Map Visualization
What is a Map Visualization?
A map visualization overlays network data onto a geographic map, where node positions correspond to real-world locations. Nodes appear as markers at their geographic coordinates, while edges show connections between locations. This combines the power of network analysis with spatial context, revealing patterns that only emerge when geography is considered.
Why Use Map Visualizations?
Map visualizations excel when you need to:
- Analyze spatial patterns: See how network structure relates to geography.
- Identify regional clusters: Discover location-based communities and hubs.
- Understand distance effects: Assess whether connections correlate with proximity.
- Plan logistics: Visualize routes, coverage areas, and service networks.
- Communicate location context: Geographic familiarity makes data immediately relatable.
How It Works in GraphPolaris
Creating and exploring a map visualization in GraphPolaris is straightforward:
- Load your network data with geographic coordinates (latitude/longitude) for nodes.
- Generate the map with automatic marker placement on the chosen base map.
- Explore connections: See edges connecting locations, hover for details.
- Filter and focus: Zoom to regions, filter by attributes, toggle layers.
GraphPolaris supports multiple base map styles and provides clustering for dense regions.
Visual Patterns
Understanding common visual patterns in map visualizations helps you quickly interpret geographic network structure. Here are the key patterns to look for:
Regional Cluster
Nodes concentrated in a geographic area with many internal connections indicate a regional cluster—a community or activity hub tied to location.
┌────────────────────────────────────┐
│ │
│ ○ │
│ ●───● │
│ ╱│╲ ╱│ │
│ ○ ●──●──● │
│ ╲│╱ │
│ ● Regional │
│ Cluster │
│ ○ │
│ │
└────────────────────────────────────┘
What to look for: Dense groups of connected markers in geographic proximity. May indicate cities, facilities, or activity centers.
Long-Distance Connection
Edges spanning large geographic distances indicate long-distance relationships—connections that transcend proximity.
┌────────────────────────────────────┐
│ │
│ ●═══════════════════════════● │
│ New York London │
│ │
│ │
│ ●══════════════● │
│ LA Tokyo │
│ │
└────────────────────────────────────┘
What to look for: Long edges crossing the map. Compare frequency of local vs long-distance connections.
Hub City
A location with many edges radiating to other locations indicates a geographic hub—a central point in the network.
┌────────────────────────────────────┐
│ ● ● │
│ ╲ ╱ │
│ ╲ ╱ │
│ ●─────◉─────● ● │
│ ╱╲ Hub │
│ ╱ ╲ │
│ ● ● │
│ │
└────────────────────────────────────┘
What to look for: A marker with many connecting edges. Multiple hubs may indicate a multi-center network.
Corridor (Route Pattern)
A chain of connected locations following a geographic path indicates a corridor—a route, pipeline, or sequential flow.
┌────────────────────────────────────┐
│ │
│ ●───●───●───●───●───● │
│ ╲ │
│ Transportation ● │
│ Corridor ╲ │
│ ● │
│ │
└────────────────────────────────────┘
What to look for: Nodes connected in sequence following roads, rivers, coastlines, or other geographic features.
Border Effect
Connections that stop at geographic or political boundaries indicate border effects—network structure influenced by regions.
┌────────────────────────────────────┐
│ Region A ┃ Region B │
│ ┃ │
│ ●───●───● ┃ ●───●───● │
│ ╲ │ ╱ ┃ ╲ │ ╱ │
│ ●──● ┃ ●──● │
│ ┃ │
│ Dense within ┃ Dense within │
│ Few crossing ┃ │
└────────────────────────────────────┘
What to look for: High connectivity within regions, sparse connections crossing boundaries. Reveals jurisdictional or cultural effects.
Proximity Pattern
When most edges connect nearby locations, it indicates a proximity-driven network—distance matters for connections.
┌────────────────────────────────────┐
│ │
│ ●──● ●──● │
│ ╲╱ │ │
│ ● ● │
│ ●──● │
│ ╲╱ │
│ ● │
│ │
│ (mostly short edges) │
└────────────────────────────────────┘
What to look for: Most edges span short distances. Long edges are rare exceptions worth investigating.
Coverage Gap
Areas without markers or connections indicate coverage gaps—regions not represented in the network.
┌────────────────────────────────────┐
│ │
│ ●───●───● │
│ ╲ │ ╱ │
│ ●──● ┌──────────┐ │
│ │ NO │ │
│ │ COVERAGE │ │
│ └──────────┘ │
│ ●──● │
│ ╲╱ │
│ ● │
└────────────────────────────────────┘
What to look for: Geographic areas with no markers. May indicate market opportunities, service gaps, or data collection issues.
Star Pattern (Distribution Center)
One location connected to many others in a region, with those others not connected to each other—indicates a distribution or service center.
┌────────────────────────────────────┐
│ │
│ ● ● │
│ ╲ ╱ │
│ ╲ ╱ │
│ ●────◉────● Distribution │
│ ╱ ╲ Center │
│ ╱ ╲ │
│ ● ● │
│ │
└────────────────────────────────────┘
What to look for: A central location with edges to many peripheral locations that don't connect to each other. Classic hub-and-spoke pattern.
Map visualizations combine network and geographic analysis—and with GraphPolaris, you can seamlessly explore how your connected data relates to the physical world.