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script_LULC.R
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########MAPA ZGODNOSCI
setwd("E:/Projekty/projekty_22_23/LULC_stat/siatka/przyciete/ostateczne/koncowe")
library(raster)
library(terra)
library(kableExtra)
library(tmap)
library(sf)
library(ggplot2)
library(tibble)
library(tidyr)
library(plotly)
lc_names <- c("bdot", "lucas", "esa", "esri", "sent", "clc", "urban")
lc_list <- list()
lc_classes <- data.frame(matrix(nrow = 6, ncol = 7))
# Wczytanie danych pokrycia terenu
for (i in 1:length(lc_names)) {
file_name <- paste0(lc_names[i], ".tif")
rast <- rast(file_name)
lc_list[[i]] <- rast
lc_classes[, i] <- as.data.frame(table(as.data.frame(rast)))[, 2]
assign(lc_names[i], rast)
}
names(lc_list) <- lc_names
names(lc_classes) <- lc_names
## odejmowanie i reklasyfikacja
lc_list <- lc_list[-1]
lc_names <- lc_names[-1]
for (i in seq_along(lc_list)){
roznica <- bdot - lc_list[[i]]
m <- c(-0.1, 0.1, 1,
-6.1, -0.9, 0,
0.9, 6.1, 0)
matrix <- matrix(m, ncol = 3, byrow = TRUE)
r_reclass <- classify(roznica, matrix, right = NA)
assign(paste0(lc_names[i], "_diff"), r_reclass)
}
result <- clc_diff + esa_diff + esri_diff + lucas_diff + sent_diff + urban_diff
#writeRaster(result, "mapa_zgodnosci_2.tif", datatype = "INT1U")
plot(result, main = "OVaccuracy")
######## MACIERZE PRZEJSC ########
setwd("E:/Projekty/projekty_22_23/LULC_stat/dane")
poznan <- vect("poznan.gpkg")
lc_list <- c(bdot, lucas, esa, esri, sent, clc, urban)
lc_names <- c("bdot", "lucas", "esa", "esri", "sent", "clc", "urban")
#### POZNAN ####
#przycięcie warstw do granicy Poznania
for (i in 1:7) {
cropped <- crop(lc_list[[i]], poznan, mask = TRUE)
assign(paste0(lc_names[[i]], "_cropped"), cropped)
}
lc_cropped <- c(bdot_cropped, lucas_cropped, esa_cropped, esri_cropped, sent_cropped, clc_cropped, urban_cropped)
#utworzenie wektorów do obliczenia macierzy przejsc dla poznania
for (i in 1:7) {
rast <- raster(lc_cropped[[i]])
factor <- rasterToPoints(rast)
factor <- as.data.frame(factor)
factor <- as.factor(factor[, 3])
assign(paste0(lc_names[[i]], "_crop_factor"), factor)
}
conf_matrix_poznan <- data.frame(matrix(nrow = 6, ncol = 2))
names(conf_matrix_poznan) <- c("accuracy_poznan", "kappa_poznan")
rownames(conf_matrix_poznan) <- lc_names[-1]
lc_factors <- list(lucas_crop_factor, esa_crop_factor, esri_crop_factor, sent_crop_factor, clc_crop_factor, urban_crop_factor)
PA_matrix_poznan <- data.frame(matrix(nrow = 6, ncol = 18))
rownames(PA_matrix_poznan) <- c("Obszary podmokłe", "Obszary wodne", "Zabudowa", "Lasy", "Obszary trawiaste i krzewiaste", "Pola uprawne")
colnames(PA_matrix_poznan)[c(1, 4, 7, 10, 13, 16)] <- paste0(lc_names[-1], "_PA")
colnames(PA_matrix_poznan)[c(2, 5, 8, 11, 14, 17)] <- paste0(lc_names[-1], "_UA")
colnames(PA_matrix_poznan)[c(3, 6, 9, 12, 15, 18)] <- paste0(lc_names[-1], "_F1")
kappa <- 0
### macierz przejść dla urban atlas
UA_matrix <- function(){
urban_atlas_matrix <<- matrix(c(0, 0, 0, 0, 0, 0,
125, 67313, 129, 1020, 936, 2,
279, 5636, 769551, 25311, 296850, 15208,
2255, 4353, 31242, 399689, 66256, 9469,
8857, 5045, 33300, 80616, 372198, 96743,
