centroid of each zones, these can be represented as straight ‘desire lines’ or as routes allocated to the transport network. 

## [1] FALSE 

## 3.1 Processing OD data 

The central input dataset was a table of origin-destination (OD) pairs from the 2011 Census. This was loaded from open access file wu03ew_v2.csv , provided by the UK Data Service. This captures the number of commuters travelling between Middle Super Output Area zones (MSOAs, average commuter population: 3300), by mode of travel (see Table 2). This dataset was derived from responses to the following questions in the 2011 Census for England and Wales: “In your main job, what is the address of your workplace?” (question 40) and “How do you usually travel to work? (Tick one box only, for the longest part, by distance, of your usual journey to work)” (Question 41). This dataset was enhanced by merging in information on the gender composition of cyclists in each OD pair (Dataset 1 in Figure 1); data at the area level on the background mortality rate (Dataset 2); and data at the OD pair-level on route distance (km) and hilliness (average gradient, as a percentage) (Dataset 3). OD data was assigned to the transport network using the R package stplanr (Lovelace et al., 2016). See the Appendix for further details. 

## 3.2 Modelling baseline propensity to cycle 

The starting point for generating our scenario-based ‘cycling futures’ was to model baseline data on cycle commuting in England and Wales. We did this using OD data from the 2011 Census, and modelling cycling commuting as a function of route distance and route hilliness. We did so using logistic regression applied at the individual level, including squared and square-root terms to capture ‘distance decay’ — the non-linear impact of distance on the 

