Polygon preparation

Introduction

This markdown file contains all of the code that prepares the polygons used to define the different regions in the Northwest Atlantic. These different regions then have their SST pixels spatially averaged to create a single time series per region. This is done so that the MHW detection algorithm may then be run on these individual time series as a general representation of the SST in those regions, rather than running the algorithm on each pixel individually, which would introduce a host of problems.

# Packages used in this vignette
library(jsonlite, lib.loc = "../R-packages/")
library(tidyverse) # Base suite of functions
library(R.matlab) # For dealing with MATLAB files
# library(marmap) # For bathymetry
# library(maptools) # Contour tools
# library(rgeos) # For intersections

Coastal region polygons

The first step in this analysis is to broadly define the coastal regions based on previous research into thermally relevant boundaries. We have chosen to use a paper by Richaud et al. (2016) to do this (https://www.sciencedirect.com/science/article/pii/S0278434316303181#f0010). Being the kind-hearted man that he is, Benjamin forwarded us the polygons [Richaud et al. (2016); Figure 2] from this work as a MATLAB file. In order to use this here we must first open the file and convert it to an R format. It should be noted that these areas were designed to not encompass depths deeper than 600 m as the investigators were interested in characterising the climatologies for the shelf and upper slope regions of the north east coast of North America.

# Load the file
NWA_polygons <- readMat("data/boundaries.mat")

# Remove index list items and attributes
NWA_polygons[grepl("[.]",names(NWA_polygons))] <- NULL
# attributes(NWA_polygons) <- NULL

# Function for neatly converting list items into a dataframe
# vec <- NWA_polygons[1]
mat_col <- function(vec){
  df <- as.data.frame(vec)
  df$region <- substr(colnames(df)[1], 2, nchar(colnames(df)[1]))
  colnames(df)[1] <- strtrim(colnames(df)[1], 1)
  df <- df[c(2,1)]
  return(df)
}

# Create multiple smaller data.frames
coords_1 <- cbind(mat_col(NWA_polygons[1]), mat_col(NWA_polygons[2])[2])
coords_2 <- cbind(mat_col(NWA_polygons[3]), mat_col(NWA_polygons[4])[2])
coords_3 <- cbind(mat_col(NWA_polygons[5]), mat_col(NWA_polygons[6])[2])
coords_4 <- cbind(mat_col(NWA_polygons[7]), mat_col(NWA_polygons[8])[2])
coords_5 <- cbind(mat_col(NWA_polygons[9]), mat_col(NWA_polygons[10])[2])
coords_6 <- cbind(mat_col(NWA_polygons[11]), mat_col(NWA_polygons[12])[2])

# Combine them into one full dataframe and save
NWA_coords <- rbind(coords_1, coords_2, coords_3, coords_4, coords_5, coords_6)
colnames(NWA_coords) <- c("region", "lon", "lat")
# saveRDS(NWA_coords, "data/NWA_coords.Rda")

With our polygons switched over from MATLAB to R we now want to visualise them to ensure that everything has gone smoothly.

# Load polygon coordinates
NWA_coords <- readRDS("data/NWA_coords.Rda")

# The base map
map_base <- ggplot2::fortify(maps::map(fill = TRUE, col = "grey80", plot = FALSE)) %>%
  dplyr::rename(lon = long) %>%
  mutate(group = ifelse(lon > 180, group+9999, group),
         lon = ifelse(lon > 180, lon-360, lon)) %>% 
  select(-region, -subregion)

# Quick map
NWA_coords_plot <- ggplot(data = NWA_coords, aes(x = lon, y = lat)) +
  geom_polygon(aes(colour = region, fill = region), size = 1.5, alpha = 0.2) +
  geom_polygon(data = map_base, aes(group = group), show.legend = F) +
  coord_cartesian(xlim = c(min(NWA_coords$lon)-2, max(NWA_coords$lon)+2),
                  ylim = c(min(NWA_coords$lat)-2, max(NWA_coords$lat)+2)) +
  labs(x = NULL, y = NULL, colour = "Region", fill = "Region") +
  theme(legend.position = "bottom")
# ggsave(NWA_coords_plot, filename = "output/NWA_coords_plot.pdf", height = 5, width = 6)

# Visualise
NWA_coords_plot

The region abbreviations are: “gm” for Gulf of Maine, “gls” for Gulf of St. Lawrence, “ls” for Labrador Shelf, “mab” for Mid-Atlantic Bight, “nfs” for Newfoundland Shelf and “ss” for Scotian Shelf.

