram # (note that one can also regress against seadist2 <- seadist^2), just do: # maurelhy2$seadist2 <- maurelhy$seadist^2 # Even with all these changes, you don't have to recompute maurelhy, # just update() it and the costly steps of calculating landscape descriptors # are reused (not the use of I() to protect calcs inside a formula)! maurelhy2 <- update(maurelhy, scale = FALSE, nbr.pc = 7, model = data ~ x + y + z + PC3 + PC6 + PC7 + seadist + I(seadist^2), vgmodel = gstat::vgm(100, "Gau", 1, 1)) maurelhy2 # Diagnostic of the new PCA on land descriptors without scaling summary(maurelhy2) plot(maurelhy2) # Interpolate with the new parameters pmrain2 <- predict(maurelhy2, mrain, "logRain") summary(pmrain2) # A couple of graphs plot(pmrain2, which = 1) # Residuals versus fitted (how residuals spread?) plot(pmrain, which = 6) # Semi-variogram and adjusted model plot(pmrain2, which = 7) # Model prediction and kriged residuals #... Explore as much as you like until you find the set of parameters that suits you!# Create an aurelhy object for the Morocco terrain data data(morocco) # The terrain model with a grid of about 924x924m data(mbord) # A shape with the area around Morocco to analyze data(mmask) # A 924x924m grid with a mask covering territory to analyze data(mseadist) # The distance to the sea for territory to analyze data(mrain) # Rain data measured at 43 stations to be interpolated # Create a map with these data image(morocco) # Plot the terrain model grid() lines(mbord, col = "red") # Add borders of territory to analyze in red # Make sure we use all the stations from mrain in the geomask mmask2 <- add.points(mmask, mrain) # Now, create an aurelhy object with landscape description, using the # first ten PCs, plus the distance to the sea (mseadist) for prediction # Use a default radial window of analysis of 26km as maximum distance # and an interpolation grid of 0.1x0.1degrees (roughly 11x11km) # The variogram model is kept simple here, see ?gstat::vgm for other choices # Be patient... this takes a little time to calculate! maurelhy <- aurelhy(morocco, mmask2, auremask(), x0 = 30, y0 = 54, step = 12, scale = TRUE, nbr.pc = 10, vgmodel = gstat::vgm(100, "Sph", 1, 1), add.vars = mseadist, var.name = "seadist") maurelhy points(maurelhy) # Add the interpolated points on the map points(mrain, col = "red") # Add location of weather stations in red # Diagnostic of the PCA on land descriptors summary(maurelhy) plot(maurelhy) # Interpolate 'rain' variable on the considered territory around Morocco # Since we do not want negative values for this variable and it is log-normally # distributed, we will interpolate log10(rain) instead mrain$logRain <- log10(mrain$rain) pmrain <- predict(maurelhy, mrain, "logRain") pmrain # Diagnostic of regression model summary(pmrain) # Significant predictors at alpha = 0.01 are x, y, PC3, PC6 and PC7 # one could simplify the model as data ~ x + y + PC3 + PC6 + PC7 # but it is faster to keep the full model for final interpolation # when we are only interested by the final interpolation or when processing # is automated... # Any of the predictors can be extracted from maurelhy as a geomat object # for further inspection. For instance, let's look at PC3, PC6 and PC7 components persp(as.geomat(maurelhy, "PC3"), expand = 50) persp(as.geomat(maurelhy, "PC6"), expand = 50) persp(as.geomat(maurelhy, "PC7"), expand = 50) plot(pmrain, which = 1) # Residuals versus fitted (how residuals spread?) plot(pmrain, which = 2) # Normal Q-Q plot of residuals (residuals distribution) plot(pmrain, which = 3) # Best graph to look at residuals homoscedasticity plot(pmrain, which = 4) # Cook's distance of residuals versus observation plot(pmrain, which = 5) # Residuals leverage: are there influencial points? # Map of predicted values filled.contour(as.geomat(pmrain, "Predicted"), asp = 1, color.palette = terrain.colors, main = "Values predicted by the linear model") # Residuals kriging diagnostic plot(pmrain, which = 6) # Semi-variogram and adjusted model filled.contour(as.geomat(pmrain, "KrigedResiduals"), asp = 1, color.palette = terrain.colors, main = "Kriged residuals") filled.contour(as.geomat(pmrain, "KrigeVariance"), asp = 1, color.palette = terrain.colors, main = "Kriged residuals