knitr::include_graphics("https://pbs.twimg.com/media/DOH94nXUIAAgcll.jpg")
knitr::include_graphics("http://citiscope.org/sites/default/files/styles/story_large/public/shutterstock_355737158_2.jpg?")
knitr::include_graphics("https://larrycuban.files.wordpress.com/2015/02/data-overload-2.jpg")
- Problem is operationalising this data
- Needs to be provided in a format that can be acted on at the local level
knitr::include_graphics("~/npct/pct-team/figures/pct-frontpage.png")
- 3 years in the making
- Origins go back further
Concept (PhD) -> Job at UoL (2009 - 2013) Discovery of R programming and shiny (2013) 'Propensity to Cycle' bid by DfT via SDG (2014) Link-up with Cambridge University and colleagues (2015) Implementation on national OD dataset, 700k routes (2016) Completed LSOA phase (4 million lines!) (2017)
- 2018: (Global PCT?)
knitr::include_graphics("https://github.com/npct/pct-team/blob/master/figures/pct-metalogo.png?raw=true")
knitr::include_graphics("../figures/jtlu-paper-front-page.png")
dft = readr::read_csv("~/npct/pct-team/data-sources/cycle-tools-wide.csv") dft$Tool = gsub("Permeability Assessment Tool", "PAT", dft$Tool) knitr::kable(dft[-5, ])
"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."
"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."
Included in Cycling and Walking Infrastructure Strategy (CWIS)
knitr::include_graphics("~/npct/pct-team/figures/front-page-leeds-pct-demo.png")
knitr::include_graphics("~/npct/pct-team/flow-model/dd-anna.jpg")
$$ logit(pcycle) = \alpha + \beta_1 d + \beta_2 d^{0.5} + \beta_3 d^2 + \gamma h + \delta_1 d h + \delta_2 d^{0.5} h $$
logit_pcycle = -3.9 + (-0.59 * distance) + (1.8 * sqrt(distance) ) + (0.008 * distance^2)
knitr::include_graphics("../../cyipt-website/images/ttwa-uptake.png")
- A $64,000 question
- Very simple model of uptake (Bristol):
## lm(formula = p_uptake ~ dist + exposure, data = l, weights = all11) ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.372e-02 4.207e-03 5.639 2.28e-08 *** ## dist -1.671e-07 8.424e-07 -0.198 0.84283 ## exposure 4.147e-02 1.523e-02 2.724 0.00658 ** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.7972 on 906 degrees of freedom ## Multiple R-squared: 0.008318, Adjusted R-squared: 0.006128 ## F-statistic: 3.799 on 2 and 906 DF, p-value: 0.02274
See https://www.cyipt.bike (password protected)
knitr::include_graphics("../../cyipt-website/images/recommended.png")
stplanr lives here: https://github.com/ropensci/stplanr
Package can be installed from CRAN or GitHub (see the package's
README for details),
it can be loaded in with library()
:
install.packages("stplanr") # stable CRAN version # devtools::install_github("ropensci/stplanr") # dev version
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