Typically plots the concentration of a pollutant by wind direction and as a function of time as an annulus. The function is good for visualising how concentrations of pollutants vary by wind direction and a time period e.g. by month, day of week.
polarAnnulus(
mydata,
pollutant = "nox",
resolution = "fine",
local.tz = NULL,
period = "hour",
type = "default",
statistic = "mean",
percentile = NA,
limits = NULL,
cols = "default",
width = "normal",
min.bin = 1,
exclude.missing = TRUE,
date.pad = FALSE,
force.positive = TRUE,
k = c(20, 10),
normalise = FALSE,
key.header = statistic,
key.footer = pollutant,
key.position = "right",
key = TRUE,
auto.text = TRUE,
alpha = 1,
plot = TRUE,
...
)an openair object
A data frame minimally containing date, wd and a
pollutant.
Mandatory. A pollutant name corresponding to a variable in a
data frame should be supplied e.g. pollutant = "nox". There can also
be more than one pollutant specified e.g. pollutant = c("nox",
"no2"). The main use of using two or more pollutants is for model
evaluation where two species would be expected to have similar
concentrations. This saves the user stacking the data and it is possible to
work with columns of data directly. A typical use would be pollutant
= c("obs", "mod") to compare two columns “obs” (the observations)
and “mod” (modelled values).
Two plot resolutions can be set: “normal” and “fine” (the default).
Should the results be calculated in local time that includes
a treatment of daylight savings time (DST)? The default is not to consider
DST issues, provided the data were imported without a DST offset. Emissions
activity tends to occur at local time e.g. rush hour is at 8 am every day.
When the clocks go forward in spring, the emissions are effectively
released into the atmosphere typically 1 hour earlier during the summertime
i.e. when DST applies. When plotting diurnal profiles, this has the effect
of “smearing-out” the concentrations. Sometimes, a useful approach
is to express time as local time. This correction tends to produce
better-defined diurnal profiles of concentration (or other variables) and
allows a better comparison to be made with emissions/activity data. If set
to FALSE then GMT is used. Examples of usage include local.tz
= "Europe/London", local.tz = "America/New_York". See
cutData and import for more details.
This determines the temporal period to consider. Options are “hour” (the default, to plot diurnal variations), “season” to plot variation throughout the year, “weekday” to plot day of the week variation and “trend” to plot the trend by wind direction.
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.
It is also possible to choose type as another variable in the data
frame. If that variable is numeric, then the data will be split into four
quantiles (if possible) and labelled accordingly. If type is an existing
character or factor variable, then those categories/levels will be used
directly. This offers great flexibility for understanding the variation of
different variables and how they depend on one another.
Type can be up length two e.g. type = c("season", "site") will
produce a 2x2 plot split by season and site. The use of two types is mostly
meant for situations where there are several sites. Note, when two types
are provided the first forms the columns and the second the rows.
Also note that for the polarAnnulus function some type/period
combinations are forbidden or make little sense. For example, type =
"season" and period = "trend" (which would result in a plot with
too many gaps in it for sensible smoothing), or type = "weekday" and
period = "weekday".
The statistic that should be applied to each wind
speed/direction bin. Can be “mean” (default), “median”,
“max” (maximum), “frequency”. “stdev” (standard
deviation), “weighted.mean” or “cpf” (Conditional Probability
Function). Because of the smoothing involved, the colour scale for some of
these statistics is only to provide an indication of overall pattern and
should not be interpreted in concentration units e.g. for statistic =
"weighted.mean" where the bin mean is multiplied by the bin frequency and
divided by the total frequency. In many cases using polarFreq will
be better. Setting statistic = "weighted.mean" can be useful because
it provides an indication of the concentration * frequency of occurrence
and will highlight the wind speed/direction conditions that dominate the
overall mean.
If statistic = "percentile" or statistic =
"cpf" then percentile is used, expressed from 0 to 100. Note that
the percentile value is calculated in the wind speed, wind direction
‘bins’. For this reason it can also be useful to set min.bin
to ensure there are a sufficient number of points available to estimate a
percentile. See quantile for more details of how percentiles are
calculated.
The function does its best to choose sensible limits
automatically. However, there are circumstances when the user will wish to
set different ones. An example would be a series of plots showing each year
of data separately. The limits are set in the form c(lower, upper),
so limits = c(0, 100) would force the plot limits to span 0-100.
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"). cols
can also take the values "viridis", "magma",
"inferno", or "plasma" which are the viridis colour maps
ported from Python's Matplotlib library.
The width of the annulus; can be “normal” (the default), “thin” or “fat”.
