Flow Map

# === 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

=== 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