Flow Map
Chart overview
Flow maps visualize movement between locations using lines or arrows, with width often proportional to volume.
Key points
- They effectively display migration patterns, trade routes, transportation flows, and any origin-destination data with geographic context.
- The core encoding is an origin-destination (OD) matrix draped over real geography, so a flow map is the right choice only when the spatial positions themselves carry meaning — where flows originate and terminate, which regions are hubs, how distance shapes movement.
- If geography is incidental and you only care about the volume of exchange between entities, a chord diagram or a Sankey diagram shows the same OD data with far less visual clutter.
Practical guidance
The perennial problem with flow maps is overplotting: dozens of straight great-circle lines crossing a continent become an unreadable hairball. Mitigate it by using curved or bundled edges, scaling line width (not just color) to volume, drawing the largest flows last so they sit on top, and filtering to the top-N flows or a single origin. In Python, folium and plotly render interactive flow maps with pan and zoom, while geopandas plus matplotlib gives publication-ready static maps; project coordinates appropriately (avoid raw lat/lon on a flat axis for anything beyond a small region) and always include a width legend so readers can decode volume.
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Python Tutorial
How to create a flow map in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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View example prompt
"Create an interactive flow map showing 'US Interstate Migration Patterns' between major metropolitan areas. Generate realistic migration data for flows between 15 cities: top outflows from California (LA, SF) and New York to Texas (Austin, Dallas, Houston), Florida (Miami, Tampa), and Arizona (Phoenix). Flow volumes range from 5,000 to 50,000 annual migrants. Draw curved arrows between origin and destination. Arrow width proportional to migration volume. Color by net flow direction: blue for inflows, red for outflows. Animate flows with moving particles. Size city markers by population. Add hover showing origin, destination, annual migrants, and percentage of origin population. Include a summary panel showing top gainers and losers. Title: 'US Domestic Migration Flows 2023'."
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Python Code Example
# === IMPORTS ===
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import FancyArrowPatch, Circle
import matplotlib.patheffects as pe
# === USER-EDITABLE PARAMETERS ===
title = "US Domestic Migration Flows 2023"
figsize = (16, 10)
# === EXAMPLE DATASET ===
# Major cities with coordinates (lat, lon) - simplified for plotting
cities = {
'Los Angeles': {'pos': (0.15, 0.35), 'pop': 3.9},
'San Francisco': {'pos': (0.08, 0.55), 'pop': 0.87},
'Seattle': {'pos': (0.12, 0.85), 'pop': 0.75},
'Phoenix': {'pos': (0.28, 0.25), 'pop': 1.6},
'Denver': {'pos': (0.38, 0.55), 'pop': 0.72},
'Dallas': {'pos': (0.52, 0.22), 'pop': 1.3},
'Houston': {'pos': (0.55, 0.12), 'pop': 2.3},
'Austin': {'pos': (0.48, 0.18), 'pop': 1.0},
'Chicago': {'pos': (0.62, 0.65), 'pop': 2.7},
'Miami': {'pos': (0.82, 0.08), 'pop': 0.45},
'New York': {'pos': (0.88, 0.68), 'pop': 8.3},
'Boston': {'pos': (0.92, 0.78), 'pop': 0.68},
}
# Migration flows: (origin, destination, migrants in thousands)
flows = [
('Los Angeles', 'Austin', 35),
('Los Angeles', 'Phoenix', 28),
('Los Angeles', 'Dallas', 22),
('San Francisco', 'Austin', 25),
('San Francisco', 'Denver', 18),
('San Francisco', 'Seattle', 15),
('New York', 'Miami', 45),
('New York', 'Austin', 20),
('New York', 'Dallas', 18),
('Chicago', 'Austin', 15),
('Chicago', 'Phoenix', 12),
('Miami', 'New York', 8),
('Seattle', 'Austin', 10),
]
# Calculate inflow/outflow
inflow = {city: 0 for city in cities}
outflow = {city: 0 for city in cities}
for origin, dest, migrants in flows:
outflow[origin] += migrants
inflow[dest] += migrants
# Print summary
print("=== US Domestic Migration Flows ===")
print(f"\nCities: {len(cities)}")
print(f"Flow Routes: {len(flows)}")
print(f"\nTop Destinations:")
for city, flow in sorted(inflow.items(), key=lambda x: x[1], reverse=True)[:5]:
if flow > 0:
print(f" {city}: +{flow}K migrants")
print(f"\nTop Origins (outflow):")
for city, flow in sorted(outflow.items(), key=lambda x: x[1], reverse=True)[:3]:
print(f" {city}: -{flow}K migrants")
# === CREATE FLOW MAP ===
fig, ax = plt.subplots(figsize=figsize, facecolor='#0a0a1a')
ax.set_facecolor('#0a0a1a')
# Draw simplified US outline
us_outline_x = [0.05, 0.12, 0.15, 0.35, 0.50, 0.65, 0.85, 0.95, 0.92, 0.85,
0.78, 0.72, 0.65, 0.55, 0.45, 0.35, 0.25, 0.15, 0.08, 0.05]
us_outline_y = [0.50, 0.80, 0.90, 0.88, 0.82, 0.78, 0.75, 0.65, 0.45, 0.25,
