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Copy path04_SpatialVectOverlayAnalysisOnVMEs.r
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04_SpatialVectOverlayAnalysisOnVMEs.r
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##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# Make closure boxes polygons!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
library(sf)
library(leaflet)
library(raster)
library(dplyr)
library(rnaturalearth)
library(ggplot2)
#background map
sf_world <- ne_countries(returnclass='sf')
# plot(sf_world, add=TRUE, col="grey", border=FALSE)
cut_polygon = function(polygon, mindepth, maxdepth){
bbox=st_bbox(polygon)
b <- as(extent(bbox[1], bbox[3], bbox[2], bbox[4]), 'SpatialPolygons')
crs(b) <- crs(bathy)
localbathy=crop(bathy_sub,b)
localbathy[localbathy > -mindepth] = NA
localbathy[localbathy < -maxdepth] = NA
localbathy[!is.na(localbathy)] = 1
localbathy_sf=st_as_sf(rasterToPolygons(localbathy))
st_intersection(st_make_valid(polygon),st_union(localbathy_sf))
}
convert_coordinates <- function(x){
require(stringr)
len=nchar(x)
degree=as.numeric(sapply(strsplit(x,split="°"), function(coord) coord[1]))
minutes=as.numeric(str_match(x,"'")[,2])
seconds=as.numeric(gsub(",",".",substr(x, str_locate(x, "''")[,1]+1,len-2)))
seconds=ifelse(seconds>60,seconds/100,seconds)
minutes2 = NA
if (any(is.na(minutes)))
minutes2=as.numeric(gsub(",",".",substr(x,str_locate(x, "'")[,1]+1,len-2)))
res=degree+ifelse(!is.na(minutes),
minutes/60+seconds/3600,
minutes2/60)
res=ifelse(endsWith(x,"W"), -res, res)
res
}
create_a_polygon <- function(x){
#coords<-cbind(convert_coordinates(x$Longitude),
# convert_coordinates(x$Latitude))
coords<-cbind(x$Longitude,
x$Latitude)
if (!all(coords[1,]==coords[nrow(coords),]))
coords <- rbind(coords,coords[1,] )
a_polygon <- st_sf(st_sfc(st_polygon(list(coords)), crs=4326))
a_polygon$year <- x$year[1]
if ("name" %in% names(x))
a_polygon$name <- x$name[1]
if ("depth" %in% names(x))
a_polygon <- cut_polygon(polygon, x$depth[1], x$depth[2])
a_polygon
}
#bathy=raster("GEBCO_04_Jul_2022/gebco.tif")
#b <- as(extent(-5.6, 10, 30.2, 44), 'SpatialPolygons')
#crs(b) <- crs(bathy)
#bathy_sub=crop(bathy,b)
# create polygons
poly_coords <- read.table (file=file.path("D:","FBA","ADVICES","STECF","STECF_ad_hoc_2023_VMEs","Contract n2","COORD sc2opt1_ImplementingAct DSAR.csv"), header=TRUE, sep=";")
poly_coords$year <- 2022
poly_coords$name <- "DG_MARE"
lst_poly_coords <- split(poly_coords, f=poly_coords$Poly_No)
lst_poly_coords <- lapply(lst_poly_coords, as.list)
allclosures_sf_list <- lapply(lst_poly_coords, create_a_polygon)
# export a shp file
allclosures_sf <- do.call(dplyr::bind_rows, allclosures_sf_list) # bind many df into one....
allclosures_sf$popup <- seq_len(nrow(allclosures_sf))
allclosures_sf <- allclosures_sf %>%
dplyr::mutate(year= dplyr::case_when(year=="2022" ~ 2022))
st_write(allclosures_sf, file.path("D:","FBA","ADVICES","STECF","STECF_ad_hoc_2023_VMEs","Contract n2","fba_closure_VMEs_2022.shp"), append=FALSE)
# a visual check
library(leaflet)
leaflet(allclosures_sf %>% dplyr::filter(year==2022)) %>% addTiles() %>% addPolygons(popup=~htmltools::htmlEscape(paste(popup, name)))
# export a plot
library(ggplot2)
pols <- allclosures_sf %>% dplyr::filter(allclosures_sf$year=="2022")
coords <- st_bbox(pols)
print(ggplot(pols) +
geom_sf(fill=NA, color=grey(0.5)) +
xlim(coords[1]-.3, coords[3]+.3)+
ylim(coords[2]-.3, coords[4]+.3) +
#geom_sf(data=continent, color="black", fill="grey", inherit.aes=FALSE)+
ggtitle("closed")+
scale_fill_manual("year",values=grey(0.5))+
theme_bw()+
xlab("")+
ylab(''))
ggsave(paste0("closed.png"),width=16/2.54, dpi=300)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# A quick spatial overlay!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# utils
what_is_inside <- function(filepath, allclosures_sf, reproject=FALSE)
{
library(terra)
library(raster)
bb <- st_bbox(allclosures_sf)
er <- try( {
rstr <- terra::rast(file.path(filepath, "spatRaster.tif")) # in "+proj=longlat +datum=WGS84"
}, silent=TRUE)
if(class(er)!="try-error"){
if(reproject){
newcrs <- "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs" # European EEA projection
rstr_eea <- project(rstr, newcrs)
} else{
rstr_eea <- rstr
}
if(length(grep( "FishingHour", colnames(rstr[])))!=0){
is_vms <- TRUE; is_fdi <- FALSE
} else{
is_vms <- FALSE ; is_fdi <- TRUE
}
# crop in a bbox define by the closure shp
cr <- as(extent(bb), 'SpatialPolygons')
crs(cr) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
if(reproject) cr <- spTransform(cr, crs(rstr_eea))
rstr_eea_sub <- crop(rstr_eea, cr)
library(viridis)
#plot(rstr_eea_sub)
#plot(rstr_eea_sub, col=viridis(50))
#plot(log(rstr_eea_sub), col=viridis(50))
# reproject closures
allclosures_sf_eea <- st_transform(allclosures_sf, crs(rstr_eea))
a_width <- 3000 ; a_height <- 4000
tiff(filename=file.path(file.path(getwd(), a_folder, a_folder2, "VectorExtract", paste0("e_mpas_",sce,"_",fs,"_",y,".tif"))), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
plot(log(rstr_eea_sub$landings_aer_in_ctry_level6_csquare), main="log of landings kg")
plot(allclosures_sf_eea, add=TRUE, lwd=0.5, col=rgb(0.2,0.2,0.2,0.2))
dev.off()
# adding a csquare code
r <- rast(nrow=dim(rstr_eea_sub)[1], ncol=dim(rstr_eea_sub)[2],
extent=ext(rstr_eea_sub), res=res(rstr_eea_sub), crs=crs(rstr_eea_sub), vals=NA, names="value") # init
cells <- 1:ncell(r)
xy <- xyFromCell(r, cells)
if(is_fdi) csquares <- vmstools::CSquare(xy[,"x"],xy[,"y"], degrees=0.5)
if(is_vms) csquares <- vmstools::CSquare(xy[,"x"],xy[,"y"], degrees=0.05)
values(r) <- csquares
csquare_coding <- data.frame(id=c(values(r)), csquare=csquares, x=xy[,"x"], y=xy[,"y"])
names(r) <- "CSQUARE_ID"
add(rstr_eea_sub) <- r
# extract what is inside...
# Caution here...It is not simple because basically the polygons are much small that the raster cells (in this FDI case)...
