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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.


  method = "Euclid",
  n.cluster = 5,
  plot = TRUE,
  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,



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.


Should a plot be produced?


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 transpency 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 require additional parameters, these must be given in the parameters argument.


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.


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.


Returns a list with two data components. The first (data) contains the original data with the cluster identified. The second (results) contains the data used to plot the clustered trajectories.


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.


David Carslaw


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)