GeoMetric

Multi-map spatial analytics portfolio in Python: projections, choropleths, proportional symbols, flows, continuous-field mapping, cartograms, and scenario-based map design, with optional interactive exports (Folium, Plotly, Dash) and spatial statistics (Moran’s I, LISA).

Requirements

• Python 3.10+ (3.11–3.12 recommended; see requirements.txt for optional notes on Python 3.13).
• Install: pip install -r requirements.txt
  On Windows, if cartopy fails via pip, install it from conda-forge first, then re-run pip for the rest.

Quick start

1. Create and activate a virtual environment.
2. pip install -r requirements.txt
3. Download raw data: python scripts/utils/data_loader.py
4. Preprocess: python scripts/utils/preprocess.py
5. Generate all maps: python run_all.py
   • Draft (150 DPI): python run_all.py --draft
   • Selected parts: python run_all.py --parts 1 2 3
   • Skip download/preprocess if data already built: --skip-download / --skip-preprocess
6. Optional batch bonus HTML (animation + Moran figures):
   python run_all.py --bonus --skip-download --skip-preprocess
   The Plotly Dash dashboard is started separately: python scripts/bonus/bonus_dashboard.py (opens port 8050).

Alternatively use make targets if you use the bundled .geovenv layout (see Makefile).

Repository layout

GeoMetric/
├── data/                 # raw/ + processed/ (generated; see .gitignore)
├── notebooks/            # One notebook per part + setup
├── scripts/
│   ├── utils/            # config, data_loader, preprocess, map_utils
│   ├── parts/            # part1_projections … part7_scenarios
│   └── bonus/            # dashboard, animation, Moran's I
├── outputs/
│   ├── figures/          # PNG maps (300 DPI by default)
│   └── interactive/      # HTML (Folium, Plotly, static dashboard export)
├── webapp/               # Optional static gallery + Leaflet explorer (serve repo root)
├── docs/                 # WORKFLOW.md, dataset_inventory.md, assignment notes
├── report/               # GeoMetric_Report.tex; build PDF via online Overleaf-Latex
├── tests/                # pytest
├── run_all.py            # Pipeline entrypoint
├── requirements.txt
└── README.md

Report (PDF)

• Manuscript: report/GeoMetric_Report.md (8–12 pages when exported; trim figures if needed).
• Build instructions: report/BUILD_PDF.md.
• After maps exist: python scripts/build_report_pdf.py (needs Pandoc + LaTeX), or python scripts/build_report_pdf.py --html then print the HTML to PDF.

Documentation

• Workflow and data-flow diagram (Mermaid): docs/WORKFLOW.md
• Dataset inventory (Part 0 table): docs/dataset_inventory.md
• Assignment text: docs/assignment_requirements.txt

Assignment alignment (summary)

• Part 0: Multiple dataset categories documented; see docs/dataset_inventory.md.
• Parts 1–7: Implemented in scripts/parts/ with narratives and CSV/table outputs under outputs/figures/.
• Deliverables: Python scripts + notebooks, PNG figures, at least one interactive HTML map (e.g. Folium/Plotly under outputs/interactive/), plus the separate PDF report.
• Stack: pandas, GeoPandas, Matplotlib, mapclassify, Folium, Plotly, SciPy, NetworkX, contextily (available for basemaps where used), optional PySAL stack for bonus Moran/LISA.

Testing

pytest tests/ -v

Web gallery (optional)

From the repository root: python -m http.server 8765 and open http://localhost:8765/webapp/ so paths to /outputs/ and /asset/ resolve.

Data sources (licences)

Natural Earth (public domain); Our World in Data (CC BY 4.0); World Bank (CC BY 4.0); OpenFlights (ODbL); temperature source as documented in preprocessing. See figure captions and text outputs for citations.

Author

Muhammad Nouman Hafeez — FAST-NUCES, Islamabad.