https://github.com/anisotropi4/parenx

Pare or simplify a network using raster image skeletonization and Voronoi polygons
https://github.com/anisotropi4/parenx

Last synced: about 2 months ago
JSON representation

Pare or simplify a network using raster image skeletonization and Voronoi polygons

• Host: GitHub
• URL: https://github.com/anisotropi4/parenx
• Owner: anisotropi4
• License: apache-2.0
• Created: 2024-01-17T22:51:09.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-01-23T19:03:11.000Z (11 months ago)
• Last Synced: 2024-07-12T10:06:40.258Z (6 months ago)
• Language: Python
• Size: 4.24 MB
• Stars: 11
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# parenx

Simplify (or "[pare](https://dictionary.cambridge.org/dictionary/english/pare)") a GeoJSON network ("nx") using raster image skeletonization an Voronoi polygons

Provides functions that use image skeletonization or Voronoi polygons to simplify geographic networks composed of linestrings. The outputs are geographic layers representing simplified or 'primal' representations of the network. Primal networks only contains straight line segments

Sample datasets include:

- Princes Street in Edinburgh, in [data/rnet_princes_street.geojson](https://github.com/anisotropi4/parenx/blob/main/data/rnet_princes_street.geojson)

- Rail lines in Doncaster, in [data/rnet_doncaster_rail.geojson](https://github.com/anisotropi4/parenx/blob/main/data/rnet_doncaster_rail.geojson)

## Installation

Install the package into an activated `python` virtual environment with the following command:

```bash

pip install parenx

```

Install the latest development version from GitHub with the following command:

```bash

pip install git+https://github.com/anisotropi4/parenx.git

```

This places the `skeletonization.py` and `voronoi.py` scripts into the executable search path.

Test to see if the package is installed with the following command:

```bash

python -c "import parenx; print(parenx.__version__)"

```

## Examples

A `bash` helper script `run.sh` and example data is available under the `sitepackage` project directory under `venv`. The exact path varies with module and `python` version

### Skeletonization

The following creates a simplified network by applying skeletonization to a buffered raster array in `output.gpkg`

```bash

# Download the data if not already present

if [ ! -f ./data/rnet_princes_street.geojson ]; then

    wget https://raw.githubusercontent.com/anisotropi4/parenx/main/data/rnet_princes_street.geojson

    # Create data folder if not already present

    if [ ! -d ./data ]; then

        mkdir ./data

    fi

    mv rnet_princes_street.geojson ./data

fi

```

```bash

skeletonize.py ./data/rnet_princes_street.geojson rnet_princes_street_skeletonized.gpkg

```

### Voronoi

The following creates a simplified network by creating set of Voronoi polygons from points on the buffer in `output.gpkg`

```bash

voronoi.py ./data/rnet_princes_street.geojson rnet_princes_street_voronoi.gpkg

```

### Simple operation

The `run.sh` script sets a python virtual environment and executes the script against a data file in the `data` directory

    $ ./run.sh

The `run.sh` script optionally takes a filename and file-extension. To simplify a file, say `somewhere.geojson` and output to `GeoPKG` files `sk-simple.gpkg` and `vr-simple.gpkg`

    

    $ ./run.sh somewhere.geojon simple

### Locating the `run.sh` script

To copy the `run.sh` script into your local directory the following could help

    $ find . -name run.sh -exec cp {} . \;

## Notes

Both are the skeletonization and Voronoi approach are generic approaches, with the following known issues:

* This does not maintain a link between attributes and the simplified network

* This does not identify a subset of edges that need simplification

* The lines are a bit wobbly

* It is quite slow