A tile grid map represents geographic regions (states, countries, provinces) as equally-sized tiles — squares or hexagons — arranged to approximate their real-world geographic positions. Unlike choropleth maps where large-area regions dominate visually, every region receives identical visual weight, making tile grid maps ideal for per-capita or per-region comparisons where the statistic matters more than physical area. Tiles are colored by a data variable and labeled with region abbreviations for identification.
""" anyplot.ai
map-tilegrid: Tile Grid Map for Equal-Area Geographic Comparison
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 88/100 | Created: 2026-05-14
"""
import os
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_text,
geom_tile,
ggplot,
labs,
scale_color_identity,
scale_fill_cmap,
theme,
)
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# US state tile grid: (state, col, row, renewable_energy_pct)
# Grid approximates geographic positions; row 0 = north, row 7 = south
records = [
("ME", 11, 0, 34),
("VT", 10, 1, 71),
("NH", 11, 1, 27),
("WA", 1, 2, 74),
("MT", 3, 2, 61),
("ND", 4, 2, 34),
("MN", 5, 2, 29),
("WI", 6, 2, 14),
("MI", 7, 2, 12),
("NY", 9, 2, 31),
("MA", 10, 2, 37),
("RI", 11, 2, 20),
("OR", 1, 3, 72),
("ID", 2, 3, 67),
("WY", 3, 3, 15),
("SD", 4, 3, 58),
("IA", 5, 3, 62),
("IL", 6, 3, 11),
("IN", 7, 3, 7),
("OH", 8, 3, 5),
("PA", 9, 3, 11),
("NJ", 10, 3, 7),
("CT", 11, 3, 10),
("CA", 1, 4, 49),
("NV", 2, 4, 22),
("UT", 3, 4, 22),
("CO", 4, 4, 32),
("NE", 5, 4, 24),
("MO", 6, 4, 8),
("KY", 7, 4, 6),
("WV", 8, 4, 4),
("VA", 9, 4, 14),
("MD", 10, 4, 12),
("DE", 11, 4, 8),
("AZ", 2, 5, 18),
("NM", 3, 5, 31),
("KS", 5, 5, 44),
("AR", 6, 5, 14),
("TN", 7, 5, 17),
("NC", 8, 5, 12),
("SC", 9, 5, 9),
("DC", 10, 5, 2),
("TX", 4, 6, 26),
("OK", 5, 6, 27),
("LA", 6, 6, 6),
("MS", 7, 6, 8),
("AL", 8, 6, 11),
("GA", 9, 6, 14),
("FL", 10, 6, 24),
("AK", 0, 7, 28),
("HI", 1, 7, 34),
]
df = pd.DataFrame(records, columns=["state", "col", "row", "renewable_pct"])
df["row_pos"] = -df["row"] # Flip so row 0 is at top (geographic north)
# Text contrast: white on dark viridis tiles, dark on bright viridis tiles
mid = (df["renewable_pct"].min() + df["renewable_pct"].max()) / 2
df["label_color"] = df["renewable_pct"].apply(lambda v: "#FFFFFF" if v < mid else "#1A1A17")
# Theme
anyplot_theme = theme(
figure_size=(16, 9),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_blank(),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
plot_title=element_text(color=INK, size=22),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=14),
legend_title=element_text(color=INK, size=16),
legend_position="right",
)
# Plot
plot = (
ggplot(df, aes(x="col", y="row_pos", fill="renewable_pct"))
+ geom_tile(color=PAGE_BG, size=1.5)
+ geom_text(aes(label="state", color="label_color"), size=11, fontweight="bold")
+ scale_fill_cmap(cmap_name="viridis", name="Renewable\nEnergy (%)")
+ scale_color_identity()
+ coord_fixed()
+ labs(title="US Renewable Energy Share · map-tilegrid · plotnine · anyplot.ai")
+ anyplot_theme
)
# Save
plot.save(f"plot-{THEME}.png", dpi=300, width=16, height=9)
Part of Tile Grid Map for Equal-Area Geographic Comparison on anyplot.ai.