3634, 1148, 2588, 13064, 66193, 237721),
nrow = 6, byrow = TRUE,
dimnames = list(c("1", "2", "3", "4", "5", "6"),
c("1", "2", "3", "4", "5", "6")))
n <- sum(urban_atlas_matrix)
OA <<- sum(diag(urban_atlas_matrix)) / n
rowsums <- apply(urban_atlas_matrix, 1, sum)
p <- rowsums / n
colsums <- apply(urban_atlas_matrix, 2, sum)
q <- colsums / n
expAccuracy <- sum(p*q)
kappa <<- (OA - expAccuracy) / (1 - expAccuracy)
conf_matrix_poznan[6, 1] <<- OA
conf_matrix_poznan[6, 2] <<- kappa
}
UA_matrix()
#uzupełnienie macierzy przejsc
for (i in 1:6) {
matrix <- table(lc_factors[[i]], bdot_crop_factor)
if (i == 6) {
matrix <- urban_atlas_matrix
}
##accuracy
n <- sum(matrix)
diag <- diag(matrix)
OA <- sum(diag) / n
if (i != 6) {
conf_matrix_poznan[i, 1] <- OA
}
##kappa
rowsums <- apply(matrix, 1, sum)
p <- rowsums / n
colsums <- apply(matrix, 2, sum)
q <- colsums / n
expAccuracy <- sum(p*q)
kappa <- (OA - expAccuracy) / (1 - expAccuracy)
if (i != 6) {
conf_matrix_poznan[i, 2] <- kappa
}
##producer accuracy
PA <- diag / colsums
col_idx <- (i - 1) * 3 + 1
PA_matrix_poznan[, col_idx] <- round(PA, 2)
# user accuracy
UA <- diag / rowsums
col_idx <- (i - 1) * 3 + 2
PA_matrix_poznan[, col_idx] <- round(UA, 2)
## F1 score
f1 = 2 * PA * UA / (PA + UA)
col_idx <- (i - 1) * 3 + 3
PA_matrix_poznan[, col_idx] <- round(f1, 2)
}
#### AGLOMERACJA ####
lc_list <- c(bdot, lucas, esa, esri, sent, clc, urban)
#utworzenie wektorów do obliczenia macierzy przejsc dla aglomeracji
for (i in 1:7) {
rast <- raster(lc_list[[i]])
factor <- rasterToPoints(rast)
factor <- as.data.frame(factor)
factor <- as.factor(factor[, 3])
assign(paste0(lc_names[[i]], "_factor"), factor)
}
lc_factors <- list(lucas_factor, esa_factor, esri_factor, sent_factor, clc_factor, urban_factor)
PA_matrix_aglo <- data.frame(matrix(nrow = 6, ncol = 18))
rownames(PA_matrix_aglo) <- c("Obszary podmokłe", "Obszary wodne", "Zabudowa", "Lasy", "Obszary trawiaste i krzewiaste", "Pola uprawne")
colnames(PA_matrix_aglo)[c(1, 4, 7, 10, 13, 16)] <- paste0(lc_names[-1], "_PA")
colnames(PA_matrix_aglo)[c(2, 5, 8, 11, 14, 17)] <- paste0(lc_names[-1], "_UA")
colnames(PA_matrix_aglo)[c(3, 6, 9, 12, 15, 18)] <- paste0(lc_names[-1], "_F1")
conf_matrix_aglo <- data.frame(matrix(nrow = 6, ncol = 2))
names(conf_matrix_aglo) <- c("accuracy_aglo", "kappa_aglo")
rownames(conf_matrix_aglo) <- c("LUCAS", "ESA", "Esri", "Sentinel-2", "CLC", "Urban Atlas")
#uzupełnienie macierzy przejsc
for (i in 1:6) {
matrix <- table(lc_factors[[i]], bdot_factor)
##accuracy
n <- sum(matrix)
diag <- diag(matrix)
OA <- sum(diag) / n
conf_matrix_aglo[i, 1] <- round(OA, 3)
##kappa
rowsums <- apply(matrix, 1, sum)
p <- rowsums / n
colsums <- apply(matrix, 2, sum)
q <- colsums / n
expAccuracy <- sum(p*q)
kappa <- (OA - expAccuracy) / (1 - expAccuracy)
conf_matrix_aglo[i, 2] <- round(kappa, 3)
##producer accuracy
PA <- diag / colsums
col_idx <- (i - 1) * 3 + 1
PA_matrix_aglo[, col_idx] <- round(PA, 2)
# user accuracy
UA <- diag / rowsums
col_idx <- (i - 1) * 3 + 2
PA_matrix_aglo[, col_idx] <- round(UA, 2)
## F1 score
f1 = 2 * PA * UA / (PA + UA)
col_idx <- (i - 1) * 3 + 3
PA_matrix_aglo[, col_idx] <- round(f1, 2)
