sync

paper.bib

@@ -199,6 +199,28 @@ @article{Veldkamp1996
   doi     = {10.1016/0304-3800(94)00151-0}
 }
 
+@article{Moreira2009,
+  author  = {Moreira, Evaldinolia and Costa, Sérgio and Aguiar, Ana Paula and
+             Câmara, Gilberto and Carneiro, Tiago},
+  title   = {Dynamical coupling of multiscale land change models},
+  journal = {Landscape Ecology},
+  volume  = {24},
+  number  = {9},
+  pages   = {1183--1194},
+  year    = {2009},
+  doi     = {10.1007/s10980-009-9397-x}
+}
+
+@software{LuccME,
+  author    = {Aguiar, Ana Paula Dutra and Carneiro, Tiago G. S. and
+               Costa, Sérgio Souza and Moreira, Evaldinolia and
+               Câmara, Gilberto},
+  title     = {{LuccME}: a {TerraME}-based framework for spatially explicit
+               land use and cover change modeling},
+  publisher = {GitHub},
+  url       = {https://github.com/terrame/luccme}
+}
+
 @inproceedings{Costa2009,
   title     = {Common Concepts to Development of the Top-Down Models of Land Changes},
   author    = {Costa, Sérgio Souza and Aguiar, Ana Paula Dutra and Câmara, Gilberto and Moreira, Evaldinolia},

paper.md

@@ -52,13 +52,13 @@ available through the DisSModel GitHub organisation and on PyPI.
 ## Statement of Need
 
 Python has become the lingua franca for geospatial data science, supported by
-libraries such as GeoPandas and PySAL — but these tools are designed for static
-analysis. Dynamic spatial modeling, simulating how landscapes evolve over time,
-has historically required specialised platforms. In Brazil, TerraME [@Carneiro2013]
-and Dinamica EGO are the most widely adopted general-purpose frameworks, while
+libraries such as GeoPandas and PySAL — but these tools target static analysis.
+Dynamic spatial modeling, simulating how landscapes evolve over time, has
+historically required specialised platforms. In Brazil, TerraME [@Carneiro2013] and
+Dinamica EGO are the most widely adopted general-purpose frameworks, while
 institutions elsewhere rely on narrower allocation models such as CLUE and CLUE-S
-[@Veldkamp1996]. This fragmentation leaves researchers choosing between a
-Lua-based toolchain and single-purpose implementations with no shared contract.
+[@Veldkamp1996]. This fragmentation leaves researchers choosing between a Lua-based
+toolchain and single-purpose implementations with no shared contract.
 
 While TerraME is conceptually robust, its reliance on Lua — a language with far
 smaller adoption in data science than Python — creates a barrier for data
@@ -108,7 +108,7 @@ neighbourhoods [@Rey2021], and a raster substrate (`RasterModel`,
 `focal_sum`, `neighbor_contact`) replacing cell-by-cell loops. **Executor** defines
 the `ModelExecutor` four-phase lifecycle (`validate`, `load`, `run`, `save`);
 subclasses self-register via `__init_subclass__`, and every run produces an
-`ExperimentRecord` capturing input checksum, parameters, timing, and output paths.
+`ExperimentRecord` with input checksum, parameters, timing, and output paths.
 **IO** provides a unified dataset abstraction (`load_dataset` / `save_dataset`)
 across GeoDataFrame, GeoTIFF, and Xarray/Zarr, with transparent `s3://`
 resolution. **Visualization** integrates Matplotlib, Streamlit-compatible widgets,
@@ -151,10 +151,10 @@ validates the raster implementation against TerraME over the Maranhão Island
 dataset (50,496 cells, 19 steps): land use and soil match exactly at every
 checkpoint (MAE 0, max error 0), and elevation on 97.3% of cells within 1 mm
 (MAE 0.00068 m) — match percentage being the appropriate metric for categorical
-outputs [@PontiusEtAl2011]. In this scenario the flood component triggers no land-use
-transition and the golden files confirm TerraME behaves identically, so the
+outputs [@PontiusEtAl2011]. In this scenario the flood component triggers no
+land-use transition and the golden files confirm TerraME does the same, so the
 agreement above exercises mangrove migration; flooding is covered separately under
-the original laboratory parameters. Reproducible via
+the laboratory parameters. Reproducible via
 `brmangue-dissmodel/src/brmangue/executors/validation_executor.py` (`end_time=19`)
 against the committed golden CSVs in `tests/fixtures/golden/`, with
 `tests/test_model_invariants.py` and `tests/test_transition_rules.py` covering
@@ -180,32 +180,33 @@ allocation [@PontiusMillones2011]. The raster substrate is 3.9× faster (44.0 ms
 172.8 ms/step). Reproducible via
 `disslucc-continuous/tests/test_benchmark_validation.py`, with
 `tests/test_benchmark_discriminance.py` confirming that perturbing the regression
-coefficients breaks the tolerance criterion. Full end-to-end provenance from raw
-inputs to final metrics is addressed by the `dissmodel-platform` satellite
-package.
+coefficients breaks the tolerance criterion. End-to-end provenance from raw inputs
+to final metrics is addressed by the `dissmodel-platform` package.
 
