Merge pull request #15 from belizcole/main

21Nov_Conflict

21Nov_Conflict/Author_Credits.txt

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+Instagram = blizcole

21Nov_Conflict/USA_Map_Script.R

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+# Step 1: Load Necessary Libraries
+library(sf)
+library(ggplot2)
+library(dplyr)
+library(viridis)
+library(magick)
+library(grid)
+library(maps)
+library(cowplot)
+
+# Step 2: Load Conflict Data
+conflict_data <- read.csv("USA_Canada_2020_2024_Nov15.csv")
+
+# Filter to only include contiguous U.S.
+conflict_data_filtered <- conflict_data %>%
+  filter(!is.na(longitude) & !is.na(latitude)) %>%
+  filter(longitude > -125 & longitude < -66.5 & latitude > 24 & latitude < 50)  # Contiguous US bounds
+
+# Convert to an sf object
+conflict_points <- st_as_sf(conflict_data_filtered, coords = c("longitude", "latitude"), crs = 4326)
+
+# Step 3: Load County Shapefile
+counties <- st_read("tl_2020_us_county.shp")
+
+# Filter counties for the contiguous U.S.
+contiguous_counties <- counties %>%
+  filter(!STATEFP %in% c("02", "15")) %>%  # Exclude Alaska (02) and Hawaii (15)
+  st_transform(crs = 4326)
+
+# Step 4: Load State Boundaries from `maps`
+states <- st_as_sf(maps::map("state", plot = FALSE, fill = TRUE)) %>%
+  st_transform(crs = 4326)  # Ensure CRS matches
+
+# Step 5: Load Population Data
+# Extract the FIPS code from STATE and COUNTY and select the population column
+population_data <- read.csv("co-est2020-alldata.csv") %>%
+  mutate(FIPS = sprintf("%02d%03d", STATE, COUNTY)) %>%  # Create FIPS column
+  select(FIPS, POPESTIMATE2020)  # Use the most recent population estimate
+
+# Join population data with county geometries
+contiguous_counties <- contiguous_counties %>%
+  left_join(population_data, by = c("GEOID" = "FIPS"))
+
+# Step 6: Count Events Per County
+# Spatial join: Assign conflict points to counties
+event_counts <- conflict_points %>%
+  st_join(contiguous_counties) %>%
+  group_by(GEOID) %>%
+  summarise(event_count = n(), .groups = "drop")
+
+# Convert event_counts to a data frame
+event_counts_df <- event_counts %>%
+  st_drop_geometry()
+
+# Join event counts with counties using a left join
+contiguous_counties <- contiguous_counties %>%
+  left_join(event_counts_df, by = "GEOID") %>%
+  mutate(
+    event_per_capita = (event_count / POPESTIMATE2020) * 10000,  # Events per 10,000 people
+    event_per_capita = ifelse(is.na(event_per_capita), 0, event_per_capita)  # Replace NAs with 0
+  )
+
+# Step 7: Apply Logarithmic Transformation
+contiguous_counties <- contiguous_counties %>%
+  mutate(log_event_per_capita = log1p(event_per_capita))  # Log transform for better visibility
+
+# Step 8: Load Rbanism Logo
+rbanism_logo <- image_read("https://rbanism.org/assets/imgs/about/vi_l.jpg")
+
+# Step 9: Create Density Map
+log_density_map <- ggplot() +
+  # County-level data
+  geom_sf(
+    data = contiguous_counties,
+    aes(fill = log_event_per_capita),
+    color = "grey90",  # Subtle light grey for county borders
+    size = 0.00001  # Thinner lines for boundaries
+  ) +
+  # State boundaries
+  geom_sf(
+    data = states,
+    color = "grey90",
+    fill = NA,
+    size = 0.0001
+  ) +
+  scale_fill_viridis(
+    name = "Events per 10,000 People (Log)",
+    option = "C",
+    na.value = "grey100",
+    guide = guide_colorbar(
+      barwidth = 15,
+      barheight = 0.8,
+      title.position = "top",
+      title.hjust = 0.5
+    ),
+    breaks = log1p(c(0, 10, 50, 100, 500)),
+    labels = c("0", "10", "50", "100", "500")
+  ) +
+  labs(
+    title = "Socio-Political Conflict Event Density Across Contiguous U.S. (2020–2024)",
+    subtitle = "#30DayMapChallenge2024",
+    caption = "Data Source: ACLED, US Census | Author: Brianna E. Cole | #Rbanism",
+    x = "Longitude",
+    y = "Latitude"
+  ) +
+  coord_sf(xlim = c(-125, -66.5), ylim = c(24, 50), expand = FALSE) +
+  theme_minimal() +
+  theme(
+    plot.background = element_rect(fill = "white", color = NA),
+    panel.background = element_rect(fill = "white", color = NA),
+    legend.position = "bottom",
+    legend.title = element_text(size = 10, face = "bold"),
+    legend.text = element_text(size = 8),
+    plot.title = element_text(hjust = 0.5, face = "bold", size = 18),
+    plot.subtitle = element_text(hjust = 0.5, size = 10),
+    plot.caption = element_text(hjust = 0.5, size = 10),
+    axis.title = element_text(size = 10, face = "bold"),
+    panel.grid = element_blank()
+  )
+
+# Step 10: Overlay Logo in Bottom-Right Corner
+final_map <- ggdraw() +
+  draw_plot(log_density_map) +  # Add the main map
+  draw_image(
+    rbanism_logo,
+    x = 0.38,  # Adjust X position for bottom-right corner
+    y = -0.12,  # Adjust Y position for bottom-right corner
+    scale = 0.13  # Adjust logo size
+  )
+
+# Display the final map with logo
+print(final_map)
+
+# Save the final map with logo
+ggsave("Day21_Conflict.png",
+       plot = final_map, width = 18, height = 12, dpi = 500)

21Nov_Conflict/co-est2020-alldata.csv

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+

21Nov_Conflict/tl_2020_us_county.cpg

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+UTF-8

21Nov_Conflict/tl_2020_us_county.prj

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+GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]