}
conf_matrix_aglo$accuracy_poznan <- round(conf_matrix_poznan$accuracy_poznan, 3)
conf_matrix_aglo$kappa_poznan <- round(conf_matrix_poznan$kappa_poznan, 3)
conf_matrix_aglo %>%
kbl() %>%
kable_minimal()
PA_matrix_aglo %>%
kbl() %>%
kable_minimal()
library(DT)
#datatable(PA_matrix_aglo, options = list(pageLength = 6,
# scrollX = TRUE, searching = FALSE))
datatable(conf_matrix_aglo, options = list(pageLength = 6,
scrollX = TRUE, searching = FALSE,
scrollY = FALSE,
fontSize = "150%",
dom = 't',
#autoWidth = TRUE,
columnDefs = list(list(width = '100px', fontSize = "150%", targets = "_all")))) %>% formatStyle(names(conf_matrix_aglo),
background = styleColorBar(range(0, 1), '#8cc0a5'),
color = "#000000",
fontSize = "160%",
backgroundSize = '98% 88%',
backgroundRepeat = 'no-repeat',
backgroundPosition = 'center')
names(conf_matrix_aglo) <- c("AGLOMERACJA - dokładność", "AGLOMERACJA - kappa", "POZNAŃ - dokładność", "POZNAŃ - kappa")
names(PA_matrix_aglo) <- c("LUCAS - precyzja", "LUCAS - czułość", "LUCAS - F1", "ESA - precyzja", "ESA - czułość", "ESA - F1", "Esri - precyzja", "Esri - czułość", "Esri - F1",
"Sentinel-2 - precyzja", "Sentinel-2 - czułość", "Sentinel-2 - F1", "CLC - precyzja", "CLC - czułość", "CLC - F1", "Urban Atlas - precyzja", "Urban Atlas - czułość", "Urban Atlas - F1")
names(PA_matrix_poznan) <- c("LUCAS - precyzja", "LUCAS - czułość", "LUCAS - F1", "ESA - precyzja", "ESA - czułość", "ESA - F1", "Esri - precyzja", "Esri - czułość", "Esri - F1",
"Sentinel-2 - precyzja", "Sentinel-2 - czułość", "Sentinel-2 - F1", "CLC - precyzja", "CLC - czułość", "CLC - F1", "Urban Atlas - precyzja", "Urban Atlas - czułość", "Urban Atlas - F1")
#### STYL DLA WYKRESÓW - artykuł####
styl <- function(){
style <<- theme(
text = element_text(family = "Calibri"),
panel.grid.major.x = element_blank(),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff"),
axis.title = element_text(size = 40,
color = "#222222"),
axis.title.x = element_text(vjust= -1),
axis.title.y = element_text(vjust= 2.2),
plot.title = element_text(size = 16,
color = "#222222",
vjust = 2,
hjust = 0.5),
legend.title = element_text(size = 42, color = "#222222"),
legend.text = element_text(size = 40, color = "#222222"),
axis.text = element_text(size = 40,
color = "#222222"),
axis.text.x = element_text(angle = 0, vjust = 0.95, hjust=0.5),
legend.spacing.y = unit(0.2, 'cm'),
legend.key = element_blank(),
legend.background = element_rect(fill = "transparent", color = "transparent"),
plot.margin = margin(0, 0, 20, 15),
panel.grid = element_line(color = "#222222"))
}
styl()
#### Procentowy udział poszczególnych zgodności (0-6) z mapy zgodności #####
### AGLOMERACJA
setwd("E:/Projekty/projekty_22_23/LULC_stat/siatka/przyciete/ostateczne/koncowe")
result <- rast("mapa_zgodnosci.tif")
result_df <- as.data.frame(result)
value_counts <- table(result_df)
value_counts <- as.data.frame(round(( value_counts / 20681815) * 100, 2))
colors <- c("#d73027", "#f46d43", "#fdae61", "#ffffbf", "#d9ef8b", "#66bd63", "#1a9850")
styl_inter <- function(){
style <<- theme(
text = element_text(family = "Calibri"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#222222"),