 ## Research Impact Statement
 
 DisSModel's scientific lineage is rooted in the TerraME/LuccME research program at
 INPE. The submitting author conducted doctoral research at INPE under Prof.
 Gilberto Câmara and Dr. Ana Paula Dutra Aguiar — principal architects of
-TerraME/LuccME — and has co-authored the LuccME modeling framework since 2009 [@Costa2009]. On 7 May 2026, DisSModel
-was presented at INPE's Graduate Program in Applied Computing seminar series
-(recording: https://youtu.be/o7pMJt0CvXU), connecting the framework to the
-institutional community that maintains TerraME and LuccME.
+TerraME/LuccME — and has co-authored the modeling program since 2009
+[@Moreira2009; @Costa2009]; the DisSLUCC packages reimplement in Python the
+continuous and discrete allocation components of that lineage [@LuccME]. On
+7 May 2026, DisSModel was presented at INPE's Graduate Program in Applied
+Computing seminar series (recording: https://youtu.be/o7pMJt0CvXU), connecting
+the framework to the institutional community that maintains TerraME and LuccME.
 
 The framework is in active use across two UFMA research groups. Within LambdaGeo,
-graduate students develop `disslucc-continuous` and `brmangue-dissmodel` as part of
-their Master's research. Independently, Prof. Denilson da Silva Bezerra (UFMA,
-former INPE), whose doctoral work established BR-MANGUE's scientific foundation
+graduate students develop `disslucc-continuous` and `brmangue-dissmodel` in their
+Master's research. Independently, Prof. Denilson da Silva Bezerra (UFMA, former
+INPE), whose doctoral work established BR-MANGUE's scientific foundation
 [@Bezerra2013], uses the DisSModel reimplementation in his own coastal dynamics
-research program (PVCBS4959-2025, PVCBS4960-2025;
+program (PVCBS4959-2025, PVCBS4960-2025;
 https://sigaa.ufma.br/sigaa/public/docente/pesquisa.jsf?siape=3104707), a
 collaboration predating DisSModel itself [@Bezerra2025BM].
 
-Starting August 2026, the project receives its first PIBIC-funded undergraduate
-research fellows, under approved institutional project PVCET5136-2026 at
-UFMA. The concentrated 2026 development effort was oriented toward this milestone:
+Starting August 2026, the project receives its first undergraduate research
+fellows, funded by UFMA and by CNPq, one of them supervised by a collaborating
+faculty member. The 2026 development effort was oriented toward this milestone:
 stabilizing the `ModelExecutor` contract so each fellow can own an independent
 repository — `disslucc-continuous`, `disslucc-discrete`, `brmangue-dissmodel`, or
 `disscube` (a data-cube layer, the Python successor to TerraME's
@@ -214,18 +215,18 @@ repository — `disslucc-continuous`, `disslucc-discrete`, `brmangue-dissmodel`,
 Since the original submission, development has continued with `disslucc-discrete`
 [@DisSLUCCDiscrete], a CLUE-S-like discrete allocation package using logistic
 regression — the discrete counterpart to `DisSLUCC-Continuous`. An initial version
-has been validated against the Lab6 case study (Moju municipality, 5,914 cells, 6
-steps) from the original TerraME/LuccME repository, reaching cell-for-cell
+has been validated against the Lab15 case study (Moju municipality, 5,914 cells,
+6 steps) from the reference LuccME implementation [@LuccME], reaching cell-for-cell
 agreement — zero quantity and zero allocation disagreement [@PontiusMillones2011] — at
-56.8 ms/step. A shipped discriminance test shows this scenario is also reproduced
-by a trivial static ranking, so it validates coefficient transcription rather than
-the allocation algorithm; a dynamic-covariate scenario is planned.
+56.8 ms/step. A shipped discriminance test shows this scenario is also reproduced by a trivial
+static ranking, so it validates coefficient transcription rather than the
+allocation algorithm; a dynamic-covariate scenario is planned.
 
 These packages — `dissmodel-ca`, `dissmodel-sysdyn`, `DisSLUCC-Continuous`,
 `disslucc-discrete`, and `brmangue-dissmodel` — demonstrate that the
 `ModelExecutor` contract generalizes across modeling paradigms without core
-modifications. Studies such as @Bezerra2022, developed using LuccME, represent the
-class of models the DisSLUCC packages are designed to reproduce. A roadmap toward
+modifications. Studies such as @Bezerra2022, developed using LuccME, are the class
+of models the DisSLUCC packages aim to reproduce. A roadmap toward
 DisSModel 1.0 (May 2027) anchors community outreach including an open textbook,
 *Geospatial Modeling with Python*
 (https://lambdageo.github.io/geospatial-modeling-python/), already in progress.
@@ -257,9 +258,9 @@ validation routines against the original TerraME scripts. AI tools also assisted
 with writing in English, not the submitting author's native language. The
 scientific design — the TerraME compatibility contract, executor pattern,
 dual-substrate architecture, and validation methodology — predates and is
-independent of this AI-assisted phase, tracing to the submitting author's
-doctoral research at INPE and the undergraduate thesis cited above. AI tools
-assisted with implementation velocity and language clarity, not scientific or
-architectural decisions. All outputs were reviewed and validated by the authors.
+independent of this AI-assisted phase, tracing to the submitting author's doctoral
+research at INPE and the undergraduate thesis cited above. AI assisted with
+implementation velocity and language clarity, not scientific or architectural
+decisions. All outputs were reviewed and validated by the authors.
 
 ## References