Divvy (Chicago)

Fetches trip data straight from the public S3 bucket via bike_network_traffic.get_bike_data and links a Celldega clustergram to the deck.gl flow map.

from bike_network_traffic import silence_warnings
silence_warnings()

import celldega as dega
from ipywidgets import HBox, Layout

from bike_network_traffic import (
    get_bike_data,
    link_flow_to_clustergram,
    make_flow_widget,
    make_station_clustergram,
)

1. Download trips and build station + transition tables

First call downloads (and caches under ~/.cache/bike_network_traffic) the monthly zip from S3 and returns a station table plus a destination-probability matrix ready for Celldega.

ds = get_bike_data('chicago', year=2026, return_trips=True)
stations, transition_prob, trips = ds.stations, ds.transition_prob, ds.trips
print('stations:', stations.shape, ' transition_prob:', transition_prob.shape, ' trips:', trips.shape)
stations.head()

stations: (1477, 4)  transition_prob: (1395, 1365)  trips: (656274, 8)

	station_id	station_name	lat	lng
0	CHI00611	2112 W Peterson Ave	41.991178	-87.683593
1	CHI01823	21st St & Pulaski Rd	41.853908	-87.724794
2	CHI00315	63rd St Beach	41.780911	-87.576323
3	CHI01748	900 W Harrison St	41.874754	-87.649807
4	CHI01976	Aberdeen St & 103rd St	41.707040	-87.650220

2. Cluster the transition matrix

mat, cgm, cluster_map = make_station_clustergram(transition_prob, n_clusters=100)

3. Build the flow map and link it to the clustergram

flow = make_flow_widget(stations, transition_prob, cluster_map, trips=trips, height=700, debug=True)
link_flow_to_clustergram(flow, cgm)

flow.layout = Layout(width='560px', height='700px')
cgm.layout  = Layout(width='720px', height='700px')
HBox([flow, cgm])