A choropleth map visualizes data by shading geographic regions (countries, states, or counties) according to a measured variable. This technique is ideal for showing regional patterns and spatial distributions, making it easy to identify areas with high or low values at a glance. The color intensity represents the data magnitude, creating an intuitive way to understand geographic variation.
""" anyplot.ai
choropleth-basic: Choropleth Map with Regional Coloring
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 73/100 | Updated: 2026-05-15
"""
import math
from bokeh.io import export_png
from bokeh.models import ColorBar, ColumnDataSource, LabelSet, LogColorMapper
from bokeh.palettes import Blues9
from bokeh.plotting import figure
# Data: US states arranged in a tile grid map layout (all 50 states + DC)
# Grid positions (col, row) - approximating geographic positions
regions = {
"AK": (0, 5),
"HI": (0, 1),
"WA": (1, 5),
"OR": (1, 4),
"CA": (0, 3),
"NV": (1, 3),
"ID": (2, 5),
"MT": (3, 5),
"WY": (3, 4),
"UT": (2, 3),
"AZ": (2, 2),
"CO": (3, 3),
"NM": (3, 2),
"ND": (4, 5),
"SD": (4, 4),
"NE": (4, 3),
"KS": (4, 2),
"OK": (4, 1),
"TX": (3, 1),
"MN": (5, 5),
"IA": (5, 4),
"MO": (5, 3),
"AR": (5, 2),
"LA": (5, 1),
"WI": (6, 5),
"IL": (6, 4),
"IN": (7, 4),
"MI": (7, 5),
"OH": (8, 4),
"KY": (7, 3),
"TN": (6, 3),
"MS": (6, 2),
"AL": (7, 2),
"GA": (8, 2),
"FL": (8, 1),
"SC": (9, 2),
"NC": (9, 3),
"VA": (9, 4),
"WV": (8, 3),
"PA": (10, 4),
"NY": (10, 5),
"VT": (11, 5),
"NH": (11, 4),
"ME": (12, 5),
"MA": (11, 3),
"RI": (12, 3),
"CT": (11, 2),
"NJ": (10, 3),
"DE": (10, 2),
"MD": (9, 1),
"DC": (10, 1),
}
# Population density values (people per sq mile) - realistic ranges
density_values = {
"AK": 1,
"HI": 226,
"CA": 253,
"TX": 112,
"FL": 411,
"NY": 408,
"PA": 286,
"IL": 227,
"OH": 289,
"GA": 185,
"NC": 218,
"MI": 177,
"NJ": 1263,
"VA": 218,
"WA": 117,
"AZ": 64,
"MA": 901,
"TN": 167,
"IN": 189,
"MO": 89,
"MD": 636,
"WI": 108,
"CO": 57,
"MN": 71,
"SC": 173,
"AL": 99,
"LA": 107,
"KY": 114,
"OR": 44,
"OK": 58,
"CT": 733,
"IA": 57,
"MS": 63,
"AR": 58,
"UT": 40,
"NV": 28,
"KS": 36,
"NM": 17,
"NE": 25,
"WV": 74,
"ID": 23,
"ME": 44,
"NH": 154,
"RI": 1061,
"MT": 8,
"DE": 508,
"SD": 12,
"ND": 11,
"VT": 68,
"WY": 6,
# DC intentionally missing to demonstrate missing data handling
}
# Prepare data for rectangles
xs = []
ys = []
widths = []
heights = []
colors = []
abbrevs = []
abbrev_x = []
abbrev_y = []
# Color mapping - use log scale to better differentiate low-density states
min_val = max(1, min(density_values.values())) # Log scale needs min > 0
max_val = max(density_values.values())
palette = list(reversed(Blues9))
color_mapper = LogColorMapper(palette=palette, low=min_val, high=max_val, nan_color="#d0d0d0")
# Process each region - rect is centered, so adjust coords to center of tile
for abbrev, (col, row) in regions.items():
center_x = col * 1.1 + 0.5
center_y = row * 1.1 + 0.5
xs.append(center_x)
ys.append(center_y)
widths.append(1.0)
heights.append(1.0)
abbrev_x.append(center_x)
abbrev_y.append(center_y)
abbrevs.append(abbrev)
if abbrev in density_values:
density = density_values[abbrev]
# Map to color index using log scale for better low-value differentiation
log_min = math.log10(min_val)
log_max = math.log10(max_val)
log_val = math.log10(max(density, 1)) # Ensure positive for log
norm = (log_val - log_min) / (log_max - log_min)
idx = min(int(norm * (len(palette) - 1)), len(palette) - 1)
colors.append(palette[idx])
else:
# Missing data - gray with pattern indication
colors.append("#d0d0d0")
# Create data sources
rect_source = ColumnDataSource(data={"x": xs, "y": ys, "width": widths, "height": heights, "fill_color": colors})
# Determine text colors based on background (using log scale)
text_colors = []
log_min = math.log10(min_val)
log_max = math.log10(max_val)
for abbrev in abbrevs:
if abbrev not in density_values:
text_colors.append("#333333")
else:
density = density_values[abbrev]
log_val = math.log10(max(density, 1))
norm = (log_val - log_min) / (log_max - log_min)
text_colors.append("white" if norm > 0.5 else "#306998")
label_source = ColumnDataSource(data={"x": abbrev_x, "y": abbrev_y, "text": abbrevs, "text_color": text_colors})
# Create figure (4800x2700 for landscape)
p = figure(
width=4800,
height=2700,
title="choropleth-basic · bokeh · pyplots.ai",
x_axis_location=None,
y_axis_location=None,
tools="",
toolbar_location=None,
x_range=(-1, 14),
y_range=(0, 7),
)
# Remove grid and axes
p.grid.grid_line_color = None
p.outline_line_color = None
p.xaxis.visible = False
p.yaxis.visible = False
# Draw rectangles for each state
p.rect(
x="x",
y="y",
width="width",
height="height",
source=rect_source,
fill_color="fill_color",
fill_alpha=0.9,
line_color="white",
line_width=2,
)
# Add state abbreviation labels
labels = LabelSet(
x="x",
y="y",
text="text",
text_color="text_color",
source=label_source,
text_align="center",
text_baseline="middle",
text_font_size="18pt",
text_font_style="bold",
)
p.add_layout(labels)
# Title styling
p.title.text_font_size = "32pt"
p.title.align = "center"
# Color bar for legend - made larger for prominence
color_bar = ColorBar(
color_mapper=color_mapper,
width=60,
height=1200,
location=(0, 0),
title="Population Density (per sq mile)",
title_text_font_size="24pt",
major_label_text_font_size="20pt",
title_standoff=20,
padding=30,
margin=40,
)
p.add_layout(color_bar, "right")
# Save output
export_png(p, filename="plot.png")
Part of Choropleth Map with Regional Coloring on anyplot.ai.