Tuning visualizations in colorist

Map monthly distributions of a species in the southern hemisphere

The sample Field Sparrow data included with colorist describe monthly occurrence probabilities for a partially migratory bird in the eastern United States. We can explore the data visually by calculating distribution metrics with metrics_pull(), adopting the default HCL-based color palette provided by palette_timecycle(), and mapping each of the data layers using the map_multiples() function.

# pull metrics, generate default palette, map layers
m1 <- metrics_pull(fiespa_occ)
p1 <- palette_timecycle(12)
map_multiples(m1, p1, labels = names(fiespa_occ), ncol = 4)

The default settings for palette_timecycle() produce a palette of hues that begins at “blue” in January and moves progressively toward “green”, “yellow”, and “red”, before returning toward “blue”. These hues align with our general sense of seasonal progression in northern temperate regions, where we move from “winter” toward “spring”, “summer”, “fall”, and back to “winter”.

Now, let us assume that our bird species actually occurs in temperate regions of the southern hemisphere. The timing of seasonal progression is offset by approximately six months from northern temperate regions. Clearly, the default settings in palette_timecycle() are no longer appropriate because the hues indicating particular seasons are not aligned with the correct months.

To realign seasonal hues and months for a southern temperate species, we need to modify the start_hue argument in palette_timecycle(). So, we specify a value of 60 rather than accept the default value of 240. This change tells palette_timecycle() to begin assigning hues to layers on the opposite side of the color wheel, 180 degrees, and six months away, from the default setting.

# change palette start position on color wheel
p1_custom <- palette_timecycle(12, start_hue = 60)

# map layers
map_multiples(m1, p1_custom, labels = names(fiespa_occ), ncol = 4)

Now, the progression from “blue“ winter, to “green“ spring, to “yellow“ summer, and “red“ fall seasons begins in July rather than January and hues in the resulting maps more closely indicate the seasons experienced by our hypothetical southern temperate bird species.

Map space use by individual animals using relevant hues

When a Rasterstack contains distributions of multiple species or individuals, it is easy to imagine that users may want to override the default assignment of hues in order to reinforce some aspect of their identity.

Here, we turn to data describing utilization distributions for two African Elephants in Etosha National Park from 2011, individuals LA11 and LA14. Let us assume that they are better known as “Purple People-eater“ and “Jolly Green Giant“. We begin by calculating distribution metrics with metrics_pull() and then generate a palette using the default settings for palette_set(). When we combine our metrics and palette using map_multiples(), the two utilization distributions generate significant cognitive dissonance.

# pull metrics, generate default palette, map layers
m2 <- metrics_pull(elephant_ud)
p2 <- palette_set(2)
map_multiples(m2, p2, labels = c("'Purple People-eater'", "'Jolly Green Giant'"), ncol = 2)

To communicate a consistent signal of identity across the labels and the maps, we need to override the default hue settings in palette_set(). We can achieve this by providing the custom_hues argument with a vector of integers indicating where on a rainbow color wheel the function should sample hues. “Purple” hues are found near 280 and “greens” are found near 120, so we specify those values in the appropriate order and visualize our adjustments.

# use custom_hues argument to make specific hue choices
p2_custom <- palette_set(2, custom_hues = c(280, 120))

# map layers
map_multiples(m2, p2_custom, labels = c("'Purple People-eater'", "'Jolly Green Giant'"), ncol = 2)

The result: cognitive harmony.

Emphasize the extent of the landscape used by an individual animal

If we continue our exploration of African Elephant data and inspect the utilization distribution for “Jolly Green Giant“ using the map_single() function, it is clear that the individual uses some areas of the landscape much more intensively than others.

# map one layer
map_single(m2, p2_custom, layer = 2)

The map does not effectively visualize the system of trails used to move between areas of intensive use, however, which may be of specific management interest. In order to visually emphasize the full extent of the landscape used by “Jolly Green Giant”, we need to provide the lambda_i argument in map_single() with a negative number rather than accept the default value of 0. This change should increase the visual weight of cells containing moderate and low intensity values.

# map one layer with adjustment to lambda_i
map_single(m2, p2_custom, layer = 2, lambda_i = -12)

After adjusting lambda_i, our map more clearly indicates the full extent of “Jolly Green Giant’s” movements throughout the year, albeit at a cost of distinguishing subtle differences in intensity of use between cells. It is important for users to recognize this trade-off and to make thoughtful decisions about how best to communicate their narrative goals while respecting the information embedded in their data.

Emphasize specific locations that are essential to an individual animal

Next, let us explore utilization distribution data for an individual Fisher that describes its use of the landscape over nine sequential nights in upstate New York. First, we use metrics_pull() to calculate distribution metrics. Then, we use palette_timeline() to generate a palette. Finally, we make a map using the map_multiples() function.

