An origin-destination flow map visualizes movement or transfer between geographic locations using curved arcs overlaid on a map. Each arc connects an origin point to a destination point, with line thickness proportional to the flow magnitude. This visualization excels at revealing spatial patterns in migration, trade, or travel data, making it easy to identify major corridors, hub locations, and directional imbalances in movement between places.
""" anyplot.ai
flowmap-origin-destination: Origin-Destination Flow Map
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 82/100 | Updated: 2026-05-20
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_rect,
element_text,
geom_curve,
geom_point,
geom_polygon,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
scale_color_viridis,
scale_size,
theme,
theme_void,
xlim,
ylim,
)
LetsPlot.setup_html()
# 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"
CONTINENT_FILL = "#E0DDD6" if THEME == "light" else "#2E2E2B"
CONTINENT_BORDER = "#C8C5BE" if THEME == "light" else "#4A4A47"
HUB_COLOR = "#4467A3"
# Data
np.random.seed(42)
hubs = {
"Los Angeles": (-118.24, 34.05),
"New York": (-74.01, 40.71),
"London": (-0.13, 51.51),
"Rotterdam": (4.48, 51.92),
"Dubai": (55.27, 25.20),
"Singapore": (103.82, 1.35),
"Shanghai": (121.47, 31.23),
"Tokyo": (139.69, 35.69),
"Sydney": (151.21, -33.87),
"Sao Paulo": (-46.63, -23.55),
}
label_offsets = {
"Los Angeles": (-5, 4),
"New York": (4, 2),
"London": (-20, 4),
"Rotterdam": (4, 2),
"Dubai": (4, 2),
"Singapore": (4, -5),
"Shanghai": (4, 2),
"Tokyo": (4, 2),
"Sydney": (4, -5),
"Sao Paulo": (-14, -5),
}
flows = [
("Shanghai", "Los Angeles", 85),
("Shanghai", "Rotterdam", 72),
("Singapore", "Rotterdam", 65),
("Tokyo", "Los Angeles", 58),
("Rotterdam", "New York", 52),
("Dubai", "London", 48),
("Shanghai", "Singapore", 45),
("Los Angeles", "Tokyo", 42),
("Singapore", "Sydney", 38),
("Sao Paulo", "Rotterdam", 35),
("New York", "London", 32),
("Dubai", "Singapore", 30),
("Shanghai", "Dubai", 28),
("Rotterdam", "Dubai", 25),
("London", "New York", 22),
("Sydney", "Singapore", 20),
("Tokyo", "Shanghai", 18),
("Los Angeles", "Shanghai", 15),
]
flow_data = []
for origin, dest, volume in flows:
o_lon, o_lat = hubs[origin]
d_lon, d_lat = hubs[dest]
flow_data.append(
{
"origin_name": origin,
"dest_name": dest,
"origin_lon": o_lon,
"origin_lat": o_lat,
"dest_lon": d_lon,
"dest_lat": d_lat,
"flow": volume,
}
)
df_flows = pd.DataFrame(flow_data)
hub_data = []
for name, (lon, lat) in hubs.items():
lx, ly = label_offsets.get(name, (4, 2))
hub_data.append({"name": name, "lon": lon, "lat": lat, "label_lon": lon + lx, "label_lat": lat + ly})
df_hubs = pd.DataFrame(hub_data)
# Simplified world polygons
world_coords = [
# North America
(-170, 70),
(-140, 70),
(-120, 60),
(-100, 50),
(-80, 45),
(-70, 45),
(-60, 50),
(-55, 50),
(-55, 45),
(-80, 25),
(-100, 20),
(-120, 30),
(-130, 50),
(-170, 60),
(-170, 70),
(None, None),
# South America
(-80, 10),
(-60, 5),
(-35, -5),
(-40, -20),
(-55, -25),
(-70, -55),
(-75, -45),
(-80, -5),
(-80, 10),
(None, None),
# Europe/Africa
(-10, 60),
(30, 70),
(40, 65),
(30, 45),
(10, 35),
(-10, 35),
(-20, 15),
(50, 10),
(45, -35),
(20, -35),
(10, 5),
(-20, 10),
(-10, 60),
(None, None),
# Asia
(30, 70),
(70, 75),
(180, 70),
(160, 60),
(140, 50),
(130, 45),
(120, 30),
(105, 20),
(90, 25),
(70, 25),
(55, 25),
(45, 30),
(35, 35),
(30, 45),
(30, 70),
(None, None),
# Australia
(115, -20),
(150, -10),
(155, -25),
(150, -40),
(135, -35),
(115, -35),
(115, -20),
]
polygons = []
current_poly = []
for lon, lat in world_coords:
if lon is None:
if current_poly:
polygons.append(current_poly)
current_poly = []
else:
current_poly.append((lon, lat))
if current_poly:
polygons.append(current_poly)
world_data = []
for i, poly in enumerate(polygons):
for lon, lat in poly:
world_data.append({"x": lon, "y": lat, "group": i})
df_world = pd.DataFrame(world_data)
# Plot
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
plot_title=element_text(size=16, hjust=0.5, color=INK),
legend_title=element_text(size=12, color=INK),
legend_text=element_text(size=10, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_position="right",
)
plot = (
ggplot()
+ geom_polygon(
data=df_world, mapping=aes(x="x", y="y", group="group"), fill=CONTINENT_FILL, color=CONTINENT_BORDER, size=0.3
)
+ geom_curve(
data=df_flows,
mapping=aes(x="origin_lon", y="origin_lat", xend="dest_lon", yend="dest_lat", size="flow", color="flow"),
curvature=-0.3,
alpha=0.6,
)
+ geom_point(data=df_hubs, mapping=aes(x="lon", y="lat"), size=7, color=HUB_COLOR, fill=HUB_COLOR)
+ geom_text(data=df_hubs, mapping=aes(x="label_lon", y="label_lat", label="name"), size=8, color=INK_SOFT)
+ scale_size(range=[1, 6], name="Trade Volume")
+ scale_color_viridis(option="viridis", name="Trade Volume")
+ labs(title="flowmap-origin-destination · python · letsplot · anyplot.ai")
+ theme_void()
+ anyplot_theme
+ ggsize(800, 450)
+ xlim(-180, 180)
+ ylim(-60, 85)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Origin-Destination Flow Map on anyplot.ai.