dissmodel

Numerical and spatial parity benchmark of DisSCube against canonical TerraME goldens: cell-by-cell comparison, spatial metrics and timing, reproducible in CI.

Agent-Based Modeling extension for dissmodel, with a Society/Agent API inspired by TerraME. Includes RandomWalk, PredatorPrey, and Schelling models.

Cellular Automata (CA) library and examples for dissmodel, supporting GeoDataFrame-based (vector) and NumPy-based (raster) models, with interactive Streamlit explorers.

Spatially explicit simulation of sea-level rise impacts on mangrove ecosystems, implementing the model by Bezerra et al. (2014) on the DisSModel framework.

QGIS interface for BR-MANGUE coastal simulations — jobs run on the DisSModel Platform, results load directly in the canvas.

Traceable land-use change experiment from open data with DisSModel, DisSCube and DisSLUCC: declared sources, a grid on the Brazil Data Cube mesh, a verification model, a run record and acceptance criteria, repeatable at four levels with make.

QGIS plugin to build DisSCube grids and pipeline files (TOML) on the map, aligned with the Brazil Data Cube tiles

External executor registry for DisSModel — TOML-based, PR-reviewed.

[Re] lulcc (Moulds et al., 2015): replication of the Plum Island Ecosystems application in Python with DisSModel and DisSLUCC, for ReScience C

Classic System Dynamics models (SIR, Lorenz, Predator-Prey) built on dissmodel. Includes interactive Streamlit explorers and CLI tools.

Land Use and Cover Change (LUCC) modeling — continuous and discrete allocation — on top of dissmodel

Reference execution outputs (goldens) of TerraME 2.0.1's ca, sysdyn and logo model packages, generated in a pinned Docker image, with SHA-256 hashes, to verify dissmodel-ca, dissmodel-sysdyn and dissmodel-abm against TerraME itself.

Parity benchmark of DisSLUCC against the canonical LuccME goldens (luccme-goldens): cell-by-cell comparison, spatial metrics and timing.

Two-volume book: Vol. I teaches geospatial Python from scratch (pandas, geopandas, rasterio, spatial statistics); Vol. II introduces DisSModel, our discrete spatial modeling framework. Work in progress, AI-assisted writing.

Declarative pipelines that turn open geospatial data into model-ready grids for DisSModel, with a spatial data catalog and raster derivation.

Domain-decomposition engine (blocks + halo/ghost cells) for cellular automata on rasters larger than RAM, integrated with dissmodel.

Prototype execution platform for DisSModel simulations: JupyterLab, FastAPI, workers and S3-compatible storage via Docker Compose.

Organization website for the DisSModel ecosystem.