• Introductory comments
• Demonstration
• Discussion

• Was initially titled "The Propensity to Cycle Tool: from conception in the clouds to implementation on the ground"

• Large surface area (countries, planets)
• Reproducible, e.g. for infinite new scenarios of future
• Accessible - so results can 'scale' to be seen and used interactively by millions of people
• Resilient: methods can operate in data rich and data poor environments
• Rationale: a 'rolled-out' simple method can have a greater impact than a non-scalable complex one

• 2 years in the making, the PCT is now part of the Cycling and Walking Infrastructure Strategy (CWIS)
• Mentioned in the forword of this legally binding document
• Being used by dozens of local authorities to design strategic cycling networks

See www.pct.bike for demo of 'finished product'.

• As a tool: to get stuff done (see github.com/homeRangeOD)

# Download all code and data to reproduce example
git clone git@github.com:homeRangeOD/homeRangeOD

## Cloning into 'homeRangeOD'...
cp -r homeRangeOD/input-data/* . # move the data

• As a language: to communicate (Knuth 1984)

library(stplanr)
oas_lds = readRDS("oas_lds.Rds")
wpz_lds = readRDS("wpz_lds.Rds")
flow = readr::read_csv("f_lds.csv")
f_sp = od2line(flow = flow, zones = oas_lds, destinations = wpz_lds)

library(tmap)
tmap_mode("view")

## tmap mode set to interactive viewing

qtm(oas_lds) +
  qtm(wpz_lds, symbols.col = "red", symbols.size = 2) +
  qtm(f_sp)

• Also see example generated yesterday representing trips to Leeds University

• Reproducible example: rpubs.com/RobinLovelace

• Mature method for estimating flow (Kariel 1968; Wilson 1971)
• Can be extended towards agent-based modelling (ABM) (Wu, Birkin, and Rees 2008; Lovelace and Dumont 2016)
• Can be calibrated with 'Big Data' (Lovelace et al. 2016)

• Theotical advance: the radiation model (Simini et al. 2012)

library(stplanr)
cents$pop = 1:nrow(cents)
plot(cents, cex = cents$pop)

flow_est = od_radiation(p = cents, pop_var = "pop")
plot(flow_est, lwd = flow_est$flow)

• For scalability generalisability is vital
• software engineering/compsci approach
• But political leadership vital

• Links with the Cycling Infrastructure Prioritisation Toolkit (CyIPT)

• How to institutionalise the open (data, science) approach
• Citizen science / crowd funded add-ons
• Next case study cities?

• How to move from simulation / theory to implementation?
• Where does prediction and validation fit in?
  • How to do natural experiments in this area?
• How can ecological theory / models inform transport simulations?
  • Defining and modelling 'home ranges' (activity spaces)
  • Modelling GPS traces ('bread crumb data')
  • Group behaviour / 'contagion' (if you cycle, I'll cycle)

Geocomputation with R book project with @jakub_nowosad now up 🎉 Contributions/suggestions welcome: https://t.co/QD1GjiVtP9 pic.twitter.com/Mi5SUalZgJ

— Robin Lovelace (@robinlovelace) May 18, 2017

It is important to know where people travel for a number of reasons. Most important among these is the urgent need to transition away from fossil fuels: models of travel patterns can help identify the most effective interventions to make this happen.

This paper explores globally scalable methods for generating estimates of travel patterns that build on areal and point-based data to estimate movements down to the road network level currently, and under scenarios of the change. The presentation is based on my experience developing the Propensity to Cycle Tool (PCT) and scaling it across all areas and major cyclable roads in England (see pct.bike) and recent experiments extending it internationally with a case study in Seville, Spain.

Methodologically I will explore the possibility of extending the methods to be dynamic and multi-modal, themes that will be prominent during the summer school.