Merge branch 'gh-pages' of https://github.com/OHI-Science/ohiprep_v2024…

… into gh-pages

Reference/CRS/crs_exercise_anna.Rmd

@@ -39,16 +39,18 @@ library(tidyverse)
 Data describing commercial landings (tonnes) in 2017.
 
 ```{r}
-# Read in commercial landings data
+# ---- Read in commercial landings data ----
 fish <- terra::rast(here("Reference", "CRS", "commercial_landings_2017.tif"))
 
+# ---- preliminary data exploration -----
 # print out geospatial data info (class, dimensions, resolution, extent, CRS, etc.)
 fish # equivalent to (fish)
 
 # print out summary of value layer
 summary(fish)
 
-## Take a look
+# ---- raw plot ----
+## take a look!
 terra::plot(fish)
 # adjust margins: par(mar = c(bottom, left, top, right))
 
@@ -57,8 +59,19 @@ par(oma = c(0,0,0,0))
 terra::plot(fish, mar = c(0, 1.5, 0, 5) )#,
             #oma = c(0,0,0,0)) # outer margin area
 
+# ---- log-transformed plot ----
 # lets log so we can visualize this a little better
-terra::plot(log(fish + 1), mar = c(0,1.5,0,3.5))
+terra::plot(log(fish + 1) , mar = c(0,1.5,0,3.5))
+
+#par(mar = c(5, 4, 4, 0))
+terra::plot(log(fish + 1), main = "Log-transformed Commercial Landings (EPSG:4326)", mar = c(1.5, 0, 2.1, 1))
+
+# library(tidyterra)
+# ggplot() +
+#   geom_spatraster(data = fish, aes(fill = commercial_landings_2017)) +
+#   coord_sf(crs = 4326) +
+#   scale_fill_grass_c(palette = "plasma")
+
 ```
 
 
@@ -78,8 +91,11 @@ Check against Sea Around Us to make sure this seems generally reasonable.
 
 ```{r}
 cell_area <- terra::cellSize(fish)
-terra::plot(cell_area, box = TRUE,
-            mar = c(2, 1.5, 1, 8)) # adjusted margins for exporting plot
+# terra::plot(cell_area, box = TRUE,
+#             mar = c(2, 1.5, 1, 8)) # adjusted margins for exporting plot
+
+terra::plot(cell_area, main = "Cell Size: Original CRS (EPSG:4326)", mar = c(0, 1.5, 0, 6))
+
 
 print(cell_area)
 ```
@@ -136,7 +152,8 @@ terra::global(fish_moll, "sum", na.rm = TRUE)
 # 389.3 million
 ```
 
--   389 million tonnes is a much higher value than the raw data's ~87 million value. Reprojecting the data impacted the values to a significant degree. Cell sizes have been stretched and increased to be uniform across the globe. When perform our global summation after transforming, the 
+-   389 million tonnes is a much higher value than the raw data's ~87 million value. Reprojecting the data impacted the values to a significant degree. Cell sizes have been stretched and increased to be uniform across the globe. When we performed our global summation after transforming, the commercial fish landings values were counts per cell, not density (counts per km^2, for example). This means that when we projected the data from EPSG:4326 to Mollweide, the count associated with one cell in the original raster was copied over to potentially multiple cells assigned to the same area in the Mollweide template. 
+
 
 ```{r}
 # calculate cell area
@@ -145,15 +162,15 @@ cell_area_moll <- terra::cellSize(fish_moll)
 #par(mai = c(1, 1, 3, 5)) # set margins: bottom, left, top, right
 #par(oma = c(1, 1, 3, 4))
 #par(mar = c(1,1,1,1)) 
-terra::plot(cell_area_moll, main = "Mollweide projection cell size", mar = c(0, 2, 0, 6))
+terra::plot(cell_area_moll, main = "Cell Size: Mollweide Projection", mar = c(0, 2, 0, 6))
 
-terra::plot(cell_area, main = "Original projection cell size")
+terra::plot(cell_area, main = "Cell Size: Original CRS (EPSG:4326)", mar = c(0, 1.5, 0, 6))
 ```
 
 
 
