A map visualization that displays data points on top of tile-based backgrounds such as OpenStreetMap, Stamen, or satellite imagery. Unlike vector-based basemaps, tile backgrounds provide rich geographic context including street-level detail, terrain, or satellite imagery that loads dynamically as the user navigates. This visualization is essential for location-based analysis where real-world geographic context enhances data interpretation.
""" anyplot.ai
map-tile-background: Map with Tile Background
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 82/100 | Updated: 2026-05-27
"""
import os
import sys
import numpy as np
import pandas as pd
# Work around naming conflict with plotnine.py script and plotnine package
script_dir = os.path.dirname(os.path.abspath(__file__))
if script_dir in sys.path:
sys.path.remove(script_dir)
if "" in sys.path:
sys.path.remove("")
if "." in sys.path:
sys.path.remove(".")
from plotnine import (
aes,
annotate,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_label,
geom_point,
geom_polygon,
geom_rect,
ggplot,
labs,
scale_color_manual,
scale_fill_manual,
scale_size_continuous,
theme,
theme_minimal,
)
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
np.random.seed(42)
# San Francisco Bay Area landmarks with visitor counts (thousands per year)
landmarks_data = {
"name": [
"Golden Gate Bridge",
"Alcatraz Island",
"Fisherman's Wharf",
"Pier 39",
"Cable Cars",
"Chinatown",
"Union Square",
"Ferry Building",
"Palace of Fine Arts",
"Coit Tower",
"AT&T Park",
"Exploratorium",
"de Young Museum",
"California Academy",
"Lombard Street",
],
"lat": [
37.8199,
37.8267,
37.8080,
37.8087,
37.7873,
37.7941,
37.7879,
37.7955,
37.8020,
37.8024,
37.7786,
37.8016,
37.7714,
37.7699,
37.8021,
],
"lon": [
-122.4783,
-122.4230,
-122.4177,
-122.4098,
-122.4119,
-122.4070,
-122.4075,
-122.3935,
-122.4486,
-122.4058,
-122.3893,
-122.3976,
-122.4687,
-122.4663,
-122.4187,
],
"visitors": [10500, 1700, 12000, 10000, 7000, 2000, 15000, 6000, 2000, 500, 3500, 1000, 1200, 2500, 2000],
"category": [
"Landmark",
"Historic",
"Tourism",
"Tourism",
"Transport",
"Cultural",
"Shopping",
"Tourism",
"Landmark",
"Landmark",
"Sports",
"Museum",
"Museum",
"Museum",
"Landmark",
],
}
df = pd.DataFrame(landmarks_data)
# Simulated tile-style background using grid rectangles
lon_min, lon_max = -122.52, -122.36
lat_min, lat_max = 37.755, 37.84
n_tiles_x = 10
n_tiles_y = 8
tile_width = (lon_max - lon_min) / n_tiles_x
tile_height = (lat_max - lat_min) / n_tiles_y
# Theme-adaptive terrain colors
water_color = "#B8D4E8" if THEME == "light" else "#1D2E3A"
land_color = "#E8E4D8" if THEME == "light" else "#2B2820"
coast_color = "#8A7A6B" if THEME == "light" else "#7A7268"
tiles = []
for i in range(n_tiles_x):
for j in range(n_tiles_y):
x_center = lon_min + tile_width * (i + 0.5)
y_center = lat_min + tile_height * (j + 0.5)
is_water = (
(x_center > -122.39 and y_center < 37.79)
or (x_center > -122.44 and y_center > 37.825)
or (x_center > -122.37)
)
tiles.append(
{
"xmin": lon_min + tile_width * i,
"xmax": lon_min + tile_width * (i + 1),
"ymin": lat_min + tile_height * j,
"ymax": lat_min + tile_height * (j + 1),
"terrain": "water" if is_water else "land",
}
)
df_tiles = pd.DataFrame(tiles)
# Coastline polygon (San Francisco peninsula outline)
coast_coords = [
(-122.52, 37.755),
(-122.48, 37.755),
(-122.42, 37.76),
(-122.39, 37.77),
(-122.37, 37.785),
(-122.36, 37.80),
(-122.38, 37.815),
(-122.42, 37.82),
(-122.46, 37.825),
(-122.50, 37.82),
(-122.52, 37.80),
(-122.52, 37.755),
]
coastline = [{"region": "sf", "order": i, "lon": c[0], "lat": c[1]} for i, c in enumerate(coast_coords)]
df_coast = pd.DataFrame(coastline)
# Imprint palette assigned alphabetically by category
categories_sorted = sorted(df["category"].unique())
category_colors = {cat: IMPRINT_PALETTE[i] for i, cat in enumerate(categories_sorted)}
# Labels for top 3 most-visited landmarks (well-separated geographically)
top3 = df.nlargest(3, "visitors") # Union Square, Fisherman's Wharf, Golden Gate Bridge
label_positions = {
"Union Square": {"nudge_x": 0.025, "nudge_y": 0.012},
"Fisherman's Wharf": {"nudge_x": -0.035, "nudge_y": 0.014},
"Golden Gate Bridge": {"nudge_x": 0.015, "nudge_y": 0.014},
}
label_records = []
for _, row in top3.iterrows():
pos = label_positions.get(row["name"], {"nudge_x": 0, "nudge_y": 0.012})
label_records.append({"name": row["name"], "lon": row["lon"] + pos["nudge_x"], "lat": row["lat"] + pos["nudge_y"]})
label_df = pd.DataFrame(label_records)
title = "map-tile-background · python · plotnine · anyplot.ai"
plot = (
ggplot()
+ geom_rect(
aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", fill="terrain"),
data=df_tiles,
color=INK_MUTED,
size=0.05,
alpha=0.85,
)
+ scale_fill_manual(values={"water": water_color, "land": land_color}, guide=None)
+ geom_polygon(aes(x="lon", y="lat", group="region"), data=df_coast, fill="none", color=coast_color, size=0.8)
+ geom_point(aes(x="lon", y="lat", color="category", size="visitors"), data=df, alpha=0.88, stroke=0.4)
+ scale_size_continuous(range=(2, 10), name="Visitors\n(K/yr)")
+ scale_color_manual(values=category_colors, name="Category")
+ geom_label(
aes(x="lon", y="lat", label="name"),
data=label_df,
size=3.5,
alpha=0.92,
fill=ELEVATED_BG,
color=INK,
label_padding=0.2,
)
+ annotate(
"text",
x=lon_max - 0.003,
y=lat_min + 0.003,
label="Simulated tiles · SF landmarks",
size=2.5,
ha="right",
va="bottom",
color=INK_MUTED,
)
+ coord_fixed(ratio=1.06, xlim=(lon_min, lon_max), ylim=(lat_min, lat_max))
+ labs(title=title, x="Longitude (°)", y="Latitude (°)")
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
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_rect(color=INK_SOFT, fill=None),
plot_title=element_text(size=12, weight="bold", ha="center", color=INK),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=9, color=INK),
legend_text=element_text(size=8, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_position="right",
legend_key_size=10,
plot_margin=0.01,
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Map with Tile Background on anyplot.ai.