In RS-eco/rISIMIP: R Package for processing ISIMIP data in R

knitr::opts_chunk$set(echo = FALSE, fig.width=8, fig.height=8, warning=FALSE, comment=NA, message=FALSE, eval=F)

Data Set-Up

Load rISIMIP package

library(rISIMIP)

First, we specify the file path, where the ISIMIP data is located.

# Specify path of file directory
filedir <- "/media/matt/Data/Documents/Wissenschaft/Data/"

Data Pre-processing

Population data

We read and summarise global population data for three 30-year time periods (1995, 2050, 2080)

# Time frames
timeframes <- c("1995", "2050", "2080")

# rcp26soc only goes until 2100
library(raster)
population_data <- stack(mapply(FUN=function(x,y){calc(stack(readISIMIP(path=filedir, type="population", scenario="rcp26soc", startyear=x, endyear=y)), mean)}, c(1980, 2036, 2066), c(2009, 2065, 2095)))
names(population_data) <- timeframes
population_data <- as.data.frame(rasterToPoints(population_data))
colnames(population_data) <- c("x", "y", "1995", "2050", "2080")

Plot data

Geographic Map

library(dplyr)

# Set 0 values to NA
population_data[population_data == 0] <- NA

# Get cell area
data("landseamask_generic", package="rISIMIP")
area <- data.frame(raster::rasterToPoints(raster::area(landseamask_generic, na.rm=TRUE)))

# Load outline
data(outline, package="ggmap2")

# Turn into long format, calculate density and create plot
library(ggplot2)
population_data %>% tidyr::gather(year, size, -c(x,y)) %>%
  mutate(year = factor(year, labels=c("1995", "2050", "2080"))) %>% 
  dplyr::left_join(area) %>% # Calculate population density
  mutate(density = size/layer) %>%
  tidyr::drop_na() %>% 
  mutate(density = cut(density, c(0, 25, 75, 150, 300, 500, 700, 1500, 10000))) %>% # Turn population density into categories
  ggplot() + geom_tile(aes(x=x, y=y, fill=density)) + 
  facet_wrap(~ year, ncol=1) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_discrete(name="Population\ndensity (per km2)", na.value="transparent") + 
  theme_bw() + theme(strip.background= element_blank()) + 
  scale_x_continuous(name=expression(paste("Longitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-180, -90, 0, 90, 180)) + 
  scale_y_continuous(name=expression(paste("Latitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-60, -40, -20, 0, 20, 40, 60,80)) + 
  coord_sf(xlim=c(-180,180), ylim=c(-60,84))

Change map

# Calculate change in population
delta_pop <- population_data %>% 
  mutate_at(vars(`2050`:`2080`), funs(. - `1995`)) %>% 
  select(-matches("1995")) %>% 
  tidyr::gather(year, size, -c(x,y))

# Set 0 values to NA
delta_pop[delta_pop == 0] <- NA

# Calculate population density
data("landseamask_generic", package="rISIMIP")
area <- data.frame(raster::rasterToPoints(raster::area(landseamask_generic, na.rm=TRUE)))
delta_pop <- dplyr::left_join(delta_pop, area)
delta_pop$density <- delta_pop$size/delta_pop$layer

# Define Year label
delta_pop$year <- factor(delta_pop$year, labels=c("2050", "2080"))

# Drop NAs
delta_pop <- tidyr::drop_na(delta_pop)

# Turn population density into categories
delta_pop$density <- cut(delta_pop$density, 
                         c(-2000, -1000, -500, 0, 500, 1000, 2000, 5000, 10000))

# Plot change in population density
ggplot() +
  geom_tile(data=delta_pop, aes(x=x, y=y, fill=density)) + 
  facet_wrap(~ year, ncol=1) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_manual(name="Population\ndensity (per km2)\nchange",
                       values=c("#00007F", "blue", "#007FFF", "cyan", "yellow", 
        "#FF7F00", "red", "#7F0000"), na.value="transparent") + 
  theme_bw() + theme(strip.background= element_blank()) + 
  scale_x_continuous(name=expression(paste("Longitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-180, -90, 0, 90, 180)) + 
  scale_y_continuous(name=expression(paste("Latitude (",degree,")")), expand=c(0.05,0.05),
                     breaks=c(-60, -40, -20, 0, 20, 40, 60,80)) + 
  coord_sf(xlim=c(-180,180), ylim=c(-60,84))

Population time series

# Get data 
pop_data <- rISIMIP::readISIMIP(path=filedir, type="population", scenario="rcp26soc",
                                startyear=2006, endyear=2099)

# Calculate total population size for every year
pop_size <- data.frame(size=raster::cellStats(pop_data, stat="sum", na.rm=TRUE),
                       year=c(2006:2099))

# Plot total population over time
ggplot(data = pop_size, aes(x = year, y = size/1000000000)) + 
  labs(x= "Year", y="Total population size (Billion)") + 
  geom_line() + theme_classic()