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Catchment Area

Catchment Area shows how far people can travel within a certain travel timeGlossaryTravel timeHow long a journey between two places actually takes by a given mode, measured along the network rather than as straight line distance.Read more ↗ or distance, using one or more transport modes, with extended output shapes, custom step sizes, and additional public transport settings.

1. Explanation​

Based on specified starting point(s), maximum travel time or distance, and transport mode(s), Catchment Areas visualize accessibility extent using real-world data. This provides insights into transport network quality, density, and extensiveness. The catchment area can be intersected with spatial datasets, such as population data, to assess reachable amenities and identify accessibility coverage for inhabitants.

Catchment Area includes the following additional features:

For all routing modes:

  • Custom step sizes: define each isochroneGlossaryIsochroneA line joining all the points reachable in the same travel time from a starting point. The travel time version of a contour line.Read more ↗ step independently (e.g., 5, 10, 20, 30 minutes) instead of using equally-spaced intervals.
  • Point Grid output shape: a new result geometry option that represents the catchment as a grid of individual points, each showing its exact travel cost value.

For Public Transport only:

  • Maximum number of transfers: limit how many PT connections a trip can include.
  • Access and egress mode: configure how users travel to and from PT stations (walking, cycling, pedelec, or car).
info

Catchment Area computation is available across over 30 European countries for Walk, Bicycle, Pedelec, and Car. For Public Transport, Germany, Switzerland, and the Haut-Rhin region of France are supported. If you need analyses beyond these regions, you can import your own routing network or contact us and we will do it for you.

2. Example use cases​

  • Which amenities are reachable within a 5, 10, and 20-minute walk? (Using custom step sizes to reflect planning standards.)
  • How does limiting PT connections to one transfer change the catchment area compared to unlimited transfers?
  • Which areas are reachable by bicycle within 5 minutes, 15 minutes, and 30 minutes from a new cycling hub?
  • How do workplace catchment areas compare between car and public transport when cyclists can access PT stations?
  • What share of the population has a general practitioner within 500m on foot?

3. How to use the tool?​

1
Click on Toolbox Toolbox . Under Accessibility Indicators, click on Catchment Area.

Routing & Configuration​

2
Select the Routing Type for your analysis.
3
Configure the parameters for your chosen Routing Type. The available fields depend on the mode:
  • Choose whether to calculate the catchment area based on Time or Distance, and set the corresponding limit. If choosing Time, you can also configure the Speed.
  • Choose the Catchment area shape. If choosing:
    • Polygon or Network: you can select the Steps and Step sizes.
    • Hexagonal grid: no further configuration is necessary.
    • Point grid: you need to select the Point grid layer where the values will be applied.

Advanced Options​

4
Optionally, click on Advanced Options to configure additional settings. The available options depend on the selected Routing Type:

Shape style​

(Walk, Bicycle, and Pedelec only, visible when Catchment area shape is set to Polygon)

Choose how polygons are shaped when there are multiple starting points:

  • Combined across origins (default): all starting points are merged into a single shared catchment polygon per step.
  • Separated by origin: each starting point gets its own individual catchment polygon per step. If your starting points come from a layer, you can also choose a Reference field. Its value is written to each catchment polygon as origin, so every polygon can be traced back to the starting point it was computed from. If you leave it empty, origin simply numbers the starting points (1, 2, 3 …), so choose a field such as an ID if you want to match the results to your points. This makes the results easier to work with later, for example to join them back to the starting points layer.

Steps style​

Choose how the isochrone steps are displayed:

  • Separated steps: each step shows only the area reachable between that step and the previous one.
  • Cumulative steps: each step shows the full area reachable up to that travel cost.

Starting Points​

5
Select the Starting point method: Select on map and click on the map to place starting point(s). Or Select from layer and choose a Point layer containing your desired starting point(s). All features in the layer will be used as starting points.

Result Layer​

6
Set the Result layer name for the output catchment area layer.
7
Set the Starting points layer name for the output starting points layer.
8
Click on Run to start the calculation.

