City2graph

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city2graph is an AI Agents & Automation tool that transforms geospatial datasets into graph representations. It provides an integrated interface for GeoPandas, NetworkX, and PyTorch Geometric to enable GeoAI and geographic data science applications.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is city2graph?

City2Graph is a Python library designed to convert diverse geospatial datasets into graph representations, making them suitable for Graph Neural Networks (GNNs) and spatial network analysis. It offers an integrated interface with popular libraries like GeoPandas, NetworkX, and PyTorch Geometric, streamlining the development of advanced GeoAI and geographic data science applications. Key features include graph construction from urban datasets (buildings, streets, land use), transportation network modeling via GTFS feeds, proximity and contiguity analysis, and mobility flow analysis. The library also facilitates seamless conversion of geospatial data into PyTorch tensors for GNNs, supporting both CPU and GPU installations for various computational needs.

Best used for

Ideal for developers and data scientists who need to convert geospatial datasets into graph representations, analyze urban mobility patterns, and build GeoAI applications. Especially valuable for researchers and practitioners working with Graph Neural Networks and spatial network analysis.

Common actions

transform geospatial data

build graph neural networks

analyze urban mobility

model transportation networks

low-code/no-codecollaborationautomated workflowdeepfakeworkflowsgithub copilotface swappingopen-source"AI Agents"

Capabilities

Key features

Geospatial data to graphs
GeoPandas, NetworkX, PyTorch Geometric integration
Transportation network modeling
Proximity and contiguity analysis
Mobility flow analysis
PyTorch tensors for GNNs

Target Audience

developerdata scientist

Integrations

geopandasnetworkxpytorch-geometricduckdb

Pricing & Plans

Open Source

Free

FAQs

What types of geospatial data can City2Graph process?

City2Graph can process diverse urban datasets, including buildings, streets, land use, GTFS feeds for transportation, and various sources for mobility flow analysis like bike-sharing and pedestrian flows. It's designed for a wide range of geographic data science applications.

Does City2Graph support GPU acceleration for Graph Neural Networks?

Yes, City2Graph supports GPU acceleration. You can install it with specific CUDA versions (e.g., cu126, cu128, cu130) using pip, allowing for faster processing and training of Graph Neural Networks on compatible hardware.

How can I contribute to the City2Graph project?

Contributions are welcome! You can contribute by forking the repository, setting up a development environment with `uv sync --group dev --extra cpu`, making your changes, and submitting a pull request. Detailed guidelines are available in the Contributing Guide.

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