• Recap on accessibility indices
• Distance decay
• Case study: Propensity to Cycle Tool
• Practical

What was accessibility again?

• Ease with which an individual can access desired services and facilities
• Different types of accessibility, depending on purpose (education, shopping, etch)
• By different modes of transport (e.g. cycling)
• Technical question: at what level does accessibility operate?

How do accessibility indicators support sustainability transport policies?

• It incorporates social, economic and environmental factors
• Provides information at local level for cost-effective interventions

What are we looking at here? How could it inform policy?

Proximity to fast food can be a bad thing (Burgoine et al. 2014) - see here.

• Access to fast food and supermarkets linked with obesity
• Few measures include information about service quality

A function that links the proportion of trips to distance:

\[ p = f(d) \] See Iacono et al. (2008)

• Source: The Propensity to Cycle Tool (Lovelace et al. 2017)

See Martinez and Vargas (2013):

• Step function, \(x < 10 km\)
• Exponential functions, \(e^{\beta x}\)
• Power functions, \(x^{\beta}\)
• Tanner functions, \(x^{\beta_1}e^{\beta_2 x}\)
• Box-Cox functions, \(exp(\beta \frac{x^{\gamma} - 1}{\gamma})\) when the parameter \(\gamma \neq 0\) and \(x^{\beta}\) when \(\gamma = 0\)

But…

• Often the functional form of distance decay is less important than other indicator design decisions - a step function will do!

See (Lovelace et al. 2017)

• Government Target
• Gender Equality
• Go Dutch
• Ebikes

Source paper on school accessibility: Moreno-Monroy, Lovelace, and Ramos (2017)

Source: paper on PCT for schools (Goodman et al. 2019)

• Indicator of walking ‘level of service’
  • Proximity to green space
  • Speed limits of roads
  • Number of paths
  • Shops within walking distance
• Indicator of car dependence
  • % who drive
  • % who could cycle
  • % who drive given nearby services
• Indicator of park accessibility

• Practical guidebook can be found here: https://itsleeds.github.io/QGIS-intro/

There are many good transport datasets out there, but they are not always easy to find.

The list below provides links to some key data sources that may be of use and interest, starting with the most universal and easy to use, and ending in rather specific datasets.

• UK geoportal, providing geographic data at many levels: https://geoportal.statistics.gov.uk
• Other national geoportals exist, such as this: http://www.geoportal.org/
• A good source of cleaned origin destination data is the Region downloads tab in the Propensity to Cycle Tool (Lovelace et al. 2017) - see the Region data tab for West Yorkshire here, for example: http://www.pct.bike/m/?r=west-yorkshire
• OpenStreetMap is an excellent source of geographic data with global coverage. You can download data on specific queries (e.g. highway=cycleway) from the overpass-turbo service: https://overpass-turbo.eu/

For other datasets, search online! Good starting points in your research may be:

• The open data section in Geocomputation with R: https://geocompr.robinlovelace.net/read-write.html#retrieving-data
• Transport datasets mentioned here: https://data.world/datasets/transportation
• UK government transport data: https://ckan.publishing.service.gov.uk/publisher/department-for-transport

• The ors and OSMTools QGIS plugins provide a range of datasets
• The stats19 package can get road crash data for anywhere in Great Britain. See here for info: https://itsleeds.github.io/stats19/
• The pct package provides access to data in the PCT: https://github.com/ITSLeeds/pct
• There are many other QGIS and R packages and plugins to help access data