variance") # As we can expect, kriging variance is larger in the south/south-west part # where density of stations is low # AURELHY interpolation diagnostic plots # Graph showing the importance of predicted versus kriged residuals for # all observations plot(pmrain, which = 7) # Model prediction and kriged residuals # Extract interpolated log(rain) and transform back into rain (mm) geomrain <- as.geomat(pmrain) geomrain <- 10^geomrain # How is interpolated rain distributed? range(geomrain, na.rm = TRUE) range(mrain$rain) # Ooops! We have some very high values! How many? sum(geomrain > 1000, na.rm = TRUE) # This is probably due to a lack of data at high altitudes # Let's truncate them to 1000 for a better graph geomrain[geomrain > 1000] <- 1000 # ... and plot the result image(geomrain, col = topo.colors(12)) contour(geomrain, add = TRUE) lines(mbord, col = "red") points(mrain, col = "red") # A better plot for these interpolated rain data filled.contour(coords(geomrain, "x"), coords(geomrain, "y"), geomrain, asp = 1, xlab = "Longitude", ylab = "Latitude", main = "AURELHY interpolated rain data", key.title = title(sub = "Rain (mm)\n\n"), color.palette = colorRampPalette(c("red", "gray", "blue"), bias = 2)) # One can experiment different interpolation parameters using update() # Suppose we (1) don't want to scale PCs, (2) to keep only first 7 PCs, # (3) we want an upgraded linear model like this: # data <- a.x + b.y + c.z + d.PC3 + e.PC6 + f.PC7 + g.seadist + h.seadist^2 + i # and (4) we want a Gaussian model for the semi-variogram # (note that one can also regress against seadist2 <- seadist^2), just do: # maurelhy2$seadist2 <- maurelhy$seadist^2 # Even with all these changes, you don't have to recompute maurelhy, # just update() it and the costly steps of calculating landscape descriptors # are reused (not the use of I() to protect calcs inside a formula)! maurelhy2 <- update(maurelhy, scale = FALSE, nbr.pc = 7, model = data ~ x + y + z + PC3 + PC6 + PC7 + seadist + I(seadist^2), vgmodel = gstat::vgm(100, "Gau", 1, 1)) maurelhy2 # Diagnostic of the new PCA on land descriptors without scaling summary(maurelhy2) plot(maurelhy2) # Interpolate with the new parameters pmrain2 <- predict(maurelhy2, mrain, "logRain") summary(pmrain2) # A couple of graphs plot(pmrain2, which = 1) # Residuals versus fitted (how residuals spread?) plot(pmrain, which = 6) # Semi-variogram and adjusted model plot(pmrain2, which = 7) # Model prediction and kriged residuals #... Explore as much as you like until you find the set of parameters that suits you!
These functions manipulate geographical coordinates in various ways to optimize computation of the AURELHY method.
deg.lat(latitude) deg.lon(latitude) polar.coords(geomat, x, y, maxdist) match.coords(points, table, tol = 0.002) coords(x, ...) resample(x, ...) add.points(x, ...) ## S3 method for class 'geomask' add.points(x, geopoints, ...) dist2sea(geotm)deg.lat(latitude) deg.lon(latitude) polar.coords(geomat, x, y, maxdist) match.coords(points, table, tol = 0.002) coords(x, ...) resample(x, ...) add.points(x, ...) ## S3 method for class 'geomask' add.points(x, geopoints, ...) dist2sea(geotm)
latitude |
the latitude in decimal degrees |
geomat |
a 'geomat' object |
x |
X coordinate of the reference point for |
y |
Y coordinate of the reference point |
maxdist |
maximum distance to consider in km. All points whose distance from the reference point is larger are not considered in the calculation |
points |
a list or data frame with X and Y coordinates of the points to match to the reference points (in decimal degrees, for instance) |
table |
a similar list or data frame with X(ref) and Y(ref) coordinates
of the reference points to be matched (in the same units as for
|
tol |
the maximum tolerance in X and Y units to consider points are matching, that is, X +/- tol = X(ref) and Y +/- tol = Y(ref) |
... |
further arguments passed to the method |
geopoints |
a geopoints object from which we want to add corresponding points in a geomask |
geotm |
a geotm object |
deg.lat() and deg.lon() provide the length of one degree in,
respectively, latitude and longitude in km, given the corresponding latitude
in decimal degrees. The ellipsoid defined in WGS84 model is used for these
calculations.
