This function carries out cluster analysis of HYSPLIT back trajectories. The
function is specifically designed to work with the trajectories imported
using the openair importTraj function, which provides
pre-calculated back trajectories at specific receptor locations.
trajCluster(
traj,
method = "Euclid",
n.cluster = 5,
type = "default",
cols = "Set1",
split.after = FALSE,
map.fill = TRUE,
map.cols = "grey40",
map.alpha = 0.4,
projection = "lambert",
parameters = c(51, 51),
orientation = c(90, 0, 0),
by.type = FALSE,
origin = TRUE,
plot = TRUE,
...
)an openair object. The data component contains
both traj (the original data appended with its cluster) and results
(the average trajectory path per cluster, shown in the trajCluster()
plot.)
An openair trajectory data frame resulting from the use of
importTraj.
Method used to calculate the distance matrix for the back trajectories. There are two methods available: “Euclid” and “Angle”.
Number of clusters to calculate.
type determines how the data are split i.e. conditioned,
and then plotted. The default is will produce a single plot using the
entire data. Type can be one of the built-in types as detailed in
cutData e.g. “season”, “year”, “weekday” and so
on. For example, type = "season" will produce four plots --- one for
each season. Note that the cluster calculations are separately made of each
level of "type".
Colours to be used for plotting. Options include
“default”, “increment”, “heat”, “jet” and
RColorBrewer colours --- see the openair openColours
function for more details. For user defined the user can supply a list of
colour names recognised by R (type colours() to see the full list).
An example would be cols = c("yellow", "green", "blue")
For type other than “default” e.g.
“season”, the trajectories can either be calculated for each level
of type independently or extracted after the cluster calculations
have been applied to the whole data set.
Should the base map be a filled polygon? Default is to fill countries.
If map.fill = TRUE map.cols controls the fill
colour. Examples include map.fill = "grey40" and map.fill =
openColours("default", 10). The latter colours the countries and can help
differentiate them.
The transparency level of the filled map which takes values from 0 (full transparency) to 1 (full opacity). Setting it below 1 can help view trajectories, trajectory surfaces etc. and a filled base map.
The map projection to be used. Different map projections
are possible through the mapproj package. See ?mapproject for
extensive details and information on setting other parameters and
orientation (see below).
From the mapproj package. Optional numeric vector of
parameters for use with the projection argument. This argument is optional
only in the sense that certain projections do not require additional
parameters. If a projection does not require additional parameters then set
to null i.e. parameters = NULL.
From the mapproj package. An optional vector
c(latitude, longitude, rotation) which describes where the "North Pole"
should be when computing the projection. Normally this is c(90, 0), which
is appropriate for cylindrical and conic projections. For a planar
projection, you should set it to the desired point of tangency. The third
value is a clockwise rotation (in degrees), which defaults to the midrange
of the longitude coordinates in the map.
The percentage of the total number of trajectories is given
for all data by default. Setting by.type = TRUE will make each panel
add up to 100.
If true a filled circle dot is shown to mark the receptor point.
Should a plot be produced? FALSE can be useful when
analysing data to extract plot components and plotting them in other ways.
Other graphical parameters passed onto lattice:levelplot
and cutData. Similarly, common axis and title labelling options
(such as xlab, ylab, main) are passed to
levelplot via quickText to handle routine formatting.
David Carslaw
Two main methods are available to cluster the back trajectories using two different calculations of the distance matrix. The default is to use the standard Euclidian distance between each pair of trajectories. Also available is an angle-based distance matrix based on Sirois and Bottenheim (1995). The latter method is useful when the interest is the direction of the trajectories in clustering.
The distance matrix calculations are made in C++ for speed. For data sets of
up to 1 year both methods should be relatively fast, although the
method = "Angle" does tend to take much longer to calculate. Further
details of these methods are given in the openair manual.
Sirois, A. and Bottenheim, J.W., 1995. Use of backward trajectories to interpret the 5-year record of PAN and O3 ambient air concentrations at Kejimkujik National Park, Nova Scotia. Journal of Geophysical Research, 100: 2867-2881.
Other trajectory analysis functions:
importTraj(),
trajLevel(),
trajPlot()
Other cluster analysis functions:
polarCluster(),
timeProp()
if (FALSE) {
## import trajectories
traj <- importTraj(site = "london", year = 2009)
## calculate clusters
clust <- trajCluster(traj, n.cluster = 5)
head(clust$data) ## note new variable 'cluster'
## use different distance matrix calculation, and calculate by season
traj <- trajCluster(traj, method = "Angle", type = "season", n.cluster = 4)
}
Run the code above in your browser using DataLab