A geographic map visualization showing connection lines (arcs or great circles) between pairs of locations. This plot excels at revealing spatial patterns in flows, routes, and relationships between places. The curved lines naturally represent shortest paths on Earth's surface while avoiding visual overlap with straight lines. Line properties like thickness or color can encode additional variables such as traffic volume or connection type.
""" anyplot.ai
map-connection-lines: Connection Lines Map (Origin-Destination)
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-28
"""
import os
import sys
# Prevent self-import (this file shares its name with the plotnine library)
_here = os.path.abspath(os.path.dirname(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p or ".") != _here]
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_cartesian,
element_blank,
element_line,
element_rect,
element_text,
geom_path,
geom_point,
geom_polygon,
ggplot,
labs,
scale_color_gradient,
scale_size_continuous,
theme,
theme_minimal,
)
# 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"
BRAND = "#009E73" # Imprint palette position 1
LAND_FILL = "#E0DED4" if THEME == "light" else "#282824"
LAND_BORDER = "#BCBBB1" if THEME == "light" else "#3A3A30"
TITLE = "Global Flight Routes · map-connection-lines · python · plotnine · anyplot.ai"
_n = len(TITLE)
TITLE_SIZE = max(8, round(12 * 67 / _n)) if _n > 67 else 12
np.random.seed(42)
# Major international airports: code → (city, lat, lon)
airports = {
"JFK": ("New York", 40.64, -73.78),
"LAX": ("Los Angeles", 33.94, -118.41),
"LHR": ("London", 51.47, -0.46),
"CDG": ("Paris", 49.01, 2.55),
"DXB": ("Dubai", 25.25, 55.36),
"HND": ("Tokyo", 35.55, 139.78),
"SIN": ("Singapore", 1.36, 103.99),
"SYD": ("Sydney", -33.95, 151.18),
"GRU": ("São Paulo", -23.43, -46.47),
"JNB": ("Johannesburg", -26.14, 28.25),
"FRA": ("Frankfurt", 50.03, 8.57),
"HKG": ("Hong Kong", 22.31, 113.92),
"PEK": ("Beijing", 40.08, 116.58),
"ORD": ("Chicago", 41.97, -87.91),
"MIA": ("Miami", 25.79, -80.29),
}
# Flight routes: (origin, destination, annual passengers in thousands)
routes = [
("JFK", "LHR", 4200),
("JFK", "CDG", 2800),
("LAX", "HND", 3500),
("LAX", "SYD", 1800),
("LHR", "DXB", 3100),
("LHR", "SIN", 2600),
("LHR", "HKG", 2400),
("CDG", "JFK", 2900),
("DXB", "SIN", 2200),
("DXB", "LHR", 3000),
("HND", "SIN", 1900),
("SIN", "SYD", 2100),
("GRU", "MIA", 1500),
("GRU", "LHR", 1700),
("JNB", "LHR", 1400),
("JNB", "DXB", 1600),
("FRA", "JFK", 2300),
("FRA", "DXB", 1800),
("HKG", "LAX", 2000),
("HKG", "SIN", 2500),
("PEK", "LAX", 2200),
("PEK", "LHR", 1900),
("ORD", "LHR", 2100),
("ORD", "FRA", 1700),
("MIA", "GRU", 1400),
]
# Build flight path dataframe — great circle arcs computed inline
flight_paths = []
N_PTS = 50
for route_i, (origin, dest, volume) in enumerate(routes):
_, olat, olon = airports[origin]
_, dlat, dlon = airports[dest]
lon1_r, lat1_r = np.radians(olon), np.radians(olat)
lon2_r, lat2_r = np.radians(dlon), np.radians(dlat)
d = np.arccos(
np.clip(np.sin(lat1_r) * np.sin(lat2_r) + np.cos(lat1_r) * np.cos(lat2_r) * np.cos(lon2_r - lon1_r), -1.0, 1.0)
)
if d < 1e-10:
arc_lons = np.array([olon, dlon])
arc_lats = np.array([olat, dlat])
else:
t = np.linspace(0, 1, N_PTS)
a_c = np.sin((1 - t) * d) / np.sin(d)
b_c = np.sin(t * d) / np.sin(d)
x = a_c * np.cos(lat1_r) * np.cos(lon1_r) + b_c * np.cos(lat2_r) * np.cos(lon2_r)
