🚢 MCIS — Maritime Conflict Intelligence System

A research-grade AIS analytics framework for maritime anomaly detection and early-warning studies.

MCIS analyzes only publicly available commercial AIS describing civilian vessel traffic; the conflict is treated purely as an exogenous disruption to civilian maritime activity, and the repository contains no operational, tactical, or defense-sensitive content.

The current primary case study is Black Sea maritime dynamics around February 24, 2022 (T0).

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✨ Why MCIS?

• Event-aware Maritime Analytics for pre/post event behavior shifts.
• End-to-end Reproducibility from raw CSV to model-ready panels.
• Methodological Guardrails to reduce leakage and unsupported claims.
• Baseline-first Modeling before moving to heavier deep-learning approaches.

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🧭 Table of Contents

• Project Layout
• Installation
• Quick Start
• CLI Guide
• Output Artifacts
• Testing
• Research Guardrails
• Recommended Workflow
• Troubleshooting
• License

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🗂 Project Layout

mcis/
├── cli/ # Command-line entrypoints
│ ├── run_pipeline.py # Load → clean → feature-engineer → aggregate
│ ├── run_analysis.py # Event-study / ITS / DiD / Granger workflows
│ └── run_model.py # Anomaly & forecasting model workflows
├── config/
│ └── settings.yaml # Central experiment/pipeline configuration
├── data/
│ ├── raw/ # Source AIS CSV files
│ ├── interim/ # Cleaned intermediate artifacts
│ ├── processed/ # Feature-engineered artifacts
│ └── aggregated/ # Panel-level Parquet outputs
├── mcis/
│ ├── compat.py # Dependency compatibility shims (NumPy, SciPy, statsmodels)
│ ├── loader.py
│ ├── cleaner.py
│ ├── features.py
│ ├── aggregator.py
│ ├── validation.py
│ ├── analysis/
│ ├── models/
│ ├── viz/
│ └── utils/
├── notebook/
│ └── mcis_pipeline.ipynb # End-to-end Jupyter notebook
├── outputs/ # Tables, metadata, model artifacts, reports
├── tests/ # Pytest suite (376 tests)
├── ROADMAP.md # Detailed technical roadmap and guardrails
├── pyproject.toml
└── requirements.txt

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⚙️ Installation

Requirements

• Python 3.11+
• pip

Base Install

pip install -e .

Optional Extras

Extra	Includes	Command
dev	pytest, coverage, notebooks	pip install -e ".[dev]"
ml	xgboost, torch, shap	pip install -e ".[ml]"
geo	geopandas, shapely, folium, plotly	pip install -e ".[geo]"
all	all optional groups	pip install -e ".[all]"

Note

Some optional packages (especially torch, geopandas) may require longer installs and additional system libraries.

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🚀 Quick Start

1) Validate config + guardrails

pytest tests/test_validation.py -v

2) Run pipeline (load → clean → features → aggregate)

python cli/run_pipeline.py \
--config config/settings.yaml \
--file data/raw/ais_blacksea_6m.csv \
--steps all

3) Run statistical analysis

python cli/run_analysis.py \
--config config/settings.yaml \
--analyses event_study its \
--metrics vessel_count mean_sog

4) Run anomaly models

python cli/run_model.py \
--config config/settings.yaml \
--panel data/aggregated/panel_blacksea.parquet \
--models rolling_zscore ewma robust_mahalanobis

5) Explore via Jupyter notebook

jupyter notebook notebook/mcis_pipeline.ipynb

The notebook covers the full pipeline: data loading, cleaning, feature engineering, aggregation, event studies, ITS/Granger/DiD analysis, anomaly detection, forecasting, model cards, and interactive maps.