Results​

Once the calculation finishes, the resulting layer(s) are added to the map:

  • Catchment Area: the calculated isochrones in the selected shape (polygon, network, hexagonal grid, or point grid). Click any feature to inspect the travel_cost attribute, which shows travel time (minutes) or distance (meters) depending on your configuration.
  • Starting Points: a point layer with the selected starting locations (only created when starting points were placed on the map, not when using a pre-existing layer).

The result layer is automatically styled with a color scale ranging from the shortest to the longest travel cost step.

4. Technical details​

Catchment areas are isolines connecting points reachable from starting point(s) within a time interval (isochrones) or distance (isodistance). The calculation uses the appropriate transport network for the selected routing mode.

Starting point limits​

Routing modeMaximum starting points
Walk / Bicycle / Pedelec / Car1,000
Public Transport100

Time window​

For public transport, the catchment is computed over a time window (defined by a day of the week and a start and end time) rather than for a single departure. The engine evaluates every departure minute within this window and retains the fastest journey to each reachable location. The result is not an average across departures, but the best case among them, yielding the largest possible catchment area. A journey is considered within the window based solely on its start time, independent of its end time or duration.

Visualization​

The algorithm used to derive the catchment shape depends on the routing mode:

  • Walk, Bicycle, Pedelec, and Public Transport: the shape is derived from the routing grid using the Marching Squares contour line algorithm, a computer graphics algorithm generating 2D contour lines from rectangular value arrays (de Queiroz Neto et al. 2016). This transforms the routing grid from a 2D array into smooth polygon contours for visualization and spatial analysis.
  • Car: the shape is derived using DuckDB's ST_ConcaveHull function, which wraps tightly around the set of reachable points to produce the catchment polygon. A dynamic concavity ratio is applied based on the number of reached nodes: 0.5 for fewer than 10,000 nodes, 0.3 for fewer than 50,000, and 0.2 otherwise. Lower values produce tighter, more concave shapes for large catchments, while higher values yield smoother outlines for small ones.
Marching Squares illustration

Scientific background​

Catchments are contour-based measures (also cumulative opportunities), valued for their interpretable results (Geurs and van Eck 2001; Albacete 2016). They do not distinguish between different travel times within the cut-off range (Bertolini, le Clercq, and Kapoen 2005), unlike heatmap-based accessibility indicators.

Hint

For further insights into the routing algorithm, visit Routing.

5. Further readings​

Further insights into catchment calculation and scientific background: this publication.

6. References​

Albacete, Xavier. 2016. "Evaluation and Improvements of Contour-Based Accessibility Measures." url: https://dspace.uef.fi/bitstream/handle/123456789/16857/urn_isbn_978-952-61-2103-1.pdf

Bertolini, Luca, F. le Clercq, and L. Kapoen. 2005. "Sustainable Accessibility: A Conceptual Framework to Integrate Transport and Land Use Plan-Making." Transport Policy 12 (3): 207–20. https://doi.org/10.1016/j.tranpol.2005.01.006

J. F. de Queiroz Neto, E. M. d. Santos, and C. A. Vidal. "MSKDE - Using Marching Squares to Quickly Make High Quality Crime Hotspot Maps". en. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). Sao Paulo, Brazil: IEEE, Oct. 2016, pp. 305–312. isbn: 978-1-5090-3568-7. doi: 10.1109/SIBGRAPI.2016.049. url: https://ieeexplore.ieee.org/document/7813048

https://fr.wikipedia.org/wiki/Marching_squares#/media/Fichier:Marching_Squares_Isoline.svg

Majk Shkurti, "Spatio-temporal public transport accessibility analysis and benchmarking in an interactive WebGIS", Sep 2022. url: https://www.researchgate.net/publication/365790691_Spatio-temporal_public_transport_accessibility_analysis_and_benchmarking_in_an_interactive_WebGIS

Matthew Wigginton Conway, Andrew Byrd, Marco Van Der Linden. "Evidence-Based Transit and Land Use Sketch Planning Using Interactive Accessibility Methods on Combined Schedule and Headway-Based Networks", 2017. url: https://journals.sagepub.com/doi/10.3141/2653-06

Geurs, Karst T., and Ritsema van Eck. 2001. "Accessibility Measures: Review and Applications." RIVM Report 408505 006. url: https://rivm.openrepository.com/handle/10029/259808