plot.background = element_rect(fill = "#eff6f1", color = NA),
panel.background = element_rect(fill = "#eff6f1"),
axis.title = element_text(size = 18,
color = "#222222"),
axis.title.y = element_text(vjust= 2.2),
plot.title = element_text(size = 18,
color = "#222222",
vjust = 2,
hjust = 0.5),
legend.margin = margin(t = -15, r = 7, l = 7, b = 7),
legend.title = element_text(size = 18, color = "#222222"),
legend.text = element_text(size = 12, color = "#222222"),
axis.text = element_text(size = 16,
color = "#222222"),
axis.text.x = element_text(angle = 0, vjust = 1.2, hjust=1),
legend.spacing.y = unit(0.01, 'cm'),
legend.position = "right",
legend.key.size = unit(0.01, "cm"),
legend.background = element_rect(fill = "transparent", color = "transparent"),
plot.margin = margin(10, 10, -40, 10))
}
styl_inter()
wykres <- ggplot(data = value_counts, aes(x = result_df, y = Freq, fill = result_df, text = paste('<span style = " font-weight:bold">Liczba zgodności:</span>',
'<span>',result_df ,'</span>',
'</br></br><span style = " font-weight:bold">Udział procentowy:</span>',
'<span>',paste(Freq, "%") ,'</span>'))) +
geom_bar(stat = "identity", color = "#222222") +
scale_fill_manual(values = colors) +
xlab("Liczba źródeł pokrycia terenu zgodnych z BDOT10k") +
ylab("Procent obszaru aglomeracji [%]") + style + theme(legend.position = "none")
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne/poprawione")
svg("udzial_procentowy_aglo.svg", width = 9, height = 6.5, family = "Calibri")
wykres + theme(axis.title = element_text(size = 24,
color = "#222222"),
axis.text = element_text(size = 24,
color = "#222222"))
invisible(dev.off())
ggplotly(wykres, tooltip = "text")%>% config(displayModeBar = FALSE)
### Poznań
setwd("E:/Projekty/projekty_22_23/LULC_stat/dane")
poznan <- vect("poznan.gpkg")
result_poznan <- crop(result, poznan)
result_poznan <- mask(result_poznan, poznan)
result_poznan_df <- as.data.frame(result_poznan)
value_counts_poznan <- table(result_poznan_df)
value_counts_poznan <- as.data.frame(round(( value_counts_poznan / 2624464) * 100, 2))
colors <- c("#d73027", "#f46d43", "#fdae61", "#ffffbf", "#d9ef8b", "#66bd63", "#1a9850")
wykres <- ggplot(data = value_counts_poznan, aes(x = result_poznan_df, y = Freq, fill = result_poznan_df, text = paste('<span style = " font-weight:bold">Liczba zgodności:</span>',
'<span>',result_poznan_df ,'</span>',
'</br></br><span style = " font-weight:bold">Udział procentowy:</span>',
'<span>',paste(Freq, "%") ,'</span>'))) +
geom_bar(stat = "identity", color = "#222222") +
scale_fill_manual(values = colors) +
xlab("Liczba źródeł pokrycia terenu zgodnych z BDOT10k") +
ylab("Procent obszaru miasta Poznań [%]") + style + theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 50))
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne/poprawione")
svg("udzial_procentowy_poznan.svg", width = 9, height = 6.5, family = "Calibri")
wykres + theme(axis.title = element_text(size = 24,
color = "#222222"),
axis.text = element_text(size = 24,
color = "#222222"))
invisible(dev.off())
ggplotly(wykres, tooltip = "text")%>%
