Description Usage Arguments Details Value Author(s) References See Also Examples
Function for identifying clusters in bivariate polar plots
polarPlot); identifying clusters in the original data for
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polarCluster(mydata, pollutant = "nox", x = "ws", wd = "wd", n.clusters = 6, cols = "Paired", angle.scale = 315, units = x, auto.text = TRUE, ...)
A data frame minimally containing
Mandatory. A pollutant name corresponding to a
variable in a data frame should be supplied e.g.
Name of variable to plot against wind direction in polar coordinates, the default is wind speed, “ws”.
Name of wind direction field.
Number of clusters to use. If
Colours to be used for plotting. Useful options for
categorical data are avilable from
The wind speed scale is by default shown at a
315 degree angle. Sometimes the placement of the scale may
interfere with an interesting feature. The user can therefore set
The units shown on the polar axis scale.
Other graphical parameters passed onto
Bivariate polar plots generated using the
function provide a very useful graphical technique for identifying
and characterising different air pollution sources. While
bivariate polar plots provide a useful graphical indication of
potential sources, their location and wind-speed or other variable
dependence, they do have several limitations. Often, a ‘feature’
will be detected in a plot but the subsequent analysis of data
meeting particular wind speed/direction criteria will be based
only on the judgement of the investigator concerning the wind
speed-direction intervals of interest. Furthermore, the
identification of a feature can depend on the choice of the colour
scale used, making the process somewhat arbitrary.
polarCluster applies Partition Around Medoids (PAM)
clustering techniques to
polarPlot surfaces to help
identify potentially interesting features for further
analysis. Details of PAM can be found in the
package (a core R package that will be pre-installed on all R
systems). PAM clustering is similar to k-means but has several
advantages e.g. is more robust to outliers. The clustering is
based on the equal contribution assumed from the u and v wind
components and the associated concentration. The data are
standardized before clustering takes place.
The function works best by first trying different numbers of
clusters and plotting them. This is achieved by setting
n.clusters to be of length more than 1. For example, if
n.clusters = 2:10 then a plot will be output showing the 9
cluster levels 2 to 10.
Note that clustering is computationally intensive and the function
can take a long time to run — particularly when the number of
clusters is increased. For this reason it can be a good idea to
run a few clusters first to get a feel for it
n.clusters = 2:5.
Once the number of clusters has been decided, the user can then
polarCluster to return the original data frame together
with a new column
cluster, which gives the cluster number
as a character (see example). Note that any rows where the value
NA are ignored so that the returned
data frame may have fewer rows than the original.
Note that there are no automatic ways in ensuring the most appropriate number of clusters as this is application dependent. However, there is often a-priori information available on what different features in polar plots correspond to. Nevertheless, the appropriateness of different clusters is best determined by post-processing the data. The Carslaw and Beevers (2012) paper discusses these issues in more detail.
Note that unlike most other
openair functions only a single
type “default” is allowed.
As well as generating the plot itself,
also returns an object of class “openair”. The object includes
three main components:
call, the command used to generate
data, the original data frame with a new field
cluster identifying the cluster; and
plot, the plot
itself. Note that any rows where the value of
NA are ignored so that the returned data frame may have
fewer rows than the original.
An openair output can be manipulated using a number of generic
Carslaw, D.C., Beevers, S.D, Ropkins, K and M.C. Bell (2006). Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmospheric Environment. 40/28 pp 5424-5434.
Carslaw, D.C., & Beevers, S.D. (2013). Characterising and understanding emission sources using bivariate polar plots and k-means clustering. Environmental Modelling & Software, 40, 325-329. doi:10.1016/j.envsoft.2012.09.005
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## Not run: # load example data from package data(mydata) ## plot 2-8 clusters. Warning! This can take several minutes... polarCluster(mydata, pollutant = "nox", n.clusters = 2:8) # basic plot with 6 clusters results <- polarCluster(mydata, pollutant = "nox", n.clusters = 6) ## get results, could read into a new data frame to make it easier to refer to ## e.g. results <- results$data... head(results$data) ## how many points are there in each cluster? table(results$data$cluster) ## plot clusters 3 and 4 as a timeVariation plot using SAME colours as in ## cluster plot timeVariation(subset(results$data, cluster %in% c("3", "4")), pollutant = "nox", group = "cluster", col = openColours("Paired", 6)[c(3, 4)]) ## End(Not run)
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