Slides, test code, figures and ideas behind the PCT
TeX R
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
R
README_files
analysis
case-studies
data-sources
documents
drafts
figure
figures
flow-model
ggplots
infra-active
input-data
loading-data
meetings
minidocs
models
plotting
slides
tmp
vignettes
vis-code
.gitignore
GISRUKPaperTemplate2018-infra-active.tex
HS2-cycling-potential.Rmd
LICENSE
README.Rmd
README.md
bibexport.bib
buildmaster.R
centroids-for-cyclestreets.geojson
cover-letter.Rmd
dutch-dd.Rmd
gis-overview.qgs
highlights.Rmd
infra-active.Rmd
l.Rds
leeds-potential.Rmd
lewes-uckfield.Rmd
pct-team.Rproj
set-up.R

README.md

pct: Propensity to cycle tool

This repo contains miscellaneous R scipts, documentation and figures related to the Propensity to Cycle Tool.

This repo is not needed to run the PCT on your computer. For that, see pct-load.

Information on the PCT

The project is funded by the Department for Transport (DfT) so the initial case studies will be taken from the UK. However, it is expected that the methods will be of use elsewhere. For that reason, attempts have been made to make the examples generalisable. All examples presented here are reproducible using data stored in the pct-data repository.

A simple example

If you run the following lines of code on from a local copy of the pct repository you will get the same results.

source("set-up.R")
# load some flow data
fleeds <- read.csv("README_files/data/sample-leeds-centre-dists.csv")
# load the zones
leeds <- readOGR("README_files/data", "leeds-central-sample")
## OGR data source with driver: ESRI Shapefile 
## Source: "README_files/data", layer: "leeds-central-sample"
## with 25 features
## It has 3 fields

Now we can estimate propensity to cycle, by using the distance decay function from (Iacono et al. 2010):

$$ p = \alpha e^{- \beta d} $$

where $\alpha$, the proportion of made for the shortest distances and $\beta$, the rate of decay are parameters to be calculated from empirical evidence.

To implement this understanding in R code we can use the following function:

# Distance-dependent mode switch probs
iac <- function(x, a = 0.3, b = 0.2){
  a * exp(-b * x)
}

Apply this function to openly accessible flow data:

fleeds$p_cycle <- iac(fleeds$dist / 1000)
fleeds$n_cycle <- fleeds$p_cycle * fleeds$All.categories..Method.of.travel.to.work
fleeds$pc1 <- fleeds$n_cycle - fleeds$Bicycle

Now we can create a simple visualisation of the result:

plot(leeds)

for(i in which(fleeds$Area.of.residence == leeds$geo_code[1])){
  from <- leeds$geo_code %in% fleeds$Area.of.residence[i]
  to <- leeds$geo_code %in% fleeds$Area.of.workplace[i]
  x <- coordinates(leeds[from, ])
  y <- coordinates(leeds[to, ])
  lines(c(x[1], y[1]), c(x[2], y[2]), lwd = fleeds$pc1[i] )
}

\

Set the CS_API_KEY Environment variable

Some of the examples pull data from the CycleStreets.net API. Once you have a token, you can add it in Ubuntu as a session variable using the following in your terminal

echo "export CS_API_KEY='my_token'" >> ~/.profile

or system wide variable

sudo echo "export CS_API_KEY='my_token'" > /etc/profile.d/cyclestreet.sh

Set up rgdal

The version of gdal needs to be newer than 1.11

rgdal::getGDALVersionInfo()
## [1] "GDAL 1.11.2, released 2015/02/10"
# Should return GDAL 1.11.2, released 2015/02/10 (or newer)

It is possible to use the following Personal Package Archive (PPA) to get the latest version of gdal on Ubuntu.

sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable && sudo apt-get update
sudo apt-get install gdal-bin libgdal-dev