The minimum number of points allowed in a wind speed/wind
direction bin. The default is 1. A value of two requires at least 2 valid
records in each bin an so on; bins with less than 2 valid records are set
to NA. Care should be taken when using a value > 1 because of the risk of
removing real data points. It is recommended to consider your data with
care. Also, the polarFreq function can be of use in such
circumstances.
Setting this option to TRUE (the default)
removes points from the plot that are too far from the original data. The
smoothing routines will produce predictions at points where no data exist
i.e. they predict. By removing the points too far from the original data
produces a plot where it is clear where the original data lie. If set to
FALSE missing data will be interpolated.
For type = "trend" (default), date.pad = TRUE
will pad-out missing data to the beginning of the first year and the end of
the last year. The purpose is to ensure that the trend plot begins and ends
at the beginning or end of year.
The default is TRUE. Sometimes if smoothing data
with steep gradients it is possible for predicted values to be negative.
force.positive = TRUE ensures that predictions remain positive. This
is useful for several reasons. First, with lots of missing data more
interpolation is needed and this can result in artefacts because the
predictions are too far from the original data. Second, if it is known
beforehand that the data are all positive, then this option carries that
assumption through to the prediction. The only likely time where setting
force.positive = FALSE would be if background concentrations were
first subtracted resulting in data that is legitimately negative. For the
vast majority of situations it is expected that the user will not need to
alter the default option.
The smoothing value supplied to gam for the temporal and wind
direction components, respectively. In some cases e.g. a trend plot with
less than 1-year of data the smoothing with the default values may become
too noisy and affected more by outliers. Choosing a lower value of k
(say 10) may help produce a better plot.
If TRUE concentrations are normalised by dividing by
their mean value. This is done after fitting the smooth surface.
This option is particularly useful if one is interested in the patterns of
concentrations for several pollutants on different scales e.g. NOx and CO.
Often useful if more than one pollutant is chosen.
Adds additional text/labels to the scale key. For example,
passing the options key.header = "header", key.footer = "footer1"
adds addition text above and below the scale key. These arguments are
passed to drawOpenKey via quickText, applying the
auto.text argument, to handle formatting.
see key.footer.
Location where the scale key is to plotted. Allowed
arguments currently include "top", "right", "bottom"
and "left".
Fine control of the scale key via drawOpenKey. See
drawOpenKey for further details.
Either TRUE (default) or FALSE. If TRUE
titles and axis labels will automatically try and format pollutant names
and units properly e.g. by subscripting the `2' in NO2.
The alpha transparency to use for the plotting surface (a value
between 0 and 1 with zero being fully transparent and 1 fully opaque).
Setting a value below 1 can be useful when plotting surfaces on a map using
the package openairmaps.
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. For example, polarAnnulus passes the option
hemisphere = "southern" on to cutData to provide southern
(rather than default northern) hemisphere handling of type =
"season". Similarly, common axis and title labelling options (such as
xlab, ylab, main) are passed to levelplot via
quickText to handle routine formatting.
David Carslaw
The polarAnnulus function shares many of the properties of the
polarPlot. However, polarAnnulus is focussed on displaying
information on how concentrations of a pollutant (values of another variable)
vary with wind direction and time. Plotting as an annulus helps to reduce
compression of information towards the centre of the plot. The circular plot
is easy to interpret because wind direction is most easily understood in
polar rather than Cartesian coordinates.
The inner part of the annulus represents the earliest time and the outer part of the annulus the latest time. The time dimension can be shown in many ways including "trend", "hour" (hour or day), "season" (month of the year) and "weekday" (day of the week). Taking hour as an example, the plot will show how concentrations vary by hour of the day and wind direction. Such plots can be very useful for understanding how different source influences affect a location.
For type = "trend" the amount of smoothing does not vary linearly with
the length of the time series i.e. a certain amount of smoothing per unit
interval in time. This is a deliberate choice because should one be
interested in a subset (in time) of data, more detail will be provided for
the subset compared with the full data set. This allows users to investigate
specific periods in more detail. Full flexibility is given through the
smoothing parameter k.
Other polar directional analysis functions:
percentileRose(),
polarCluster(),
polarDiff(),
polarFreq(),
polarPlot(),
pollutionRose(),
windRose()
# diurnal plot for PM10 at Marylebone Rd
if (FALSE) polarAnnulus(mydata, pollutant = "pm10",
main = "diurnal variation in pm10 at Marylebone Road")
# seasonal plot for PM10 at Marylebone Rd
if (FALSE) polarAnnulus(mydata, poll="pm10", period = "season")
# trend in coarse particles (PMc = PM10 - PM2.5), calculate PMc first
mydata$pmc <- mydata$pm10 - mydata$pm25
if (FALSE) polarAnnulus(mydata, poll="pmc", period = "trend",
main = "trend in pmc at Marylebone Road")
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