corr_transport_street_pattern

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Built with:

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Table of Contents

• Overview
• Installation
• Getting Started
• Documentation
• Contributing
• Citation

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Overview

This project is a notebook-centric Python workspace for exploratory analysis of how transport-related indicators relate to street-pattern structure. It is aimed at researchers and data scientists who want to reproduce or extend correlation-oriented experiments in an interactive environment. The repository centers on Jupyter-based analysis workflows rather than a packaged application, and newcomers should use the Getting Started instructions to run the notebooks and inspect the results.

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Installation

Prerequisites: requires Python >=3.11

Install corr_transport_street_pattern using one of the following methods:

Build from source:

1. Clone the corr_transport_street_pattern repository:

git clone https://github.com/GeorgeKontsevik/corr_transport_street_pattern

2. Navigate to the project directory:

cd corr_transport_street_pattern

3. Install the project dependencies:

pip install -r requirements.txt

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Getting Started

Prerequisites:

• Python with Jupyter notebook support.
• The notebooks use geopandas, pandas, numpy, osmnx, networkx, shapely, matplotlib, scipy, tqdm, pickle, json, and torch / torch_geometric in the shown setup cells.
• Notebook runs expect local geospatial inputs and serialized helper artifacts referenced in the notebooks.

1. Open the notebook you want to reproduce, such as cleaned_ver.ipynb, pattern_mode_corr.ipynb, or pt_exp.ipynb.
2. Install the packages used in the notebook setup cells before running it. The notebooks show pip install commands for huggingface_hub, geopandas, osmnx, torch_geometric, folium>=0.12, matplotlib, and mapclassify.
3. Run the import and data-loading cells from top to bottom so the notebook can read the required geospatial inputs and serialized dictionaries or graphs.
4. If you are working in pattern_mode_corr.ipynb, follow the notebook cells that filter the polygon data, convert the CRS, and prepare the geo_place values before downstream analysis.
5. Continue executing the analysis cells in order to generate the correlation and visualization outputs.

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Documentation

A detailed corr_transport_street_pattern description is available here.

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Contributing

• Report Issues: Submit bugs found or log feature requests for the project.

• Submit Pull Requests: To learn more about making a contribution to corr_transport_street_pattern.

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Citation

If you use this software, please cite it as below.

APA format:

GeorgeKontsevik (2026). corr_transport_street_pattern repository [Computer software]. https://github.com/GeorgeKontsevik/corr_transport_street_pattern

BibTeX format:

@misc{corr_transport_street_pattern,

    author = {GeorgeKontsevik},

    title = {corr_transport_street_pattern repository},

    year = {2026},

    publisher = {github.com},

    journal = {github.com repository},

    howpublished = {\url{https://github.com/GeorgeKontsevik/corr_transport_street_pattern}},

    url = {https://github.com/GeorgeKontsevik/corr_transport_street_pattern}

}

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