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library(sf)
library(areal)
library(dplyr)
setwd("cw4")
list_race = c("WHITE", "BLACK", "ASIAN", "HISPANIC", "INDIAN", "OTHER_RACE")
dat90 = st_read("data/san_francisco_tracts_1990_attr.shp")
dat00 = st_read("data/san_francisco_tracts_2000_attr.shp")
dat10 = st_read("data/san_francisco_tracts_2010_attr.shp")
target_units <- dat10[, c("GISJOIN")]
source_units <- dat90[, c("GISJOIN", list_race, "POP")]
source_units2 <- dat00[, c("GISJOIN", list_race, "POP")]
colnames(source_units) <- c("GJOIN", list_race, "POP", "geometry")
colnames(source_units2) <- c("GJOIN", list_race, "POP", "geometry")
intersect <- aw_intersect(target_units,
source = source_units,
areaVar = "area")
intersect <- aw_total(intersect,
source = source_units, #granice zrodlowe
id = GJOIN, #id jednostek zrodlowych
areaVar = "area", #nazwa pola z powierzchnia jednostek z przeciecia, Ai
totalVar = "totalArea",
type = "extensive",
weight = "sum")
intersect <- aw_weight(intersect,
areaVar = "area", #Ai - obliczane przez aw_intersect
totalVar = "totalArea", #Aj, obliczane przez aw_total
areaWeight = "areaWeight") #Wi
intersect <- aw_calculate(intersect,
value = "POP", #kolumna do przeliczenia
newVar = "POPnew", #nazwa nowej kolumny z przeliczonymi danymi.
areaWeight = "areaWeight")
result <- aw_aggregate(intersect,
target = target_units, #granice docelowe
tid = GISJOIN, #id docelowe (target units)
interVar = "POPnew") #nazwa przeliczonej zmiennej
result90_10 <- aw_interpolate(target_units, #granice jednostek docelowych (target_units)
tid = GISJOIN, #id jednostek docelowych
source = source_units, #dane zrodlowe
sid = GJOIN, #id jednostek zrodlowych (source units)
weight = "sum",
output = "tibble", #wynik w jako tabela, moze tez być obiekt sf
extensive = list_race) #lista zmiennych do przeliczenia
result00_10 <- aw_interpolate(target_units, #granice jednostek docelowych (target_units)
tid = GISJOIN, #id jednostek docelowych
source = source_units2, #dane zrodlowe
sid = GJOIN, #id jednostek zrodlowych (source units)
weight = "sum",
output = "tibble", #wynik w jako tabela, moze tez być obiekt sf
extensive = list_race) #lista zmiennych do przeliczenia
write.csv(result90_10, "result90_10.csv", row.names = FALSE)
write.csv(result00_10, "result00_10.csv", row.names = FALSE)
dat10
cls_df <- st_read("data/klasyfikacja_1990_2010.shp")
result90_10$ASIAN = as.integer(result90_10$ASIAN)
result90_10$BLACK = as.integer(result90_10$BLACK)
result90_10$HISPANIC = as.integer(result90_10$HISPANIC)
result90_10$INDIAN = as.integer(result90_10$INDIAN)
result90_10$OTHER_RACE = as.integer(result90_10$OTHER_RACE)
result90_10$WHITE = as.integer(result90_10$WHITE)
result90_10$POP = result90_10$ASIAN + result90_10$BLACK + result90_10$HISPANIC + result90_10$INDIAN+result90_10$OTHER_RACE+result90_10$WHITE
View(result90_10)
result00_10$ASIAN = as.integer(result00_10$ASIAN)
result00_10$BLACK = as.integer(result00_10$BLACK)
result00_10$HISPANIC = as.integer(result00_10$HISPANIC)
result00_10$INDIAN = as.integer(result00_10$INDIAN)
result00_10$OTHER_RACE = as.integer(result00_10$OTHER_RACE)
result00_10$WHITE = as.integer(result00_10$WHITE)
result00_10$POP = result00_10$ASIAN + result00_10$BLACK + result00_10$HISPANIC + result00_10$INDIAN+result00_10$OTHER_RACE+result00_10$WHITE
View(result00_10)
res9_1 = result90_10 %>% summarize(GISJOIN, ASIAN = ASIAN/POP, BLACK = BLACK/POP,
HISPANIC = HISPANIC/POP, INDIAN = INDIAN/POP,
OTHER_RACE = OTHER_RACE/POP, WHITE = WHITE/POP)
res0_1 = result00_10 %>% summarize(GISJOIN, ASIAN = ASIAN/POP, BLACK = BLACK/POP,
