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: matplotlib 3.10.9 | Python 3.13.13
Quality: 86/100 | Created: 2026-05-14
"""
import os
import matplotlib
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
from matplotlib.colors import Normalize
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# US states tile grid layout: (abbreviation, row, col, renewable_energy_pct)
states = [
("ME", 0, 10, 72),
("MT", 1, 3, 65),
("ND", 1, 4, 54),
("MN", 1, 5, 48),
("WI", 1, 6, 22),
("MI", 1, 7, 18),
("VT", 1, 9, 88),
("NH", 1, 10, 32),
("WA", 2, 0, 84),
("ID", 2, 1, 78),
("WY", 2, 2, 15),
("SD", 2, 3, 81),
("IA", 2, 4, 62),
("IL", 2, 5, 24),
("IN", 2, 6, 11),
("OH", 2, 7, 10),
("PA", 2, 8, 16),
("NY", 2, 9, 38),
("MA", 2, 10, 19),
("OR", 3, 0, 72),
("NV", 3, 1, 42),
("CO", 3, 2, 35),
("NE", 3, 3, 43),
("MO", 3, 4, 17),
("KY", 3, 5, 9),
("WV", 3, 6, 6),
("VA", 3, 7, 21),
("NJ", 3, 8, 11),
("CT", 3, 9, 14),
("RI", 3, 10, 22),
("CA", 4, 0, 58),
("UT", 4, 1, 24),
("AZ", 4, 2, 38),
("KS", 4, 3, 55),
("AR", 4, 4, 18),
("TN", 4, 5, 16),
("NC", 4, 6, 24),
("SC", 4, 7, 18),
("MD", 4, 8, 12),
("DE", 4, 9, 10),
("NM", 5, 2, 45),
("OK", 5, 3, 52),
("LA", 5, 4, 11),
("MS", 5, 5, 8),
("AL", 5, 6, 13),
("GA", 5, 7, 17),
("TX", 6, 3, 28),
("FL", 6, 7, 20),
("DC", 6, 8, 5),
("AK", 7, 0, 30),
("HI", 7, 1, 36),
]
# Color mapping — sequential viridis for unipolar renewable energy data
values = [v for _, _, _, v in states]
vmin, vmax = min(values), max(values)
norm = Normalize(vmin=vmin, vmax=vmax)
cmap = matplotlib.colormaps["viridis"]
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
TILE = 0.82
PAD = (1.0 - TILE) / 2.0
for abbr, row, col, value in states:
tile_color = cmap(norm(value))
rect = mpatches.Rectangle(
(col + PAD, -(row + 1) + PAD), TILE, TILE, facecolor=tile_color, edgecolor=PAGE_BG, linewidth=2.0, zorder=2
)
ax.add_patch(rect)
r_c, g_c, b_c = tile_color[:3]
lum = 0.2126 * r_c + 0.7152 * g_c + 0.0722 * b_c
label_color = "#1A1A17" if lum > 0.45 else "#F0EFE8"
ax.text(
col + 0.5,
-(row + 0.5),
abbr,
ha="center",
va="center",
fontsize=13,
fontweight="bold",
color=label_color,
zorder=3,
)
# Axes bounds — grid spans cols 0–10 (12 units) × rows 0–7 (8.5 units)
ax.set_xlim(-0.5, 11.5)
ax.set_ylim(-8.5, 0.5)
ax.set_aspect("equal", adjustable="box")
ax.axis("off")
# Colorbar
sm = ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = plt.colorbar(sm, ax=ax, fraction=0.025, pad=0.04, shrink=0.75, aspect=25)
cbar.set_label("Renewable Energy Share (%)", fontsize=16, color=INK_SOFT, labelpad=10)
cbar.ax.tick_params(labelsize=13, labelcolor=INK_SOFT, colors=INK_SOFT)
cbar.outline.set_edgecolor(INK_SOFT)
# Title
ax.set_title(
"US Renewable Energy Share · map-tilegrid · matplotlib · anyplot.ai",
fontsize=22,
fontweight="medium",
color=INK,
pad=15,
)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Tile Grid Map for Equal-Area Geographic Comparison on anyplot.ai.