Road Network Density Analysis in the Netherlands

Author

Claudiu Forgaci, Daniele Cannatella

Classification: Intermediate GIS Analysis | Spatial Network Analysis | Infrastructure Assessment

Learning Level: Intermediate

Time Estimate: 1–2 hours

Software Required: QGIS 3.24+

Author: Updated from original by Claudiu Forgaci (2024)

Overview

This tutorial guides you through analyzing the spatial distribution of road network density across the Netherlands. You will use OpenStreetMap (OSM) highway data and a 2×2 km reference grid to calculate total road length per grid cell, then visualize patterns of road infrastructure density at the regional scale.

Learning Outcomes:

By completing this tutorial, you will be able to:

✅ Import and filter OpenStreetMap highway data by road classification
✅ Prepare road network data for density analysis
✅ Calculate total road length within grid cells using line length analysis
✅ Visualize spatial distribution of road density using classified maps
✅ Interpret regional patterns in transportation infrastructure
✅ Apply the workflow to different geographic regions and road types

System Requirements & Setup

Software Installation

QGIS: - Download version 3.24 or later from https://qgis.org/download/ - Installation instructions vary by operating system

Data Requirements

You will need the following datasets:

OpenStreetMap (OSM) Highway Data - Vector line layer containing road network - Available from: geofabriek.de or QuickOSM plugin - Must contain a highway field with road classification values - Relevant road types for this analysis: - motorway, motorway_link – High-speed limited access - primary, primary_link – Major through routes - secondary, secondary_link – Important secondary routes - tertiary, tertiary_link – Local roads

2x2 km Reference Grid - Regular grid covering the Netherlands - Must contain a fid field for unique cell identification - Grid cells are 2×2 km (4 km²)

Coordinate Reference System (CRS):

  • Both datasets must be in EPSG:28992 (Amersfoort / RD New)
  • This projected CRS ensures accurate distance/length calculations
  • All data should be pre-reprojected before analysis

Spatial Extent

This tutorial covers the Netherlands using a 2x2 grid extent.

Workflow Overview

The analysis follows a simple, direct workflow:

Phase Tasks Output
Data Preparation Import OSM and grid data; verify CRS Verified layers in EPSG:28992
Data Filtering Select relevant road types (motorway, primary, secondary, tertiary) Filtered OSM road layer
Density Calculation Calculate total road length per grid cell Grid with road length field
Classification Apply natural breaks classification to visualize density Classified density map
Interpretation Analyze spatial patterns of road infrastructure Regional insights

Background: Understanding Road Networks and Density

Road Network Classification:

Roads are typically classified by their functional importance and connectivity:

  • Motorway (motorway) – Limited-access, high-speed infrastructure
  • Primary (primary) – Major through routes connecting urban centers
  • Secondary (secondary) – Important regional connections
  • Tertiary (tertiary) – Local roads with mixed traffic
  • Links (e.g., motorway_link) – Connector roads between road classes

Road Density Concept:

Road density = Total length of roads (kilometers) per unit area (km²)

Units: - For a 2×2 km grid cell (4 km² area): Density = Total road length (km) / 4 - High density indicates well-developed transportation infrastructure - Low density indicates rural or sparse settlement areas

Spatial Patterns:

Road density typically shows: - High density in urban cores, metropolitan regions, and developed areas - Medium density in towns and agricultural regions - Low density in rural, mountainous, or sparsely settled areas

Step 1: Import and Prepare Data

1.1 Import Layers into QGIS

  1. Launch QGIS 3.24+

  2. Import the OSM highway data:

    • Go to Layer → Add Layer → Add Vector Layer
    • Navigate to your OSM roads shapefile or GeoPackage
    • Click Open
    • The layer appears in the Layers Panel

  3. Import the 2×2 km grid:

    • Go to Layer → Add Layer → Add Vector Layer
    • Navigate to the grid layer
    • Click Open
    • The layer appears in the Layers Panel

  4. Verify visibility:

    • Both layers should be visible in the main canvas
    • The grid will appear as a regular pattern of square cells

1.2 Verify Coordinate Reference System

Both layers must be in EPSG:28992 for accurate length calculations.

