`diffMapStatic()`

creates a `ggplot2`

map using bivariate "difference" 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

```
diffMapStatic(
before,
after,
pollutant = NULL,
ggmap,
limits = "free",
x = "ws",
latitude = NULL,
longitude = NULL,
facet = NULL,
cols = c("#002F70", "#3167BB", "#879FDB", "#C8D2F1", "#F6F6F6", "#F4C8C8", "#DA8A8B",
"#AE4647", "#5F1415"),
alpha = 1,
key = FALSE,
facet.nrow = NULL,
d.icon = 150,
d.fig = 3,
...
)
```

## Arguments

- before
A data frame that represents the "before" case. See

`polarPlot()`

for details of different input requirements.- after
A data frame that represents the "after" case. See

`polarPlot()`

for details of different input requirements.- 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.

- ggmap
A

`ggmap`

object obtained using`ggmap::get_map()`

or a similar function to use as the basemap.- 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.

- x
The radial axis variable to plot.

- 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.- 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"`

.`upper`

This sets the upper limit wind speed to be used. Often there are only a relatively few data points at very high wind speeds and plotting all of them can reduce the useful information in the plot.

`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.`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.`formula.label`

When pair-wise statistics such as regression slopes are calculated and plotted, should a formula label be displayed?

`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.`plot`

Should a plot be produced?

`FALSE`

can be useful when analysing data to extract plot components and plotting them in other ways.

## 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.

## See also

the original `openair::polarDiff()`

`diffMap()`

for the interactive `leaflet`

equivalent of
`diffMapStatic()`

Other static directional analysis maps:
`annulusMapStatic()`

,
`freqMapStatic()`

,
`percentileMapStatic()`

,
`polarMapStatic()`

,
`pollroseMapStatic()`

,
`windroseMapStatic()`