polar.coords() calculates polar coordinates of points.
match.coords() selects points with matching coordinates, given a
tolerance distance between the reference points (i.e., from a geotm grid,
using coords(my_geotm, "xy")) and the points to match (stations).
coords() is a generic function that extracts geographical coordinates
from one object in different fashions.
resample is a generic function to resample a grid ('geomat' object).
add.points add points from a geopoints object in a geomask.
dist2sea() calculate the distance of points in a geotm object to the
sea.
deg.lat() and deg.lon() return the length of one degree in km.
polar.coords() returns a data frame with 'angle' in rad and 'dist'(ance)
in km for the reference point to each point in the grid, within 'maxdist'.
There is also a 'geomat' attribute containing the window of the initial
'geomat' object containing the considered points.
match.coords() returns a vector of logical of the same length as the
number of colunms in the points data frame (that must contain 'x' and 'y'
columns with coordinates of points to be matched).
Philippe Grosjean <[email protected]>, and Francois Delobel for
dist2sea()
# Size of one degree in latitude and longitude, given the latitude in decimal degrees deg.lat(c(0, 15, 30, 45, 60, 75, 90)) # 110.574 110.649 110.852 111.132 111.412 111.618 111.694 deg.lon(c(0, 15, 30, 45, 60, 75, 90)) # 111.320 107.550 96.486 78.847 55.800 28.902 0.000# Size of one degree in latitude and longitude, given the latitude in decimal degrees deg.lat(c(0, 15, 30, 45, 60, 75, 90)) # 110.574 110.649 110.852 111.132 111.412 111.618 111.694 deg.lon(c(0, 15, 30, 45, 60, 75, 90)) # 111.320 107.550 96.486 78.847 55.800 28.902 0.000
An AURELHY window of analysis ('auremask' object) specifies the regions relative to the point that define the various variables describing the landscape.
auremask(type = "radial", dist = c(1, 6, 11, 16, 21, 26), angles = 0:7 * pi/4 + 0.01, n = 11, keep.origin = FALSE) ## S3 method for class 'auremask' print(x, geomat, ...) ## S3 method for class 'auremask' plot(x, y, ...)auremask(type = "radial", dist = c(1, 6, 11, 16, 21, 26), angles = 0:7 * pi/4 + 0.01, n = 11, keep.origin = FALSE) ## S3 method for class 'auremask' print(x, geomat, ...) ## S3 method for class 'auremask' plot(x, y, ...)
type |
the type of window, either |
dist |
A vector of distances (in km) to consider in the window for the
|
angles |
A vector of angles in radians to use to construct a
|
n |
The number of grid points in latitude and longitude to use for a
|
keep.origin |
Is the origin where the window is centered considered as one point of the grid, or not (by default, not, as in the original implementation of the AURELHY method) |
x |
An 'auremask' object |
geomat |
A reference grid, as a 'geomat' object against which the window of analysis is tested (print or plot the number of points that are located in each sector of the window) |
y |
Same as |
... |
further arguments passed to the function |
auremask() creates a new window of analysis. The object has print() and
plot() methods.
An 'auremask' object with all information required to mask a 'geotm' object (terrain model) for creating landscape variables required by the AURELHY method.
