Calculate clusters for back trajectories

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.

Usage

trajCluster(
  traj,
  method = "Euclid",
  n.cluster = 5,
  type = "default",
  split.after = FALSE,
  by.type = FALSE,
  crs = 4326,
  cols = "Set1",
  plot = TRUE,
  ...
)

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.
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".
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.
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.
crs: The coordinate reference system to use for plotting. Defaults to 4326, which is the WGS84 geographic coordinate system, the standard, unprojected latitude/longitude system used in GPS, Google Earth, and GIS mapping. Other crs values are available - for example, 27700 will use the the OSGB36/British National Grid.
cols: Colours for plotting. Passed to openColours().
plot: Should a plot be produced? FALSE can be useful when analysing data to extract plot components and plotting them in other ways.
...: Passed to trajPlot().

Value

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.)

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.

Author

David Carslaw

Jack Davison

Examples

if (FALSE) { # \dontrun{
## 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)
} # }