Plugin guide

This page covers installation, a first run, the data downloader, the run dialog, the outputs, and troubleshooting. The introduction explains the model itself, the theory page relates it to the published model, and the scenario library provides prepared data and parameters to start from.

Install

  1. In QGIS 4: Plugins → Manage and Install Plugins → Settings, tick “Show also experimental plugins”, then search for isobenefit and install it.
  2. Two toolbar buttons appear: Isobenefit Urbanism (the simulation) and Extract from OpenStreetMap (the data downloader).
  3. The first time you run the simulation, the plugin checks for its isobenefit engine and, if missing, offers to install it into the QGIS Python environment (this requires an internet connection). Restart QGIS once it finishes.

If the automatic install is not available or not working on your system, run the shown command yourself with the QGIS Python:

<qgis-python> -m pip install "isobenefit>=0.12.18,<0.13"

Quick start: your first run

The fastest route uses the OSM downloader for the data and accepts most defaults.

  1. Zoom the map to a place you want to test (a town and its surroundings; the area must be more than twice the walking distance across, and a window of a few kilometres works well).
  2. Open Extract from OpenStreetMap. Click Draw area on map…: the dialog hides, left-clicks add corners, a right-click finishes the polygon (Esc cancels).
  3. Leave all datasets ticked, choose an output GeoPackage path, and press Fetch. The layers are saved to the GeoPackage and added to the project as an “OSM” group.
  4. Open Isobenefit Urbanism. The dialog pre-fills its layer pickers from the OSM download, suggests a local projected CRS, and validates as you type; the status line under the form lists what is still missing.
  5. Confirm the output folder and the run name. With an OSM download present, the folder is pre-filled as a scenarios folder beside the downloaded data (numbered upward when earlier runs exist); all of the run’s files take the name.
  6. Set the target population: the number of new residents to house. Existing buildings are context only and are never counted.
  7. Check the Development density group: three densities (people per km²) and the share of new blocks built at each. The shares must sum to 1; the feedback line shows the running total and the resulting mean density.
  8. Press Run. The simulation runs as a background task; the progress bar tracks it, and a run can be cancelled safely. Per-stage detail (grid size, ensemble progress, post-processing candidates, warnings) streams to the Log Messages panel: View → Panels → Log Messages, Isobenefit tab. With the default Development likelihood mode, several layers load on completion; start with the moderately clustered centres plan.
  9. The run’s full settings are saved next to the output as <name>_params.json. To repeat or adjust the run later, use Load parameters at the top of the dialog.

If the first result shows less growth than expected, some constraint is usually binding harder than intended for the place. The usual suspects, in order: the target population against the iterations available (the run report states both); the centre walk, which bounds growth around each centre until a new one seeds; a minimum green span wider than the gaps growth would need to fill; and unbuildable land fragmenting the window. The troubleshooting section walks through each.

To start from a prepared case instead, use a scenario download, described in the next section.

Using a downloaded scenario

Each entry in the scenario library downloads as one ZIP with the data and parameters for a run.

The Extract from OpenStreetMap tool

Downloading and simulating are separate steps. The layers are on disk and can be edited or swapped before any run. The simulation dialog recognises the downloaded layers and pre-selects them.

The run dialog, group by group

Parameters. Load parameters repopulates the dialog from a previous run’s *_params.json sidecar or from a scenario preset. Every run writes such a sidecar next to its output.

Simulation.

Field Default What it does
Max iterations 100 Cap on growth steps; a run stops early at the target population
Grid size (m) 50 Cell size of the simulation grid
Target population 100,000 New residents to house; growth stops once reached (checked between iterations, so the final count can slightly overshoot)
Build probability 0.25 Per-step chance an eligible cell develops (the growth rate)
Dispersed development Moderate Leapfrog rate: Off / Moderate / Aggressive
Random seed 42 The same seed reproduces the same run and the same ensemble, independent of core count

Walkable access. Centre walk (800 m) and Green walk (400 m): how far people walk to a mixed-use centre and to a park. The defaults follow common practice: a ten-minute walk to a neighbourhood centre, and everyday green within the stricter reach the WHO and Natural England standards use. During growth the engine uses one walk radius for its checks, set to the larger of these two values, so growth is never cut off by the stricter one mid-run. The finished plan is then scored against each walk separately, and any shortfall shows in the coverage figures and steers the centre re-positioning.

Post-processing.

Field Default What it does
Optimise centre placement on Re-position centres central to their development, add one wherever new development lacks a centre of its own (a nearby existing centre does not stand in), cull redundant ones; saves moderately and tightly clustered options. Off keeps the grown centres (one plan)
Centre area (m² per person) 20 Mixed-use centre land provided per new resident served
Min settlement (people) 1000 A detached new cluster housing fewer people than this reverts to green (converted to cells via the mean density); the raw plan keeps everything for comparison
Min green span (m) 400 A green patch must span this to count as a park; also a build rule protecting corridors

Development density. Three densities (people per km²) for the high, medium and low tiers, each with a share. The dialog requires positive, strictly descending densities and shares between 0 and 1 that sum to 1; the feedback line shows the running total and the mean. Every new block is built at one of the three densities; post-processing arranges the highest nearest the mixed-use centres.

Output. Development likelihood (the default) blends many runs; the Detail picker sets how many (Quick 10 / Standard 50 / Thorough 100). Untick it for a single run written as a growth animation. The output folder and run name determine where the run’s files land (see Outputs below); the CRS must be a local projected CRS (a suggestion is made from the extents layer; geographic lat/lon CRSs are rejected so the model always works in metres).

Input layers. Extents (required, polygon) plus optional existing urban, existing green, unbuildable, urban centres (points or polygon areas), PT stops, and rail/tram stations. All layers may be in any CRS; they are reprojected to the chosen run CRS.

The Run button stays disabled until four things are set: an extents layer, an output folder and run name, a projected CRS, and valid densities and shares. The red status line names whichever are missing.

How distances, barriers and public transport are treated

Outputs and how to read them

Ensemble mode writes a family of files into the output folder, sharing the run name: <name>.tif (the built and green likelihood bands), <name>_existing.tif (the starting fabric), <name>_pre.tif (the chosen run before post-processing), <name>_moderate.tif and <name>_tight.tif (the two clustering options, each coloured by density tier: built as a yellow-to-brown ramp, mixed-use centres as a reds ramp, existing fabric muted), <name>_report.txt (parameters and per-option statistics) and <name>_params.json (the reloadable settings). QGIS loads them as a layer group in that order.

Every population figure counts new residents only; existing fabric is assumed served by its own centres. The per-person readouts follow the same convention: m² of mixed-use centre per person is new centre land over new residents, and m² of green per person is new green over new residents. Coverage percentages include every home, existing and new.

Single-run mode writes one band per growth step. QGIS loads it as a temporal animation: open View → Panels → Temporal Controller, press the play button, and the town grows step by step.

Troubleshooting