Philippe Grosjean <[email protected]>
# Default window of analysis am <- auremask() am # Get an example terrain model and apply the window on it data(morocco) plot(am, morocco) # Further statistics are displayed with print() if a grid is provided too print(am, morocco)# Default window of analysis am <- auremask() am # Get an example terrain model and apply the window on it data(morocco) plot(am, morocco) # Further statistics are displayed with print() if a grid is provided too print(am, morocco)
Geomat are matrices of geographically referenced data. These are essentially georeferenced rectangular, regular grids of points. Data can be numeric (reals), integer, or logical (booleans). Objects 'geotm' are special 'geomat' matrices containing always integers and representing terrain models. Objects 'geomask' are also special 'geomat' that only contain logical values. They are mainly used to define a mask on top of a grid (which points to consider and which ones to eliminate from a calculation).
geomat(x, size, xcenter, ycenter, coords = c(size = size, x = xcenter, y = ycenter), datatype = c("numeric", "integer", "logical"), nodata = NA) geotm(x, size, xcenter, ycenter, coords = c(size = size, x = xcenter, y = ycenter)) geomask(x, size, xcenter, ycenter, coords = c(size = size, x = xcenter, y = ycenter)) read.geomat(file, type = "ascii", datatype = c("numeric", "integer", "logical"), ...) read.geotm(file, type = "ascii", ...) read.geomask(file, type = "ascii", threshold = 0, ...) write.geomat(x, file, type = "ascii", integers = FALSE, nodata = -9999, ...) write.geotm(x, file, type = "ascii", nodata = -9999, ...) write.geomask(x, file, type = "ascii", nodata = -9999, ...) as.geomat(x, ...) ## S3 method for class 'geomat' print(x, ...) ## S3 method for class 'geomat' coords(x, type = "par", ...) ## S3 method for class 'geomat' resample(x, x0 = 1, y0 = 1, step = NULL, nx = 100, ny = nx, strict = FALSE, ...) ## S3 method for class 'geomat' window(x, xlim, ylim, ...) ## S3 method for class 'geomat' plot(x, y = NULL, max.xgrid = 100, nlevels = 50, color.palette = terrain.colors, xlab = "Longitude", ylab = "Latitude", asp = 1, ...) ## S3 method for class 'geomat' image(x, max.xgrid = 500, col = terrain.colors(50), add = FALSE, xlab = if (add) "" else "Longitude", ylab = if (add) "" else "Latitude", asp = 1, ...) ## S3 method for class 'geomat' contour(x, max.xgrid = 100, nlevels = 10, col = par("fg"), add = FALSE, xlab = if (add) "" else "Longitude", ylab = if (add) "" else "Latitude", asp = 1, ...) ## S3 method for class 'geomat' persp(x, max.xgrid = 500, col = "green3", xlab = "Longitude", ylab = "Latitude", asp = 1, theta = 10, phi = 30, expand = 1, shade = 0.75, border = NA, box = TRUE, ...)geomat(x, size, xcenter, ycenter, coords = c(size = size, x = xcenter, y = ycenter), datatype = c("numeric", "integer", "logical"), nodata = NA) geotm(x, size, xcenter, ycenter, coords = c(size = size, x = xcenter, y = ycenter)) geomask(x, size, xcenter, ycenter, coords = c(size = size, x = xcenter, y = ycenter)) read.geomat(file, type = "ascii", datatype = c("numeric", "integer", "logical"), ...) read.geotm(file, type = "ascii", ...) read.geomask(file, type = "ascii", threshold = 0, ...) write.geomat(x, file, type = "ascii", integers = FALSE, nodata = -9999, ...) write.geotm(x, file, type = "ascii", nodata = -9999, ...) write.geomask(x, file, type = "ascii", nodata = -9999, ...) as.geomat(x, ...) ## S3 method for class 'geomat' print(x, ...) ## S3 method for class 'geomat' coords(x, type = "par", ...) ## S3 method for class 'geomat' resample(x, x0 = 1, y0 = 1, step = NULL, nx = 100, ny = nx, strict = FALSE, ...) ## S3 method for class 'geomat' window(x, xlim, ylim, ...) ## S3 method for class 'geomat' plot(x, y = NULL, max.xgrid = 100, nlevels = 50, color.palette = terrain.colors, xlab = "Longitude", ylab = "Latitude", asp = 1, ...) ## S3 method for class 'geomat' image(x, max.xgrid = 500, col = terrain.colors(50), add = FALSE, xlab = if (add) "" else "Longitude", ylab = if (add) "" else "Latitude", asp = 1, ...) ## S3 method for class 'geomat' contour(x, max.xgrid = 100, nlevels = 10, col = par("fg"), add = FALSE, xlab = if (add) "" else "Longitude", ylab = if (add) "" else "Latitude", asp = 1, ...) ## S3 method for class 'geomat' persp(x, max.xgrid = 500, col = "green3", xlab = "Longitude", ylab = "Latitude", asp = 1, theta = 10, phi = 30, expand = 1, shade = 0.75, border = NA, box = TRUE, ...)
