Bivariate polar plots on a static ggmap

polarMapStatic() creates a ggplot2 map using bivariate polar plots as markers. As this function returns a ggplot2 object, further customisation can be achieved using functions like ggplot2::theme() and ggplot2::guides().

Usage

polarMapStatic(
  data,
  pollutant = NULL,
  x = "ws",
  limits = "free",
  upper = "fixed",
  latitude = NULL,
  longitude = NULL,
  facet = NULL,
  zoom = 13,
  ggmap = NULL,
  cols = "turbo",
  alpha = 1,
  key = FALSE,
  facet.nrow = NULL,
  d.icon = 150,
  d.fig = 3,
  ...
)

Arguments

data

A data frame. The data frame must contain the data to plot the directional analysis marker, which includes wind speed (ws), wind direction (wd), and the column representing the concentration of a pollutant. In addition, data must include a decimal latitude and longitude.

pollutant

The column name(s) of the pollutant(s) to plot. If multiple pollutants are specified, they will each form part of a separate panel.

x

The radial axis variable to plot.

limits

One of:

"fixed" which ensures all of the markers use the same colour scale.
"free" (the default) which allows all of the markers to use different colour scales.
A numeric vector in the form c(lower, upper) used to define the colour scale. For example, limits = c(0, 100) would force the plot limits to span 0-100.

upper

One of:

"fixed" (the default) which ensures all of the markers use the same radial axis scale.
"free" which allows all of the markers to use different radial axis scales.
A numeric value, used as the upper limit for the radial axis scale.

latitude, longitude

The decimal latitude/longitude. If not provided, will be automatically inferred from data by looking for a column named "lat"/"latitude" or "lon"/"lng"/"long"/"longitude" (case-insensitively).

facet

Used for splitting the input data into different panels, passed to the type argument of openair::cutData(). facet cannot be used if multiple pollutant columns have been provided.

zoom

The zoom level to use for the basemap, passed to ggmap::get_stamenmap(). Alternatively, the ggmap argument can be used for more precise control of the basemap.

ggmap

By default, openairmaps will try to estimate an appropriate bounding box for the input data and then run ggmap::get_stamenmap() to import a basemap. The ggmap argument allows users to provide their own ggmap object to override this, which allows for alternative bounding boxes, map types and colours.

cols

The colours used for plotting. See openair::openColours() for more information.

alpha

The alpha transparency to use for the plotting surface (a value between 0 and 1 with zero being fully transparent and 1 fully opaque).

key

Should a key for each marker be drawn? Default is FALSE.

facet.nrow

Passed to the nrow argument of ggplot2::facet_wrap().

d.icon

The diameter of the plot on the map in pixels. This will affect the size of the individual polar markers. Alternatively, a vector in the form c(width, height) can be provided if a non-circular marker is desired.

d.fig

The diameter of the plots to be produced using openair in inches. This will affect the resolution of the markers on the map. Alternatively, a vector in the form c(width, height) can be provided if a non-circular marker is desired.

...

Arguments passed on to openair::polarPlot

wd

Name of wind direction field.

statistic

The statistic that should be applied to each wind speed/direction bin. 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.Can be:

“mean” (default), “median”, “max” (maximum), “frequency”. “stdev” (standard deviation), “weighted.mean”.
statistic = "nwr" Implements the Non-parametric Wind Regression approach of Henry et al. (2009) that uses kernel smoothers. The openair implementation is not identical because Gaussian kernels are used for both wind direction and speed. The smoothing is controlled by ws_spread and wd_spread.
statistic = "cpf" the conditional probability function (CPF) is plotted and a single (usually high) percentile level is supplied. The CPF is defined as CPF = my/ny, where my is the number of samples in the y bin (by default a wind direction, wind speed interval) with mixing ratios greater than the overall percentile concentration, and ny is the total number of samples in the same wind sector (see Ashbaugh et al., 1985). Note that percentile intervals can also be considered; see percentile for details.
When statistic = "r" or statistic = "Pearson", the Pearson correlation coefficient is calculated for two pollutants. The calculation involves a weighted Pearson correlation coefficient, which is weighted by Gaussian kernels for wind direction an the radial variable (by default wind speed). More weight is assigned to values close to a wind speed-direction interval. Kernel weighting is used to ensure that all data are used rather than relying on the potentially small number of values in a wind speed-direction interval.
When statistic = "Spearman", the Spearman correlation coefficient is calculated for two pollutants. The calculation involves a weighted Spearman correlation coefficient, which is weighted by Gaussian kernels for wind direction an the radial variable (by default wind speed). More weight is assigned to values close to a wind speed-direction interval. Kernel weighting is used to ensure that all data are used rather than relying on the potentially small number of values in a wind speed-direction interval.
"robust_slope" is another option for pair-wise statistics and "quantile.slope", which uses quantile regression to estimate the slope for a particular quantile level (see also tau for setting the quantile level).
"york_slope" is another option for pair-wise statistics which uses the York regression method to estimate the slope. In this method the uncertainties in x and y are used in the determination of the slope. The uncertainties are provided by x_error and y_error --- see below.

