How to plot spatial data when grid cell size varies - r

Question

I am trying to make a map of global model predictions with the outline of the world on it.
The data I have cover most of the worlds oceans and the grid cells have a resolution of 4 degrees. This is my desired output: Map of global predictions
This is the code I have tried using:
# Packages
library(rnaturalearth)
library(ggplot2)
library(sp)
library(ggspatial)
# Subset of data (500 rows, 4 columns; Longitude, Latitude, Prediction, Grid cell area)
data <- structure(list(Longitude = c(-10.16956752, 126.1689468, -61.99876686,
-94.00000289, 126.0007703, 170.000033, 82.55726146, -6.495772811,
-149.9999879, -61.99897757, -89.99998854, 2.000011459, 122.1211726,
-2.593090202, -17.99998854, -170.0001433, 121.8262874, 10.21256204,
166.0000115, 158.3111356, 134.3679695, -13.73648415, 170.0000423,
92.91243971, 5.996191823, 62.00001146, -53.99998854, -21.99998854,
125.9910976, 154.0000115, 149.9995576, -77.88731531, -45.99998854,
-33.99998854, -5.878402842, -157.9999885, -169.9929349, 66.00001146,
-138.5853402, -17.99998854, -69.99998854, -56.79830902, 110.0502773,
50.00001146, 39.56890984, -146.0004371, -5.999521688, 18.72192101,
-69.77517581, 21.3289268, 42.00001146, 162.0000115, 165.9995769,
-162.0365539, 170.0000138, 142.0000446, -45.99998854, -165.9999885,
110.0000115, -25.99998854, 6.000011459, -89.99998854, -53.99998854,
50.00187439, 146.0000115, -134.3123896, 165.9543502, -169.9999885,
-81.89972865, 72.42284406, 134.0000115, -170.0197871, -96.6556298,
17.92812, -152.9139565, -9.999988541, -73.99998854, 158.0000115,
54.20603343, 157.9995854, 74.00034763, -157.9999902, 102.4769658,
-73.99191424, 6.000011459, 162.0000177, -10.07971318, -93.99998854,
90.00001146, 153.9999475, 162.0000115, 117.9915582, -33.99998854,
38.00001146, -77.99998854, -81.99998854, 74.00022314, 114.1420482,
-173.9974719, 118.4796001, 178.0206217, 42.00001146, 178, -138.000174,
146.6226982, 165.999998, -49.73916727, -37.99998854, 137.9999465,
38.00001146, -82.04904949, 119.0280587, -169.9999946, 162.855343,
113.9999563, -61.9672714, 50.12191729, -173.9999885, 41.85382837,
130.0000115, 126.5154259, -37.93913378, 62.00001146, 129.9747567,
-161.9999885, -137.9999787, -1.958606869, 165.9999802, 150.1568838,
13.72012633, -37.59668977, 162.0000061, -29.99998854, -177.9999885,
117.9999058, -149.9999885, 114.0000063, 96.91839389, -29.99998854,
-25.99998854, -93.99998854, -29.99998854, 170.0000115, -173.9999885,
142.0105738, -37.9593374, 9.937003613, 145.9958965, -178.0133745,
-78.74777464, -122.0023764, -178.0000115, 162.0000115, -169.9999885,
-141.9999885, 158.0000115, -85.99998854, -15.44057437, 30.01655738,
-161.9999885, 35.50200234, 173.9999943, 5.999766664, 58.00001146,
-14.27262379, 53.59629681, 146.0999455, 53.97807051, 178.3714871,
174.0000115, -173.9999885, 46.05658123, 154.0000115, -150.0013408,
130.3202993, 5.888053059, 70.00001146, -137.9971843, -57.99998854,
-153.9999885, 38.51576452, 5.356846647, -177.9999885, 38.21244542,
22.06404301, -85.99990114, 165.9999621, 42.06833294, 82.38551366,
-69.26776454, -149.9999885, 105.7885139, -162.0000326, 17.01965058,
-74.66124649, -61.84333457, 154.0000125, 170.0162654, 1.824314695,
13.82919973, -166.2490329, 2.000011459, 58.00381545, 162.269909,
70.00001146, 126.2275842, -85.99069255, -161.9999885, -145.9998563,
-110.8963681, -45.6718642, 106.1072479, 150.0124763, 173.9999974,
129.7397288, -53.99998854, -174.0231047, 142.4679548, 6.084793515,
165.951299, 174.0000115, 58.00001146, 54.00001146, -78.41571925,
22.04138072, -87.07057574, -105.9999885, -109.9999885, -37.99998854,
9.440334846, -74.33053694, -65.97004746, -49.99998854, -73.95814747,
