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: pygal 3.1.0 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-28
"""
import os
import sys
# Remove cwd temporarily to resolve the pygal.py → pygal library name conflict
_cwd = sys.path[0] if sys.path else None
if _cwd:
sys.path.remove(_cwd)
import numpy as np
import pygal
from pygal.style import Style
if _cwd:
sys.path.insert(0, _cwd)
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
OCEAN_BG = "#E8F3F9" if THEME == "light" else "#0C1822"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
GRID_COLOR = "#1A1A1722" if THEME == "light" else "#F0EFE822"
# Data - Major international flight routes with passenger volumes (thousands/year)
np.random.seed(42)
airports = {
"JFK": (40.64, -73.78, "New York"),
"LAX": (33.94, -118.41, "Los Angeles"),
"LHR": (51.47, -0.46, "London"),
"CDG": (49.01, 2.55, "Paris"),
"DXB": (25.25, 55.36, "Dubai"),
"HND": (35.55, 139.78, "Tokyo"),
"SIN": (1.36, 103.99, "Singapore"),
"SYD": (-33.95, 151.18, "Sydney"),
"HKG": (22.31, 113.91, "Hong Kong"),
"FRA": (50.03, 8.57, "Frankfurt"),
"ORD": (41.98, -87.90, "Chicago"),
"PEK": (40.08, 116.58, "Beijing"),
"GRU": (-23.43, -46.47, "Sao Paulo"),
"JNB": (-26.13, 28.23, "Johannesburg"),
}
routes = [
("JFK", "LHR", 4200),
("JFK", "CDG", 2100),
("LAX", "HND", 3500),
("LAX", "SYD", 1800),
("LHR", "DXB", 3800),
("LHR", "HKG", 2900),
("LHR", "JFK", 4200),
("CDG", "JFK", 2100),
("DXB", "SIN", 3200),
("DXB", "LHR", 3800),
("HND", "SIN", 2400),
("HND", "LAX", 3500),
("SIN", "SYD", 2800),
("SIN", "HKG", 3100),
("HKG", "LHR", 2900),
("HKG", "SIN", 3100),
("FRA", "JFK", 2600),
("FRA", "DXB", 2200),
("ORD", "LHR", 2800),
("ORD", "FRA", 1900),
("PEK", "LAX", 2300),
("PEK", "FRA", 1700),
("GRU", "JFK", 1500),
("GRU", "LHR", 1200),
("JNB", "DXB", 1400),
("JNB", "LHR", 1100),
("SYD", "LAX", 1800),
("SYD", "SIN", 2800),
]
route_data = []
for origin, dest, volume in routes:
o_lat, o_lon, o_city = airports[origin]
d_lat, d_lon, d_city = airports[dest]
route_data.append(
{
"origin_lat": o_lat,
"origin_lon": o_lon,
"dest_lat": d_lat,
"dest_lon": d_lon,
"volume": volume,
"origin_city": o_city,
"dest_city": d_city,
}
)
coastlines = [
[(-125, 50), (-124, 45), (-122, 38), (-117, 33), (-110, 32), (-105, 28)],
[(-67, 45), (-70, 42), (-74, 40), (-76, 37), (-80, 32), (-81, 28), (-82, 25)],
[(-82, 25), (-85, 30), (-90, 30), (-95, 28), (-97, 26), (-105, 28)],
[(-125, 50), (-130, 55), (-141, 60), (-150, 61), (-165, 55), (-168, 65)],
[(-45, 60), (-40, 65), (-35, 70), (-25, 72), (-20, 65), (-30, 60), (-45, 60)],
[(-35, -6), (-38, -13), (-42, -23), (-48, -28), (-53, -33), (-58, -38), (-66, -55)],
[(-78, 10), (-80, 0), (-81, -5), (-77, -15), (-72, -30), (-75, -45), (-66, -55)],
[(-10, 36), (-9, 42), (-5, 44), (0, 43), (3, 43), (5, 47), (3, 51)],
[(3, 51), (5, 53), (8, 55), (10, 58), (18, 60), (25, 66), (28, 70)],
[(-10, 36), (0, 37), (10, 43), (18, 40), (23, 37), (26, 35), (30, 31)],
[(-17, 14), (-15, 12), (-13, 10), (-5, 5), (0, 6), (5, 4), (10, 5)],
[(30, 31), (34, 30), (42, 14), (48, 8), (45, 0), (40, -5), (35, -22), (27, -34)],
[(27, -34), (20, -34), (17, -30), (12, -17), (10, 5)],
[(30, 31), (35, 32), (42, 30), (50, 27), (55, 25), (60, 25)],
[(60, 25), (66, 24), (72, 22), (72, 8), (80, 8), (88, 22), (90, 22)],
[(90, 22), (100, 14), (104, 2), (102, -5), (106, -7), (110, -8)],
[(120, 32), (122, 37), (124, 40), (130, 43), (135, 44), (141, 45)],
[(100, 22), (106, 22), (110, 20), (117, 24), (120, 32)],
[(130, 32), (132, 34), (135, 35), (140, 36), (141, 41), (145, 44)],
[
(113, -22),
(130, -14),
(145, -15),
(150, -23),
(153, -28),
(150, -38),
(142, -38),
(130, -32),
(117, -35),
(113, -22),
],
[(173, -41), (175, -37), (178, -38), (177, -44), (170, -46), (168, -45), (173, -41)],
]
