---
title: "Geocomputation for active transport planning"
subtitle: "🚲<br/>a case study of cycle network design<br/>Slides: [github.com/Robinlovelace/erum18-transport](https://github.com/Robinlovelace/erum18-transport) "
author: "Robin Lovelace"
date: "`r Sys.Date()`"
bibliography: references.bib
output:
xaringan::moon_reader:
lib_dir: libs2
nature:
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
---
```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE)
library(RefManageR)
BibOptions(check.entries = FALSE,
bib.style = "authoryear",
cite.style = 'alphabetic',
style = "markdown",
first.inits = FALSE,
hyperlink = FALSE,
dashed = FALSE)
my_bib = ReadBib("references.bib", check = FALSE)
# [@lovelace_propensity_2017] [@lovelace_geocomputation_2018a]
```
# Outline
.pull-left[
## The problem
## Solutions
## How R can help
]
--
.pull-right[
]
---
# whoami
.pull-left[
- Environmental geographer
- Learned R for PhD on energy and transport
- Now work at the University of Leeds (ITS and LIDA)
- Working on Geocomputation with R
```{r, eval=FALSE}
devtools::install_github("r-rust/gifski")
system("youtube-dl https://youtu.be/CzxeJlgePV4 -o v.mp4")
system("ffmpeg -i v.mp4 -t 00:00:03 -c copy out.mp4")
system("ffmpeg -i out.mp4 frame%04d.png ")
f = list.files(pattern = "frame")
gifski::gifski(f, gif_file = "g.gif", width = 200, height = 200)
```
]
--
.pull-right[
Image credit: Jeroen Ooms + others
```{r, out.width="100%"}
knitr::include_graphics("https://user-images.githubusercontent.com/1825120/39661313-534efd66-5047-11e8-8d99-a5597fe160ff.gif")
```
]
---
class: inverse, center, middle
# The problem
---
background-image: url(https://pbs.twimg.com/media/DOH94nXUIAAgcll.jpg)
background-position: 50% 50%
class: center, bottom, inverse
# Cities look a bit like this
---
# Transport: growing source of emissions
.pull-left[
```{r}
knitr::include_graphics("https://raw.githubusercontent.com/Robinlovelace/erum18-transport/master/transport-projections-ipcc.png")
```
]
--
.pull-right[
- People like to travel!
- Does not 'saturate' with income
- Hard to decarbonise via technology
]
---
class: inverse, center, middle
# Solutions
---
# Make cycling the natural choice
```{r, echo=FALSE, out.width="70%"}
knitr::include_graphics("https://pbs.twimg.com/media/DJaWCo0U8AAzQGW.jpg:large")
```
Source: [Brent Toderian](https://twitter.com/BrentToderian)
--
## For everyone: a political problem
---
# Another problem...
```{r, echo=FALSE, out.width="80%"}
knitr::include_graphics("https://larrycuban.files.wordpress.com/2015/02/data-overload-2.jpg")
```
--
## Data overload
<!-- Source: [Larry Cuban](https://larrycuban.files.wordpress.com/2015/02/data-overload-2.jpg) -->
---
class: inverse, center, middle
# Technical solutions
---
# Simplify the data deluge
Cycling network in Seville: 'basic' (77 km) and ‘complementary’ (120 km, dashed line) cycleways (from Marqués et al. 2015). Led to fourfold increase in cycling.
```{r, echo=FALSE}
knitr::include_graphics("https://raw.githubusercontent.com/ATFutures/who/master/fig/sevnet2.png")
```
---
## Estimate cycling pontential: the Propensity to Cycle Tool - see [w](http://npct0.vs.mythic-beasts.com/shiny_interface/?r=west-yorkshire)[ww.pct.bike](www.pct.bike)
Included in UK policy (CWIS) used by many local authorities (LCWIP)
`r Citep(my_bib, "lovelace_propensity_2017", .opts = list(cite.style = "authoryear"))`
```{r, echo=FALSE, out.width="70%"}
knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/figures/front-page-leeds-pct-demo.png")
```
---
# Build infrastructure
- Link between infrastructure and uptake between 2001 and 2011 in English regions
- But how to ensure that infrastructure is effective?