# applying a multiplier on GVA will be later necessary.
allclosures_vect_terra <- vect(allclosures_sf_eea)
if(is_fdi) col_names <- c("daysatsea_aer_in_ctry_level6_csquare","landings_aer_in_ctry_level6_csquare","value_aer_in_ctry_level6_csquare",
"varcosts_in_ctry_level6_csquare", "oth_non_var_costs_in_csquare", "other_income_in_csquare","unpaid_labour_in_csquare", "personnelcosts_in_ctry_level6_csquare", "KwFishingdays_aer_in_ctry_level6_csquare",
"lpue_csquare_fdi_kgperfday", "CSQUARE_ID")
if(is_vms) col_names <- c("daysatsea_aer_in_ctry_level6_csquare","landings_aer_in_ctry_level6_csquare","value_aer_in_ctry_level6_csquare",
"varcosts_in_ctry_level6_csquare", "oth_non_var_costs_in_csquare", "other_income_in_csquare","unpaid_labour_in_csquare", "personnelcosts_in_ctry_level6_csquare", "KwFishingdays_aer_in_ctry_level6_csquare",
"lpue_csquare_vms_kgperfhour", "CSQUARE_ID")
# extract
if(is_vms){
# with VMS data, some polygons lay on several csquares...
e_mpas <- extract(subset(rstr_eea_sub, col_names), vect(allclosures_sf_eea), cells=TRUE, na.rm=TRUE) # extract the raster cell numbers from each polygon pts for all polygons
e_mpas <- e_mpas[!duplicated(e_mpas [,c("ID", "cell")]),] # avoid double counting because polygons are by def having several pts...
}
if(is_fdi){
# do an extract (use the centroid...it is acceptable given the closed boxes are much smaller than the FDI c-squares)
coord <- crds(rstr_eea_sub, na.rm=FALSE)
coords <- st_coordinates(st_centroid(st_make_valid(allclosures_sf_eea$geometry)))
e_mpas <- extract(rstr_eea_sub, vect(SpatialPoints(coords)), cellnumbers=TRUE)
}
# retrieve the csquare coding
e_mpas <- cbind.data.frame(e_mpas, csquare=csquare_coding[match(e_mpas[, "CSQUARE_ID"], csquare_coding$id), "csquare"])
e_mpas <- data.table(e_mpas)
# then aggregate per c-square
e_mpas_1 <- e_mpas[,lapply(.SD, sum, na.rm=TRUE),
.SDcols=c("daysatsea_aer_in_ctry_level6_csquare", "landings_aer_in_ctry_level6_csquare", "value_aer_in_ctry_level6_csquare",
"varcosts_in_ctry_level6_csquare", "oth_non_var_costs_in_csquare",
"other_income_in_csquare" ,"unpaid_labour_in_csquare", "personnelcosts_in_ctry_level6_csquare",
"KwFishingdays_aer_in_ctry_level6_csquare"), by=c("ID", "csquare")]
e_mpas_1 <- as.data.frame(e_mpas_1)
if(is_fdi) e_mpas_2 <- e_mpas[,lapply(.SD, sum, na.rm=TRUE),
.SDcols=c("lpue_csquare_fdi_kgperfday"), by=c("ID","csquare")]
if(is_vms) e_mpas_2 <- e_mpas[,lapply(.SD, sum, na.rm=TRUE),
.SDcols=c("lpue_csquare_vms_kgperfhour"), by=c("ID","csquare")]
e_mpas_2 <- as.data.frame(e_mpas_2)
e_mpas <- cbind.data.frame(what="inside_this_poly", e_mpas_1, e_mpas_2[,c(-c(1:2)), drop=FALSE])
## => export to TABULATE later!!!
# what about outside? which outside to get a percentage?
#=> need to do the analysis per fs to compare with total per fs....
library(data.table)
a_dt <- data.table(as.data.frame(rstr_eea))
## aggregate per grID
a_dt_1 <-
a_dt[,lapply(.SD, sum, na.rm=TRUE),
.SDcols=c("daysatsea_aer_in_ctry_level6_csquare", "landings_aer_in_ctry_level6_csquare", "value_aer_in_ctry_level6_csquare",
"varcosts_in_ctry_level6_csquare", "oth_non_var_costs_in_csquare",
"other_income_in_csquare" ,"unpaid_labour_in_csquare", "personnelcosts_in_ctry_level6_csquare",
"KwFishingdays_aer_in_ctry_level6_csquare")]
if(is_fdi) a_dt_2 <-
a_dt[,lapply(.SD, mean, na.rm=TRUE),
.SDcols=c("lpue_csquare_fdi_kgperfday")]
if(is_vms) a_dt_2 <-
a_dt[,lapply(.SD, mean, na.rm=TRUE),
.SDcols=c("lpue_csquare_vms_kgperfhour")]
a_dt <- cbind(a_dt_1, a_dt_2)
a_df <- cbind.data.frame(what="all layer cells", ID=0, csquare=0, data.frame(a_dt))
e_mpas <- rbind.data.frame(a_df, e_mpas) # Adding a Marginal Total for ALL (i.e. INSIDE+OUTSIDE) as ID=0
if(FALSE){
# try this, for fun:
library(mapview)
mapviewOptions(fgb = FALSE) # needed when creating web pages
mapview(st_geometry(allclosures_sf_eea))
# raster to terra
#rstr_eea_sub_terra <- terra::rast(rstr_eea_sub)
#vect_eea_sub_terra <- terra::as.polygons(rstr_eea_sub_terra, dissolve=FALSE, value=TRUE, na.rm=TRUE) # broken...values are lost!
# raster to polygon to sf
vect_eea_sub <- rasterToPolygons(raster(rstr_eea_sub))
vect_eea_sub_sf <- st_as_sf(vect_eea_sub)
# transform in mercator to be compatible to mapview
a_crs <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs"
allclosures_sf_merc <- st_transform(allclosures_sf_eea, crs(a_crs))
vect_sub_sf_merc <- st_transform(vect_eea_sub_sf, crs(a_crs))
# the fun:
mapview(st_geometry(allclosures_sf_merc), native.crs=FALSE)+
mapview(vect_sub_sf_merc, col.regions = colorRampPalette(c("white", "green", "red")), native.crs=FALSE, alpha.regions=0.4, at=c())
} # end FALSE
return(e_mpas)
} else{ cat(paste0("no spatRast file found in ", filepath, "\n"))}
}
##------------------------------------
##--CALLS-----------------------------
# read in closure shp
#allclosures_sf <- st_read(file.path("D:","FBA","ADVICES","STECF","STECF_ad_hoc_2023_VMEs","Contract n2","fba_closure_VMEs_2022.shp"))
#allclosures_sf <- st_read(file.path(getwd(), "INPUT_SPATIAL_LAYERS", "OWF", "EMODnet_HA_WindFarms_20221219", "EMODnet_HA_WindFarms_pg_20221219.shp"))
