# Geoai City2graph Pattern

> Usa a convertir geoespacial a grafos con City2Graph.

- Skill: `ntizar/geoai-city2graph-pattern` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ntizar/geoai-city2graph-pattern`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ntizar/geoai-city2graph-pattern/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ntizar (https://skillmd.com/u/ntizar)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ntizar/geoai-city2graph-pattern

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# City2Graph — datos geoespaciales → grafos (API real)

> ⚠️ Corrección 2026-09-05 (auditoría): `c2g.build_graph`/`build_proximity_graph`/`build_transit_graph`/`compute_isochrones` **no existen**. La API real usa `knn_graph`/`delaunay_graph`/`waxman_graph` (proximity), `morphological_graph(s)` (morphology), `load_gtfs`/`load_gbfs`/`travel_summary_graph` (transporte), `od_matrix_to_graph` (mobility), `gdf_to_pyg`/`pyg_to_nx` (grafos).

**Repo:** `https://github.com/c2g-dev/city2graph` (Python, ~1.9K⭐).

## When to Use

- Cuando pidas **convertir datos geoespaciales** (geodataframes, GTFS/GBFS) en **grafos** para análisis de redes/IA urbana.

## Uso (API real)

```python
import city2graph as c2g
graph = c2g.knn_graph(gdf, k=... )            # proximity: knn_graph / delaunay_graph / waxman_graph
graph = c2g.morphological_graph(...)          # morphology
graph = c2g.travel_summary_graph(...)          # transporte (load_gtfs / load_gbfs)
graph = c2g.od_matrix_to_graph(...)            # movilidad
gfp = c2g.gdf_to_pyg(gdf, ...)                 # GeoDataFrame → PyG Data
nx_g = c2g.pyg_to_nx(gfp)
```

## Pitfalls

- **No** `build_graph`/`build_proximity_graph`/`build_transit_graph`/`compute_isochrones`.
- Atributos: usa las funciones de arriba; no `graph.node_features`/`edge_index`/`labels` directos.
- DOI real del README no es 10.5281/zenodo.15858845 (verificar en el repo).

## Verificación

- `knn_graph(gdf)` de un dataset y mirar `gdf_to_pyg` para alimentar una GNN.