Check CRS for each layer:

  1. Right-click the OSM roads layer and select Layer CRS
    • Note the current CRS (should be EPSG:4326)
  2. Repeat for the grid layer

If CRS differs from EPSG:28992, reproject:

  1. Go to Processing → Toolbox
  2. Search for Reproject layer
  3. Double-click to open

For each layer that needs reprojection:

  1. Input layer: Select the layer to reproject
  2. Target CRS: Select EPSG:28992
  3. Click Run
  4. A reprojected layer is created and added to the Layers Panel

1.3 Examine the OSM Data

  1. Right-click the OSM roads layer and select Open Attribute Table

  2. Locate the highway classification field:

    • Find the column named highway
    • This contains values like “motorway,” “primary,” “secondary,” etc.

  3. Review the data:

    • Scroll through several rows to understand road types present
    • Note the number of features (roads)
    • Check for any data quality issues

  4. Close the attribute table

Step 2: Filter Road Data by Classification (Optional)

You can analyze all road types together or focus on specific classifications (e.g., only motorways and primary roads). This step filters the data; skip if you want to include all roads.

2.1 Apply a Filter to the OSM Layer

  1. Right-click the OSM roads layer and select Filter…
    • Alternatively: Vector → Filter…
  2. The Query Builder dialog opens

Create a filter expression for major roads:

Build an expression to select only important road classes:

"highway" IN ('motorway', 'motorway_link', 'primary', 'primary_link', 'secondary', 'secondary_link'. `tertiary`, `tertiary_link`)

Explanation: - IN (...) selects records where the fclass field matches any listed value - This includes motorways, primary, secondary, and tertiary roads with their link roads - Excludes local/minor roads

Alternative: Include all road types

If you want to analyze the complete road network, skip filtering or use:

"fclass" IS NOT NULL
  1. Click OK to apply the filter

Result: - The OSM layer now displays only selected road types - Filtered-out roads are hidden but not deleted

Step 3: Calculate Total Road Length per Grid Cell

This is the core analysis step. We use QGIS’s Sum Line Lengths tool to calculate the total length of roads within each grid cell.

3.1 Open Sum Line Lengths Tool

  1. Go to Vector → Analysis Tools → Sum Line Lengths
    • Alternatively: Processing → Toolbox → Search for Sum Line Lengths → Double-click
  2. The Sum Line Lengths dialog opens

3.2 Configure Sum Line Lengths

  1. Input polygon layer: Select the EEA grid layer
    • This defines the zones (grid cells) for which we calculate road lengths
  2. Input line layer: Select the OSM roads layer
    • This contains the road network data
    • If filtered, the tool uses only visible (filtered) roads
  3. Output field name: Enter LENGTH
    • This field will store the total road length per grid cell
    • The name is descriptive and follows standard conventions
  4. Output: Click dropdown and select Save to File
    • Click folder icon to choose save location
    • Enter filename: Grid_with_Road_Length.shp or .gpkg
  5. Click Run

Processing: - QGIS analyzes each grid cell polygon - For each cell, finds all road segments within it - Calculates the total length of these road segments - Units are in the CRS measurement units (meters in EPSG:28992)

Result: - A new layer Grid_with_Road_Length is created - The attribute table includes: - All original grid fields (e.g., CellCode) - New LENGTH field containing total road length per cell in meters

3.3 Verify the Results

  1. Open the Attribute Table of Grid_with_Road_Length

  2. Locate the LENGTH column (scroll right if needed)

  3. Review values:

    • All values should be ≥ 0 (non-negative)
    • Cells with high urban density should show higher values
    • Cells in rural areas should show lower values
    • Most cells should have some roads (LENGTH > 0)

  4. Inspect a sample of values:

    • Note the range (e.g., minimum and maximum values)
    • These help inform the classification in the next step

  5. Close the attribute table

Example length values (in meters): - Urban cell: 200,000–300,000 m - Town cell: 100,000–200,000 m - Rural cell: 20,000–50,000 m - Sparsely populated: 0–20,000 m

Step 4: Visualize Road Density Using Natural Breaks Classification

Natural breaks classification groups the data into meaningful categories based on natural clustering, making patterns in road density visually apparent.

