Pip repository
Full documentation
It's recommended to use a virtual environment to install this tool, since its dependencies may require different versions than what is installed on your system.
Recommended installation is with pip:
python -m pip install geo-adjacency
You must have Python <3.13,>=3.9 installed.
$ git clone [email protected]:asmyth01/geo-adjacency.git
$ cd geo-adjacency
$ poetry install
Or with build.
$ git clone [email protected]:asmyth01/geo-adjacency.git
$ cd geo-adjacency
$ python -m build
See the docs for details.
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Load the data. geo-adjacency expects you to provide your data as Shapely geometries. You will provide three lists: source_geoemtries, target_geometries, and obstacle_geometries. What we are analyzing is which of the source geometries are adjacent to which of the target geometries. Obstacles can prevent a source and target from being adjacent, but they do not participate in the adjacency dictionary.
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Create an AdjacencyEngine. In this case, we'll load the sample data which is available on
Github <https://github.com/asmyth01/geo-adjacency/>_.s, t, o = load_test_geoms("../tests/sample_data") engine = AdjacencyEngine(s , t, o, True)
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Run the analysis
output = engine.get_adjacency_dict() # defaultdict(<class 'list'>, {0: [1, 2], 1: [1], 2: [1], 3: [2], 6: [1], 7: [1]})
The output is a dictionary. Keys are the indices of source geometries in the input list, and values are a list of indices of adjacent target geometries in the input list.
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You can visualize the output with a handy built-in method which uses pyplot.
engine.plot_adjacency_dict(). (Source geoms are grey, targets are blue, obstacles are red. Linkages are green.
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You probably will want to match the adjacency dictionary back to the original data so that you can do something cool with it.
for source_i, target_i_list in output.items():
source_geom = source_geometries[source_i]
target_geoms = [target_geometries[i] for i in target_i_list]