config(displayModeBar = FALSE)
########## UDZIAŁ PROCENTOWY KLAS #############
library(ggplot2)
library(dplyr)
data <- data.frame()
for (i in 1:7) {
data_frame <- as.data.frame(table(as.data.frame(lc_cropped[[i]])))##lc_list
data_frame$LC <- lc_names[i]
data_frame$proc <- round((data_frame$Freq / sum(data_frame$Freq)) * 100, 2)
data <- rbind(data, data_frame)
}
data <- data %>%
mutate(Var1 = case_when(
Var1 == 1 ~ "Obszary podmokłe",
Var1 == 2 ~ "Obszary wodne",
Var1 == 3 ~ "Zabudowa",
Var1 == 4 ~ "Lasy",
Var1 == 5 ~ "Obszary trawiaste i krzewiaste",
Var1 == 6 ~ "Pola uprawne",
TRUE ~ as.character(Var1)
),
LC = case_when(
LC == "bdot" ~ "BDOT10k",
LC == "clc" ~ "CLC",
LC == "esa" ~ "ESA",
LC == "esri" ~ "Esri",
LC == "lucas" ~ "LUCAS",
LC == "sent" ~ "Sentinel-2",
LC == "urban" ~ "Urban Atlas",
TRUE ~ as.character(LC)
))
## styl dla wykresu
styl <- function(){
style <<- theme(
text = element_text(family = "Calibri"),
panel.grid.major.x = element_blank(),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff"),
axis.title = element_text(size = 36,
color = "#222222"),
axis.title.y = element_text(vjust= 2.2),
plot.title = element_text(size = 16,
color = "#222222",
vjust = 2,
hjust = 0.5),
legend.title = element_text(size = 40, color = "#222222"),
legend.margin = margin(t = 30, r = 7, l = 7, b = 7),
legend.text = element_text(size = 40, color = "#222222"),
axis.text = element_text(size = 36,
color = "#222222"),
axis.text.x = element_text(angle = 45, vjust = 0.9, hjust=1),
legend.spacing.y = unit(0.2, 'cm'),
legend.key = element_blank(),
legend.background = element_rect(fill = "transparent", color = "transparent"),
plot.margin = margin(10, 10, 40, 10),
panel.grid = element_line(color = "#222222"))
}
styl()
styl_inter <- function(){
style <<- theme(
text = element_text(family = "Calibri"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#222222"),
plot.background = element_rect(fill = "#eff6f1", color = NA),
panel.background = element_rect(fill = "#eff6f1"),
axis.title = element_text(size = 18,
color = "#222222"),
axis.title.y = element_text(vjust= 2.2),
plot.title = element_text(size = 18,
color = "#222222",
vjust = 2,
hjust = 0.5),
legend.margin = margin(t = -15, r = 7, l = 7, b = 7),
legend.title = element_text(size = 18, color = "#222222"),
legend.text = element_text(size = 12, color = "#222222"),
axis.text = element_text(size = 16,
color = "#222222"),
axis.text.x = element_text(angle = 45, vjust = 1.2, hjust=1),
legend.spacing.y = unit(0.01, 'cm'),
legend.position = "right",
legend.key.size = unit(0.01, "cm"),
legend.background = element_rect(fill = "transparent", color = "transparent"),
plot.margin = margin(10, 10, -40, 10))
}
styl_inter()
wykres1 <- ggplot(data, aes(x = LC, y = proc, fill = Var1, text = paste('<span style = " font-weight:bold"> Źródło:</span>',
'<span>',LC ,'</span>',
'</br></br>',
'<span style = " font-weight:bold">Klasa:</span>',
'<span>',Var1 ,'</span>','</br>',
'<span style = " font-weight:bold">Udział procentowy:</span>',
'<span>',paste(proc, "%") ,'</span>'))) +
geom_bar(stat = "identity", position = "stack") +
labs(x = "", y = "Procentowy udział klas [%]", fill = "") +
