A Python toolkit for simplifying street network geometry by converting transportation-focused representations to morphological centerlines.
neatnet is a Python toolkit for processing and simplifying street network geometry. It transforms transportation-focused street representations (like those from OpenStreetMap) into morphological centerlines by removing dual carriageways, simplifying roundabouts, and preserving network continuity. The tool addresses the need for street networks that better represent urban space morphology rather than just transportation infrastructure.
Urban researchers, GIS analysts, and data scientists working with street network data who need simplified, morphologically accurate representations for spatial analysis, urban studies, or network science applications.
Developers choose neatnet because it provides a specialized, scientifically-grounded approach to street network simplification that preserves topological continuity while converting transportation geometries to morphological representations, filling a gap in existing GIS tooling for urban analysis.
Street geometry processing toolkit
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Removes redundant parallel road segments to create single centerlines, specifically addressing transportation-focused geometries in OSM data. This simplifies networks for morphological urban analysis.
Converts complex roundabout geometries into centerline representations, enhancing readability and making street networks better suited for spatial research. Evidence from the README's key features highlights this core functionality.
Maintains network connectivity while simplifying geometry, ensuring that the resulting street centerlines remain usable for analysis. This is emphasized in the philosophy as a priority for urban space representation.
Tailored to process OpenStreetMap-derived street network data, making it ideal for urban researchers using common, open data sources. The README specifies this compatibility as a key feature.
The README states the project is 'young and may be evolving fast,' which could lead to breaking changes or unstable APIs, potentially disrupting production workflows.
Primarily designed for OpenStreetMap data, so it may not handle other street network formats without additional preprocessing, limiting flexibility for non-OSM datasets.
Focused on morphological urban analysis, so it lacks features for broader GIS tasks like routing or real-time visualization, making it less versatile for general-purpose use.