Toronto General Hospital to the world via webinar, 2017-05-12

Presentation structure

  • Existing tools for planning
  • A case study of the PCT
  • Future directions
  • But first: a test of interactivity
  • Who uses (broadly defined) tools for policy-making?
  • What % of those are open access?
  • Open source?

Context

  • In January 2015 we were commissioned by the Department for Transport
  • In April 2017 the Propensity to Cycle Tool is finally officially launched
  • As part of the UK's Cycling and Walking Infrastructure Strategy (CWIS)
  • Our tool is a key part of the local implementation (quote below from the Transport Minister Chris Grayling)

Reaction to the Cycling and Walking Infrastructure Strategy

Definition of health

A state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.

  • From the World Health Organisation's Constitution, 1946 (Grad 2002).
  • We need to shift towards tackling the root causes of bad health
  • Rather than "fixing people who've fallen off a cliff"
  • Good (and bad) health behaviours are learned early on -> work of SickKids = vital

Tools for planning healthy cities

Modelling context

Interactive online tools

Importance of open data and methods

  • If the data underlying policy is hidden, it can be represented to push certain aims (solved by open data)
  • If the data is 'open' but the tools are closed, results open to political influence
  • Which brings us onto our next topic…

A case study of the Propensity to Cycle Tool

Context: from concept to implementation

A life-course of my involvement with the PCT

Concept of algorithms for cycling uptake (PhD 2009 - 2013)
 Discovery of programming (R) and shiny (2013)
  'Propensity to Cycle' bid by DfT via SDG (2014)
    Start work w. Cambridge University and colleagues (2015)
     Implementation on national OD dataset, 700k routes (2016)
       Addition of school and near-market prototypes (late 2016)
         LSOA phase (Malcolm Morgan) (early 2017)
           ...
  • 2018: (Global PCT?)
  • Academic write-up (Lovelace et al. 2017)

A definition of Propensity to Cycle and its uses

Propensity to cycle refers to the modelled uptake of cycling at area, desire line and route network levels under different scenarios of the future. Policy relevant scenarios include meeting national or local targets, the potential uptake if people in the study area cycled as much as the Dutch do or the impact of electric bikes on people's willingness to cycle longer distances. (see Get Britain Cycling article, 2016)

The tool aims to help prioritise where interventions are most needed based on where cyclable trips are most common

Policy impact

“The PCT shows the country’s great potential to get on their bikes, highlights the areas of highest possible growth and will be a useful innovation for local authorities to get the greatest bang for their buck from cycling investments  and realise cycling potential.” Andrew Jones, Parliamentary Under Secretary of State for Transport

“A world first from a brilliant academic team. As a Department we should be celebrating this example of innovation in promoting the UK’s capability to deliver innovation in transport planning.” Pauline Reeves, DfT Deputy Director Sustainable Accessible Transport

Prior work (source: Lovelace et al. 2017)

Tool Scale Coverage Public access Format of output Levels of analysis Software licence
Propensity to Cycle Tool National England Yes Online map A, OD, R, RN Open source
Prioritization Index City Montreal No GIS-based P, A, R Proprietary
PAT Local Parts of Dublin No GIS-based A, OD, R Proprietary
Usage intensity index City Belo Horizonte No GIS-based A, OD, R, I Proprietary
Bicycle share model National England, Wales No Static A, R Unknown
Cycling Potential Tool City London No Static A, I Unknown
Santa Monica model City Santa Monica No Static P, OD, A Unknown

The PCT team

"If you want to go far, go as a team"

Robin Lovelace (Lead Developer, University of Leeds)

  • James Woodcock (Principal Investigator, Cambridge University)
  • Anna Goodman (Lead Data Analyst, LSHTM)
  • Rachel Aldred (Lead Policy and Practice, Westminster University)
  • Ali Abbas (User Interface, University of Cambridge)
  • Alvaro Ullrich (Data Management, University of Cambridge)
  • Nikolai Berkoff (System Architecture, Independent Developer)
  • Malcolm Morgan (GIS and infrastructure expert, UoL)

Policy feedback

"The PCT is a brilliant example of using Big Data to better plan infrastructure investment. It will allow us to have more confidence that new schemes are built in places and along travel corridors where there is high latent demand."

  • Shane Snow: Head of Seamless Travel Team, Sustainable and Acessible Travel Division

"The PCT shows the country’s great potential to get on their bikes, highlights the areas of highest possible growth and will be a useful innovation for local authorities to get the greatest bang for their buck from cycling investments  and realise cycling potential."

  • Andrew Jones, Parliamentary Under Secretary of State for Transport

The PCT in CWIS and LCWIP

Included in Cycling and Walking Infrastructure Strategy (CWIS) and the Local Cycling and Walking Infrastructure Plan (LCWIP)