scale_fill_manual(values = c("Obszary podmokłe" = "#122b0e", "Obszary wodne" = "#61b5ff", "Zabudowa" = "#b83938",
"Lasy" = "#2d5e47", "Obszary trawiaste i krzewiaste" = "#9dd3b6", "Pola uprawne" = "#f4f430")) +
style + guides(fill = guide_legend(byrow = TRUE)) #+ theme(legend.position = "none")#+ theme(plot.margin = margin(0, 0, 0, 70))
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne")
svg("udzial_procentowy_aglo.svg", width = 9, height = 6.5, family = "Calibri")
wykres
invisible(dev.off())
ggplotly(wykres1, tooltip = c("text")) %>%
config(displayModeBar = FALSE) #%>% layout(legend = list(x = 0, y = -0.3, orientation = "h"))
library(patchwork)
combined <- wykres + wykres1 & theme(legend.position = "bottom")
combined <- combined + plot_layout(guides = "collect")
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne/poprawione")
svg("udzial_proc.svg", width = 24, height = 11.25, family = "Calibri")
combined
invisible(dev.off())
####### wykres błędów klasyfikacji dla aglomeracji ########
error_factors <- list(lucas_factor, esa_factor, esri_factor, sent_factor, clc_factor, urban_factor)
error_aglo <- data.frame(matrix(nrow = 6, ncol = 6))
colnames(error_aglo) <- c("LUCAS", "ESA", "Esri", "Sentinel-2", "CLC", "Urban Atlas")
for (i in 1:6){
matrix <- table(error_factors[[i]], bdot_factor)
error_aglo[, i] <- round((1 - (diag(matrix) / apply(matrix, 1, sum))) * 100, 2)
}
#error_aglo$error <- round(apply(error_aglo, 1, mean), 2)
error_aglo$LC <- c("Obszary podmokłe", "Obszary wodne", "Zabudowa", "Lasy", "Obszary trawiaste i krzewiaste", "Pola uprawne")
## styl dla wykresu
styl <- function(){
style <<- theme(
text = element_text(family = "Calibri"),
panel.grid.major.y = element_line(color = "#222222"),
panel.grid.minor.y = element_line(color = "#222222"),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff"),
axis.title = element_text(size = 32,
color = "#222222"),
axis.title.y = element_text(vjust= 2.2, hjust = 0.6),
plot.title = element_text(size = 16,
color = "#222222",
vjust = 2,
hjust = 0.5),
legend.margin = margin(t = 15, r = 7, l = 7, b = 7),
legend.title = element_text(size = 16, color = "#222222"),
legend.text = element_text(size = 36, color = "#222222"),
axis.text = element_text(size = 36,
color = "#222222"),
axis.text.x = element_text(size = 36, angle = 45, vjust = 1, hjust=1),
legend.spacing.y = unit(0.2, 'cm'),
legend.key = element_blank(),
legend.background = element_rect(fill = "transparent", color = "transparent"),
plot.margin = margin(10, 10, 0, 10))
}
styl()
styl_inter <- function(){
style <<- theme(
text = element_text(family = "Calibri"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#222222"),
panel.grid.minor.y = element_line(color = "#222222"),
plot.background = element_rect(fill = "#eff6f1", color = NA),
panel.background = element_rect(fill = "#eff6f1"),
axis.title = element_text(size = 18,
color = "#222222"),
axis.title.y = element_text(vjust= 2.2),
plot.title = element_text(size = 18,
color = "#222222",
vjust = 2,
hjust = 0.5),
legend.margin = margin(t = -15, r = 7, l = 7, b = 7),
legend.title = element_text(size = 18, color = "#222222"),
