peartree: A library for converting transit data into a directed graph for network analysis.
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README.rst

peartree 🍐🌳

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

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.

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

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.

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

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

Generating comparative acyclic route graphs

Coalescing transit network graphs and spectral clustering methods

Exploratory graph analysis with betweenness and load centrality