Rush Hour Dynamics: Using Python to Study the London Underground¶ – Introduction¶ – Background¶

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Rush Hour Dynamics: Using Python to Study the London Underground¶ – Introduction¶ – Background¶

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Rush Hour Dynamics: Using Python to Study the London Underground

Camilla Montonen

PyData Paris 2015

Full slides available at http://cs-with-python.github.io/

Introduction

Background

  • Bryn Mawr College 2013
  • University of Edinburgh 2014
  • Currently working in QA at Caplin Systems Ltd.
  • Member of Pyladies London and Women in Data. If you're ever in London, please drop in to one of our meetups!

There are interesting data problems everywhere...

  • Python gives you the tools, but you have to ask the questions!

Back in August 2014...

Which Tube line should I take to work?

Some days it was all good...

Other days ...not so good

A pattern starts to emerge

Source: BBC News

Observation: delays or suspensions on one station can affect remote stations

Questions

What are the most "important" stations in the London Underground network?

How does suspending these "important" stations affect the rest of the network

Let's bring the Python to the Data

In the beginning, there was the 'Data'

How do I translate a physical map of the London Underground into a Graph I can process with Python?

Start

Goal

Goal

Data collection:

It would be cool to program some kind of OCR to automatically read the data from the map and produce a data file! But alas, I had to resort to manually creating a data file:

#Station #Neighbour(line)
Acton Town            Chiswick Park (District), South Ealing (Picadilly), Turnham Green (Picadilly)
Aldgate                Tower Hill (Circle; District), Liverpool Street (Metropolitan; Circle; District)
Aldgate East        Tower Hill (District), Liverpool Street (HammersmithCity; Metropolitan)
Alperton            Sudbury Town (Picadilly), Park Royal (Picadilly)

Now it's a piece of cake...

... to perform some analysis

Let's go back to our question 1

What is the most "important" station in the London Underground network?

Defining "importance"

Let's talk about betweenness centrality

Betweenness seems like a good metric to measure the "importance" of a station

The higher the betweenness of a station, the more commuters will pass through it

How can we compute betweenness on our London Underground graph?

Graphs and Python: graph-tool

  • graph-tool is a Python library written by Tiago Peixoto that provides a number of tools for analyzing and plotting graphs.

What can you do with graph-tool ?

Create a graph object

In [14]:
from graph_tool.all import Graph

#create a new Graph object
graph_object=Graph()

Add edges and vertices to the graph

In [15]:
# add a vertex 
vertex1 = graph_object.add_vertex()
vertex2 = graph_object.add_vertex()
In [16]:
# add an edge
edge1 = graph_object.add_edge(vertex1, vertex2)

Create property maps

helpful for storing information about your nodes and edges

In [17]:
# create a property map
vertex_names = graph_object.new_vertex_property("string")

## iterate through the vertices in the graph
for vertex in graph_object.vertices():
    vertex_names[vertex]="some_name"

Create visualizations

In [10]:
from graph_tool.draw import graph_draw
from graph_tool.all import price_network

# draw a small graph
graph_draw(graph_object, output="somefile.png")

#create a price network 
price_graph=price_network(5000)
graph_draw(price_graph, output="price.png")
Out[10]:
<PropertyMap object with key type 'Vertex' and value type 'vector<double>', for Graph 0x7f27b0121190, at 0x7f277c05cf10>

A Simple Graph

A Price Network

Filter vertices and edges

A sample visualization of the London Underground

Let's go back to betweenness

Easily calculate betweenness by calling the betweenness function in graph_tool.centrality

Betweenness

We have our answer for question 1...

Let's take our analysis of betweenness one step further... and answer question 2

How do problems on one of these important stations affect the Underground network?

Bokeh: creating interactive data visualization

A basic visualization of the London Underground

A Basic Visualization of Betweenness

How does the betwenness of each station change when Baker Street is suspended?

Bokeh allows us to visualize this interactively in the browser

In [13]:
from IPython.display import YouTubeVideo

YouTubeVideo('VouLqY-Uegs')
Out[13]:

Bokeh can do alot more than this

In fact, we can build "real time" simulations by using the built-in bokeh-server app to stream data to a graph

A Simple Bokeh Simulation of the Underground

Each station is assigned a random number of commuters Each commuter is assigned a random destination At each step in the simulation, commuters travel over one edge Bokeh allows us to observe how the number of commuters at each station changes over time
In [11]:
YouTubeVideo('ZKHMtu1eKtc')
Out[11]:

Summary

At the beginning, we set off to answer two questions:

1. What are the most important stations on the Underground?

We used graph-tool to calculate betweenness

We determined that Baker Street, King's Cross St. Pancras and Liverpool Street are the most important

2. How does suspending one of the important stations affect the rest of the network?

We used bokeh to create interactive graphics

We saw that removing Baker Street can put more pressure on almost an entire Tube line worth of stations

Thank you very much!

Questions, comments and critique are very welcome!

Please get in touch at camillamon[at]gmail.com or info[at]winterflower.net