# Plot - colors ordered: routes first (pos 0-2 = brand green/lavender/blue),
# then airports (pos 3), then coastlines last (pos 4 = INK_MUTED)
custom_style = Style(
background=PAGE_BG,
plot_background=OCEAN_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
guide_stroke_color=GRID_COLOR,
guide_stroke_dasharray="2,6",
colors=(
"#009E73", # Routes < 2M (Imprint palette pos 0 — brand green, first series)
"#C475FD", # Routes 2-3M (Imprint palette pos 1)
"#4467A3", # Routes > 3M (Imprint palette pos 2)
INK, # Airports (theme-adaptive endpoint markers)
INK_MUTED, # Coastlines (geographic context, drawn last)
),
opacity=0.65,
opacity_hover=0.95,
title_font_size=66,
label_font_size=56,
major_label_font_size=44,
legend_font_size=44,
value_font_size=36,
stroke_width=2.5,
)
chart = pygal.XY(
width=3200,
height=1800,
style=custom_style,
title="map-connection-lines · python · pygal · anyplot.ai",
x_title="Longitude (°)",
y_title="Latitude (°)",
show_legend=True,
legend_at_bottom=True,
legend_at_bottom_columns=5,
legend_box_size=28,
stroke=True,
dots_size=3,
show_x_guides=True,
show_y_guides=True,
explicit_size=True,
print_values=False,
xrange=(-180, 180),
range=(-60, 80),
margin=70,
margin_top=120,
margin_bottom=130,
)
n_segments = 20
def build_bezier_curves(route_list):
curves = []
for route in route_list:
o_lat, o_lon = route["origin_lat"], route["origin_lon"]
d_lat, d_lon = route["dest_lat"], route["dest_lon"]
label = f"{route['origin_city']} → {route['dest_city']}: {route['volume']}K passengers"
mid_lon = (o_lon + d_lon) / 2
mid_lat = (o_lat + d_lat) / 2
dx, dy = d_lon - o_lon, d_lat - o_lat
length = np.sqrt(dx * dx + dy * dy)
if length > 0:
perp_x, perp_y = -dy / length, dx / length
# Pacific-crossing routes: flip perpendicular to arc northward over the pole
if abs(d_lon - o_lon) > 150:
perp_x, perp_y = -perp_x, -perp_y
offset_amount = min(length * 0.15, 20.0)
ctrl_lon = mid_lon + perp_x * offset_amount
ctrl_lat = mid_lat + perp_y * offset_amount
else:
ctrl_lon, ctrl_lat = mid_lon, mid_lat
for i in range(n_segments + 1):
t = i / n_segments
lon = (1 - t) ** 2 * o_lon + 2 * (1 - t) * t * ctrl_lon + t**2 * d_lon
lat = (1 - t) ** 2 * o_lat + 2 * (1 - t) * t * ctrl_lat + t**2 * d_lat
curves.append({"value": (lon, lat), "label": label})
curves.append({"value": (None, None)})
return curves
low_routes = [r for r in route_data if r["volume"] < 2000]
medium_routes = [r for r in route_data if 2000 <= r["volume"] <= 3000]
high_routes = [r for r in route_data if r["volume"] > 3000]
# Routes added first — occupy palette positions 0-2 (brand green, lavender, blue)
chart.add(
"Routes < 2M",
build_bezier_curves(low_routes),
stroke=True,
show_dots=False,
stroke_style={"width": 3, "linecap": "round", "opacity": 0.55},
)
chart.add(
"Routes 2-3M",
build_bezier_curves(medium_routes),
stroke=True,
show_dots=False,
stroke_style={"width": 5, "linecap": "round", "opacity": 0.60},
)
chart.add(
"Routes > 3M",
build_bezier_curves(high_routes),
stroke=True,
show_dots=False,
stroke_style={"width": 8, "linecap": "round", "opacity": 0.65},
)
# Airport markers — palette position 3 (INK, theme-adaptive)
airport_points = []
for code, (lat, lon, city) in airports.items():
airport_points.append({"value": (lon, lat), "label": f"{city} ({code})"})
chart.add("Airports", airport_points, stroke=False, dots_size=16)
# Coastlines last — palette position 4 (INK_MUTED), low opacity so routes show through
coastline_points = []
for coastline in coastlines:
for lon, lat in coastline:
coastline_points.append({"value": (lon, lat), "label": "Coastline"})
coastline_points.append({"value": (None, None)})
chart.add("Coastlines", coastline_points, stroke=True, show_dots=False, stroke_style={"width": 2, "opacity": 0.25})
# Save
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
f.write(chart.render())
Part of Connection Lines Map (Origin-Destination) on anyplot.ai.