4.1 Open Layer Styling

  1. Select the Grid_with_Road_Length layer in the Layers Panel

  2. Open Layer Styling:

    • Go to View → Panels → Layer Styling
    • Or press F7

  3. The Layer Styling panel opens on the right side

4.2 Configure Graduated Classification

  1. In the Symbology tab, change the classification type

    • Click the dropdown showing “Single Symbol”
    • Select Graduated

  2. Configure classification parameters:

    • Value: Click dropdown and select LENGTH
    • Classification Mode: Click dropdown and select Natural Breaks (Jenks)
      • This algorithm identifies natural clustering in the data
      • Produces visually meaningful categories
    • Number of Classes: Set to 5
      • Adjust to 4–7 depending on your data distribution

  3. Choose a color ramp:

    • Click the Color Ramp dropdown
    • Select a sequential scheme suitable for representing density:
      • YlOrRd – Yellow to Orange to Red (classic density visualization)
      • YlGnBu – Yellow to Green to Blue
      • Viridis – Purple to Green to Yellow (perceptually uniform)
      • RdYlBu_r – Red to Yellow to Blue (intensity gradient)

  4. Click Classify to generate categories

    • The panel shows the computed class breaks and their associated colors
    • Review the breaks to ensure they make sense

  5. (Optional) Customize class labels:

    • In the Label column, replace numeric break values with descriptive names:
      • “Very Low,” “Low,” “Medium,” “High,” “Very High”
    • Double-click a label cell to edit

  6. Click Apply to visualize on the map

Result: - The main canvas displays the grid with cells colored by road density - Distinct spatial patterns should emerge: - Red/warm colors indicate high road density (urban areas) - Yellow/medium colors indicate moderate density (towns) - Blue/cool colors indicate low density (rural areas)

4.3 Enhance Map Visibility

  1. Adjust transparency (optional):
    • In Layer Styling, go to the General or Transparency tab
    • Set Opacity to 85–90% to allow underlying features to show through
  2. Customize borders:
    • In Symbology, adjust Stroke width to 0.2–0.5 pt for subtle cell outlines
  3. Click Apply again to update

Step 5: Create a Professional Map Layout

Compile the styled grid into a publication-ready map with legend, scale, and attribution.

Step 6: Interpretation and Analysis

Spatial Patterns to Observe

Examine your road density map for characteristic patterns:

High Density Zones (warm colors): - Metropolitan areas (London, Paris, Amsterdam, Brussels, Cologne, Ruhr Valley) - Dense urban networks with well-developed street grids - Major highway corridors and interchanges

Medium Density Zones (neutral colors): - Secondary cities and towns - Agricultural regions with moderate road networks - Suburban and periurban areas

Low Density Zones (cool colors): - Rural and sparsely populated areas - Mountainous regions with limited accessibility - Natural protected areas with minimal infrastructure

Linear Patterns: - Major motorway corridors visible as distinct density bands - Trans-regional routes connecting major urban centers

Regional Insights

Questions to explore with your analysis:

  1. Which regions show highest road density?
    • Compare urban centers vs. surrounding countryside
    • Identify infrastructure hubs
  2. Do density patterns reflect economic development?
    • Do wealthier regions show higher infrastructure investment?
    • How do historical development patterns influence modern density?
  3. Are there geographic constraints?
    • How do mountains, water bodies, or political boundaries affect road networks?
  4. How does road classification correlate with density?
    • Are motorways concentrated in high-density regions?
    • Do secondary roads provide more even coverage?

Troubleshooting Guide

Problem Cause Solution
Sum Line Lengths produces all zero values Wrong CRS (geographic instead of projected); grid and roads not properly aligned Verify CRS is EPSG:28992 (projected). Check grid and roads overlap spatially.
No roads found in cells Spatial extent mismatch; roads layer filtered out Verify grid and roads cover the same area. Remove any filters on roads layer.
Classification produces poor visualization Too many or too few classes; extreme outliers Adjust number of classes (4–7). Try different break algorithms (Equal Interval, Quantiles).
Map is too cluttered Grid cell outlines too thick; overlap with basemap Reduce stroke width to 0.2 pt. Increase cell opacity or adjust colors.
LENGTH values in unusual units Wrong CRS interpretation Verify CRS is EPSG:28992. Recalculate if needed.
Some grid cells show no roads but should Topological issues; roads don’t perfectly intersect cells Verify road data quality. Run “Check Validity” on roads layer.

