a sample of the variables as a table at the OD pair level. Figure 6 shows how the proportion of trips made by cycling varies as a function of distance in two regions currently, and under the PCT’s four scenarios of change. The frequency of all mode trips by distance band (the red lines) illustrates the two regions have different spatial structures. Oxfordshire has a high proportion short (under 5km) trips, helping to explain the relatively high level of cycling there. West Yorkshire (Figure 6, left), by contrast, has a higher proportion of longer distance commutes and a lower level of cycling than Oxford. Note that under Go Dutch and Ebikes scenarios, regional differences in the rate of cycling diminish, however, illustrating that these scenarios are not influenced by the current level of cycling. ## [1] FALSE ## [1] FALSE The spatial distribution of cycling potential differs markedly between scenarios, as illustrated in Figure 7 for the city of Leeds, West Yorkshire. The top 50 OD pairs in Leeds under 4.3 The shifting spatial distribution of cycling and associated im- pacts 4.2 Trip distance distributions • Areas : This tab is the equivalent of the ‘Lines’ tab, but with data at the area level. • Model output : This tab provides key statistics, diagnostic plots and model results for each region. The document is produced by a ‘dynamic document’ which runs embedded code for each regional dataset. Diagnostic plots include the distribution of cycling by trip distance under each scenario (see Figure 6), providing insight into local travel patterns and how they relate to cycling potential in the region overall. 23