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https://github.com/kuanb/peartree

peartree: A library for converting transit data into a directed graph for sketch network analysis.
https://github.com/kuanb/peartree

gis graphs gtfs modeling network-analysis spatial-analysis transit

Last synced: about 1 month ago
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peartree: A library for converting transit data into a directed graph for sketch network analysis.

Lists

README 

        
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peartree 🍐🌳

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.. image:: https://img.shields.io/gitter/room/nwjs/nw.js.svg

        :target: https://gitter.im/peartree_transit



.. image:: https://img.shields.io/pypi/v/peartree.svg

        :target: https://pypi.python.org/pypi/peartree



.. image:: https://img.shields.io/travis/kuanb/peartree.svg?branch=master

        :target: https://travis-ci.org/kuanb/peartree



.. image:: https://codecov.io/gh/kuanb/peartree/branch/master/graph/badge.svg

  :target: https://codecov.io/gh/kuanb/peartree



peartree is a library for converting `GTFS `_ feed schedules into a representative directed network graph. The tool uses `Partridge `__ to convert the target operator schedule data into `Pandas `__ dataframes and then `NetworkX `_ to hold the manipulated schedule data as a directed multigraph.



.. image:: https://raw.githubusercontent.com/kuanb/peartree/master/examples/example.gif



Above, an example of multiple Bay Area transit operators being incrementally loaded into peartree.



Installation

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.. code:: console



    pip install peartree



Usage

-----



See a full notebook at `this gist `_ to see a simple, step-by-step iPython Notebook pulling in an AC Transit GTFS feed and converting it to a NetworkX graph.



.. code:: python



    import peartree as pt



    path = 'path/to/actransit_gtfs.zip'



    # Automatically identify the busiest day and

    # read that in as a Partidge feed

    feed = pt.get_representative_feed(path)



    # Set a target time period to

    # use to summarize impedance

    start = 7*60*60  # 7:00 AM

    end = 10*60*60  # 10:00 AM



    # Converts feed subset into a directed

    # network multigraph

    G = pt.load_feed_as_graph(feed, start, end)



Examples

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I've yet to produce a full how-to guide for this library, but will begin to populate this section with any blog posts or notebooks that I or others produce, that include workflows using peartree.



`Calculating betweeness centrality with Brooklyn bus network `_



`Combining a peartree transit network and an OpenStreetMap walk network `_



`Generating comparative acyclic route graphs `_



`Coalescing transit network graphs and spectral clustering methods `_



`Exploratory graph analysis with betweenness and load centrality `_