Geographic Heatmap for Spatial Density in ggplot2 (R)

The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Geographic Heatmap for Spatial Density in Python, R, Julia and JavaScript.

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

Renders

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
)

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/heatmap-geographic/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.

{
  "spec_id": "heatmap-geographic",
  "language": "r",
  "library": "ggplot2",
  "page": "https://anyplot.ai/heatmap-geographic/r/ggplot2",
  "hub": "https://anyplot.ai/heatmap-geographic",
  "code_json": "https://api.anyplot.ai/specs/heatmap-geographic/ggplot2/code",
  "spec_json": "https://api.anyplot.ai/specs/heatmap-geographic",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-geographic/r/ggplot2/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-geographic/r/ggplot2/plot-dark.png",
  "quality_score": 87.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

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

Other implementations