# etc.
# The recent shp sent by EC:
library(sf)
VMEs_MARE <- st_read(file.path(getwd(),"INPUT_SPATIAL_LAYERS","VMEs","DGMARE_Sc2Opt1", "Scenario2_option1_DG_Mare_Poly.shp"))
ICES_VMEs_SceC <- st_read(file.path(getwd(),"INPUT_SPATIAL_LAYERS","VMEs","ICES_VMEs_Coordinates_shp", "Scenario_C.shp"))
ICES_VMEs_SceD <- st_read(file.path(getwd(),"INPUT_SPATIAL_LAYERS","VMEs","ICES_VMEs_Coordinates_shp", "Scenario_D.shp"))
plot(st_geometry(VMEs_MARE))
plot(st_geometry(ICES_VMEs_SceC), col=rgb(0.5,0.5,0.5,0.5), add=TRUE)
plot(st_geometry(ICES_VMEs_SceD), col=rgb(0.8,0.2,0.5,0.8), add=TRUE)
# a stacked visual
library(leaflet)
map <- leaflet() %>%
addTiles()
map <- map %>% addPolygons(data=VMEs_MARE, popup=~htmltools::htmlEscape(paste(Poly_No, ISO_TER1)), col="blue", group = "VMEs_MARE") %>%
addPolygons(data=ICES_VMEs_SceC, popup=~htmltools::htmlEscape(paste(poly_id)), col="green", group = "ICES_VMEs_SceC") %>%
addPolygons(data=ICES_VMEs_SceD, popup=~htmltools::htmlEscape(paste(poly_id)), col="red", group = "ICES_VMEs_SceD")
map <- map %>% addLayersControl(
baseGroups = "",
overlayGroups = c("VMEs_MARE","ICES_VMEs_SceC", "ICES_VMEs_SceD"),
options = layersControlOptions(collapsed = FALSE))
map
library(htmlwidgets)
saveWidget(map, file=file.path(getwd(),"INPUT_SPATIAL_LAYERS","VMEs","leaflet_map_VMEs.html"))
#!!!!!!!!!!!!!!!!!!!!#
#!!!!!!!!!!!!!!!!!!!!#
sces <- c("Closure2022", "ICES_SceC", "ICES_SceD")
years <- 2018:2021
a_folder <- "OUTCOME_OVERLAY_VMEs"
a_folder2 <- "OUTCOME_FISHERIES_DISTR_FDI_AER"
#a_folder <- "OUTCOME_OVERLAY_VMEs"
#a_folder2 <- "OUTCOME_FISHERIES_DISTR_VMS_AER_VMEs"
#!!!!!!!!!!!!!!!!!!!!#
#!!!!!!!!!!!!!!!!!!!!#
if(length(grep( "VMS", a_folder2))!=0){
is_vms <- TRUE; is_fdi <- FALSE
} else{
is_vms <- FALSE ; is_fdi <- TRUE
}
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# FOR EACH IMPACTED PER C-SQUARE,
# FIND THE SURFACE AREA, THE FISHABLE AREA, AND THE CLOSED AREA
# TO FIND OUT THE GVA PER FISHABLE AREA IN EACH IMPACTED C-SQUARE
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# the goal is to obtain a multiplier to do: GVA impacted = GVA in the impacted csquare * CLOSED AREA / FISHABLE AREA
## GET A PREPARATORY TABLE
library(terra)
library(doBy)
lookup_table_prop_fishable_area <- NULL
an <- function(x) as.numeric(x)
bathy_400_800 <- rast(file.path(getwd(), "INPUT_SPATIAL_LAYERS","GEBCO_May_2023","gebco.tif"))
bathy_0_800_1000 <- rast(file.path(getwd(), "INPUT_SPATIAL_LAYERS","GEBCO_May_2023","GEBCO_bathy_coding_for_o800_and_o1000m.tif"))
years <- c("2018", "2019", "2020", "2021","2018_2021")
if (is_vms) metiers <- "all_metiers" # ICES VMS is bottom metiers only...
if (is_fdi) metiers <- "all_bottom_metiers"
for(yrs in years)
{
for (sce in sces){
cat(paste0("Scenario ", sce, "\n"))
if(sce=="Closure2022") allclosures_sf <- VMEs_MARE
if(sce=="ICES_SceC") allclosures_sf <- ICES_VMEs_SceC
if(sce=="ICES_SceD") allclosures_sf <- ICES_VMEs_SceD
filepath <- file.path(getwd(), a_folder2, metiers, yrs)
rstr <- terra::rast(file.path(filepath, "spatRaster.tif")) # in "+proj=longlat +datum=WGS84"
rstr$GVA <- (an(rstr$landings_aer_in_ctry_level6_csquare) * # landing kg * price
(an(rstr$value_aer_in_ctry_level6_csquare)/an(rstr$landings_aer_in_ctry_level6_csquare))) +
an(rstr$other_income_in_csquare) - # plus other income
an(rstr$unpaid_labour_in_csquare) - an(rstr$varcosts_in_ctry_level6_csquare) - an(rstr$oth_non_var_costs_in_csquare) # minus var and fixed costs
r <- rast(nrow=dim(rstr)[1], ncol=dim(rstr)[2],
extent=ext(rstr), res=res(rstr), crs=crs(rstr), vals=NA, names="value") # init
# adding a csquare code
cells <- 1:ncell(r)
xy <- xyFromCell(r, cells)
if(is_fdi) csquares <- vmstools::CSquare(xy[,"x"],xy[,"y"], degrees=0.5)
if(is_vms) csquares <- vmstools::CSquare(xy[,"x"],xy[,"y"], degrees=0.05)
values(r) <- csquares
csquare_coding <- data.frame(id=c(values(r)), csquare=csquares, x=xy[,"x"], y=xy[,"y"])
names(r) <- "CSQUARE_ID"
add(rstr) <- r
if(is_fdi){
# figuring out what is the csquare code of each polygon
e_csquares <- extract(rstr, vect(allclosures_sf), fun=modal) # modal => get the more frequent
csquare_per_polygon_id_for_this_sce <- cbind.data.frame(e_csquares[, c("ID", "CSQUARE_ID")], csquare=csquare_coding[match(e_csquares[, "CSQUARE_ID"], csquare_coding$id), c("csquare", "x", "y")])
}
if(is_vms){
# figuring out what is the csquare codes of each polygon
e_csquares <- extract(rstr, vect(allclosures_sf))
csquare_per_polygon_id_for_this_sce <- cbind.data.frame(e_csquares[, c("ID", "CSQUARE_ID")], csquare=csquare_coding[match(e_csquares[, "CSQUARE_ID"], csquare_coding$id), c("csquare", "x", "y")])
}
# subsetting to only keep the info on csquares containing the closure polygons
dd <- values(rstr$CSQUARE_ID)
idx_to_discard <- c(1:ncell(rstr))[!c(dd) %in% csquare_per_polygon_id_for_this_sce$CSQUARE_ID]
rstr[idx_to_discard] <- NA
rstr <- trim(rstr)
plot(rstr$GVA)
# align both bathy and data rasters
if(is_fdi) n <- 30
if(is_vms) n <- 4 # caution: estimates are quite sensitive to this choice...