x |
An object (a matrix or data frame for |
size |
The size of a grid square (in decimal degrees) |
xcenter |
The position of the center of the top-left square of the grid, that is, its longitude in decimal degrees |
ycenter |
Idem, but latitude in decimal degrees |
coords |
A named vector of three numbers: 'size', 'x' and 'y' as above |
datatype |
The type of data to store in the grid, ort to read/write on the file. Can be 'numeric' (reals), 'integer', or 'logical' (booleans) |
nodata |
The number to use to represent missing data in the grid (by
default it is |
file |
The path to the file used for reading or writing data |
type |
The type of data to read/write. Currently, only \"ascii\", which
means ARC/INFO ASCII GRID format (.asc file). For |
threshold |
Value (single integer) above which all data are converted to
|
integers |
Do we read/write integers (saves memory and disk space used to represent the grid) |
x0 |
The X origin of the new grid |
y0 |
The Y origin of the new grid |
step |
The step to use for resampling ( |
nx |
The desired number of points in the X direction (longitude).
|
ny |
idem than nx, but in the Y direction (latitude) |
strict |
do we interpolated the grid so that we obtain exactly |
xlim |
A vector of two numbers defining the limits to use in X direction (longitude) for the window |
ylim |
A vector of two numbers defining the limits to use in Y direction (latitude) for the window |
y |
Unused argument to match |
max.xgrid |
The maximum number of points in x direction to use. If the grid that is plotted is denser, it is furst resampled to avoid drawing a graph with too much points |
nlevels |
the number of contour levles to calculate |
color.palette |
a color palette generation function |
col |
A vector of colors to use for the plot |
xlab |
The label of the X axis ( |
ylab |
The label of the Y axis ( |
asp |
The aspect ratio between 'x' and 'y'. The default value of
|
add |
Do we add the graph to an existing graph device, or do we plot a fresh new graph? |
theta |
angles defining the viewing direction. |
phi |
|
expand |
the expansion level to use for the z-axis in the perspective |
shade |
the shade at a surface facet is computed as |
border |
the color of the borders of facets. If |
box |
If |
... |
Further arguments passed to the functions (only used for the plotting method) |
An object of class, respectively 'geomat', 'geotm' or 'geomask' inheriting from 'matrix' is created. Methods either return an object of same class, or are used for their side effect of plotting a graph. Objects 'geotm' and 'geomask' also inherit from 'geomat'.
A 'geomat' object. For the print() method, size of the grid is presented
in km.