```{r, echo=FALSE, out.width="80%"}
knitr::include_graphics("https://raw.githubusercontent.com/cyipt/cyipt-website/master/images/ttwa-uptake.png")
```
---
# Identify cost-effective schemes: the CyIPT
- A ~~publicly available~~ password protected web app providing accessible evidence on cycling infrastructure hosted at [www.cyipt.bike](https://www.cyipt.bike/)
```{r, echo=FALSE}
knitr::include_graphics("https://www.cyipt.bike/images/home-example.png")
```
---
class: inverse, center, middle
# How R can help
An open source language for statistical computing
`r Citep(my_bib, "rcoreteam_language_2018", .opts = list(cite.style = "authoryear"))`
---
# Scalability
```{r, tidy=FALSE, message=FALSE, echo=FALSE}
d = readr::read_csv("https://github.com/npct/pct-team/raw/master/data-sources/cycle-tools.csv")
DT::datatable(
d,
fillContainer = FALSE, options = list(pageLength = 8)
)
```
---
# Visualisation
- Live demo...
```{r, eval=FALSE}
# try it!
shiny::runGitHub("robinlovelace/erum18-transport")
```
- More on shiny-leaflet integration: [Section 9.5](http://geocompr.robinlovelace.net/adv-map.html#mapping-applications) in
`r Citep(my_bib, "lovelace_geocomputation_2018a", .opts = list(cite.style = "authoryear"))`
- **stplanr**
`r Citep(my_bib, "R-sf", .opts = list(cite.style = "authoryear"))`
making use of **sf**
`r Citep(my_bib, "R-stplanr", .opts = list(cite.style = "authoryear"))`
---
# Some example shiny code
```{r, eval=FALSE}
# non-reproducible snippet
getroads = reactive({
roads[roads$highway == input$type, ]
})
renderLeaflet({
m = tm_shape(getroads()) +
tm_lines(col = "red", lwd = 5) +
tmap_leaflet(m) # you can use tmap in shiny!
})
```
---
# Local routing
.pull-left[
```{r, eval=FALSE}
fr = stplanr::geo_code(
"Budapest airport")
to = stplanr::geo_code(
"akvarium budapest")
# install.packages("cyclestreets")
library(cyclestreets)
r = journey(fr, to)
```
```{r, echo=FALSE}
r = sf::read_sf("r.geojson")
```
```{r}
# From Martin's workshop:
library(tmap)
bm = leaflet::providers
bm_cycle = bm$Thunderforest.OpenCycleMap
m = tm_shape(r) +
tm_lines(col = "busynance",
lwd = 5) +
tm_scale_bar()
```
]
.pull-right[
See: [rpubs.com/RobinLovelace/389709](http://rpubs.com/RobinLovelace/389709)
```{r, out.width="350", out.height="400", message=FALSE}
tmap_leaflet(m)
```
]
---
# Extracting data from routes
```{r}
r$distances[1:5]
r$time[1:5]
sum(r$distances) / 1000
```
For something on remote routing see **stplanr** or **dodgr** packages.
- Vignette on fast local routing: [cran.r-project.org/package=dodgr](https://cran.r-project.org/web/packages/dodgr/vignettes/dodgr.html)
---
class: center, middle
# Thanks and safe 🚶, 🚲 + 🚀!
- Reproducible slides + app: [github.com/Robinlovelace/erum18-transport](https://github.com/Robinlovelace/erum18-transport)
- Transport chapter in Geocomputation with R (feedback welcome):
[geocompr.robinlovelace.net](http://geocompr.robinlovelace.net/transport.html)
Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).
---
class: small
# References
```{r, 'refs', results="asis", echo=FALSE}
PrintBibliography(my_bib)
```
```{r, echo=FALSE, out.height="100", fig.show='hold'}
knitr::include_graphics(
c(
"https://www.cyipt.bike/images/logo.png",
"https://raw.githubusercontent.com/cyipt/cyipt/master/figures/its-logo-square.png"
))
```