bathy_0_800_1000_c <- crop(bathy_0_800_1000, rstr)
rstr$surface_area_csquare <- cellSize(rstr$GVA, unit="km", names="surface_area") # returns only for cells!=NA
rstr_disagg <- disagg(rstr, n) # increase the resolution to line up with bathy
bathy_0_800_1000_c <- resample(bathy_0_800_1000_c, rstr_disagg, method="near") # align
bathy_0_800_c <- bathy_0_800_1000_c# init
bathy_0_800_c[bathy_0_800_c > 1] <- NA # no bottom trawl allowed if bathy >-800
rstr_in_bathy_0_800 <- rstr_disagg + bathy_0_800_c
# rasterize the closure shape
dd <- terra::vect(allclosures_sf)
dd$value <- 1
closure_rast_terra <- terra::rasterize(dd, y=rstr_in_bathy_0_800, field="value", fun=sum, na.rm=TRUE)
rstr_closure <- rstr_disagg + closure_rast_terra + bathy_0_800_c
surface_area_of_closure_csquare <- cellSize(rstr_closure$CSQUARE_ID, unit="km", names="surface_area_closure") # returns only for cells!=NA
surface_area_of_closure_csquare <- aggregate(surface_area_of_closure_csquare, n, fun= "sum", by="CSQUARE_ID", na.rm=TRUE)
#=> this gives the surface of the closure in the fishable area
# find out the TOTAL closure surface for info
rstr_closure_tot <- closure_rast_terra
surface_area_of_closure_tot_csquare <- cellSize(rstr_closure_tot$value, unit="km", names="surface_area_closure_tot") # returns only for cells!=NA
surface_area_of_closure_tot_csquare <- aggregate(surface_area_of_closure_tot_csquare, n, fun= "sum", by="CSQUARE_ID", na.rm=TRUE)
totsurface <- sum(surface_area_of_closure_tot_csquare[], na.rm=TRUE)
# find out the surface area per clipped Csquare
surface_area_of_clipped_csquare <- cellSize(rstr_in_bathy_0_800$CSQUARE_ID, unit="km", names="surface_area") # returns only for cells!=NA
surface_area_of_clipped_csquare <- aggregate(surface_area_of_clipped_csquare, n, fun= "sum", by="CSQUARE_ID", na.rm=TRUE)
# build a raster prop
prop_surface_area_of_clipped_csquare <- surface_area_of_clipped_csquare/rstr$surface_area_csquare
names(prop_surface_area_of_clipped_csquare) <- "prop_fishable_area"
add(prop_surface_area_of_clipped_csquare) <- rstr$CSQUARE_ID
add(prop_surface_area_of_clipped_csquare) <- rstr$surface_area_csquare
add(prop_surface_area_of_clipped_csquare) <- surface_area_of_closure_csquare
prop_surface_area_of_clipped_csquare_df <- prop_surface_area_of_clipped_csquare[]
prop_surface_area_of_clipped_csquare_df <- prop_surface_area_of_clipped_csquare_df[complete.cases(prop_surface_area_of_clipped_csquare_df),]
lookup_table_prop_fishable_area_per_csquare_and_ID_polygon <- merge(prop_surface_area_of_clipped_csquare_df, csquare_per_polygon_id_for_this_sce, all=TRUE)
lookup_table_prop_fishable_area_per_csquare_and_ID_polygon <- orderBy(~ID, lookup_table_prop_fishable_area_per_csquare_and_ID_polygon)
# collect for later use
lookup_table_prop_fishable_area <- rbind.data.frame(lookup_table_prop_fishable_area,
cbind.data.frame(sce=sce, year=yrs, metier=metiers, lookup_table_prop_fishable_area_per_csquare_and_ID_polygon, totsurfaceclosure=totsurface))
#=> some NAs because no fishable area into it...
# check fishable area in each c-square
plot(surface_area_of_clipped_csquare)
plot(rstr_in_bathy_0_800$CSQUARE_ID, add=TRUE, col=rgb(0.2,0.2,0.2,0.2), leg=FALSE)
plot(prop_surface_area_of_clipped_csquare)
plot(rstr_in_bathy_0_800$CSQUARE_ID, add=TRUE, col=rgb(0.2,0.2,0.2,0.2), leg=FALSE)
n2 <- rstr$CSQUARE_ID %in% 51853
plot(n2)
# a plot
a_width <- 3000 ; a_height <- 4000
tiff(filename=file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2,paste0("GVA_",metiers,"_in_impacted_csquare_from_", yrs,"_", sce, ".tif")), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
if(is_vms) plot(rstr_in_bathy_0_800$GVA/1e6, main=paste0(yrs, "GVA (MEuro) ", sce), breaks=c(-2, seq(0, 1, by=0.25), 2), lwd=0.1)
if(is_fdi) plot(rstr_in_bathy_0_800$GVA/1e6, main=paste0(yrs, "GVA (MEuro) ", sce), breaks=c(-15, -5, -1, seq(0, 5, by=1), 50), lwd=0.5)
plot(as.polygons(rstr$GVA), add=TRUE, lwd=0.1)
plot(vect(allclosures_sf), add=TRUE, lwd=0.5)
library(rnaturalearth)
sf_world <- ne_countries(returnclass='sf')
spdf_europe <- ne_countries(continent = "europe", scale=10)
plot(vect(spdf_europe), col=grey(0.5), add=TRUE)
dev.off()
} # end sce
} # end yrs
save(lookup_table_prop_fishable_area,
file=file.path(file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2,
paste0("lookup_prop_fishable_area.RData"))))
# prop of fishable area is constrained by bathymetry >-800
# surface closure area specific to sce
# area surface of csquares vary along latitudinal range if longlat coord
library(data.table)
dd <- data.table(lookup_table_prop_fishable_area)
dd <- dd[,.(impacted_surface_closed_km2=sum(surface_area_closure, na.rm=TRUE), tot_surface_closed_km2=mean(totsurfaceclosure, na.rm=TRUE)),by=c("sce","year","metier")]
dd$impacted_surface_closed_km2 <- round(dd$impacted_surface_closed_km2)
dd$tot_surface_closed_km2 <- signif(an(dd$tot_surface_closed_km2),3)
#=> caution: it varies alongside years depending on the year footprint
dd <- knitr::kable(as.data.frame(dd), format = "html")
library(readr)
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("impacted_surface_closed_km2.html")))
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# EXTRACT PER POLYGON ID
# TO FIND OUT THE GVA PER POLYGON IN EACH IMPACTED C-SQUARE
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
years <- c("2018_2021", "2018", "2019", "2020", "2021")
#years <- c("2018_2021")
load(file=file.path(file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2,
paste0("lookup_prop_fishable_area.RData")))) # lookup_table_prop_fishable_area
dir.create(file.path(getwd(), a_folder, a_folder2, "VectorExtract"), recursive=TRUE)
output <- NULL
collector_extraction_per_fs_per_boxID <- NULL
for (sce in sces){
cat(paste0("Scenario ", sce, "\n"))
if(sce=="Closure2022") allclosures_sf <- VMEs_MARE
if(sce=="ICES_SceC") allclosures_sf <- ICES_VMEs_SceC
if(sce=="ICES_SceD") allclosures_sf <- ICES_VMEs_SceD
## PER METIER----------
# read-in the merged disagregated AER variables in shapfiles
for(y in years){
cat(paste0("Year ", y, "\n"))
all_fs <- list.files(file.path(getwd(), a_folder2))
idx_fs_to_keep <- c(grep("DTS", all_fs), grep("TBB", all_fs), grep("DRB", all_fs), grep("HOK", all_fs), grep("DFN", all_fs)) # bottom contacting gears + hooks(longline) and nets (in FDI). only bottom gears in the ICES VMS
#idx_fs_to_keep <- c(grep("DTS", all_fs), grep("TBB", all_fs), grep("DRB", all_fs)) # bottom contacting gears only
all_fs <- all_fs[idx_fs_to_keep]
all_fs <- c("all_metiers", all_fs) #, "PRT_VL1218", "PRT_VL1824", "PRT_VL2440", "PRT_VL40XX")
for (fs in all_fs)
{
cat(paste0("Fleet-segment ", fs, "\n"))
library(data.table)
filepath <- file.path(getwd(), a_folder2, fs, y)
e_mpas_this_metier <- what_is_inside (filepath, allclosures_sf, reproject=FALSE) # caution with a reproject at TRUE cause the resampling will blur estimates....