Philippe Grosjean <[email protected]>
# Create a simple geomat object containing random numbers (gm <- geomat(matrix(rnorm(120), nrow = 10), 0.1, 10, 20)) # Get coordinates for this grid coords(gm) # Longitudes (x) and latitudes (y) for the center of all squares coords(gm, type = "x") coords(gm, type = "y") # Coordinates of the center of all squares coords(gm, type = "xy") # Resample the grid to take one point every second points in the original grid resample(gm, step = 2) # Extract a window from the grid (keep only squares with centers in the window) window(gm, xlim = c(9.5, 10.2), ylim = c(19.5, 20.6)) # Plot this grid in different ways plot(gm) image(gm) contour(gm) persp(gm, expand = 100) # Now load real data (Morocco terrain model) data(morocco) morocco image(morocco) contour(morocco, add = TRUE) grid() # The mask of points inside Morocco territory was obtained like that: #library(splancs) #data(mbord) #inm <- inout(coords(morocco, "xy"), mbord[[1]]) #mmask <- morocco #mmask[inm] <- 1 #mmask[!inm] <- 0 #mmask[is.na(morocco)] <- NA #mmask <- geomask(mmask, coords = coords(mmask)) data(mmask) image(mmask) # Get Morocco frontiers from a shapefile # To read it from an ESRI shape #mbord <- read.geoshapes("morocco_border.shp") data(mbord) lines(mbord, col = "red")# Create a simple geomat object containing random numbers (gm <- geomat(matrix(rnorm(120), nrow = 10), 0.1, 10, 20)) # Get coordinates for this grid coords(gm) # Longitudes (x) and latitudes (y) for the center of all squares coords(gm, type = "x") coords(gm, type = "y") # Coordinates of the center of all squares coords(gm, type = "xy") # Resample the grid to take one point every second points in the original grid resample(gm, step = 2) # Extract a window from the grid (keep only squares with centers in the window) window(gm, xlim = c(9.5, 10.2), ylim = c(19.5, 20.6)) # Plot this grid in different ways plot(gm) image(gm) contour(gm) persp(gm, expand = 100) # Now load real data (Morocco terrain model) data(morocco) morocco image(morocco) contour(morocco, add = TRUE) grid() # The mask of points inside Morocco territory was obtained like that: #library(splancs) #data(mbord) #inm <- inout(coords(morocco, "xy"), mbord[[1]]) #mmask <- morocco #mmask[inm] <- 1 #mmask[!inm] <- 0 #mmask[is.na(morocco)] <- NA #mmask <- geomask(mmask, coords = coords(mmask)) data(mmask) image(mmask) # Get Morocco frontiers from a shapefile # To read it from an ESRI shape #mbord <- read.geoshapes("morocco_border.shp") data(mbord) lines(mbord, col = "red")
Geospoints objects contain data for one or more points defined by their longitude and latitude in decimal degrees. These objects can be read or write to ERSI shape files, or DBF database.
geopoints(x) read.geopoints(File, format) write.geopoints(x, file, arcgis = FALSE,...) ## S3 method for class 'geopoints' print(x, ...) ## S3 method for class 'geopoints' points(x, ...)geopoints(x) read.geopoints(File, format) write.geopoints(x, file, arcgis = FALSE,...) ## S3 method for class 'geopoints' print(x, ...) ## S3 method for class 'geopoints' points(x, ...)
x |
A 'geoshapes' object or a data frame with columns 'x' and 'y'
for longitudes and latitudes of the points in decimal degrees. For
|
File |
The path to a .shp (ESRI shape file) or .dbf (DBase) file to import |
format |
Either |
file |
The path to an ESRI file where to write data, without extension. Three files are created, with respective extensions .shp, .shx, and .dbf |
arcgis |
If |
... |
Further arguments passed to the functions (not used yet) |
geopoints() converts a 'geoshapes' object or a data frame into a
'geopoints' object.
read.geoshapes() and write.geoshapes() read and write shapes
from or to ESRI shape files or DBase files on disk.
The 'geoshapes' objects have methods to print them, and to
add them to graphs (points at corresponding coordinates).
A 'geopoints' object is returned from geopoints() and
read.geopoints(). The other functions are used for their side-effect
rather than for returning something useful.
Philippe Grosjean <[email protected]>
data(mpet) mpet # Plot of Morocco terrain and add the stations location in red data(morocco) image(morocco) points(mpet, col = 2)data(mpet) mpet # Plot of Morocco terrain and add the stations location in red data(morocco) image(morocco) points(mpet, col = 2)
Geoshapes objects contain one or more shapes (that is, polygons, points, or polylines) defined by their longitude and latitude in decimal degrees. These objects can be read or write to ERSI shape files.
geoshapes(x, name = "1", dbf = NULL) read.geoshapes(shpFile, dbf = TRUE) write.geoshapes(x, file, type = c("polygon", "point", "polyLine"), dbf = TRUE, arcgis = FALSE,...) ## S3 method for class 'geoshapes' print(x, ...) ## S3 method for class 'geoshapes' lines(x, which = 1, ...) ## S3 method for class 'geoshapes' points(x, which = "all", ...)geoshapes(x, name = "1", dbf = NULL) read.geoshapes(shpFile, dbf = TRUE) write.geoshapes(x, file, type = c("polygon", "point", "polyLine"), dbf = TRUE, arcgis = FALSE,...) ## S3 method for class 'geoshapes' print(x, ...) ## S3 method for class 'geoshapes' lines(x, which = 1, ...) ## S3 method for class 'geoshapes' points(x, which = "all", ...)