-74.74355347, 117.7688064, 78.00001146, 170.0004612, -126.0474866,
-10.14156626, 41.44639846, -65.99998854, 150.0007478, 138.1284291,
166.0000115, -177.9999885, 165.9998751, 122.5803857, -1.832865108,
-78.22084003, 145.9777733, 154.0040729, -57.99998854, -73.99998854,
-17.99998854, 97.66893601, -73.99998854, 58.00448, 158.0000123,
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122.0381743, 90.02820747, 114.0000115, 178.0002266, 94.03546334,
-13.99998854, 2.026556417, 170.0816676, 170.0000105, 174.0000115,
-177.9999885, -157.9999885, 134.0000243, -74.01378632, 14.07846074,
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-165.9999885, -173.9999848, 117.8546849, 13.97253564, 138.0000115,
2.000011459, -57.86039422, -162.000012, -169.9967526, -60.85299577,
-37.53072648, -81.99781314, 138.0875793, 169.8994304, 107.4007518,
-73.99998854, 53.99801704, 46.00001146, 25.98144338, 66.00001146,
114.0000316, 110.0000115, 112.9024546, 177.9564699, -1.999988541,
-82.00000426, 162.0082822, -69.24184951, 101.9020192, -173.9995473,
-49.27150176, 21.92369036, 138.4587056, -66.29757086, -26.0078251,
174.0000131, 133.9889887, 38.16885146, -5.999988541, 133.9391315,
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166.0000115, 13.64867252, -90.74983985, -54.5837941, 150.0593517,
-30.00737278, 10.14403385, 165.9999579, 141.99997, -71.49830857,
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-61.96780638, -137.9996316, 106.0499776, 158.0000115, -65.08399512,
178, 65.83642764, 125.9426387, -157.9992733, 50.00001146, 38.06053256,
69.99999724, 129.6863334, -166.3675674, -11.16300072, 53.62409287,
137.7173457, 126.0031256, 118.9525785, 154.0010114, -162.0000077,
34.00001146, -5.999988541, -94.32614301, 25.43306958, 142.2242709,
37.21917664, 138.0000115, -49.99998854, -109.999023, 173.6014161,
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174.0000115, -74.05492837, -17.99998854, -96.82143996, 10.00001146,
165.989779, 54.00001146, 9.648264537, -153.9996769, -1.379131047,
-33.99967232, -13.99998854, -13.99998863, -29.99998854, -157.9993798,
130.3986089, -149.9540973, -66.05141375, -117.9995487, -69.99998854,
-10.08258389, 113.9349012, 50.88704952, -48.62667494, -49.99998854,
-114.0201716, -133.999895, -102.1160738, -174.0044266, 145.728285,
-21.99998854, 138.0034532, -93.99998854, 125.1209634, 133.9987429,
-141.9999885, 133.94514, -75.16942252, 13.30257806, -57.91461701,
58.00001146, 169.9995982, 38.92821472, 70.00001146, 12.76082336,
-77.99998854, -78.00228912, -81.799125, 130.0491847, 46.00091358,
154.117492, -82.0003813, 102.1876905, 50.11824049, 6.000011459,
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146.0000115, 142.0000115, 58.78666498, -45.99998854, 174.0000115,
-153.999987, 102.0000115, 125.9624191, 157.9999468, 170.027281,
154.0000115, -57.99998854, -177.994726, 46.00001146, 153.9999931,
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58.00001146, 118.0000115, -122.5807138, 46.00001146, 110.0000115,
121.829682, 46.59514055, -157.9999885, -153.9999885, 62.00001146,
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142.6960182, -153.9999943, -57.89841693, 70.00001146, 101.5055217,
170.0000115, 14.00001146, -86.04945349, 113.4322028, -158.0211136
), Latitude = c(3.820825091, 12.08126747, -55.97816596, 0.022599112,
16.02343874, 0.022652324, 8.210545822, 35.69956109, -7.977383247,
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12.02263258, 32.02264073, 48.02261086, -39.97738914, -23.97738914,
12.02261086, 72.02261086, 8.022610864, -39.97738914, -23.97920822,
52.02261086, 55.56880819, -15.99570073, 0.022610864, 16.10672797,
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59.08122925, 64.02261086, -31.97738914, 48.02261086, 15.52636519,