if(!is.null(e_mpas_this_metier)){
an <- function (x) as.numeric(x)
e_mpas_this_metier$GVA_cell <- (an(e_mpas_this_metier$landings_aer_in_ctry_level6_csquare) * # landing kg * price
(an(e_mpas_this_metier$value_aer_in_ctry_level6_csquare)/an(e_mpas_this_metier$landings_aer_in_ctry_level6_csquare))) +
an(e_mpas_this_metier$other_income_in_csquare) - # plus other income
an(e_mpas_this_metier$unpaid_labour_in_csquare) - an(e_mpas_this_metier$varcosts_in_ctry_level6_csquare) - an(e_mpas_this_metier$oth_non_var_costs_in_csquare) # minus var and fixed costs
e_mpas_this_metier$GVA_all <- e_mpas_this_metier[e_mpas_this_metier$what=="all layer cells","GVA_cell"] # track the value to get a proportion later on
e_mpas_this_metier$GrossProfit_cell <- e_mpas_this_metier$GVA_cell - an(e_mpas_this_metier$personnelcosts_in_ctry_level6_csquare)
e_mpas_this_metier$GrossProfit_GVA_all <- e_mpas_this_metier[e_mpas_this_metier$what=="all layer cells","GrossProfit_cell"] # track the value to get a proportion later on
# merge with lookup_table_prop_fishable_area
e_mpas_this_metier <- cbind.data.frame(sce=sce, year=y, e_mpas_this_metier)
e_mpas_this_metier_m <- merge(e_mpas_this_metier, lookup_table_prop_fishable_area, by.x=c("sce","year","ID", "csquare"), by.y=c("sce","year","ID", "csquare.csquare"))
# apply a multiplier accordingly
is_bottom_trawls <- c(grep("DTS", fs), grep("TBB", fs), grep("DRB", fs))
a_multiplier <- e_mpas_this_metier_m$surface_area_closure/(e_mpas_this_metier_m$surface_area_csquare) # default e.g. longliners, netters, ie.not restricted by the -800m bathy
if(length(is_bottom_trawls)!=0) a_multiplier <- e_mpas_this_metier_m$surface_area_closure/(e_mpas_this_metier_m$surface_area_csquare*e_mpas_this_metier_m$prop_fishable_area) # restricted by the -800m bathy
a_multiplier[!is.na(a_multiplier) & a_multiplier[]>1] <- 1 # if VMS data
e_mpas_this_metier_m$GVAimpacted <- e_mpas_this_metier_m$GVA_cell * a_multiplier
e_mpas_this_metier_m$GrossProfitimpacted <- e_mpas_this_metier_m$GrossProfit_cell * a_multiplier
# then the proportion impacted
e_mpas_this_metier_m$prop_GVA_inside <- e_mpas_this_metier_m$GVAimpacted/ e_mpas_this_metier_m$GVA_all
dd <- knitr::kable(as.data.frame(e_mpas_this_metier_m), format = "html")
library(readr)
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, "VectorExtract", paste0("e_mpas_",sce,"_",fs,"_",y,".html")))
# collector
collector_extraction_per_fs_per_boxID <- rbind.data.frame(collector_extraction_per_fs_per_boxID,
cbind.data.frame(fs, e_mpas_this_metier_m))
} # end
} # end fs
} # end y
} # end sce
# general outcome
library(readr)
dd <- knitr::kable(as.data.frame(collector_extraction_per_fs_per_boxID), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("collector_extraction_per_fs_per_boxID_","2018", "_","2021",".html")))
save(collector_extraction_per_fs_per_boxID, file=file.path(getwd(), a_folder, a_folder2, paste0("collector_extraction_per_fs_per_boxID_","2018", "_","2021",".RData")))
# caution with this dataset here because of doubling counting if deduced from the fdi given there might be several boxes within a same coarse 0.5 c-square...remove duplicates if needed
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# do a ggplot barplot out of this..........
library(ggplot2)
#!!!!!!!!!!!!!!!!!!!!#
#!!!!!!!!!!!!!!!!!!!!#
sces <- c("Closure2022", "ICES_SceC", "ICES_SceD")
years <- 2018:2021
a_folder <- "OUTCOME_OVERLAY_VMEs"
a_folder2 <- "OUTCOME_FISHERIES_DISTR_FDI_AER"
#a_folder <- "OUTCOME_OVERLAY_VMEs"
#a_folder2 <- "OUTCOME_FISHERIES_DISTR_VMS_AER_VMEs"
yrs <- "2018_2021"
#!!!!!!!!!!!!!!!!!!!!#
#!!!!!!!!!!!!!!!!!!!!#
load(file=file.path(getwd(), a_folder, a_folder2, paste0("collector_extraction_per_fs_per_boxID_","2018", "_","2021",".RData"))) # collector_extraction_per_fs_per_boxID
extract_df <- collector_extraction_per_fs_per_boxID
library(stringr)
temp <- as.data.frame(str_split_fixed(extract_df$fs,"_",3))
extract_df$country <- temp[,1]
extract_df$fishing_tech <- temp[,2]
extract_df$vessel_size <- temp[,3]
extract_df <- extract_df[extract_df$year==yrs,] # threshold for visualisation
# check
head(extract_df[extract_df$sce==sce & extract_df$csquare=="3002:100:3" & extract_df$year ==yrs & extract_df$fs=="ESP_DTS_VL1824",])
# Remove duplicates of values to avoid double counting if several box ID lying within the same a c-square
extract_df <- extract_df [!duplicated(extract_df[,c("sce","csquare", "fs", "year")]),]
dd <- aggregate(extract_df[extract_df$sce=="Closure2022",][,c("prop_GVA_inside")], by=list(extract_df[extract_df$sce=="Closure2022",]$fs), sum, na.rm=TRUE) # sum over ID
library(doBy)
dd <- dd[round(dd$x, 5)!=0,]
dd <- orderBy(~ - x, dd)
fs_to_keep <- dd[,1] [1:40]
fs_to_keep <- fs_to_keep[fs_to_keep!="all_metiers"]
extract_df <- extract_df[!is.na(extract_df$prop_GVA_inside) & extract_df$fs %in% fs_to_keep,] # threshold for visualisation
extract_df$fs <- factor (extract_df$fs, levels=fs_to_keep) # re-order in consistence with a_df
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
## GGPLOT 1
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
p1 <- ggplot(extract_df[,c("prop_GVA_inside", "fs", "vessel_size", "sce")], aes(x = prop_GVA_inside*100, y=fs, fill=vessel_size)) + geom_bar(stat = "summary", fun = "sum", position = "dodge") + facet_wrap(~sce, ncol=3) +
ggtitle(paste0("Extract in SWW and NWW")) + xlab(paste0("Percent in ",yrs, " of impacted GVA inside VMEs areas")) + ylab("AER Fleet-segments") +
scale_fill_manual(values=c(VL0010="#F8766D", VL1012="#B79F00", VL1218="#00BA38", VL1824="#00BFC4", VL2440="#619CFF", VL40XX="#F564E3"))
a_width <- 6000 ; a_height <- 4500
tiff(filename=file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2, paste0("Proportion_of_GVA_inside_impacted_c-squares.tif")), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
print(p1)
dev.off()
# extract data corresponding to the plot
pg <- ggplot_build(p1)
pd <- pg$data[[1]]
colnames(pd)[colnames(pd) %in% "group"] <- "fs"
pd$fs <- factor(pd$fs) ; levels(pd$fs) <- levels(extract_df$fs)
colnames(pd)[colnames(pd) %in% "PANEL"] <- "sce"
pd$sce <- factor(pd$sce) ; extract_df$sce <- factor(extract_df$sce) ; levels(pd$sce) <- levels(extract_df$sce)
colnames(pd)[colnames(pd) %in% "fill"] <- "vessel_size"
pd$vessel_size <- factor(pd$vessel_size) ; extract_df$vessel_size <- factor(extract_df$vessel_size); levels(pd$vessel_size) <- levels(extract_df$vessel_size)
colnames(pd)[colnames(pd) %in% "x"] <- "Percent"
pd$Percent <- round(pd$Percent, 2)
# export
library(readr)
dd <- knitr::kable(as.data.frame(pd[, c("fs", "sce", "Percent")]), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("ggplot_data_percent_of_GVA_inside_impacted_c-squares_","2018", "_","2021",".html")))
save(pd, file=file.path(getwd(), a_folder, a_folder2, paste0("ggplot_data_percent_of_GVA_inside_impacted_c-squares_","2018", "_","2021",".RData")))
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
## GGPLOT 2
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
a_df <- extract_df[,c("GVAimpacted", "fs", "vessel_size", "sce", "csquare")]
a_df <- a_df[an(a_df$GVAimpacted)>15000,] # put a threshold of 100000 Euros to lighten the figure...
