Geographic Risk Analysis

Overview

RiskPlot's geographic analysis capabilities provide powerful tools for visualizing country-level data through multiple perspectives. Whether you need traditional flat world maps for reports or interactive 3D globes for presentations, our tools deliver publication-quality results.

2D World Maps

Create professional flat world maps using choropleth visualization. Perfect for country risk analysis, economic indicators, and regulatory reporting.

import riskplot
import pandas as pd

# Create sample country risk data
data = pd.DataFrame({
    'country': ['USA', 'Germany', 'China', 'Japan', 'Brazil'],
    'risk_score': [0.25, 0.18, 0.45, 0.22, 0.52]
})

# Create 2D world map
fig = riskplot.country_choropleth_map(
    data,
    country_col='country',
    value_col='risk_score',
    title='Global Risk Assessment',
    color_scale='RdYlGn_r'  # Red-Yellow-Green (reversed for risk)
)

# Save and display
fig.write_html('risk_map.html')
fig.show()

3D Interactive Globes

Create stunning 3D globe visualizations that engage your audience and provide intuitive geographic context for your risk data.

# Create 3D globe visualization
fig = riskplot.country_risk_globe(
    data,
    country='country',
    risk='risk_score',
    title='Global Risk Distribution - 3D View',
    center_lon=0,    # Center longitude
    center_lat=30    # Center latitude
)

# Customize and display
fig.show()

# Create connection data for trade flows or risk spillovers
connections = pd.DataFrame({
    'source_country': ['USA', 'Germany', 'China'],
    'target_country': ['China', 'USA', 'Germany'],
    'source_lat': [39.8, 51.2, 35.9],
    'source_lon': [-98.6, 10.4, 104.2],
    'target_lat': [35.9, 39.8, 51.2],
    'target_lon': [104.2, -98.6, 10.4],
    'strength': [0.8, 0.6, 0.9]
})

# Create globe with connections
globe = riskplot.GlobeRiskPlot()
fig = globe.plot_connections(
    connections,
    title='Global Risk Interconnectedness'
)
fig.show()

Regional Risk Analysis

Group countries by regions to identify patterns and compare risk levels across different geographic areas.

# Add regional data
regional_data = pd.DataFrame({
    'region': ['North America', 'Europe', 'Asia', 'Latin America', 'North America'],
    'country': ['USA', 'Germany', 'China', 'Brazil', 'Canada'],
    'risk_score': [0.25, 0.18, 0.45, 0.52, 0.22]
})

# Create regional heatmap
fig, ax = riskplot.regional_risk_heatmap(
    regional_data,
    region_col='region',
    country_col='country',
    value_col='risk_score',
    title='Risk Analysis by Region'
)

plt.show()

Advanced Customization

Use the WorldMapPlot class for advanced customization and multiple backend support.

# Advanced customization with WorldMapPlot class
world_map = riskplot.WorldMapPlot(backend='plotly')

# Create custom map
fig = world_map.plot_choropleth(
    data,
    country_col='country',
    value_col='risk_score',
    title='Custom Risk Assessment',
    color_scale='Viridis',
    show_borders=True,
    ocean_color='lightsteelblue',
    missing_color='lightgray',
    width=1200,
    height=800
)

# Add annotations
annotations = pd.DataFrame({
    'latitude': [40.7, 51.5, 35.7],
    'longitude': [-74.0, -0.1, 139.7],
    'text': ['NYC', 'London', 'Tokyo']
})

world_map.add_annotations(
    annotations,
    marker_color='red',
    marker_size=10
)

# Save in multiple formats
world_map.save('risk_map.html')
world_map.save('risk_map.png')
world_map.show()

Best Practices

Common Use Cases