How A Booming Population And Climate Change Made California’s Wildfires Worse Than Ever

Data and R code to reproduce graphics in this Jul. 28, 2018 BuzzFeed News post on wildfires in California. Supporting files are in this GitHub repository.

Data

To analyze trends in California wildfires, we used the California Department of Forestry and Fire Protection (Cal Fire) Fire Perimeters Geodatabase, which records forest fires of 10 acres or greater, brush fires of 30 acres and greater, and grass fires of 300 acres or greater. The file calfire_frap.csv is derived from this database.

To put California’s wildfires in a national context, we used the US Forest Service’s Spatial Wildfire Occurrence Data For The United States, which records wildfires across the nation from 1992 to 2015. This data is in a series of CSV files in the us_fires folder.

Cal Fire’s data on buildings destroyed by wildfires per year is in the file calfire_damage.csv.

Setting up

Required packages and color palette for major fire causes (human, natural, unknown).

# load required packages
library(dplyr)
library(readr)
library(ggplot2)
library(ggthemes)
library(scales)
library(maps)
library(mapproj)

# color palette for major fire causes
cause_pal <- c("#ffff00","#d397fc","#ffffff")

Big fires have gotten more common

To plot fires by date, we used the Cal Fire alarm date.

# load and process data
calfire <- read_csv("data/calfire_frap.csv") %>%
  mutate(cause2 = case_when(cause == 1 | cause == 17 ~ "Natural",
                            cause == 14 | is.na(cause) ~ "Unknown",
                            cause != 1 | cause != 14 | cause != 17 ~ "Human"),
         plot_date = as.Date(format(alarm_date,"2017-%m-%d")))
  
# plot template
plot_template <- ggplot(calfire, aes(y=year_)) +
  geom_hline(yintercept = seq(1950, 2017, by = 1), color = "gray", size = 0.05) +
  scale_size_area(max_size = 10, guide = FALSE) +
  scale_x_date(date_breaks = "months", date_labels = "%b") +
  scale_y_reverse(limits = c(2017,1950), breaks = c(2010,1990,1970,1950)) +
  xlab("") +
  ylab("") +
  theme_hc(bgcolor = "darkunica", base_size = 20, base_family = "ProximaNova-Semibold") +
  theme(axis.text = element_text(color = "#ffffff"))

plot_template +
  geom_point(aes(size=gis_acres, x=plot_date), color="#ffa500", alpha=0.7)

But the pattern is different for natural and human-started fires

# plot template
cause_plot <- plot_template +
  scale_color_manual(values = cause_pal, guide = FALSE) +
  geom_point(aes(size = gis_acres, x = plot_date, color = cause2, alpha = cause2))

# plot natural fires
opacity <- c(0,0.7,0)
cause_plot +  
  scale_alpha_manual(values = opacity, guide = FALSE) +
  ggtitle("Natural") + theme(plot.title = element_text(color = "#d397fc", size = 16, hjust = 0.5))

# plot human-caused fires
opacity <- c(0.7,0,0)
cause_plot +
  scale_alpha_manual(values = opacity, guide = FALSE) +
  ggtitle("Human") + theme(plot.title = element_text(color = "#ffff00", size = 16, hjust = 0.5))

# plot unknown cause fires
opacity <- c(0,0,0.7)
cause_plot +
  scale_alpha_manual(values = opacity, guide = FALSE) +
  ggtitle("Unknown") + theme(plot.title = element_text(color = "#ffffff", size = 16, hjust = 0.5))

California’s problems with human-caused fires set it apart from most of the West

# load data
files <- list.files("data/us_fires")

us_fires <- data_frame()

for (f in files) {
  tmp <- read_csv(paste0("data/us_fires/",f), col_types = cols(
    .default = col_character(),
    stat_cause_code = col_double(),
    cont_date = col_datetime(format = ""),
    discovery_date = col_datetime(format = ""),
    cont_doy = col_integer(),
    cont_time = col_integer(),
    fire_size = col_double(),
    latitude = col_double(),
    longitude = col_double()
    ))
  us_fires <- bind_rows(us_fires,tmp)
}
rm(tmp)

# assign fires to main causes
us_fires <- us_fires %>%
  mutate(cause = case_when(stat_cause_code == 1  ~ "Natural",
                           stat_cause_code == 13 | is.na(stat_cause_code) ~ "Unknown",
                           stat_cause_code >= 2 | stat_cause_code <= 12 ~ "Human"),
         date = as.Date(case_when(is.na(discovery_date) ~ cont_date,
                          !is.na(discovery_date) ~ discovery_date)))

We assigned the fires to a grid with a resolution of half a degree latitude and longitude and then calculated:

• the total area burned per grid cell over the entire period;
• the area burned in natural fires and in those started by human activities or infrastructure;
• the percentage burned in human-caused fires, where the cause was known.

(In these calculations, repeated burns of the same area are added together.)