p2 <- ggplot(a_df, aes(x = GVAimpacted/1e6, y=csquare, fill=fs)) + geom_bar(stat = "summary", fun = "mean", position = "stack") + facet_wrap(~sce, ncol=3) +
ggtitle(paste0("Extract in SWW and NWW")) + xlab(paste0("GVA (MEuro) of ",yrs, " estimated inside VMEs areas")) + ylab("Csquare code") +guides(fill=guide_legend(ncol =1))
a_width <- 3000 ; a_height <- 5000
tiff(filename=file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2, paste0("Absolute_GVA_inside_VMEs_per_csquare.tif")), width = a_width, height = a_height,
units = "px", pointsize = 12, res=300, compression = c("lzw"))
p2
dev.off()
# extract data corresponding to the plot
pg <- ggplot_build(p2)
pd <- pg$data[[1]]
colnames(pd)[colnames(pd) %in% "y"] <- "csquare"
pd$csquare <- factor(pd$csquare) ; extract_df$csquare <- factor(extract_df$csquare) ; levels(pd$csquare) <- levels(extract_df$csquare)
colnames(pd)[colnames(pd) %in% "PANEL"] <- "sce"
pd$sce <- factor(pd$sce) ; extract_df$sce <- factor(extract_df$sce) ; levels(pd$sce) <- levels(extract_df$sce)
colnames(pd)[colnames(pd) %in% "fill"] <- "fs"
pd$fs <- factor(pd$fs) ; extract_df$fs <- factor(extract_df$fs); levels(pd$fs) <- levels(extract_df$fs)
colnames(pd)[colnames(pd) %in% "x"] <- "GVAimpactedMEuro"
pd$GVAimpactedMEuro <- round(pd$GVAimpactedMEuro, 2)
# export
library(readr)
dd <- knitr::kable(as.data.frame(pd[, c("fs", "sce", "csquare", "GVAimpactedMEuro")]), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("ggplot_data_GVAimpactedMEuro _inside_impacted_c-squares_",years[1], "_",years[length(years)],".html")))
save(pd, file=file.path(getwd(), a_folder, a_folder2, paste0("ggplot_data_GVAimpactedMEuro_inside_impacted_c-squares_",years[1], "_",years[length(years)],".RData")))
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
## MAP 1
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
# try a mapping the most impacting squares
for (sce in c("Closure2022","ICES_SceC", "ICES_SceD"))
{
if(sce=="Closure2022") allclosures_sf <- VMEs_MARE
if(sce=="ICES_SceC") allclosures_sf <- ICES_VMEs_SceC
if(sce=="ICES_SceD") allclosures_sf <- ICES_VMEs_SceD
if(is_fdi) n <- 15
if(is_vms) n <- 100
impacting_sq <- tapply(extract_df[extract_df$sce==sce,]$GVAimpacted, factor(extract_df[extract_df$sce==sce,]$CSQUARE_ID), sum, na.rm=TRUE) # sum over ID
impacting_sq <- impacting_sq[order(impacting_sq, decreasing=TRUE)] [1:n]
filepath <- file.path(getwd(), a_folder2, "all_metiers", "2018_2021")
rstr <- terra::rast(file.path(filepath, "spatRaster.tif")) # in "+proj=longlat +datum=WGS84"
r <- rast(nrow=dim(rstr)[1], ncol=dim(rstr)[2], extent=ext(rstr), res=res(rstr), crs=crs(rstr), vals=NA, names="value") # init
# adding a csquare code
cells <- 1:ncell(r)
xy <- xyFromCell(r, cells)
if(is_fdi) csquares <- vmstools::CSquare(xy[,"x"],xy[,"y"], degrees=0.5)
if(is_vms) csquares <- vmstools::CSquare(xy[,"x"],xy[,"y"], degrees=0.05)
values(r) <- csquares
csquare_coding <- data.frame(id=c(values(r)), csquare=csquares, x=xy[,"x"], y=xy[,"y"])
names(r) <- "CSQUARE_ID"
add(rstr) <- r
# dd <- values(rstr$CSQUARE_ID)
# # figuring out what is the csquare code of each polygon
# e_csquares <- extract(rstr, vect(allclosures_sf), fun=modal) # modal => get the more frequent
# csquare_per_polygon_id_for_this_sce <- cbind.data.frame(e_csquares[, c("ID", "CSQUARE_ID")], csquare=csquare_coding[match(e_csquares[, "CSQUARE_ID"], csquare_coding$id), "csquare"])
# idx_to_discard <- c(1:ncell(rstr))[!c(dd) %in% csquare_per_polygon_id_for_this_sce$CSQUARE_ID]
# rstr[idx_to_discard] <- NA
# rstr <- trim(rstr)
top5ImpactingCsquare <- rstr$CSQUARE_ID %in% as.numeric(names(impacting_sq)) [1:n] # the first ten most impacting!!!!!!