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), class = "data.frame")
# X and Y limits
x_min <- min(data$Longitude)
x_max <- max(data$Longitude)
y_min <- min(data$Latitude)
y_max <- max(data$Latitude)
# World map
world <- ne_countries(scale = "medium", returnclass = "sf")
# CRF
crf <- CRS(projargs = "+proj=longlat +datum=NAD83 +no_defs")
# Plot
ggplot() +
geom_raster(data = data, aes(x = Longitude, y = Latitude, fill = pred)) +
scale_fill_viridis_c(option = "plasma") +
geom_sf(data = world, fill = alpha("lightgrey", 1), color = "lightgrey") +
coord_sf(xlim=c(x_min, x_max), ylim=c(y_min, y_max), crs = crf, expand = FALSE)
I get this warning when I run the above code:
Error in matrix(NA_character_, nrow = nrow, ncol = ncol) :
invalid 'nrow' value (too large or NA)
In addition: Warning messages:
1: Raster pixels are placed at uneven horizontal intervals and will be shifted. Consider using geom_tile() instead.
2: Raster pixels are placed at uneven vertical intervals and will be shifted. Consider using geom_tile() instead.
3: In f(...) : NAs introduced by coercion to integer range
4: In f(...) : NAs introduced by coercion to integer range
I have tried using geom_tile / geom_stars and rounding my lat/lon values to no avail.
I think the issue might be arising due to the projection used which means the area of a grid cell decreases as you move away from the equator, hence I have also included grid cell area in the data (4th column).
Any help/suggestions would be greatly appreciated! Ideally without interpolating as these are already model predictions.
If you need the full dataset it can be found here: https://github.com/Rawizard/stack_overflow/blob/main/SO_question_data1.RDS

Answer 1

One approach to solve this would be to perform an interpolation on your data.
Here is an option based on the practical guide found here
Code
Starting by rounding the longitude and latitude with 0 digit. (unique() is to get rid of the duplicate data only 2 rows here)
library(dplyr)
df = data
df = df %>% dplyr::select(c(Longitude, Latitude, prediction))
df = df %>% mutate(across(c(Latitude, Longitude), round, digit = 0)) %>% unique()
Then creating a grid which will be used to interpolate the data:
df_template = expand.grid(Longitude= seq(min(df3$Longitude), max(df3$Longitude), 1), Latitude = seq(min(df3$Latitude), max(df3$Latitude), 1))
> head(df_template)
Longitude Latitude
1 -178 -83
2 -177 -83
3 -176 -83
4 -175 -83
5 -174 -83
6 -173 -83
Rasterizing the grid:
grd_template_raster <- df_template %>% dplyr::mutate(Z = 0) %>% raster::rasterFromXYZ(crs = crf)
Transformation of the data into simple feature:
sf_df <- st_as_sf(df, coords = c("Longitude", "Latitude"), crs = crf)
Model fitting in this case Nearest Neighbour
library(gstat)
fit_NN <- gstat::gstat( # using package {gstat}
formula = prediction ~ 1, # The column `NH4` is what we are interested in
data = as(sf_df, "Spatial"), # using {sf} and converting to {sp}, which is expected
nmax = 10, nmin = 5 # Number of neighboring observations used for the fit
)
Interpolate by running the model the data:
library(raster)
interp_NN = interpolate(grd_template_raster, fit_NN)
Extract the data to build the plot with ggplot:
df_final <- rasterToPoints(interp_NN) %>% as_tibble()
colnames(df_final) <- c("Longitude", "Latitude", "prediction")
Plotting the data:
ggplot() +
geom_raster(data = df_final, aes(x = Longitude, y = Latitude, fill = prediction)) +
scale_fill_viridis_c(option = "plasma") +
geom_sf(data = world, fill = alpha("lightgrey", 1), color = "lightgrey") +
coord_sf(xlim=c(x_min, x_max), ylim=c(y_min, y_max), crs = crf, expand = FALSE)