 ```{r}
-(cell_area_moll$area)
+cell_area_moll$area
 # min 0
 # max is 547 million
 cell_area$area
@@ -165,7 +182,7 @@ cell_area$area
 Compare to the original tonnes? What is happening? How can you improve this?
 -   original tons: 86331119
 -   Mollweide tons: 389271065
--   the cell size area is now relatively consistent across the globe -- old cell sizes have stretched etc., to fit a standardized cell size (I tried to extend the margins to see the full scalebar label values but struggled)
+-   the cell size area is now relatively consistent across the globe -- old cell sizes have stretched etc., to fit a standardized cell size
 -     perform global calculations before reprojecting
 
 
@@ -182,24 +199,26 @@ Compare to the original tonnes? What is happening? How can you improve this?
 # get density (tonnes/area)
 fish_density <- fish / cell_area
 
-plot(log(fish_density))
+par(mar = c(4, 4, 4, 0)) # set margins: bottom, left, top, right
+
+terra::plot(log(fish_density), main = "Commercial Landings Density (original CRS: EPSG 4326)")
 
 # reproject to Mollweide
 fish_density_moll <- terra::project(fish_density, crs(moll))
 fish_density_moll
 
-plot(log(fish_density_moll))
+terra::plot(log(fish_density_moll), main = "Commercial Landings Density (projected CRS: Mollweide)")
 
 # get area of cells
 new_cell_size <- cellSize(fish_density_moll)
-plot(new_cell_size)
+terra::plot(new_cell_size)
 
 # multiply density by area to get count (tonnes)
 new_tonnes <- fish_density_moll * new_cell_size
-plot(log(new_tonnes + 1))
+terra::plot(log(new_tonnes + 1), main = "Density to Counts: Mollweide")
 