plot(top5ImpactingCsquare, main=paste0(yrs, "- most impacting csquares ", sce), breaks=c(-15, seq(0, 2, by=0.5), 15))
plot(vect(allclosures_sf), add=TRUE)
top5ImpactingCsquare[top5ImpactingCsquare<1] <- NA
# reinject GVAimpacted data to the raster
impacting_sq <- impacting_sq[!is.na(impacting_sq)]
idx <- match (as.numeric(names(impacting_sq)), rstr$CSQUARE_ID[])
values(top5ImpactingCsquare)[idx] <- impacting_sq
csq <- rstr$CSQUARE_ID
values(csq)[-idx] <- NA
# a plot
a_width <- 3000 ; a_height <- 4000
tiff(filename=file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2, paste0("most_impacted_csquares_on_GVA_from_", yrs,"_", sce, ".tif")), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
par(mar=c(0,0,0,0))
par(oma=c(0,0,0,0))
if(is_fdi) plot(trim(top5ImpactingCsquare)/1e6, main=paste0(yrs, "- 15 most impacted csquares ", sce), breaks=c( -2, -1, seq(0, 1, by=0.25), 2))
if(is_vms) plot(trim(top5ImpactingCsquare)/1e6, main=paste0(yrs, "- 100 most impacted csquares ", sce), breaks=c(-2, seq(-1, 0, by=0.1), 2))
#text(csq, cex=0.5, label="123")
xy <- xyFromCell(top5ImpactingCsquare, idx)
if(is_fdi) text(x = xy[,"x"], y = xy[,"y"], labels = csquare_coding[match(names(impacting_sq), csquare_coding[,1]), 2 ], cex=0.18, srt=90, col="white")
library(rnaturalearth)
#plot(as.polygons(rstr$landings_aer_in_ctry_level6_csquare), add=TRUE)
plot(vect(allclosures_sf), add=TRUE, lwd=0.5)
library(rnaturalearth)
sf_world <- ne_countries(returnclass='sf')
spdf_europe <- ne_countries(continent = "europe", scale=10)
plot(vect(spdf_europe), col=grey(0.5), add=TRUE)
dev.off()
aa <- cbind.data.frame("C-SQUARE"=csquare_coding[match(names(impacting_sq), csquare_coding[,1]), 2 ], "GVA impacted (Euro)"=round(impacting_sq))
rownames(aa) <- NULL
# export
library(readr)
dd <- knitr::kable(as.data.frame(aa), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("most_impacted_csquares_on_GVA_from_", yrs,"_", sce, ".html")))
# export for later use with leaflet
a_rstr <- trim(top5ImpactingCsquare)
names(a_rstr) <- "MEuroGVA"
a_sf <- st_as_sf(as.polygons(a_rstr/1e6, dissolve=FALSE, values=TRUE))
assign(paste0("Impacted_CSquares_",sce), a_sf)
} # end sce
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
## INTERACTIVE MAP embedded in a HTML page ##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# a visual check
# stacked:
library(leaflet)
map <- leaflet() %>%
addTiles()
map <- map %>% addPolygons(data=VMEs_MARE, popup=~htmltools::htmlEscape(paste(Poly_No)), col="blue", group = "VMEs_Closure2022") %>%
addPolygons(data=ICES_VMEs_SceC, popup=~htmltools::htmlEscape(paste(poly_id)), col="green", group = "ICES_VMEs_SceC") %>%
addPolygons(data=ICES_VMEs_SceD, popup=~htmltools::htmlEscape(paste(poly_id)), col="red", group = "ICES_VMEs_SceD") %>%
addPolygons(data=Impacted_CSquares_Closure2022, popup=~htmltools::htmlEscape(paste(MEuroGVA)), col="purple", group = "Impacted_CSquares_Closure2022") %>%
addPolygons(data=Impacted_CSquares_ICES_SceC, popup=~htmltools::htmlEscape(paste(MEuroGVA)), col="cyan", group = "Impacted_CSquares_ICES_SceC") %>%
addPolygons(data=Impacted_CSquares_ICES_SceD, popup=~htmltools::htmlEscape(paste(MEuroGVA)), col="yellow", group = "Impacted_CSquares_ICES_SceD")
map <- map %>% addLayersControl(
baseGroups = "",
overlayGroups = c("VMEs_Closure2022","ICES_VMEs_SceC", "ICES_VMEs_SceD", "Impacted_CSquares_Closure2022", "Impacted_CSquares_ICES_SceC", "Impacted_CSquares_ICES_SceD"),
options = layersControlOptions(collapsed = FALSE))
map
library(htmlwidgets)
saveWidget(map, file=file.path(getwd(), a_folder, a_folder2, "map_impacted_csquares.html"))
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# COMPUTE ECONOMY!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
library(data.table)
ROutputPathToDatasets <- file.path(getwd(), "OUTCOME_DATASETS")
if(is_fdi) load(file=file.path(ROutputPathToDatasets, "agg_2018_2021_fdi_2023aer_eco_fs.RData")) # get agg_eco_fs
if(is_vms) load(file=file.path(ROutputPathToDatasets, "agg_ices_vms_2018_2021_aer_eco_fs.RData")) # get agg_eco_fs
head(agg_eco_fs[fs=="ESP_DTS_VL2440",])
load(file=file.path(getwd(), a_folder, a_folder2, paste0("collector_extraction_per_fs_per_boxID_2018_2021.RData"))) # collector_extraction_per_fs_per_boxID
extract_df <- collector_extraction_per_fs_per_boxID
library(stringr)
temp <- as.data.frame(str_split_fixed(extract_df$fs,"_",3))
extract_df$country <- temp[,1]
extract_df$fishing_tech <- temp[,2]
extract_df$vessel_size <- temp[,3]
# Remove duplicates of values to avoid double counting if several box ID lying within the same a c-square
extract_df <- extract_df [!duplicated(extract_df[,c("sce","csquare", "fs", "year")]),]
dd <- aggregate(extract_df[extract_df$sce=="Closure2022",][,c("prop_GVA_inside")], by=list(extract_df[extract_df$sce=="Closure2022",]$fs), sum, na.rm=TRUE) # sum over ID
library(doBy)
dd <- dd[round(dd$x, 5)!=0,]
dd <- orderBy(~ - x, dd)
fs_to_keep <- dd[,1] [1:40]
fs_to_keep <- fs_to_keep[fs_to_keep!="all_metiers"]
extract_df <- extract_df[!is.na(extract_df$prop_GVA_inside) & extract_df$fs %in% fs_to_keep,] # threshold for visualisation
# check
agg_eco_fs[agg_eco_fs$fs=="ESP_DTS_VL2440",]
opportunity_interest_rate <- 4.0 # ??
annual_depreciation_rate <- 2.0 # ??
agg_eco_fs$CapitalOpportunityCosts <- an(agg_eco_fs$value_of_physical_capital) * opportunity_interest_rate/100.0
extract_df[extract_df$fs=="ESP_DTS_VL2440",]
extract_dt <- data.table(extract_df)
extract_dt <- extract_dt[,.(GVAimpacted=sum(GVAimpacted, na.rm=TRUE), GrossProfitimpacted=sum(GrossProfitimpacted, na.rm=TRUE)),by=c("sce", "year", "fs")]
extract_dt$year <- an(extract_dt$year)
agg_eco_fs$year <- an(agg_eco_fs$year)
agg_eco_fs_with_impacted_gva <- merge(extract_dt, agg_eco_fs)
# check
agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$fs=="ESP_DTS_VL1218",]
# anticipated GVA
agg_eco_fs_with_impacted_gva$anticipated_GVA <- agg_eco_fs_with_impacted_gva$GVA - agg_eco_fs_with_impacted_gva$GVAimpacted
agg_eco_fs_with_impacted_gva$anticipated_GrossProfit <- agg_eco_fs_with_impacted_gva$GrossProfit - agg_eco_fs_with_impacted_gva$GrossProfitimpacted
# then recompute net profit
agg_eco_fs_with_impacted_gva$anticipated_OperatingProfit <- agg_eco_fs_with_impacted_gva$anticipated_GrossProfit - an(agg_eco_fs_with_impacted_gva$cons_of_fixed_capital)
agg_eco_fs_with_impacted_gva$anticipated_NetProfit <- agg_eco_fs_with_impacted_gva$anticipated_OperatingProfit - agg_eco_fs_with_impacted_gva$CapitalOpportunityCosts - (an(agg_eco_fs_with_impacted_gva$value_of_physical_capital) * (1-((100.0-annual_depreciation_rate)/100.0)))
