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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.

pip install dissmodel

Why DisSModel?

DisSModel was designed as a modern, Pythonic alternative to the TerraME framework, replacing the TerraLib/Lua stack with the standardized GeoPandas/Python stack. This transition gives researchers direct access to the full Python data science ecosystem while maintaining the modeling expressiveness required for Land Use and Cover Change (LUCC) applications.

Core objectives:

  • Multi-paradigm support — Cellular Automata, System Dynamics, and Agent-Based Models in a unified environment
  • Geospatial ecosystem integration — GeoPandas, libpysal, and rasterstats for advanced spatial operations
  • Open science and reproducibility — open-source, installable via PyPI, examples included
  • Standardized implementation — pure Python lowers the barrier for interdisciplinary collaboration

Architecture

DisSModel is organized into four modules:

Module Description
dissmodel.core Simulation clock and execution lifecycle powered by Salabim
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

Quickstart

System Dynamics

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

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

streamlit run examples/streamlit/ca_all.py
streamlit run examples/streamlit/sysdyn_all.py

Instantiation order

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

Environment  →  Model  →  display_inputs  →  initialize()  →  Visualization
     ↑             ↑            ↑                  ↑               ↑
  first         second        third              fourth           fifth

Included Models

Cellular Automata

Model Description
GameOfLife Conway's Game of Life with built-in classic patterns
FireModel Forest fire spread
FireModelProb Probabilistic fire with spontaneous combustion and regrowth
Snow Snowfall and accumulation
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

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

Python Ecosystem

DisSModel builds on well-established, industry-standard libraries:

Library Role
GeoPandas Vector data and GeoDataFrame operations
Salabim Discrete event simulation engine
libpysal Spatial weights and neighborhood analysis
rasterstats Raster/vector zonal statistics
Shapely Geometric operations
Matplotlib Time-series and spatial visualization
Streamlit Reactive web application UI

License

MIT © 2026 Sérgio Costa & Nerval Santos Junior — LambdaGeo/UFMA