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🧰 CLI Guide

cli/run_pipeline.py

Purpose

• Load raw AIS CSV data
• Apply cleaning and quality-flagging
• Engineer vessel-level features
• Aggregate to grid/day and Black Sea/day panels

Example

python cli/run_pipeline.py \
--config config/settings.yaml \
--file data/raw/ais_blacksea_12m.csv \
--steps load,clean,features,aggregate \
--date-start 2021-08-24 \
--date-end 2022-08-24

cli/run_analysis.py

Purpose

• Run selected analyses (event study, ITS, DiD, Granger) by metric
• Save outputs as structured JSON/table artifacts

Example

python cli/run_analysis.py \
--config config/settings.yaml \
--panel data/aggregated/panel_blacksea.parquet \
--analyses event_study its granger \
--metrics vessel_count unique_mmsi mean_sog

cli/run_model.py

Purpose

• Train/evaluate temporal anomaly and forecasting-error models
• Generate model artifacts, model cards, and registry records

Example

python cli/run_model.py \
--config config/settings.yaml \
--panel data/aggregated/panel_blacksea.parquet \
--models rolling_zscore ewma robust_mahalanobis var_residual

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📦 Output Artifacts

Typical outputs after successful execution:

• data/interim/ais_blacksea_cleaned.parquet
• data/processed/ais_blacksea_features.parquet
• data/aggregated/panel_daily.parquet
• data/aggregated/panel_blacksea.parquet
• outputs/tables/*.json
• outputs/metadata/*.json
• outputs/models/*.json
• outputs/models/*_model_card_*.md
• outputs/models/registry/registry_entries.json

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✅ Testing

Run all tests:

pytest tests/ -v

Run with coverage:

pytest tests/ --cov=mcis --cov-report=term-missing

Run core pipeline-module tests only:

pytest tests/test_loader.py tests/test_cleaner.py tests/test_features.py tests/test_aggregator.py -v

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🔧 Dependency Compatibility

MCIS includes a compatibility shim (mcis/compat.py) that patches breaking changes in commonly paired versions of NumPy, SciPy, and statsmodels:

Issue	Symptom	Fix
np.MachAr removed in NumPy ≥2.0	AttributeError in statsmodels internals	compat.py restores np.MachAr
scipy.signal.signaltools._centered moved in SciPy ≥1.17	ImportError in statsmodels internals	compat.py restores the import path

The compat module is imported automatically at the top of all CLI entrypoints and package __init__.py files so patches apply before any statsmodels-dependent code runs.

If you encounter missing-attribute errors from statsmodels, ensure import mcis.compat runs before any statsmodels imports (lazy imports are used in mcis/analysis/its.py, mcis/analysis/did.py, mcis/analysis/granger.py and optional geo deps in mcis/viz/maps.py).

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🛡 Research Guardrails

MCIS intentionally enforces methodological constraints for research validity:

1. No random train/test split — temporal split only.
2. No leakage features in model inputs — e.g., days_to_t0, post_conflict.
3. Validity/claim consistency — inferential claims are restricted under non-empirical modes.
4. Baseline-first strategy — interpretable methods before complex deep models.

For full policy details, see:

• ROADMAP.md
• config/settings.yaml
• mcis/validation.py

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🔁 Recommended Workflow

1. Update config/settings.yaml.
2. Run pytest tests/test_validation.py -v.
3. Run cli/run_pipeline.py to generate data artifacts.
4. Run cli/run_analysis.py for statistical outputs.
5. Run cli/run_model.py for model outputs and model cards.
6. Run regression checks with pytest tests/ --cov=mcis.

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🧯 Troubleshooting

• ModuleNotFoundError: No module named 'mcis'
  Run editable install from repo root: pip install -e .

• SHAP not installed in model CLI
  Install optional dependency: pip install shap>=0.44.0

• Panel file not found
  Run the pipeline first with --steps all.

• Missing model feature configuration
  Check model.features_to_use in config/settings.yaml.

• Memory pressure on large CSV files
  Use --date-start, --date-end, and/or --limit for development runs.

• AttributeError: module 'numpy' has no attribute 'MachAr'
  Upgrade mismatch between NumPy ≥2.0 and statsmodels 0.14.x. Fixed automatically by mcis/compat.py — ensure you import mcis.compat before any statsmodels calls.

• ImportError: cannot import name '_centered' from 'scipy.signal.signaltools'
  SciPy ≥1.17 moved this private function. Fixed automatically by mcis/compat.py.

• ModuleNotFoundError: No module named 'folium' or 'plotly'
  Install geo extras: pip install -e ".[geo]". The notebook and code gracefully degrade with clear error messages when these are missing.

• Notebook cells fail with stale imports after code changes
  Restart the kernel (Kernel → Restart & Run All) to pick up updated .py files.

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📄 License

MIT License.