exclude.missing

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.

uncertainty

Should the uncertainty in the calculated surface be shown? If TRUE three plots are produced on the same scale showing the predicted surface together with the estimated lower and upper uncertainties at the 95% confidence interval. Calculating the uncertainties is useful to understand whether features are real or not. For example, at high wind speeds where there are few data there is greater uncertainty over the predicted values. The uncertainties are calculated using the GAM and weighting is done by the frequency of measurements in each wind speed-direction bin. Note that if uncertainties are calculated then the type is set to "default".

percentile

If statistic = "percentile" 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.

percentile is also used for the Conditional Probability Function (CPF) plots. percentile can be of length two, in which case the percentile interval is considered for use with CPF. For example, percentile = c(90, 100) will plot the CPF for concentrations between the 90 and 100th percentiles. Percentile intervals can be useful for identifying specific sources. In addition, percentile can also be of length 3. The third value is the ‘trim’ value to be applied. When calculating percentile intervals many can cover very low values where there is no useful information. The trim value ensures that values greater than or equal to the trim * mean value are considered before the percentile intervals are calculated. The effect is to extract more detail from many source signatures. See the manual for examples. Finally, if the trim value is less than zero the percentile range is interpreted as absolute concentration values and subsetting is carried out directly.

weights

At the edges of the plot there may only be a few data points in each wind speed-direction interval, which could in some situations distort the plot if the concentrations are high. weights applies a weighting to reduce their influence. For example and by default if only a single data point exists then the weighting factor is 0.25 and for two points 0.5. To not apply any weighting and use the data as is, use weights = c(1, 1, 1).

An alternative to down-weighting these points they can be removed altogether using min.bin.

min.bin

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.

mis.col

When min.bin is > 1 it can be useful to show where data are removed on the plots. This is done by shading the missing data in mis.col. To not highlight missing data when min.bin > 1 choose mis.col = "transparent".

angle.scale

Sometimes the placement of the scale may interfere with an interesting feature. The user can therefore set angle.scale to any value between 0 and 360 degrees to mitigate such problems. For example angle.scale = 45 will draw the scale heading in a NE direction.

units

The units shown on the polar axis scale.

force.positive

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.

k

This is the smoothing parameter used by the gam function in package mgcv. Typically, value of around 100 (the default) seems to be suitable and will resolve important features in the plot. The most appropriate choice of k is problem-dependent; but extensive testing of polar plots for many different problems suggests a value of k of about 100 is suitable. Setting k to higher values will not tend to affect the surface predictions by much but will add to the computation time. Lower values of k will increase smoothing. Sometimes with few data to plot polarPlot will fail. Under these circumstances it can be worth lowering the value of k.

normalise

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.

key.header

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.

key.footer

see key.footer.

key.position

Location where the scale key is to plotted. Allowed arguments currently include "top", "right", "bottom" and "left".

auto.text

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.

ws_spread

The value of sigma used for Gaussian kernel weighting of wind speed when statistic = "nwr" or when correlation and regression statistics are used such as r. Default is 0.5.

wd_spread

The value of sigma used for Gaussian kernel weighting of wind direction when statistic = "nwr" or when correlation and regression statistics are used such as r. Default is 4.

x_error

The x error / uncertainty used when statistic = "york_slope".

y_error

The y error / uncertainty used when statistic = "york_slope".

kernel

Type of kernel used for the weighting procedure for when correlation or regression techniques are used. Only "gaussian" is supported but this may be enhanced in the future.

tau

The quantile to be estimated when statistic is set to "quantile.slope". Default is 0.5 which is equal to the median and will be ignored if "quantile.slope" is not used.

Value

a ggplot2 plot with a ggmap basemap

Further customisation using ggplot2

As the outputs of the static directional analysis functions are ggplot2 figures, further customisation is possible using functions such as ggplot2::theme(), ggplot2::guides() and ggplot2::labs().

If multiple pollutants are specified, subscripting (e.g., the "x" in "NOx") is achieved using the ggtext package. Therefore if you choose to override the plot theme, it is recommended to use [ggplot2::theme()] and [ggtext::element_markdown()] to define the strip.text parameter.

When arguments like limits, percentile or breaks are defined, a legend is automatically added to the figure. Legends can be removed using ggplot2::theme(legend.position = "none"), or further customised using ggplot2::guides() and either color = ggplot2::guide_colourbar() for continuous legends or fill = ggplot2::guide_legend() for discrete legends.

Usage

Arguments

Value

Further customisation using ggplot2

See also