y = a_c * np.cos(lat1_r) * np.sin(lon1_r) + b_c * np.cos(lat2_r) * np.sin(lon2_r)
z = a_c * np.sin(lat1_r) + b_c * np.sin(lat2_r)
arc_lats = np.degrees(np.arctan2(z, np.sqrt(x**2 + y**2)))
arc_lons = np.degrees(np.arctan2(y, x))
for step, (lon, lat) in enumerate(zip(arc_lons, arc_lats, strict=True)):
flight_paths.append({"route_id": route_i, "step": step, "lon": lon, "lat": lat, "volume": volume})
df_flights = pd.DataFrame(flight_paths)
# Airport endpoint markers
df_airports = pd.DataFrame([{"code": code, "lat": lat, "lon": lon} for code, (_, lat, lon) in airports.items()])
# Simplified continent polygons for basemap context
continents = []
for i, (lo, la) in enumerate(
zip(
[
-170,
-168,
-140,
-125,
-124,
-117,
-105,
-97,
-82,
-77,
-68,
-55,
-52,
-80,
-87,
-97,
-105,
-125,
-145,
-165,
-170,
],
[60, 65, 70, 55, 48, 33, 25, 26, 25, 35, 45, 48, 45, 27, 30, 20, 22, 50, 60, 55, 60],
strict=True,
)
):
continents.append({"continent": "N. America", "order": i, "lon": lo, "lat": la})
for i, (lo, la) in enumerate(
zip(
[-80, -68, -60, -50, -35, -40, -50, -55, -68, -72, -75, -80, -82, -80],
[10, 12, 5, 0, -5, -22, -35, -52, -55, -18, -5, 0, 8, 10],
strict=True,
)
):
continents.append({"continent": "S. America", "order": i, "lon": lo, "lat": la})
for i, (lo, la) in enumerate(
zip(
[-10, 0, 10, 20, 30, 40, 50, 60, 50, 35, 25, 20, 10, 0, -10, -10],
[35, 37, 36, 35, 35, 40, 45, 55, 70, 70, 70, 65, 60, 50, 40, 35],
strict=True,
)
):
continents.append({"continent": "Europe", "order": i, "lon": lo, "lat": la})
for i, (lo, la) in enumerate(
zip(
[-17, -5, 10, 35, 50, 52, 43, 35, 30, 15, 0, -17, -17],
[15, 37, 37, 32, 12, 0, -25, -35, -35, -25, 5, 20, 15],
strict=True,
)
):
continents.append({"continent": "Africa", "order": i, "lon": lo, "lat": la})
for i, (lo, la) in enumerate(
zip(
[60, 80, 100, 120, 140, 145, 140, 130, 105, 100, 80, 60, 45, 30, 25, 30, 35, 50, 60],
[55, 70, 75, 70, 55, 45, 35, 30, 0, 5, 10, 25, 30, 35, 42, 55, 70, 70, 55],
strict=True,
)
):
continents.append({"continent": "Asia", "order": i, "lon": lo, "lat": la})
for i, (lo, la) in enumerate(
zip(
[113, 125, 135, 145, 152, 150, 140, 130, 115, 113],
[-22, -15, -12, -15, -25, -38, -38, -33, -35, -22],
strict=True,
)
):
continents.append({"continent": "Australia", "order": i, "lon": lo, "lat": la})
df_continents = pd.DataFrame(continents)
# Plot
plot = (
ggplot()
+ geom_polygon(
aes(x="lon", y="lat", group="continent"),
data=df_continents,
fill=LAND_FILL,
color=LAND_BORDER,
size=0.3,
alpha=0.95,
)
+ geom_path(
aes(x="lon", y="lat", group="route_id", color="volume", size="volume"),
data=df_flights,
alpha=0.55,
lineend="round",
)
+ geom_point(
aes(x="lon", y="lat"), data=df_airports, color=INK_SOFT, fill=BRAND, size=3.5, shape="o", stroke=0.8, alpha=0.95
)
+ scale_color_gradient(low="#009E73", high="#4467A3", name="Passengers\n(thousands/yr)")
+ scale_size_continuous(range=(0.5, 2.0), guide=None)
+ coord_cartesian(xlim=(-180, 180), ylim=(-60, 80))
+ 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_line(color=INK, size=0.2, alpha=0.12),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
plot_title=element_text(size=TITLE_SIZE, color=INK, weight="bold"),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=8, color=INK),
legend_position="right",
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Connection Lines Map (Origin-Destination) on anyplot.ai.