7 Table 1: Summary of tools and methods to explore the geographical distribution of cycling potential. Levels of analysis refer to whether data is analysed at point (P), area (A), origin-destination (OD), route (R), route network (RN) or individual (I) levels. that have been short-listed. Unlike the prioritisation index of Larsen et al. (2013), which is primarily aimed at informing a city-wide strategic cycling network, the results of the PAT are designed to guide smaller, site specific interventions such as ‘contraflow’ paths and cyclist priority traffic signals. The Santa Monica model is an example of a direct facility demand model, which are based on “observed counts and context-driven regression models” (Kuzmyak et al., 2014). The model uses environmental explanatory variables such as employment density and speed limits of surrounding roads to estimate the dependent variable: walking and cycling activity (e.g. as recorded by screenline count devices). By adjusting regression parameters (e.g. based on areas that have experienced growth in cycling), such models can be used to “forecast demand levels for walk or bike at a point or intersection level” and “evaluate and prioritize projects” (ibid., p. 75). Tool Scale Coverage Public ac- cess 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 Permeability Assessment Tool Local Parts of Dublin No GIS- based A, OD, R Proprietary Usage inten- sity index City Belo Hori- zonte No GIS- based A, OD, R, I Proprietary Bicycle share model National England, Wales No Static A, R Unknown Cycling Po- tential Tool City London No Static A, I Unknown Santa Monica model City Santa Mon- ica No Static P, OD, A Unknown