Extensions and Advanced Topics

Once you complete this tutorial, consider:

  1. Road Type-Specific Analysis:
    • Calculate separate density layers for motorways, primary roads, secondary roads
    • Compare density patterns by road classification
    • Identify regions dominated by different road types
  2. Density Gradients:
    • Analyze how road density decreases with distance from urban centers
    • Create distance-decay analysis
  3. Temporal Analysis:
    • If historical OSM snapshots available, assess road network growth over time
    • Compare 2010 vs. 2024 infrastructure development
  4. Accessibility Analysis:
    • Combine road density with travel time analysis
    • Identify regions with high density but limited connectivity
  5. Infrastructure Planning:
    • Compare current density with planned infrastructure projects
    • Identify underserved areas needing road development
  6. Multi-modal Networks:
    • Extend analysis to include railways, waterways, or cycling infrastructure
    • Compare transportation modes

References & Resources

Data Sources:

  • OpenStreetMap: https://www.openstreetmap.org/
  • QuickOSM Plugin (for direct OSM import): https://plugins.qgis.org/plugins/QuickOSM/

QGIS Documentation:

  • Sum Line Lengths Tool: https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/qgis/vectoranalysis.html
  • Vector Analysis Tools: https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/qgis/vectoranalysis.html
  • Classification Methods: https://docs.qgis.org/latest/en/docs/user_manual/working_with_vector/vector_properties.html

Related Resources:

  • QGIS Official Training Manual: https://docs.qgis.org/latest/en/docs/training_manual/
  • OpenStreetMap Tag Documentation: https://wiki.openstreetmap.org/wiki/Key:highway
  • EEA Transport Resources: https://www.eea.europa.eu/themes/transport

Quick Reference: Key Tools

Task Tool Path Purpose
Import data Layer → Add Layer → Add Vector Layer Load OSM roads and grid
Check CRS Right-click layer → Layer CRS Verify coordinate system
Reproject Processing → Reproject layer Convert to EPSG:28992
Filter roads Vector → Filter Select specific road types
Calculate lengths Vector → Analysis Tools → Sum Line Lengths Compute road length per cell
Visualize View → Layer Styling (F7) Apply classification and colors
Classify Symbology → Graduated → Natural Breaks Create density categories
Create layout Project → New Print Layout Build publication map
Export Layout → Export as PDF/Image Save final map

Appendix: OSM Road Classification Hierarchy

Road Types Included in This Analysis:

Classification Meaning Typical Speed Examples
motorway Limited-access highway 100–130 km/h A-roads, Autobahns
motorway_link Motorway connector Variable Ramps, off-ramps
primary Major through route 60–100 km/h National routes
primary_link Primary connector Variable Primary exit ramps
secondary Regional route 50–80 km/h Regional highways
secondary_link Secondary connector Variable Secondary connections
tertiary Local connecting road 40–60 km/h Town center roads
tertiary_link Tertiary connector Variable Tertiary connections

Road Types Excluded (Optional): - residential – Streets in residential neighborhoods - unclassified – Minor local roads - service – Service roads, parking areas - living_street – Low-traffic residential areas - track – Agricultural/forestry roads

Document Version: 1.0 (February 2026)
QGIS Version: 3.24+
Difficulty Level: Intermediate (Beginner-friendly)
Expected Time: 1–2 hours
Data Scale: Regional (North-West Europe, 10×10 km cells)
CRS: EPSG:28992 (Amersfoort / RD New)

Feedback & Support

If you encounter issues:

  • Review the Troubleshooting Guide above
  • Check QGIS documentation at https://docs.qgis.org/
  • Consult OpenStreetMap wiki for data questions
  • Contact your instructor or GIS support team
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