dissmodel 0.2.0

Discrete Spatial Modeling framework for raster and vector simulations

DisSModel

Discrete Spatial Modeling framework for Python

DisSModel is a modular, open-source Python framework for spatially explicit dynamic modeling. Developed by the LambdaGeo group at the Federal University of Maranhão (UFMA), it provides a unified environment for building Cellular Automata (CA) and System Dynamics (SysDyn) models on top of the Python geospatial ecosystem.

DisSModel is designed as a modern, Pythonic alternative to the TerraME framework, replacing the TerraLib/Lua stack with GeoPandas, PySAL, and Salabim.

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Features

• Cellular Automata — spatial grid models with configurable neighborhood strategies (Queen, Rook, KNN)
• System Dynamics — compartmental models with automatic live plotting via @track_plot
• Flexible grid generation — from dimensions, bounds, or an existing GeoDataFrame
• Fill strategies — random sampling, zonal statistics, minimum distance, and pattern-based initialization
• Three execution modes — CLI scripts, Jupyter notebooks, and Streamlit web apps
• Reactive UI — display_inputs reads annotated model attributes and generates sidebar widgets automatically
• Built on standard tools — GeoPandas, libpysal, Salabim, Matplotlib, Streamlit

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Installation

pip install dissmodel

Requires Python 3.10+.

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Quickstart

System Dynamics — SIR Model

from dissmodel.core import Environment
from dissmodel.models.sysdyn import SIR
from dissmodel.visualization import Chart

env = Environment()
SIR(susceptible=9998, infected=2, recovered=0, duration=2, contacts=6, probability=0.25)
Chart(show_legend=True)
env.run(30)

Cellular Automaton — Forest Fire

from dissmodel.core import Environment
from dissmodel.geo import regular_grid
from dissmodel.models.ca import FireModel
from dissmodel.models.ca.fire_model import FireState

gdf = regular_grid(dimension=(30, 30), resolution=1, attrs={"state": FireState.FOREST})
env = Environment(end_time=20)
fire = FireModel(gdf=gdf)
fire.initialize()
env.run()

Streamlit App

streamlit run examples/streamlit/ca_all.py

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Instantiation order

The Environment must always be created before any model. Models connect to the active environment automatically on creation.

Environment  →  Model  →  Visualization
     ↑             ↑            ↑
  first         second        third

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Architecture

DisSModel is organized into four modules:

Module	Description
dissmodel.core	Simulation clock and execution lifecycle (Environment, Model)
dissmodel.geo	Spatial data structures — grid generation, fill strategies, neighborhood
dissmodel.models	Ready-to-use CA and SysDyn reference implementations
dissmodel.visualization	Observer-based visualization — Chart, Map, display_inputs, @track_plot

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Included Models

Cellular Automata (dissmodel.models.ca)

Model	Description
GameOfLife	Conway's Game of Life with classic built-in patterns
FireModel	Forest fire spread with Rook neighborhood
FireModelProb	Probabilistic fire with spontaneous combustion and regrowth
Snow	Snowfall and accumulation from top row
Growth	Stochastic radial growth from a center seed
Propagation	Active state transmission with KNN neighborhood
Anneal	Binary system relaxation via majority-vote rule

System Dynamics (dissmodel.models.sysdyn)

Model	Description
SIR	Susceptible–Infected–Recovered epidemiological model
PredatorPrey	Lotka–Volterra ecological dynamics
PopulationGrowth	Exponential growth with variable rate
Lorenz	Deterministic chaos — Lorenz attractor
Coffee	Newton's Law of Cooling

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Execution Modes

All models can be run in three modes:

CLI

python examples/cli/sysdyn_sir.py
python examples/cli/ca_game_of_life.py

Streamlit

streamlit run examples/streamlit/sysdyn_all.py   # all SysDyn models
streamlit run examples/streamlit/ca_all.py        # all CA models

Running examples/streamlit/ca_all.py ...

Jupyter Notebook

See examples/notebooks/ for interactive notebooks with step-by-step explanations.

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Reactive Streamlit Interface

display_inputs reads annotated model attributes and generates sidebar widgets automatically — no extra configuration needed:

env = Environment(start_time=0, end_time=steps)
gdf = regular_grid(dimension=(grid_size, grid_size), resolution=1, attrs={"state": 0})
model = FireModel(gdf=gdf)
display_inputs(model, st.sidebar)  # generates sliders from type annotations
model.initialize()                 # uses parameters set by display_inputs

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Grid and Neighborhood

from dissmodel.geo import regular_grid, fill, FillStrategy

# Create a 20x20 grid
gdf = regular_grid(dimension=(20, 20), resolution=1.0, attrs={"state": 0})

# Fill with random values
fill(FillStrategy.RANDOM_SAMPLE, gdf=gdf, attr="state", data={0: 0.7, 1: 0.3}, seed=42)

Supported neighborhood strategies: Queen, Rook, KNN (via libpysal).

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Development

git clone https://github.com/LambdaGeo/dissmodel
cd dissmodel
pip install -e .
pip install -r requirements.txt  # dev dependencies
pytest tests/

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Version 0.2.0

Major improvements:

• New raster backend architecture
• Examples for raster and vector simulations
• Improved modular structure

Documentation

Full documentation available at: https://lambdageo.github.io/dissmodel/

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Citation

If you use DisSModel in your research, please cite:

Costa, S. & Santos Junior, N. (2025). DisSModel: A Discrete Spatial Modeling
Framework for Python. LambdaGeo, Federal University of Maranhão (UFMA).
https://github.com/LambdaGeo/dissmodel

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License

MIT © LambdaGeo — UFMA