0.08, 0.05, 0.08, 0.10, 0.12, 0.15, 0.20, 0.25, 0.35, 0.50]
ax.fill(us_outline_x, us_outline_y, color='#1a1a3a', alpha=0.5, edgecolor='#404080', linewidth=2)
# Draw flows with curved arrows
for origin, dest, migrants in flows:
x1, y1 = cities[origin]['pos']
x2, y2 = cities[dest]['pos']
# Arrow properties based on flow size
width = migrants / 15
alpha = min(0.8, migrants / 50)
# Gradient color: blue to purple
color = '#00D9FF' if migrants > 25 else '#6C5CE7' if migrants > 15 else '#A29BFE'
# Create curved arrow
style = f"arc3,rad=0.15"
arrow = FancyArrowPatch(
(x1, y1), (x2, y2),
connectionstyle=style,
arrowstyle='-|>',
mutation_scale=10 + migrants/5,
color=color,
linewidth=width,
alpha=alpha,
zorder=2
)
ax.add_patch(arrow)
# Draw cities
for city, data in cities.items():
x, y = data['pos']
# Net flow determines color
net = inflow[city] - outflow[city]
if net > 20:
color = '#00E676' # Strong net inflow - green
elif net > 0:
color = '#69F0AE' # Mild net inflow
elif net > -20:
color = '#FF8A65' # Mild net outflow
else:
color = '#FF5252' # Strong net outflow - red
# Size based on total activity
activity = inflow[city] + outflow[city]
size = 100 + activity * 8
# Glow effect
for i in range(3, 0, -1):
circle = plt.Circle((x, y), 0.02 * i, color=color, alpha=0.1)
ax.add_patch(circle)
ax.scatter(x, y, s=size, c=color, edgecolors='white',
linewidths=2, zorder=5, alpha=0.9)
# City label
label_offset = 0.04
ax.text(x, y - label_offset, city.replace(' ', '\n'), fontsize=9,
fontweight='bold', ha='center', va='top', color='white',
path_effects=[pe.withStroke(linewidth=2, foreground='#0a0a1a')])
# Title
ax.set_title(title, fontsize=24, fontweight='bold', color='white', pad=20,
path_effects=[pe.withStroke(linewidth=3, foreground='#6C5CE7')])
# Legend
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
legend_elements = [
Patch(facecolor='#00E676', label='Net Inflow (gaining pop.)', edgecolor='white'),
Patch(facecolor='#FF5252', label='Net Outflow (losing pop.)', edgecolor='white'),
Line2D([0], [0], color='#00D9FF', linewidth=4, label='Major flow (>25K)'),
Line2D([0], [0], color='#A29BFE', linewidth=2, label='Minor flow (<15K)'),
]
legend = ax.legend(handles=legend_elements, loc='lower left',
facecolor='#1a1a3a', edgecolor='#404080',
labelcolor='white', fontsize=10)
# Info box
total_migrants = sum(m for _, _, m in flows)
ax.text(0.98, 0.02, f'Total Migration: {total_migrants}K people\n{len(flows)} major routes shown',
transform=ax.transAxes, fontsize=10, color='#888', ha='right', va='bottom',
bbox=dict(boxstyle='round', facecolor='#1a1a3a', alpha=0.8, edgecolor='#404080'))
ax.set_xlim(-0.02, 1.02)
ax.set_ylim(-0.02, 1.02)
ax.axis('off')
plt.tight_layout()
plt.savefig('chart.png', dpi=150, bbox_inches='tight', facecolor='#0a0a1a')
print("Saved: chart.png")
plt.show()
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
=== US Domestic Migration Flows === Cities: 12 Flow Routes: 13 Top Destinations: Austin: +105K migrants Miami: +45K migrants Phoenix: +40K migrants Dallas: +40K migrants Denver: +18K migrants Top Origins (outflow): Los Angeles: -85K migrants New York: -83K migrants San Francisco: -58K migrants Saved: chart.png
Common Use Cases
- 1Migration visualization
- 2Trade flow analysis
- 3Transportation planning
- 4Supply chain mapping
Pro Tips
Use curved lines to reduce overlap
Scale line width by volume
Add directional arrows
Frequently asked questions
When should you use a flow map?
Flow maps visualize movement between locations using lines or arrows, with width often proportional to volume. They effectively display migration patterns, trade routes, transportation flows, and any origin-destination data with geographic context. Common applications include migration visualization, trade flow analysis, and transportation planning.
Which Python libraries can create a flow map?
A flow map can be built in Python with folium, geopandas, and plotly — folium, pandas for quick plots straight from a DataFrame, and Plotly for interactive hover, zoom, and web sharing. In Plotivy you describe the figure and it writes the folium code for you.
Can I make a flow map without writing Python code?
Yes. Describe the flow map you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable folium source, so nothing is locked in a black box.
What are best practices for a clear flow map?
Use curved lines to reduce overlap. Scale line width by volume.
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
folium
Useful in specialized workflows that complement core Python plotting libraries for flow-map analysis tasks.
geopandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
plotly
Best for interactive hover, zoom, and web sharing when collaborators need to inspect values directly from flow-map figures.
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