Before we move on, we’ll do a bit of house keeping to establish a consistent study area for this project based on our polygons. We’ll simply extend the study area by the nearest 2 whole degrees of longitude and latitude from the furthest edges of the polygons, as seen in the figure above. This will encompass broad synoptic scale variables that may be driving MHWs in our study regions, but should not be so broad as to begin to account for teleconnections, which are currently beyond the scope of this project.

# Set the max/min lon/at values
lon_min <- round(min(NWA_coords$lon)-2)
lon_max <- round(max(NWA_coords$lon)+2)
lat_min <- round(min(NWA_coords$lat)-2)
lat_max <- round(max(NWA_coords$lat)+2)

# Combine and save
NWA_corners <- c(lon_min, lon_max, lat_min, lat_max)
# saveRDS(NWA_corners, file = "data/NWA_corners.Rda")

# Extract the gsl region only
gsl_sub <- NWA_coords[NWA_coords$region == "gsl",]

# Add a simple integer column for ease of plotting
gsl_sub$row_count <- 1:nrow(gsl_sub)

ggplot(data = gsl_sub, aes(x = lon, y = lat)) +
  geom_polygon(aes(fill = region)) +
  geom_label(aes(label = row_count)) +
  labs(x = NULL, y = NULL)

# Create smaller gsl polygon
gsl_new <- NWA_coords[NWA_coords$region == "gsl",] %>% 
  slice(-c(7:9))

# Create new cbs (Cabot Strait) polygon
cbs <- NWA_coords[NWA_coords$region == "gsl",] %>% 
  slice(6:10) %>% 
  mutate(region = "cbs")

# Attach the new polygons to the original polygons
NWA_coords_cabot <- NWA_coords %>% 
  filter(region != "gsl") %>% 
  rbind(., gsl_new, cbs)
# saveRDS(NWA_coords_cabot, "data/NWA_coords_cabot.Rda")

# Plot the new areas to ensure everything worked
NWA_coords_cabot_plot <- ggplot(data = NWA_coords_cabot, aes(x = lon, y = lat)) +
  geom_polygon(aes(colour = region, fill = region), size = 1.5, alpha = 0.2) +
  geom_polygon(data = map_base, aes(group = group), show.legend = F) +
  coord_cartesian(xlim = NWA_corners[1:2],
                  ylim = NWA_corners[3:4]) +
  labs(x = NULL, y = NULL, colour = "Region", fill = "Region") +
  theme(legend.position = "bottom")
# ggsave(NWA_coords_cabot_plot, filename = "output/NWA_coords_cabot_plot.pdf", height = 5, width = 6)

# Visualise
NWA_coords_cabot_plot

References

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  R.matlab_3.6.1  forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1 jsonlite_1.6   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] workflowr_1.1.1   cellranger_1.1.0  rvest_0.3.2      
[22] R.methodsS3_1.7.1 evaluate_0.11     labeling_0.3     
[25] knitr_1.20        broom_0.5.0       Rcpp_0.12.18     
[28] backports_1.1.2   scales_1.0.0      hms_0.4.2        
[31] digest_0.6.16     stringi_1.2.4     grid_3.6.1       
[34] rprojroot_1.3-2   cli_1.0.0         tools_3.6.1      
[37] maps_3.3.0        magrittr_1.5      lazyeval_0.2.1   
[40] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[43] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.0 
[46] rmarkdown_1.10    httr_1.3.1        rstudioapi_0.7   
[49] R6_2.2.2          nlme_3.1-137      git2r_0.23.0     
[52] compiler_3.6.1