legend.text = element_text(size = 12, color = "#222222"),
axis.text = element_text(size = 16,
color = "#222222"),
axis.text.x = element_text(angle = 45, vjust = 1.2, hjust=1),
legend.spacing.y = unit(0.01, 'cm'),
legend.position = "bottom",
legend.key.size = unit(0.01, "cm"),
legend.background = element_rect(fill = "transparent", color = "transparent"),
plot.margin = margin(10, 10, -40, 10))
}
styl_inter()
ggplot(error_aglo, aes(x = reorder(LC, - error), y = error, fill = LC)) + geom_bar(stat = "identity") + style +
scale_fill_manual(values = c("Obszary podmokłe" = "#122b0e", "Obszary wodne" = "#61b5ff", "Zabudowa" = "#b83938",
"Lasy" = "#2d5e47", "Obszary trawiaste i krzewiaste" = "#9dd3b6", "Pola uprawne" = "#f4f430")) +
ylab("% powierzchni poszczególnych klas") +
xlab("") + style + theme(legend.position = "none") + theme(plot.margin = margin(10, 10, 10, 10)) +
scale_y_continuous(limits = c(0, 80))
#Średni procent obszarów niepoprawnie zaklasyfikowanych względem BDOT w obu powiatach
### błędy klasyfikacji dla Poznania
error_factors <- list(lucas_crop_factor, esa_crop_factor, esri_crop_factor, sent_crop_factor, clc_crop_factor)
error_poznan <- data.frame(matrix(nrow = 6, ncol = 6))
colnames(error_poznan) <- c("LUCAS", "ESA", "Esri", "Sentinel-2", "CLC", "Urban Atlas")
for (i in 1:5){
matrix <- table(error_factors[[i]], bdot_crop_factor)
error_poznan[, i] <- round((1 - (diag(matrix) / apply(matrix, 1, sum))) * 100, 2)
}
UA_matrix <- function(){
urban_atlas_matrix <- matrix(c(0, 0, 0, 0, 0, 0,
125, 67313, 129, 1020, 936, 2,
279, 5636, 769551, 25311, 296850, 15208,
2255, 4353, 31242, 399689, 66256, 9469,
8857, 5045, 33300, 80616, 372198, 96743,
3634, 1148, 2588, 13064, 66193, 237721),
nrow = 6, byrow = TRUE,
dimnames = list(c("1", "2", "3", "4", "5", "6"),
c("1", "2", "3", "4", "5", "6")))
error_poznan[, 6] <<- round((1 - (diag(urban_atlas_matrix) / apply(urban_atlas_matrix, 1, sum))) * 100, 2)
error_poznan[1, 6] <<- 0
}
UA_matrix()
#error_poznan$error <- apply(error_poznan, 1, function(x) mean(x, na.rm = TRUE))
error_poznan$LC <- c("Obszary podmokłe", "Obszary wodne", "Zabudowa", "Lasy", "Obszary trawiaste i krzewiaste", "Pola uprawne")
ggplot(error_poznan, aes(x = reorder(LC, - error), y = error, fill = LC)) + geom_bar(stat = "identity") + style +
scale_fill_manual(values = c("Obszary podmokłe" = "#122b0e", "Obszary wodne" = "#61b5ff", "Zabudowa" = "#b83938",
"Lasy" = "#2d5e47", "Obszary trawiaste i krzewiaste" = "#9dd3b6", "Pola uprawne" = "#f4f430")) +
ylab("% powierzchni poszczególnych klas") +
xlab("") + style + theme(legend.position = "none") + theme(plot.margin = margin(10, 10, 10, 10)) +
scale_y_continuous(limits = c(0, 80))
# wykres pogrupowany dla poznania
error_poznan_long <- error_poznan %>%
pivot_longer(cols = -LC, names_to = "LandCover", values_to = "error")
error_poznan_long <- error_poznan_long %>%
mutate(LandCover = case_when(
LandCover == "Urban Atlas" ~ "Urban Atlas*",
TRUE ~ as.character(LandCover)
))
plot <- function() {
wykres <<- ggplot(error_poznan_long, aes(fill=LC, y=error, x=LandCover, text = paste('<span style = " font-weight:bold"> Źródło:</span>',