 # sum to check
-global(new_tonnes, "sum", na.rm = TRUE)
+terra::global(new_tonnes, "sum", na.rm = TRUE)
 # 85 million!
 ```
 
@@ -249,6 +268,9 @@ rob <- "+proj=robin +datum=WGS84 +units=m +no_defs"
 fish_moll_rob <- terra::project(fish_moll, crs(rob))
 plot(log(fish_moll_rob + 1))
 
+
+terra::global(fish_moll_rob, "sum", na.rm = TRUE)
+
 # now back to lat long:
 fish_moll_rob_latlon <- project(fish_moll_rob, fish)
 

Reference/CRS/testing-tabs.md

@@ -0,0 +1,72 @@
+
+
+CSS
+
+
+<style>
+.tab {
+  overflow: hidden;
+  border: 1px solid #ccc;
+  background-color: #f1f1f1;
+}
+
+.tab button {
+  background-color: inherit;
+  float: left;
+  border: none;
+  outline: none;
+  cursor: pointer;
+  padding: 14px 16px;
+  transition: 0.3s;
+}
+
+.tab button:hover {
+  background-color: #ddd;
+}
+
+.tab button.active {
+  background-color: #ccc;
+}
+
+.tabcontent {
+  display: none;
+  padding: 6px 12px;
+  border: 1px solid #ccc;
+  border-top: none;
+}
+</style>
+
+
+
+Text introducing plots
+
+<div class="tab">
+  <button class="tablinks" onclick="openTab(event, 'Figure1')" id="defaultOpen">Mollweide Projection Cell Size</button>
+  <button class="tablinks" onclick="openTab(event, 'Figure2')">OG CRS Cell Size</button>
+</div>
+
+<div id="Figure1" class="tabcontent">
+  <img src="figs/cell_size_mollweide.png" alt="Mollweide Projection">
+</div>
+
+<div id="Figure2" class="tabcontent">
+  <img src="figs/cell_size_og_crs.png" alt="Original CRS EPSG:4326">
+</div>
+
+<script>
+function openTab(evt, tabName) {
+  var i, tabcontent, tablinks;
+  tabcontent = document.getElementsByClassName("tabcontent");
+  for (i = 0; i < tabcontent.length; i++) {
+    tabcontent[i].style.display = "none";
+  }
+  tablinks = document.getElementsByClassName("tablinks");
+  for (i = 0; i < tablinks.length; i++) {
+    tablinks[i].className = tablinks[i].className.replace(" active", "");
+  }
+  document.getElementById(tabName).style.display = "block";
+  evt.currentTarget.className += " active";
+}
+
+document.getElementById("defaultOpen").click();
+</script>

globalprep/ao/v2024/ao_need_data_prep.Rmd

@@ -30,7 +30,7 @@ v2024
 One more year of data
 # Data Source 
 
-**Downloaded**: 2023-07-12
+**Downloaded**: 2024-07-30
 
 **Description**:  
 GDP adjusted per capita by PPP (ppppcgdp)
@@ -41,7 +41,7 @@ GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic
 
 Data is available directly to R through the WDI package.
 
-**Time range**: 1990-2022
+**Time range**: 1990-2023
 
 ***
 

globalprep/ao/v2024/ao_need_data_prep.html

@@ -9,7 +9,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
 
 
-<meta name="author" content="Compiled on Fri Aug 23 17:40:31 2024 by sujeet" />
+<meta name="author" content="Compiled on Thu Sep 5 17:42:48 2024 by lecuona" />
 
 
 <title>OHI 2024 - Artisanal Opportunities: Preparing need data</title>
@@ -1647,8 +1647,8 @@
 
 <h1 class="title toc-ignore">OHI 2024 - Artisanal Opportunities:
 Preparing need data</h1>
-<h4 class="author"><em>Compiled on Fri Aug 23 17:40:31 2024 by
-sujeet</em></h4>
+<h4 class="author"><em>Compiled on Thu Sep 5 17:42:48 2024 by
+lecuona</em></h4>
 
 </div>
 
@@ -1664,7 +1664,7 @@ <h1><span class="header-section-number">2</span> Updates from previous
 <p>v2023 One more year of data. Updated so that pop_weights data is also
 read in through the WDI package, and saved as annual data instead of
 only the most recent year. v2024 One more year of data # Data Source</p>
-<p><strong>Downloaded</strong>: 2023-07-12</p>
+<p><strong>Downloaded</strong>: 2024-07-30</p>
 <p><strong>Description</strong>:<br />
 GDP adjusted per capita by PPP (ppppcgdp) <a href="http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD" class="uri">http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD</a>
 Reported at country scale.</p>
@@ -1679,7 +1679,7 @@ <h1><span class="header-section-number">2</span> Updates from previous
 depletion and degradation of natural resources. Data are in constant
 international dollars based on the 2011 ICP round.</p>
 <p>Data is available directly to R through the WDI package.</p>
-<p><strong>Time range</strong>: 1990-2022</p>
+<p><strong>Time range</strong>: 1990-2023</p>
 <hr />
 </div>
 <div id="methods" class="section level1" number="3">

globalprep/lsp/v2024/lsp_data_prep.Rmd

@@ -9,6 +9,7 @@ output:
     number_sections: yes
     theme: cerulean
     toc: yes
+    toc_float: true
   pdf_document:
     toc: yes
   word_document:
@@ -48,7 +49,7 @@ v2021 Using updated February 2021 data
 
 # Data Source
 
-**Reference**: IUCN and UNEP-WCMC (2022), The World Database on Protected Areas (WDPA) [On-line], May 2024. Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net.
+**Reference**: IUCN and UNEP-WCMC (2023), The World Database on Protected Areas (WDPA) [On-line], May 2024. Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net.
 
 **Downloaded**: May 24, 2024
 
@@ -118,9 +119,9 @@ Comparing the global WDPA raster to the 3 nautical miles offshore and 1 km inlan
 ```{r lsp_zonal_stats, eval = FALSE}
 
 # list intermedite file paths
-zonal_files <- c('zonal_3nm' =  file.path(dir_goal, 'intermediate', 'zonal_stats_3nm.csv'),
-                 'zonal_1km' =  file.path(dir_goal, 'intermediate', 'zonal_stats_1km.csv'),
-                 'zonal_eez' =  file.path(dir_goal, 'intermediate', 'zonal_stats_eez.csv'))
+zonal_files <- c('zonal_3nm' =  file.path(dir_goal, 'int', 'zonal_stats_3nm.csv'),
+                 'zonal_1km' =  file.path(dir_goal, 'int', 'zonal_stats_1km.csv'),
+                 'zonal_eez' =  file.path(dir_goal, 'int', 'zonal_stats_eez.csv'))
 