x |
A data frame with columns 'x' and 'y' for longitudes and latitudes of
the points in decimal degrees, or a list of such data frames for
|
name |
The name to use for the shape in case a data frame is passed to
|
dbf |
A data frame to record as 'dbf' attribute for |
shpFile |
The path to a .shp file (ESRI shape file) to import |
file |
The path to an ESRI file where to write data, without extension. Three files are created, with respective extensions .shp, .shx, and .dbf |
type |
The type of shape to write in the ESRI shape file |
arcgis |
If |
which |
The index of the shape to use, or its name |
... |
Further arguments passed to the functions (not used yet) |
geoshapes() converts a data frame or a list into a 'geoshapes' object.
read.geoshapes() and write.geoshapes() read and write shapes
from or to ESRI shape files on disk.
The 'geoshapes' objects have methods to print them (very concisely), and to
add them to graphs, as polygons lines(), or as separate points
points().
A 'geoshapes' object is returned from geoshapes() and
read.geoshapes(). The other functions are used for their side-effect
rather than for returning something useful.
Philippe Grosjean <[email protected]>
data(mbord) # Morocco borders mbord # Plot of Morocco terrain and add the borders in red data(morocco) image(morocco) lines(mbord, col = 2) # Simulate the creation of a geoshapes object with two shapes geoshapes(list(a = mbord[[1]], b = mbord[[1]]))data(mbord) # Morocco borders mbord # Plot of Morocco terrain and add the borders in red data(morocco) image(morocco) lines(mbord, col = 2) # Simulate the creation of a geoshapes object with two shapes geoshapes(list(a = mbord[[1]], b = mbord[[1]]))
The mbord dataset is a 'geoshapes' object (georeferenced shapes).
data(mbord)data(mbord)
This 'geoshapes' object contains a polygon defining the area to analyze around Morocco in decimal degrees (longitudes - latitudes).
The mmask dataset is a 'geomask' object (georeferenced mask). It
indicates which ones of the points in the morocco terrain model do belong
to the territory to analyze around Morocco (TRUE), or are land outside
(FALSE). Points in the sea are flagged as NA.
data(mmask)data(mmask)
This 'geomask' object contains a grid of 1986 x 1866 booleans masking the
morocco terrain model.
The morocco dataset is a 'geotm' object (georeferenced terrain model).
data(morocco)data(morocco)
This 'geotm' object contains a grid of 1986x1866 elevation points (in m).
The mpet dataset is a 'geopoints' object (georeferenced points data).
data(mpet)data(mpet)
This 'geopoints' object contains a data frame with PET measurements (D1 - D36)
made at 18 weather stations (names in STATION and coordinates in x and y).
geopoints, morocco,
mbord, mrain
The mrain dataset is a 'geopoints' object (georeferenced points data).
data(mrain)data(mrain)
This 'geopoints' object contains a data frame with rain measurements (rain variable)
made at 43 weather stations (coordinates in x and y variables).
geopoints, morocco,
mbord, mpet
The mseadist dataset is a 'geomat' object (georeferenced data).
data(mseadist)data(mseadist)
This 'geomat' object contains a matrix with distance from the sea for all
points in the morocco dataset. It can be used as supplemntary variable
for AURELHY interpolation.
Performs unit tests defined in this package by running
example(unitTests.aurelhy). Tests are in runit*.R files located
in the '/unitTests' subdirectory or one of its subdirectories ('/inst/unitTests'
and subdirectories in package sources).
Philippe Grosjean ([email protected])
if (require(svUnit)) { clearLog() ## This test is now moved to the tests directory runTest(svSuite("package:aurelhy"), "aurelhy") ## Check errors at the end (needed to interrupt R CMD check) errorLog() }if (require(svUnit)) { clearLog() ## This test is now moved to the tests directory runTest(svSuite("package:aurelhy"), "aurelhy") ## Check errors at the end (needed to interrupt R CMD check) errorLog() }