Geographic Heatmap for Spatial Density — ggplot2

A geographic heatmap visualizes spatial density or intensity values across a map using continuous color gradients. Unlike choropleth maps that color discrete regions, this plot shows smooth density variations computed from point data or gridded values. The color intensity at each location represents the concentration or magnitude of the underlying data, making it ideal for identifying hotspots, clusters, and spatial patterns in geographic data.

Geographic Heatmap for Spatial Density rendered with ggplot2

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R source (ggplot2)

#' anyplot.ai
#' heatmap-geographic: Geographic Heatmap for Spatial Density
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 87/100 | Updated: 2026-05-19

library(ggplot2)
library(ragg)

set.seed(42)

# Theme tokens
THEME       <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG     <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK         <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT    <- if (THEME == "light") "#4A4A44" else "#B8B7B0"

# Data: simulated convective storm event locations over the eastern United States
gulf_coast <- data.frame(
  longitude = rnorm(420, mean = -90, sd = 3.0),
  latitude  = rnorm(420, mean = 30,  sd = 1.8)
)
great_plains <- data.frame(
  longitude = rnorm(500, mean = -96, sd = 4.5),
  latitude  = rnorm(500, mean = 37,  sd = 3.0)
)
appalachians <- data.frame(
  longitude = rnorm(330, mean = -79, sd = 2.5),
  latitude  = rnorm(330, mean = 40,  sd = 2.2)
)
florida_pen <- data.frame(
  longitude = rnorm(250, mean = -82, sd = 1.5),
  latitude  = rnorm(250, mean = 27,  sd = 1.5)
)

events <- rbind(gulf_coast, great_plains, appalachians, florida_pen)

# Kernel density estimation over eastern US
lon_range <- c(-105, -65)
lat_range <- c(24,   51)

kde_out <- MASS::kde2d(
  x    = events$longitude,
  y    = events$latitude,
  n    = 200,
  h    = c(2.5, 2.0),
  lims = c(lon_range, lat_range)
)

kde_df          <- expand.grid(longitude = kde_out$x, latitude = kde_out$y)
kde_df$density  <- as.vector(kde_out$z)
kde_df$density[kde_df$density < quantile(kde_df$density, 0.15)] <- NA

# Plot
p <- ggplot() +
  geom_raster(
    data        = kde_df,
    aes(x = longitude, y = latitude, fill = density),
    interpolate = TRUE
  ) +
  scale_fill_viridis_c(
    option   = "inferno",
    name     = "Storm\nDensity",
    na.value = "transparent",
    guide    = guide_colorbar(
      barwidth       = 1.5,
      barheight      = 8,
      title.position = "top",
      title.hjust    = 0.5
    )
  ) +
  coord_cartesian(xlim = lon_range, ylim = lat_range, expand = FALSE) +
  labs(
    title = "Convective Storm Event Density · heatmap-geographic · r · ggplot2 · anyplot.ai",
    x     = "Longitude",
    y     = "Latitude"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.background   = element_rect(fill = PAGE_BG, color = PAGE_BG),
    panel.background  = element_rect(fill = PAGE_BG, color = NA),
    panel.grid.major  = element_line(color = INK_SOFT, linewidth = 0.25, linetype = "dotted"),
    panel.grid.minor  = element_blank(),
    panel.border      = element_blank(),
    axis.title        = element_text(color = INK,      size = 20),
    axis.text         = element_text(color = INK_SOFT, size = 16),
    plot.title        = element_text(color = INK,      size = 22, margin = margin(b = 12)),
    legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.4),
    legend.text       = element_text(color = INK_SOFT, size = 16),
    legend.title      = element_text(color = INK,      size = 18),
    legend.position   = "right",
    plot.margin       = margin(15, 15, 15, 15)
  )

# Add geographic borders when the maps package is available
if (requireNamespace("maps", quietly = TRUE)) {
  countries <- map_data("world", region = c("USA", "Canada", "Mexico"))
  us_states <- map_data("state")

  p <- p +
    geom_polygon(
      data    = countries,
      mapping = aes(x = long, y = lat, group = group),
      fill    = NA, color = INK_SOFT, linewidth = 0.5,
      inherit.aes = FALSE
    ) +
    geom_polygon(
      data    = us_states,
      mapping = aes(x = long, y = lat, group = group),
      fill    = NA, color = INK_SOFT, linewidth = 0.2,
      inherit.aes = FALSE
    )
}

# Save
ggsave(
  filename = sprintf("plot-%s.png", THEME),
  plot     = p,
  device   = ragg::agg_png,
  width    = 16,
  height   = 9,
  units    = "in",
  dpi      = 300
)

Part of Geographic Heatmap for Spatial Density on anyplot.ai.

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