 # load raster created in 1_prep_wdpa_rast.rmd
 rast_wdpa <- terra::rast(file.path(dir_goal_anx, 'rast', 'wdpa_2024_moll_500m.tif'))
@@ -134,7 +135,7 @@ rgn_rast_list <- c(
   'zonal_1km' = file.path(dir_zones, 'rgn_inland1km_mol_500mcell.tif'),
   'zonal_eez' = file.path(dir_zones, 'rgn_eez_mol_500mcell.tif'))
 
-### Remove all files in `intermediate` if it's the first time working through this data prep for this assessment
+### Remove all files in `int` if it's the first time working through this data prep for this assessment
 ### Filters out finished zonal files: if zonal files don't exist yet, they will be created (comment out to recalculate)
 zonal_files_to_run <- zonal_files[!file.exists(zonal_files)]
 rgn_rast_list <- rgn_rast_list[!file.exists(zonal_files)]
@@ -201,7 +202,7 @@ cat('writeRaster elapsed: ', (proc.time() - ptm)[3])
 z <- lsp_crosstab(rgn_rast_list[3], rast_values = rast_wdpa) #~51 min minutes to run #eez
 cat('writeRaster elapsed: ', (proc.time() - ptm)[3])
 
-## Save these files to the intermediate folder
+## Save these files to the int folder
 write_csv(x, zonal_files_to_run[1])
 write_csv(y, zonal_files_to_run[2])
 write_csv(z, zonal_files_to_run[3])
@@ -306,10 +307,10 @@ prot_1km <- stats_1km %>% calc_areas()
 prot_3nm <- stats_3nm %>% calc_areas()
 prot_eez <- stats_eez %>% calc_areas()
 
-# write results to intermediate folder as csv
-write_csv(prot_3nm, file.path(dir_goal, 'intermediate', 'area_protected_3nm.csv'))
-write_csv(prot_1km, file.path(dir_goal, 'intermediate', 'area_protected_1km.csv'))
-write_csv(prot_eez, file.path(dir_goal, 'intermediate', 'area_protected_eez.csv'))
+# write results to int folder as csv
+write_csv(prot_3nm, file.path(dir_goal, 'int', 'area_protected_3nm.csv'))
+write_csv(prot_1km, file.path(dir_goal, 'int', 'area_protected_1km.csv'))
+write_csv(prot_eez, file.path(dir_goal, 'int', 'area_protected_eez.csv'))
 ```
 
 ------------------------------------------------------------------------
@@ -323,10 +324,10 @@ From the protected area files, write out the individual layers ready for the Too
 
 ```{r write_layers, eval = FALSE}
 # read in files and rename
-prot_3nm <- read_csv(file.path(dir_goal, 'intermediate', 'area_protected_3nm.csv')) %>%
+prot_3nm <- read_csv(file.path(dir_goal, 'int', 'area_protected_3nm.csv')) %>%
   rename(area = a_tot_km2,
          a_prot_3nm = a_prot_km2)
-prot_1km <- read_csv(file.path(dir_goal, 'intermediate', 'area_protected_1km.csv')) %>%
+prot_1km <- read_csv(file.path(dir_goal, 'int', 'area_protected_1km.csv')) %>%
   rename(area = a_tot_km2,
          a_prot_1km = a_prot_km2)
 
@@ -353,12 +354,12 @@ Some goals require calculation of resilience nearshore (3nm) or entire EEZ.
 
 area_ref = .30 ### 30% of area protected = reference point
 
-# read in regional data csvs from intermediate
-resil_3nm <- read_csv(file.path(dir_goal, 'intermediate', 'area_protected_3nm.csv')) %>%
+# read in regional data csvs from int
+resil_3nm <- read_csv(file.path(dir_goal, 'int', 'area_protected_3nm.csv')) %>%
   mutate(resilience.score = (a_prot_km2 / a_tot_km2) / area_ref,
          resilience.score = ifelse(resilience.score > 1, 1, resilience.score))
 
-resil_eez <- read_csv(file.path(dir_goal, 'intermediate', 'area_protected_eez.csv')) %>%
+resil_eez <- read_csv(file.path(dir_goal, 'int', 'area_protected_eez.csv')) %>%
   mutate(resilience.score = (a_prot_km2 / a_tot_km2) / area_ref,
          resilience.score = ifelse(resilience.score > 1, 1, resilience.score))
 # ask Mel about using file.path versus here function