# assign fires to a grid with half-degree latitude and longitude resolution
cells <- function(xy, origin = c(0,0), cellsize = c(0.5,0.5)) {
  t(apply(xy, 1, function(z) cellsize/2+origin+cellsize*(floor((z - origin)/cellsize))))
}
centroids <- cells(cbind(us_fires$latitude, us_fires$longitude))
us_fires$x <- centroids[, 2]
us_fires$y <- centroids[, 1]
us_fires$cell <- paste(us_fires$x, us_fires$y)

# total area burned per cell
grid_us_fires_total <- us_fires %>%
  group_by(x,y,cell) %>%
  summarize(total_acres = sum(fire_size))

# area burned per cell for natural fires
grid_us_fires_natural <- us_fires %>%
  filter(cause == "Natural") %>%
  group_by(cause,x,y,cell) %>%
  summarize(natural_acres = sum(fire_size)) %>%
  ungroup() %>%
  select(-cause)

# area burned per cell for human-caused fires
grid_us_fires_human <- us_fires %>%
  filter(cause == "Human") %>%
  group_by(cause,x,y,cell) %>%
  summarize(human_acres = sum(fire_size)) %>%
  ungroup() %>%
  select(-cause)

# combine into a single data frame and replace NAs with zeros
grid_us_fires <- left_join(grid_us_fires_total, grid_us_fires_natural) %>%
  left_join(grid_us_fires_human)
grid_us_fires[is.na(grid_us_fires)] <- 0

# calculate % acres burned in fires cause by humans (where cause is known)
grid_us_fires <- grid_us_fires %>%
  mutate(pc_human_acres = human_acres/(human_acres+natural_acres)*100)

# for cells in which all fires are of unknown cause, assign a value of 50%
grid_us_fires$pc_human_acres[is.nan(grid_us_fires$pc_human_acres)] <- 50

We then filtered the data to show only the continental US, removed grid cells with an average of less than 50 acres burned per year, and plotted on a map. The circles for each grid cell were scaled by the total area burned, and colored according to the percentage of that area burned in fires started by human activities or infrastructure.

# filter for continental US and remove cells with less than 50 acres burned per year
grid_us_fires <- grid_us_fires %>%
  filter(x < -65 & x > -125 & y > 24 & y < 50 & total_acres > 1200)

# plot
ggplot(grid_us_fires) +
  geom_point(aes(x = x, y = y, size = total_acres, color = pc_human_acres), alpha = 0.7) +
  borders("state", xlim = c(-125, -65), ylim = c(24, 50), size = 0.2) +
  scale_size_area(max_size = 4, guide = FALSE) +
  scale_color_gradient2(low = "#950fdf", mid = "#ffffff", high = "#ffff00", midpoint = 50, guide = "legend", name = "% burned in human-caused fires") +
  coord_map("mercator") +
  theme_map(base_size = 16, base_family = "ProximaNova-Semibold") +
  theme(axis.line = element_blank(),axis.text.x = element_blank(),
        axis.text.y = element_blank(),axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        panel.grid.minor = element_blank(),
        plot.background = element_rect(fill = "#2c2c2d"),
        legend.background = element_rect(fill = "#2c2c2d"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.justification = "center",
        legend.text = element_text(color = "#ffffff"),
        legend.title = element_text(color = "#ffffff"),
        legend.key = element_rect(fill = "#2c2c2d")) +
  guides(color = guide_legend(title.position="top", title.hjust = 0.5))

If California can’t reduce the number of catastrophic fires, last year’s record season may become the new normal.

These charts show how 2017 was the most devasting year for wildfires in California on record. The first shows the total area burned per year, recorded in Cal Fire’s Fire Perimeters Geodatabase.

# calculate total acres burned per year
acres_year <- calfire %>%
  group_by(year_) %>%
  summarize(acres = sum(gis_acres, na.rm=T)) 

# plot
ggplot(acres_year, aes(x = year_, y = acres/10^6)) +
  geom_bar(stat = "identity", fill = "#ffa500", color = "#ffa500", size = 0, alpha = 0.7) +
  ylab("Acres burned (millions)") +
  xlab("") +
  scale_x_continuous(breaks = c(1950,1970,1990,2010)) +
  theme_hc(bgcolor = "darkunica", base_size = 20, base_family = "ProximaNova-Semibold") +
  theme(axis.text = element_text(color = "#ffffff"))

The second shows the number of buildings destroyed per year from 1989 to 2017, from Cal Fire data.

# load data
damage <- read_csv("data/calfire_damage.csv")

# plot
ggplot(damage, aes(x = year, y = structures)) + 
  geom_bar(stat = "identity", fill = "#ffa500", color = "#ffa500", size = 0, alpha = 0.7) +
  scale_y_continuous(labels = comma) +
  xlab("") +
  ylab("Structures destroyed") +
  theme_hc(bgcolor = "darkunica", base_size = 20, base_family = "ProximaNova-Semibold") +
  theme(axis.text = element_text(color = "#ffffff"))