Skip to main content

neatnet: Street Geometry Processing Toolkit

Continuous Integration codecov

Introduction

neatnet offers a set of tools pre-processing of street network geometry aimed at its simplification. This typically means removal of dual carrieageways, roundabouts and similar transportation-focused geometries and their replacement with a new geometry representing the street space via its centerline. The resulting geometry shall be closer to a morphological representation of space than the original source, that is typically drawn with transportation in mind (e.g. OpenStreetMap).

Examples

A fully-reproducible example can be found in the User Guide.

import neatnet

simplified = neatnet.neatify(gdf)

Installing

You can install neatnet from PyPI or from conda-forge using the tool of your choice:

pip install neatnet

Or (recommended):

conda install neatnet -c conda-forge

Contribution

While we consider the API stable, the project is young and may be evolving fast. All contributions are very welcome, see our guidelines in CONTRIBUTING.md.

Recommended Citations

The package is a result of a scientific collaboration between The Research Team on Urban Structure of Charles University (USCUNI), NEtwoRks, Data, and Society research group of IT University Copenhagen (NERDS) and Oak Ridge National Laboratory.

If you use neatnet for a research purpose, please consider citing the original paper introducing it.

Canonical Citation (primary)

Fleischmann, M., Vybornova, A., Gaboardi, J.D., Brázdová, A., Dančejová, D., 2026. Adaptive continuity-preserving simplification of street networks. Computers, Environment and Urban Systems 123, 102354. https://doi.org/10.1016/j.compenvurbsys.2025.102354

BibTeX:

@article{fleischmann2026Adaptive,
  title = {Adaptive Continuity-Preserving Simplification of Street Networks},
  author = {Fleischmann, Martin and Vybornova, Anastassia and Gaboardi, James D. and Br{\'a}zdov{\'a}, Anna and Dan{\v c}ejov{\'a}, Daniela},
  year = 2026,
  month = jan,
  journal = {Computers, Environment and Urban Systems},
  volume = {123},
  pages = {102354},
  issn = {01989715},
  doi = {10.1016/j.compenvurbsys.2025.102354},
  urldate = {2025-10-31},
  langid = {english}
}

Repository Citation (secondary)

DOI

Funding

The development has been supported by the Charles University’s Primus program through the project "Influence of Socioeconomic and Cultural Factors on Urban Structure in Central Europe", project reference PRIMUS/24/SCI/023.


This package developed & and maintained by:

Copyright (c) 2024-, neatnet Developers

Release files for neatnet 0.1.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for neatnet 0.1.6
File Size Uploaded
neatnet-0.1.6.tar.gz 25.2 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for neatnet 0.1.6
File Interpreter ABI Platform
neatnet-0.1.6-py3-none-any.whl Python 3 none any Details

Total release size: 25.4 MB

Release files / neatnet-0.1.6.tar.gz

Download URL neatnet-0.1.6.tar.gz
Size 25.2 MB
Tags Source
SHA-256 checksum
How to use checksums
a1f760e0875a7f42a778d86293c397b37ee61e71fba49769c2b3db30271a7fff
BLAKE2b-256 checksum
How to use checksums
f59c14cc38e097458ab163209d14159d9b1dccaaa73fb25736ea49c1824d225d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 28, 2026.

Transparency log

Release files / neatnet-0.1.6-py3-none-any.whl

Download URL neatnet-0.1.6-py3-none-any.whl
Size 146.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7ff98abef9d1ac4cee55b731ede67404ba6c5d3be796ffd57736b3eca33f90e5
BLAKE2b-256 checksum
How to use checksums
826e2678862e74a071d6d8a05dc9d4647b5886db806200501bec5416a00eb6c0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 28, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.1.6 This release

2 release files

0.1.5

2 release files

0.1.4

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

0.0.5

2 release files

0.0.4

2 release files

0.0.3

2 release files

0.0.2

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page