# pull metrics, generate default palette, map layers
m3 <- metrics_pull(fisher_ud)
p3 <- palette_timeline(fisher_ud)
map_multiples(m3, p3, labels = names(fisher_ud))

It is clear that the Fisher is using different parts of its territory to different degrees within and among nights, but if we want to more clearly understand and illustrate which areas were used most intensively we need to change the relationship between intensity and opacity values. In order to highlight intensively used cells, we provide the lambda_i argument in map_multiples() with a positive number rather than accept the default value of 0.

# map layers with adjustment to lambda_i
map_multiples(m3, p3, labels = names(fisher_ud), lambda_i = 12)

At first glance, it may appear that we have made a mistake of some kind. Nearly all of the data seems to have disappeared. This is because our (extreme) adjustment to lambda_i has decreased the opacity and apparent intensity of cells containing intermediate and low intensity values and preserved opacity values only in the cells that were used very intensively. It makes for a very sparse series of maps, but we can now clearly see that two locations were used very intensively by the Fisher, one on the second night of the study period and one on the third night.

Emphasize differences in how consistently locations are used by an individual animal

Using the sample Fisher data which contain utilization distributions for nine sequential nights, we can visualize patterns of spatiotemporal overlap by calculating distribution metrics using metrics_distill(), creating a palette using palette_timeline(), and generating a map with map_single(). To make rarely used areas more conspicuous we also set lambda_i = -5 in map_single() and then we create a legend to aid interpretation of our map using legend_timeline().

# distill metrics, visualize metrics in a single map, create legend
m3_distill <- metrics_distill(fisher_ud)
map_single(m3_distill, p3, lambda_i = -5)
legend_timeline(p3, time_labels = c("April 7", "April 15"))

The abundance of brightly colored cells indicates that the Fisher used different parts of its territory almost every night, and the different hues indicate which night each cell was used most intensively. If we want to more clearly indicate which parts of the Fisher territory were used repeatedly, we need to adjust how specificity values are represented using the lambda_s argument in map_single(). To emphasize patterns of consistent use across nights we need to specify a positive number for lambda_s, an adjustment that decreases chroma and apparent specificity values in cells that contain moderate specificity values, making them appear more “gray“.

# visualize metrics in a single map with adjustment to lambda_s
map_single(m3_distill, p3, lambda_i = -5, lambda_s = 12)

The revised map and the original map have very similar perceptual weights but, overall, the new map is less “colorful” as a result of supplying lambda_s with a positive number. By changing the relationship between specificity and chroma values, we now have a map that more clearly indicates which areas were used repeatedly by the Fisher across nights.

Emphasize timing of peak occurrence for a seasonal migrant

There are other situations in which researchers may want to emphasize when a species is most likely to be found in a particular place rather than emphasizing how consistently it is likely to be found in a particular place. For example, in the case of migratory birds, a researcher may want to communicate to birders the specific time of year they are most likely to find a given species in their backyard. We can address this challenge using our Field Sparrow data.

As with the Fisher, we start by calculating distribution metrics with metrics_distill() and then we generate a map by feeding metrics and the palette we created in the first example to the map_single() function. We also create a legend based on that palette using legend_timecycle() which helps us find Field Sparrows in space and time.

# distill metrics, visualize metrics in a single map, create legend
m1_distill <- metrics_distill(fiespa_occ)
map_single(m1_distill, p1)
legend_timecycle(p1, origin_label = "Jan 1")

Cells where populations of Field Sparrows persist at approximately the same level throughout the year have low specificity values and low chroma values and appear “gray“ in our map. Cells where populations occur only seasonally have high specificity values and high chroma values and appear “colorful“ with the hue in a cell indicating the month of peak occurrence.

If we want to emphasize information about when a species is most likely to be found (i.e., timing of peak occurrence) we can adjust the relationship between specificity and chroma values. We inflate the chroma and apparent specificity values for cells that contain moderate and low specificity values by providing the lambda_s argument with a negative number.

# visualize metrics in a single map with adjustment to lambda_s
map_single(m1_distill, p1, lambda_s = -12)

The revised distribution map has approximately the same visual weight as the original, but our adjustment to lambda_s has made “gray” cells much more “colorful”. Now, information about timing of maximum occurrence is readily available to viewers and they should be able to identify in which month Field Sparrows are most likely to occur in their backyard and at what intensity.

Again, it is important for users to recognize that adjustments to lambda_s require a sacrifice of information about specificity. In the revised map, it is much more difficult for viewers to reliably assess how consistently Field Sparrows occur in a given cell throughout the year.