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Exploring the London Transport Data

After loading the data using 1 - Load London Transport.py, you can visually explore the graph in Neo4j Aura.

Step 1: Navigate to Aura Explore

  1. Go to the Neo4j Aura Console (https://console.neo4j.io)
  2. Click on your instance (e.g., london_transport)
  3. In the left sidebar under Tools, click on Explore

Aura Explore

If you see "No instance connected", click the Connect to instance button.

Step 2: Connect to Your Instance

  1. Click Connect to instance
  2. Your instance should connect automatically (green indicator shows "Instance: london_transport")
  3. You'll see the Explore interface with a search bar and options

Step 3: Search for Stations

To visualize all stations and their tube line connections:

  1. In the search bar at the top, construct the pattern:
    • Click Station (orange node)
    • Click the relationship connector —(any)—
    • Click Station (orange node)

This creates the pattern: Station — (any) — Station

  1. You'll see the available properties for Station nodes:

    • station_id: value
    • name: value
    • postcode: value
    • zone: value
    • latitude: value
    • longitude: value

  2. Click Search the graph (or press TAB and select it)

Search Interface

Step 4: View the Graph

You should now see a beautiful visualization of the London Transport Network:

  • Orange nodes = Station nodes
  • Colored edges = Different tube lines (BAKERLOO, CENTRAL, CIRCLE, etc.)
  • Each relationship type has a different color for easy identification

All Stations Graph

Tips for Exploring

Zoom and Pan

  • Zoom: Scroll wheel or pinch gesture
  • Pan: Click and drag the canvas
  • Center: Double-click on empty space

Inspect Nodes and Relationships

  • Click on a node to see its properties
  • Click on a relationship to see its type
  • Expand nodes to see more connections

Filter by Properties

You can filter stations by specific properties:

  • Zone: Station zone: value — Enter a zone number (1, 2, 3, etc.)
  • Name: Station name: value — Search for specific stations (e.g., "King's Cross")
  • Postcode: Station postcode: value — Filter by postcode area

Search by Tube Line

To see only specific tube lines, use the relationship filter:

  1. Click the relationship selector —(any)—
  2. Choose a specific line type:
    • BAKERLOO
    • CENTRAL
    • CIRCLE
    • DISTRICT
    • HAMMERSMITH_AND_CITY
    • JUBILEE
    • METROPOLITAN
    • NORTHERN
    • PICCADILLY
    • VICTORIA
    • WATERLOO_AND_CITY

Example: Station — BAKERLOO → Station shows only Bakerloo line connections

Example Explorations

Find Major Interchange Stations

Major interchanges will have many connections (multiple colored edges):

  • Look for nodes with 6+ connections
  • Examples: King's Cross St. Pancras, Oxford Circus, Bank

Trace a Tube Line

  1. Select a specific tube line relationship (e.g., CENTRAL)
  2. Follow the path from end to end
  3. See which stations it connects

Find Stations by Zone

  1. Use Station zone: value filter
  2. Enter zone number (e.g., 1 for central London)
  3. See all zone 1 stations

Shortest Path Between Stations

  1. Right-click on a station (e.g., "King's Cross St. Pancras")
  2. Select "Find shortest path"
  3. Right-click on another station (e.g., "Victoria")
  4. View the shortest connection between them

Using Graph Data Science

Neo4j Aura includes built-in Graph Data Science (GDS) capabilities to analyze your network and discover insights. Here's how to apply algorithms to identify important stations:

Step 1: Open Graph Data Science

  1. In the left sidebar, click the Graph Data Science icon (looks like interconnected nodes)
  2. You'll see the GDS panel open on the left side of the screen

Graph Data Science Panel

Step 2: Add Degree Centrality Algorithm

Degree Centrality identifies the most connected stations (major hubs and interchanges):

  1. Click Add algorithm in the GDS panel
  2. From the algorithm dropdown, select Degree Centrality
  3. Read the description: "Degree centrality measures the number of incoming and outgoing relationships from a node"
  4. Click Apply algorithm

Degree Centrality Configuration

The algorithm will run and calculate centrality scores for all 302 stations.

Step 3: Apply Color Gradient

After the algorithm completes, visualize the results:

  1. You'll see a success message: "652 scores added by Degree Centrality"
  2. Two options appear:
    • Size nodes based on scores - Makes hub stations larger
    • Apply color gradient based on scores - Colors nodes by importance
  3. Click Apply color gradient based on scores

Apply Color Gradient

Note: Scores only exist within the visualization and are not written to the database.

Step 4: View the Results

Your graph will now display a color-coded visualization:

  • Darker/brighter nodes = Higher degree centrality (major interchanges like King's Cross, Oxford Circus, Bank)
  • Lighter nodes = Lower degree centrality (end-of-line or less connected stations)

GDS Color-Coded Results

This visualization immediately reveals:

  • Major interchange stations with 6+ tube line connections
  • Network bottlenecks where multiple lines converge
  • Peripheral stations with fewer connections

Other Useful GDS Algorithms

Try these algorithms for different insights:

  • Betweenness Centrality - Identifies stations that act as bridges between different parts of the network
  • PageRank - Finds stations that are connected to other important stations (transfer hubs)
  • Community Detection (Louvain) - Groups stations into geographic clusters
  • Shortest Path - Finds optimal routes between stations (available via right-click menu)

Perspectives

Neo4j Aura Explore uses Perspectives to customize your view:

  • Default Perspective: Basic node and relationship display
  • Custom Perspectives: Create your own color schemes and layouts
  • Export: Save your perspective settings for future use

You can customize:

  • Node colors by label or property
  • Relationship colors by type
  • Node size by property values
  • Display captions (what property to show on nodes)

Query Tab

For more advanced exploration, switch to the Query tab to run Cypher queries directly:

Example queries:

// Count stations by zone
MATCH (s:Station)
WHERE s.zone IS NOT NULL
RETURN s.zone as zone, count(s) as stations
ORDER BY zone

// Find busiest interchange stations
MATCH (s:Station)-[r]-()
RETURN s.name as station, count(r) as connections
ORDER BY connections DESC
LIMIT 10

// Shortest path between two stations
MATCH path = shortestPath(
  (from:Station {name: "King's Cross St. Pancras"})-[*]-(to:Station {name: "Victoria"})
)
RETURN path

Next Steps

  • Try the Text-to-Cypher Agent: Use labs/2 - Query London Transport.py to ask questions in natural language
  • Build Dashboards: Create custom visualizations in the Dashboards section
  • Learn Cypher: Explore the Developer hub for Cypher query tutorials

Enjoy exploring the London Transport Network! 🚇