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: pygal 3.1.0 | Python 3.13.13
Quality: 74/100 | Created: 2026-05-14
"""
import os
import sys
sys.path.pop(0) # remove script dir so "import pygal" finds the installed package, not this file
import matplotlib.colors as mcolors
import numpy as np
import pygal
from pygal.style import Style
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# US state tile grid positions (row, col) — geographic approximation
STATE_GRID = {
"AK": (0, 0),
"ME": (0, 10),
"WA": (1, 1),
"MT": (1, 2),
"ND": (1, 3),
"MN": (1, 4),
"WI": (1, 5),
"VT": (1, 9),
"NH": (1, 10),
"OR": (2, 1),
"ID": (2, 2),
"WY": (2, 3),
"SD": (2, 4),
"IA": (2, 5),
"MI": (2, 6),
"NY": (2, 7),
"MA": (2, 9),
"RI": (2, 10),
"CA": (3, 1),
"NV": (3, 2),
"CO": (3, 3),
"NE": (3, 4),
"IL": (3, 5),
"IN": (3, 6),
"OH": (3, 7),
"PA": (3, 8),
"NJ": (3, 9),
"CT": (3, 10),
"AZ": (4, 2),
"UT": (4, 3),
"KS": (4, 4),
"MO": (4, 5),
"KY": (4, 6),
"WV": (4, 7),
"VA": (4, 8),
"MD": (4, 9),
"DE": (4, 10),
"NM": (5, 3),
"OK": (5, 4),
"AR": (5, 5),
"TN": (5, 6),
"NC": (5, 7),
"SC": (5, 8),
"TX": (6, 4),
"LA": (6, 5),
"MS": (6, 6),
"AL": (6, 7),
"GA": (6, 8),
"HI": (7, 1),
"FL": (7, 9),
}
# Synthetic renewable energy % of total electricity generation
np.random.seed(42)
state_values = {s: round(float(np.random.uniform(18, 78)), 1) for s in STATE_GRID}
state_values.update(
{
"WA": 88.2, # Pacific Northwest hydropower
"OR": 72.1,
"ID": 81.5,
"MT": 65.3,
"WY": 18.5, # Coal-heavy states
"WV": 12.3,
"KY": 15.7,
"TX": 24.8,
"LA": 20.4,
"MS": 18.9,
"CA": 52.3,
"ND": 41.8,
"SD": 76.4, # High hydro + wind
}
)
# Quintile assignment using numpy digitize
values_array = np.array(list(state_values.values()))
quintile_bins = np.percentile(values_array, [20, 40, 60, 80])
quintile_assignments = {state: int(np.digitize(val, quintile_bins)) for state, val in state_values.items()}
# Viridis colors for 5 quintiles (dark purple = low, bright yellow = high)
viridis = mcolors.LinearSegmentedColormap.from_list(
"viridis_approx", ["#440154", "#482475", "#355f8d", "#21918c", "#44bf70", "#bddf26", "#fde725"]
)
quintile_hex = tuple(mcolors.to_hex(viridis(t)) for t in (0.1, 0.3, 0.5, 0.7, 0.9))
# Quintile legend labels
b = quintile_bins
quintile_labels = [
f"< {b[0]:.0f}% (lowest fifth)",
f"{b[0]:.0f}–{b[1]:.0f}%",
f"{b[1]:.0f}–{b[2]:.0f}%",
f"{b[2]:.0f}–{b[3]:.0f}%",
f"> {b[3]:.0f}% (highest fifth)",
]
# Organise data by quintile — offset grid by 1 to prevent edge clipping
quintile_data = [[] for _ in range(5)]
for state, (row, col) in STATE_GRID.items():
q = quintile_assignments[state]
quintile_data[q].append({"value": (col + 1, -(row + 1)), "label": f"{state}: {state_values[state]:.1f}%"})
# Pygal style
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=quintile_hex,
title_font_size=32,
label_font_size=22,
major_label_font_size=22,
legend_font_size=24,
value_font_size=50,
)
# Chart
chart = pygal.XY(
width=4800,
height=2700,
title="map-tilegrid · pygal · anyplot.ai",
stroke=False,
dots_size=50,
print_labels=True,
show_x_labels=False,
show_y_labels=False,
show_x_guides=False,
show_y_guides=False,
legend_at_bottom=True,
style=custom_style,
)
for q_data, q_label in zip(quintile_data, quintile_labels, strict=False):
chart.add(q_label, q_data)
# Save
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
f.write(chart.render())
Part of Tile Grid Map for Equal-Area Geographic Comparison on anyplot.ai.