• Context
• The impacts of transport infrastructure
• Methods
• Results and discussion

• Bad news: it's ****ing HUGE

• Good news: can be broken down into chunks
• E.g.: How to transition to active cities? From this…

Source: Brent Toderian

• Needs to be framed by academics as potential solutions
• E.g.: How do we get to cities like this?

Source: Brent Toderian

• Presenting the 'PCT' work to Secretary of State for Transport

Robin Lovelace (Lead Developer, University of Leeds)

• James Woodcock (Principal Investigator, Cambridge University)
• Anna Goodman (Lead Data Analyst, LSHTM)
• Rachel Aldred (Lead Policy and Practice, Westminster University)
• Ali Abbas (User Interface, University of Cambridge)
• Alvaro Ullrich (Data Management, University of Cambridge)
• Nikolai Berkoff (System Architecture, Independent Developer)
• Malcolm Morgan (GIS and infrastructure expert, UoL)

"The PCT is a brilliant example of using Big Data to better plan infrastructure investment. It will allow us to have more confidence that new schemes are built in places and along travel corridors where there is high latent demand."

• Shane Snow: Head of Seamless Travel Team, Sustainable and Acessible Travel Division

"The PCT shows the country’s great potential to get on their bikes, highlights the areas of highest possible growth and will be a useful innovation for local authorities to get the greatest bang for their buck from cycling investments  and realise cycling potential."

• Andrew Jones, Parliamentary Under Secretary of State for Transport

• Test version of pct.bike

• Geography vs GIS vs Geocomputation vs GDS

• Why geocomputation?

• involves writing code + development (in stplanr R package)

• The importance of reproducibility (Lovelace, Nowosad, and Muenchow 2018):

• A process is reproducible only if the same results can be generated by others using publicly accessible code.

• GeoComputation is about using the various different types of geodata and about developing relevant geo-tools within the overall context of a 'scientific' approach (Openshaw and Abrahart 2000).

• What distinguishes geocomputation from the older quantitative geography, is its emphasis on "creative and experimental" GIS applications (Longley 1999).

• Source: Geocomputation with R a free, open source book (Lovelace, Nowosad, and Muenchow 2018)

d = frame_data(
  ~Attribute, ~GIS, ~GDS,
  "Home disciplines", "Geography", "Geography, Computing, Statistics",
  "Software focus", "Graphic User Interface", "Code",
  "Reproduciblility", "Minimal", "Maximal"
)

The extension of the M74 motorway = 'natural experiment':

• People who live near motorways seem to be less physically active ( ???)
• And more car dependent (Prins et al. 2017)
  • Although no evidence of impacts on active travel

3 stage methodology to identify parallel lines:

• Subset desire lines whose cetrepoints are far from the proposed route
• Segment the proposed route
• Calculate the angle of each route

Then keep only lines parallel to proposed route segments

This involved the development of a new R function:

From the R package stplanr

stplanr:::angle_diff.Spatial

## function(l, angle, bidirectional = FALSE, absolute = TRUE){
##   if(is(object = l, "Spatial")){
##     line_angles = line_bearing(l)
##   } else {
##     line_angles = l
##   }
##   angle_diff = angle - line_angles
##   angle_diff[angle_diff <= -180] = angle_diff[angle_diff <= -180] + 180
##   angle_diff[angle_diff >= 180] = angle_diff[angle_diff >= 180] - 180
##   if(bidirectional){
##     angle_diff[angle_diff <= -90] = 180 + angle_diff[angle_diff <= -90]
##     angle_diff[angle_diff >= 90] = 180 - angle_diff[angle_diff >= 90]
##   }
##   if(absolute)
##     angle_diff = abs(angle_diff)
##   angle_diff
## }
## <bytecode: 0x1297fc98>
## <environment: namespace:stplanr>

The same method!

• Divide each route into 3

• Break the train line into segments

Method of splitting the route into discrete segments using the line segment function from the stplanr R package (a) and cycling potential severed (b).

• How to integrate this work in existing tools?
• How to ensure maximum policy impact?
• How reproducibility can help?

• Thanks for listening - get in touch via r.lovelace@leeds.ac.uk or @robinlovelace