pysgn 0.4.1
A Python package for constructing synthetic geospatial networks
License: MIT
Requires Python: >=3.10

# PySGN: A Python package for constructing synthetic geospatial networks

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## Introduction

PySGN (**Py**thon for **S**ynthetic **G**eospatial **N**etworks) is a Python package for constructing synthetic geospatial networks. It is built on top of the [NetworkX](https://networkx.github.io/) package, which provides a flexible and efficient data structure for representing complex networks and [GeoPandas](https://geopandas.org/), which extends the datatypes used by pandas to allow spatial operations on geometric types. PySGN is designed to be easy to use and flexible, allowing users to generate networks with a wide range of characteristics.

## Installation

PySGN can be installed using pip:

```bash
pip install pysgn
```

If you plan to run the code snippets below or the Getting Started notebook `docs/getting_started.ipynb` locally, install the optional `docs` extras to get the other dependencies such as geodatasets, Jupyter and Sphinx:

```bash
pip install "pysgn[docs]"
```

When working from a clone of this repository, you can install the same extras in editable mode:

```bash
pip install -e ".[docs]"
```

## Usage Example

### Geospatial Erdős-Rényi Network

Here's a simple example of how to use the `geo_erdos_renyi_network` function to create a geospatial Erdős-Rényi network. It generates a network where each pair of nodes is connected with probability `p`, which depends on the spatial distance between the nodes. The parameter `a` controls the rate of decay of the connection probability with distance.

All PySGN functions expect the input GeoDataFrame to contain a single geometry type (Points or Polygons).

```python
import geodatasets
import geopandas as gpd
from pysgn import geo_erdos_renyi_network

# Load the sample grocery-store points from geodatasets
# and explode the GeoDataFrame into single points (one point per row).
gdf = (
    gpd.read_file(geodatasets.get_path("geoda.groceries"))
    .explode(index_parts=False)
    .reset_index(drop=True)
    .to_crs("EPSG:26971")
)

# Create a geospatial Erdős-Rényi network
graph = geo_erdos_renyi_network(gdf, a=3)

# Output the number of nodes and edges
print(f"Number of nodes: {graph.number_of_nodes()}")
print(f"Number of edges: {graph.number_of_edges()}")
```

### Geospatial Watts-Strogatz Network

Similarly you can use the `geo_watts_strogatz_network` function to create a geospatial Watts-Strogatz network. It first creates a network where each node is connected to its `k` nearest neighbors. Then, it rewires each edge with probability `p`. If an edge is chosen to be rewired, it is replaced with a new edge to a random node, where the probability of connecting to this new node is inversely proportional to the spatial distance.

```python
import geodatasets
import geopandas as gpd
from pysgn import geo_watts_strogatz_network

gdf = (
    gpd.read_file(geodatasets.get_path("geoda.groceries"))
    .explode(index_parts=False)
    .reset_index(drop=True)
    .to_crs("EPSG:26971")
)

# Create a geospatial Watts-Strogatz network
graph = geo_watts_strogatz_network(
    gdf,
    k=4,    # Each node is connected to k nearest neighbors
    p=0.1,  # Probability of rewiring each edge
    a=2,    # Distance decay exponent
)

# Output the number of nodes and edges
print(f"Number of nodes: {graph.number_of_nodes()}")
print(f"Number of edges: {graph.number_of_edges()}")
```

### Geospatial Barabási-Albert Network

You can also use the `geo_barabasi_albert_network` function to create a geospatial Barabási-Albert network. It creates a network using geospatial preferential attachment, where the probability of connecting to existing nodes depends on both their degrees and the spatial distances.

```python
import geodatasets
import geopandas as gpd
from pysgn import geo_barabasi_albert_network
from pysgn.ordering import density_order

gdf = (
    gpd.read_file(geodatasets.get_path("geoda.groceries"))
    .explode(index_parts=False)
    .reset_index(drop=True)
    .to_crs("EPSG:26971")
)

# Create a geospatial Barabási-Albert network
graph = geo_barabasi_albert_network(
    gdf,
    m=3,                # Each new node connects to 3 existing nodes
    a=2,                # Distance decay exponent
    max_degree=150,     # Maximum degree constraint
    # Use density-based node ordering (nodes in dense areas join first)
    node_order=lambda gdf: density_order(gdf, method='knn'),
)

# Output the number of nodes and edges
print(f"Number of nodes: {graph.number_of_nodes()}")
print(f"Number of edges: {graph.number_of_edges()}")
```

### Export to GeoDataFrames

Once you have a graph, you can convert it back to GeoPandas GeoDataFrames for
GIS workflows or file export.

```python
from pysgn import graph_to_gdf

nodes_gdf, edges_gdf = graph_to_gdf(graph)
```

## Documentation

For more information on how to use PySGN, please refer to the [documentation](https://pysgn.readthedocs.io/en/stable).

## Contributing

If you run into an issue, please file a [ticket](https://github.com/wang-boyu/pysgn/issues) for us to discuss. If possible, follow up with a pull request.

If you would like to add a feature, please reach out via [ticket](https://github.com/wang-boyu/pysgn/issues) or start a [discussion](https://github.com/wang-boyu/pysgn/discussions).
A feature is most likely to be added if you build it!

Don't forget to check out the [Contributors guide](https://github.com/wang-boyu/pysgn/blob/main/CONTRIBUTING.md).

## License

PySGN is released under the MIT License.