'<span>',LC ,'</span>',
'</br></br>',
'<span style = " font-weight:bold">Klasa:</span>',
'<span>',LandCover ,'</span>','</br>',
'<span style = " font-weight:bold">Udział procentowy:</span>',
'<span>',paste(error, "%") ,'</span>'))) +
geom_bar(position=position_dodge(width = 0.7), stat="identity", color = "#555555") +
scale_fill_manual(values = c("Obszary podmokłe" = "#122b0e", "Obszary wodne" = "#61b5ff", "Zabudowa" = "#b83938",
"Lasy" = "#2d5e47", "Obszary trawiaste i krzewiaste" = "#9dd3b6", "Pola uprawne" = "#f4f430")) +
style + labs(x = "", y = "Procent powierzchni klas [%]", fill = "", caption = "*brak obszarów podmokłych\n na terenie Poznania") +
guides(fill = guide_legend(byrow = TRUE)) + theme(plot.caption = element_text(vjust = 30, hjust = 0.05, size = 20, color = "#222222"),
plot.caption.position = "plot")
}
plot()
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne")
svg("error_poznan.svg", width = 9, height = 6.5, family = "Calibri")
wykres
invisible(dev.off())
ggplotly(wykres, tooltip = c("text")) %>%
config(displayModeBar = FALSE) %>%
layout(annotations = list(x = 1.4, y = 0.05, text = "*brak obszarów podmokłych\n na terenie Poznania",
xref='paper', yref='paper', showarrow = F,
font = list(size = 14, color = "#222222"))) #%>% layout(legend = list(x = 0, y = -0.3, orientation = "h"))
# wykres pogrupowany dla aglomeracji
error_aglo_long <- error_aglo %>%
pivot_longer(cols = -LC, names_to = "LandCover", values_to = "error")
plot <- function() {
wykres1 <<-ggplot(error_aglo_long, aes(fill=LC, y=error, x=LandCover, text = paste('<span style = " font-weight:bold"> Źródło:</span>',
'<span>',LC ,'</span>',
'</br></br>',
'<span style = " font-weight:bold">Klasa:</span>',
'<span>',LandCover ,'</span>','</br>',
'<span style = " font-weight:bold">Udział procentowy:</span>',
'<span>',paste(error, "%") ,'</span>'))) +
geom_bar(position=position_dodge(width = 0.7), stat="identity", color = "#555555") +
scale_fill_manual(values = c("Obszary podmokłe" = "#122b0e", "Obszary wodne" = "#61b5ff", "Zabudowa" = "#b83938",
"Lasy" = "#2d5e47", "Obszary trawiaste i krzewiaste" = "#9dd3b6", "Pola uprawne" = "#f4f430")) +
style + labs(x = "", y = "Procent powierzchni klas [%]", fill = "") +
guides(fill = guide_legend(byrow = TRUE)) + scale_y_continuous(limits = c(0, 100)) + theme(legend.position = "none")#+ theme(plot.margin = margin(0, 0, 0, 70))
}
plot()
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne")
svg("error_aglo.svg", width = 9, height = 6.5, family = "Calibri")
wykres
invisible(dev.off())
ggplotly(wykres1, tooltip = c("text")) %>%
config(displayModeBar = FALSE)
#library()
combined <- wykres + wykres1 & theme(legend.position = "bottom")
combined <- combined + plot_layout(guides = "collect")
setwd("E:/Projekty/projekty_22_23/LULC_stat/wykresy_artykul/poprawne/poprawione")
svg("error.svg", width = 24, height = 11.25, family = "Calibri")
combined
invisible(dev.off())
ggplotly(combined)
#subplot(style(a, showlegend = F), b) %>%
# layout(title = 'Procentowy udział klas po reklasyfikacji dla wszystkich źródeł danych \n
# Aglomeracja Poznań', legend = list(orientation = 'h', x = 0, y = -0.3))