HISPANIC = HISPANIC/POP, INDIAN = INDIAN/POP,
OTHER_RACE = OTHER_RACE/POP, WHITE = WHITE/POP)
View(res9_1)
View(res0_1)
res_9_1 = res9_1 %>% mutate(cls90 = case_when(
ASIAN > 0.8 ~ "AL",
BLACK > 0.8 ~ "BL",
HISPANIC > 0.8 ~ "HL",
WHITE > 0.8 ~ "WL",
WHITE >= 0.5 & WHITE <= 0.8 ~ "WM",
BLACK >= 0.5 & BLACK <= 0.8 ~ "BM",
ASIAN >= 0.5 & ASIAN <= 0.8 ~ "AM",
HISPANIC >= 0.5 & HISPANIC <= 0.8 ~ "HM",
HISPANIC < 0.5 | ASIAN < 0.5 | BLACK < 0.5 | WHITE < 0.5 ~ "HD"
))
res_0_1 = res0_1 %>% mutate(cls00 = case_when(
ASIAN > 0.8 ~ "AL",
BLACK > 0.8 ~ "BL",
HISPANIC > 0.8 ~ "HL",
WHITE > 0.8 ~ "WL",
WHITE >= 0.5 & WHITE <= 0.8 ~ "WM",
BLACK >= 0.5 & BLACK <= 0.8 ~ "BM",
ASIAN >= 0.5 & ASIAN <= 0.8 ~ "AM",
HISPANIC >= 0.5 & HISPANIC <= 0.8 ~ "HM",
HISPANIC < 0.5 | ASIAN < 0.5 | BLACK < 0.5 | WHITE < 0.5 ~ "HD"
))
cls_df <- st_read("data/klasyfikacja_1990_2010.shp")
cls_df$cls90 = res_9_1$cls90
cls_df$cls00 = res_0_1$cls00
View(cls_df)
table(cls_df$cls90)
tab90 <- prop.table(table(cls_df$cls90))*100
a = round(tab90, 1)
a = data.frame(rbind(a))
write.csv(a, "udzial_procentowy_90.csv", row.names = FALSE)
table(cls_df$cls00)
tab90 <- prop.table(table(cls_df$cls00))*100
a = round(tab90, 1)
a = data.frame(rbind(a))
write.csv(a, "udzial_procentowy_00.csv", row.names = FALSE)
table(cls_df$cls10)
tab90 <- prop.table(table(cls_df$cls10))*100
a = round(tab90, 1)
a = data.frame(rbind(a))
write.csv(a, "udzial_procentowy_10.csv", row.names = FALSE)
dat10
result10_10$ASIAN = as.integer(result00_10$ASIAN)
result10_10$BLACK = as.integer(result00_10$BLACK)
result10_10$HISPANIC = as.integer(result00_10$HISPANIC)
result10_10$INDIAN = as.integer(result00_10$INDIAN)
result10_10$OTHER_RACE = as.integer(result00_10$OTHER_RACE)
result10_10$WHITE = as.integer(result00_10$WHITE)
result10_10$POP = result00_10$ASIAN + result00_10$BLACK + result00_10$HISPANIC + result00_10$INDIAN+result00_10$OTHER_RACE+result00_10$WHITE
View(result00_10)
res1_1 = dat10 %>% summarize(GISJOIN, ASIAN = ASIAN/POP, BLACK = BLACK/POP,
HISPANIC = HISPANIC/POP, INDIAN = INDIAN/POP,
OTHER_RACE = OTHER_RACE/POP, WHITE = WHITE/POP)
res1_1 = res1_1 %>% mutate(cls10 = case_when(
ASIAN > 0.8 ~ "AL",
BLACK > 0.8 ~ "BL",
HISPANIC > 0.8 ~ "HL",
WHITE > 0.8 ~ "WL",
WHITE >= 0.5 & WHITE <= 0.8 ~ "WM",
BLACK >= 0.5 & BLACK <= 0.8 ~ "BM",
ASIAN >= 0.5 & ASIAN <= 0.8 ~ "AM",
HISPANIC >= 0.5 & HISPANIC <= 0.8 ~ "HM",
HISPANIC < 0.5 | ASIAN < 0.5 | BLACK < 0.5 | WHITE < 0.5 ~ "HD"
))
cls_df$cls10 = res1_1$cls10
View(cls_df)
cls_color <- c("AL"= "#CD5555", "AM"= "#FF6A6A", "BL"= "#006400", "BM"= "#32CD32", "HD"= "#8F8F8F", "HL"= "#5D478B", "HM"= "#9370DB", "WL"= "#FF8C00", "WM"= "#FFD700")
col90 <- cls_color[names(cls_color)%in%unique(cls_df$cls90)]
col00 <- cls_color[names(cls_color)%in%unique(cls_df$cls00)]
col10 <- cls_color[names(cls_color)%in%unique(cls_df$cls10)]
library(ggplot2)
library(gridExtra)
p1 <- ggplot(data = cls_df) +
geom_sf(aes(fill = cls90)) +
scale_fill_manual(values = col90) +
labs(title = "San Francisco, 1990") +
theme_bw() +
theme(legend.position="bottom")
p2 <- ggplot(data = cls_df) +
geom_sf(aes(fill = cls00)) +
scale_fill_manual(values = col10) +
labs(title = "San Francisco, 2000") +
theme_bw() +
theme(legend.position="bottom")
p3 <- ggplot(data = cls_df) +
geom_sf(aes(fill = cls10)) +
scale_fill_manual(values = col10) +
labs(title = "San Francisco, 2010") +
theme_bw() +
theme(legend.position="bottom")
grid.arrange(p1, p2, p3, nrow = 2)
png("strukturyrasowe.png")
grid.arrange(p1, p2, p3, nrow = 2)
dev.off()
cls_df
write_sf(cls_df, "classification.shp")
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