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.

## Arguments

- traj
An openair trajectory data frame resulting from the use of

`importTraj`

.- method
Method used to calculate the distance matrix for the back trajectories. There are two methods available: “Euclid” and “Angle”.

- n.cluster
Number of clusters to calculate.

- plot
Should a plot be produced?

- type
`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".- cols
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")`

- split.after
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.- map.fill
Should the base map be a filled polygon? Default is to fill countries.

- map.cols
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.- map.alpha
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.- projection
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).- parameters
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.- orientation
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.- by.type
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.- origin
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.

## Value

Returns a list with two data components. The first
(`data`

) contains the orginal data with the cluster
identified. The second (`results`

) contains the data used
to plot the clustered trajectories.

## Details

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.

## References

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.

## Examples

```
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)
}
```