# percent change:
agg_eco_fs_with_impacted_gva$percent_change_GVA <- round(((agg_eco_fs_with_impacted_gva$anticipated_GVA/agg_eco_fs_with_impacted_gva$GVA)*100) -100, 2)
agg_eco_fs_with_impacted_gva$percent_change_GrossProfit <- round(((agg_eco_fs_with_impacted_gva$anticipated_GrossProfit/agg_eco_fs_with_impacted_gva$GrossProfit)*100) -100, 2)
agg_eco_fs_with_impacted_gva$percent_change_NetProfit <- round(((agg_eco_fs_with_impacted_gva$anticipated_NetProfit/agg_eco_fs_with_impacted_gva$NetProfit)*100) -100, 2)
# correct sign
agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$anticipated_NetProfit<0 & agg_eco_fs_with_impacted_gva$anticipated_NetProfit<0,"percent_change_NetProfit"] <-
agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$anticipated_NetProfit<0 & agg_eco_fs_with_impacted_gva$anticipated_NetProfit<0,"percent_change_NetProfit"] * -1
# check
agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$fs=="ESP_DTS_VL1824"]
agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$fs=="FRA_DFN_VL2440"]
agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$fs=="ESP_DTS_VL2440"]
agg_eco_fs_with_impacted_gva$GVAimpactedMEuro <- round(agg_eco_fs_with_impacted_gva$GVAimpacted/1e6,2)
agg_eco_fs_with_impacted_gva$GVAMEuro <- round(agg_eco_fs_with_impacted_gva$GVA/1e6,2)
agg_eco_fs_with_impacted_gva$GrossProfitMEuro <- round(agg_eco_fs_with_impacted_gva$GrossProfit/1e6,2)
agg_eco_fs_with_impacted_gva$NetProfitMEuro <- round(agg_eco_fs_with_impacted_gva$NetProfit/1e6,2)
agg_eco_fs_with_impacted_gva <- orderBy(~year + fs + sce, agg_eco_fs_with_impacted_gva)
agg_eco_fs_with_impacted_gva <- orderBy(~ - year - GVAMEuro + sce, as.data.frame(agg_eco_fs_with_impacted_gva))
# export
library(readr)
dd <- knitr::kable(as.data.frame(agg_eco_fs_with_impacted_gva[, c("year","fs", "sce", "GVAMEuro", "GrossProfitMEuro", "NetProfitMEuro", "GVAimpactedMEuro", "percent_change_GVA", "percent_change_GrossProfit", "percent_change_NetProfit", "engagedCrew")]), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("agg_eco_fs_with_impacted_gva_","2018", "_","2021",".html")))
save(agg_eco_fs_with_impacted_gva, file=file.path(getwd(), a_folder, a_folder2, paste0("agg_eco_fs_with_impacted_gva_","2018", "_","2021",".RData")))
a_subset_of_agg_eco_fs_with_impacted_gva <- agg_eco_fs_with_impacted_gva[an(agg_eco_fs_with_impacted_gva$percent_change_GVA)< -1,]
a_subset_of_agg_eco_fs_with_impacted_gva <- orderBy(~ - year - GVAMEuro + sce, as.data.frame(a_subset_of_agg_eco_fs_with_impacted_gva))
dd <- knitr::kable(as.data.frame(a_subset_of_agg_eco_fs_with_impacted_gva[, c("year","fs", "sce", "GVAMEuro", "GrossProfitMEuro", "NetProfitMEuro", "GVAimpactedMEuro", "percent_change_GVA", "percent_change_GrossProfit", "percent_change_NetProfit", "engagedCrew")]), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("agg_eco_fs_with_impacted_gva_","2018", "_","2021_a_subset",".html")))
save(a_subset_of_agg_eco_fs_with_impacted_gva, file=file.path(getwd(), a_folder, a_folder2, paste0("agg_eco_fs_with_impacted_gva_","2018", "_","2021_a_subset",".RData")))
agg_eco_fs_with_impacted_gva <- orderBy(~ - year - GVAMEuro + sce, as.data.frame(agg_eco_fs_with_impacted_gva))
dd <- knitr::kable(as.data.frame(agg_eco_fs_with_impacted_gva[agg_eco_fs_with_impacted_gva$fs %in% c("ESP_DTS_VL1824", "ESP_DTS_VL2440"), c("year","fs", "sce", "GVAMEuro", "GrossProfitMEuro", "NetProfitMEuro", "GVAimpactedMEuro", "percent_change_GVA", "percent_change_GrossProfit", "percent_change_NetProfit", "engagedCrew")]), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("agg_eco_fs_with_impacted_gva_","2018", "_","2021_MAIN_FLEETS",".html")))
save(agg_eco_fs_with_impacted_gva, file=file.path(getwd(), a_folder, a_folder2, paste0("agg_eco_fs_with_impacted_gva_","2018", "_","2021_MAIN_FLEETS",".RData")))
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
## GGPLOT 4
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------
library(stringr)
temp <- as.data.frame(str_split_fixed(agg_eco_fs_with_impacted_gva$fs,"_",3))
agg_eco_fs_with_impacted_gva$country <- temp[,1]
agg_eco_fs_with_impacted_gva$fishing_tech <- temp[,2]
agg_eco_fs_with_impacted_gva$vessel_size <- temp[,3]
agg_eco_fs_with_impacted_gva$fs <- factor (agg_eco_fs_with_impacted_gva$fs, levels=fs_to_keep) # re-order in consistence with a_df
p4 <- ggplot(agg_eco_fs_with_impacted_gva[,c("percent_change_NetProfit", "fs", "vessel_size", "sce")], aes(x = percent_change_NetProfit, y=fs, fill=vessel_size)) + geom_bar(stat = "summary", fun = "mean", position = "dodge") + facet_wrap(~sce, ncol=3) +
ggtitle(paste0("Anticipated change in fleet economy")) + xlab(paste0("Change in ",yrs, " of impacted Net Profit")) + ylab("AER Fleet-segments") +
scale_fill_manual(values=c(VL0010="#F8766D", VL1012="#B79F00", VL1218="#00BA38", VL1824="#00BFC4", VL2440="#619CFF", VL40XX="#F564E3"))
a_width <- 6000 ; a_height <- 4500
tiff(filename=file.path(getwd(), "OUTCOME_OVERLAY_VMEs", a_folder2, paste0("Anticipated_change_of_net_profit_in ",yrs, ".tif")), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
print(p4)
dev.off()
# extract data corresponding to the plot
pg <- ggplot_build(p4)
pd <- pg$data[[1]]
colnames(pd)[colnames(pd) %in% "group"] <- "fs"
pd$fs <- factor(pd$fs) ; extract_df$fs <- factor(extract_df$fs); levels(pd$fs) <- levels(extract_df$fs)
colnames(pd)[colnames(pd) %in% "PANEL"] <- "sce"
pd$sce <- factor(pd$sce) ; extract_df$sce <- factor(extract_df$sce) ; levels(pd$sce) <- levels(extract_df$sce)
colnames(pd)[colnames(pd) %in% "fill"] <- "vessel_size"
pd$vessel_size <- factor(pd$vessel_size) ; extract_df$vessel_size <- factor(extract_df$vessel_size); levels(pd$vessel_size) <- levels(extract_df$vessel_size)
colnames(pd)[colnames(pd) %in% "x"] <- "Percent"
pd$Percent <- round(pd$Percent, 2)
# export
library(readr)
dd <- knitr::kable(as.data.frame(pd[, c("fs", "sce", "Percent")]), format = "html")
readr::write_file(dd, file.path(getwd(), a_folder, a_folder2, paste0("ggplot_data_percent_of_Net_profit_change_","2018", "_","2021",".html")))
save(pd, file=file.path(getwd(), a_folder, a_folder2, paste0("ggplot_data_percent_of_Net_profit_change_","2018", "_","2021",".RData")))
##-----------------------------------------------------------------------------
##-----------------------------------------------------------------------------