• Introductory comments
• An example: the Propensity to Cycle Tool
• dplyr vs tibbles
• Discussion: can GDS save the world?

Many ways of saying the same thing:

• 'Policy-led research'
• 'Impact'
• 'Socially beneficial research'
• Don't be evil (Google)

My definition: building an evidence-base for sustainable systems.

• In the context of climate change that means:

• Building an evidence-base to transition away from fossil fuels

• But could also be interpretted in terms of other (quantifiable) social/economic/environmental indicators

• You tell me!

• How does it differ from good old 'GIS'?
• What does the science in the title mean?
• Why the focus on data rather than information

Code example:

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

knitr::kable(d)

Reasoning:

• Evidence inevitably gets skewed by political aims

• If the people doing the research are influenced by dominant political forces, findings will be biases for political gain (solved by independent well-funded public research).

• People doing policy relevant research watch out (regarding politicians):

"Their very spirit undergoes a pervasive transformation,” and they finally end up as “experts at exchanging smiles, handshakes, and favors." (Reclus 2013, original: 1898)

• If the data underlying policy is hidden, it can be represented to push certain aims (solved by open data)

• If the data is 'open' but the tools are closed, results open to political influence

• Which brings us onto our next topic…

• Challenge: operationalise data
• Challenge: make locally specific

• Travel behaviour data
• Route network data
• Existing infrastructure (road widths, traffic, future possibilities)
• Road safety data
• Air pollution data
• Crowdsourced data

~200 km cycle network in Seville, Spain. Source: WHO report at ATFutures/who

• Not a UK-specific issue, but benefits of country-specific tools

• 3 years in the making
• Origins go back further
• "An algorithm to decide where to build next"!
• Internationalisation of methods (World Health Organisation funded project)

"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

Included in Cycling and Walking Infrastructure Strategy (CWIS)

• Origin-destination data shows 'desire lines'
• How will these shift with cycling uptake

• 12 month project funded by DfT's Innovation Challenge Fund (ICF)

• Aim: tackle the challenge that cycling uptake is often limited by infrastructural barriers which could be remediated cost-effectively, yet investment is often spent on less cost-effective interventions, based on assessment of only a few options.

• Project team:
  • Robin Lovelace (University of Leeds)
  • Malcolm Morgan (University of Leeds)
  • John Parkin (University of West of England)
  • Martin Lucas-Smith (Cyclestreets.net)
  • Adrian Lord (Phil Jones Associates)

• We can use 'backcasting' to estimate long-term potential under ideal questions (PCT)
• But transport authorities need forecasts of future uptake
• From specific interventions in order to do this
• There is much existing work on this
• But none that is 'operationalisable'

• How to operationalise available data?

• Clear link between infrastructure and uptake

• DfT's Transport Direct data
• 2001 OD data (manipulated and joined with 2011 data)

See: https://www.cyipt.bike (password protected)

• Online interfaces reduce barriers
• But there are benefits of running analysis locally
• Various software options, including:

• QGIS mapping software
• sDNA QGIS plugin
• R (see upcoming course 26th - 27th April)
• Key feature of CyIPT and PCT:
• Open source and provides open data downloads

• Hilliness and distance are (relatively) unchanging over time
• Model based on polynomial logit model of both:

\[ logit(pcycle) = \alpha + \beta_1 d + \beta_2 d^{0.5} + \beta_3 d^2 + \gamma h + \delta_1 d h + \delta_2 d^{0.5} h \]

logit_pcycle = -3.9 + (-0.59 * distance) + (1.8 * sqrt(distance) ) + (0.008 * distance^2)

• Test version of pwww.pct.bike

Data analysts and 'scientists': don't wrangle, munge or 'hack' your valuable datasets. Use #datacarpentry: https://t.co/gXrlIJH91R pic.twitter.com/GSWS7O7zBz

— Robin Lovelace (robinlovelace) February 20, 2017

• If you 'hack' or 'munge' data, it won't scale
• So ultimately it's about being able to handle Big Data
• We'll cover the basics of data frames and tibbles
• And the basics of dplyr, an excellent package for data carpentry
  • dplyr is also compatible with the sf package

The humble data frame is at the heart of most analysis projects:

d = data.frame(x = 1:3, y = c("A", "B", "C"))
d

##   x y
## 1 1 A
## 2 2 B
## 3 3 C

In reality this is a list, making function work on each column:

summary(d)

##        x       y    
##  Min.   :1.0   A:1  
##  1st Qu.:1.5   B:1  
##  Median :2.0   C:1  
##  Mean   :2.0        
##  3rd Qu.:2.5        
##  Max.   :3.0

plot(d)

In base R, there are many ways to subset:

d[1,] # the first line

##   x y
## 1 1 A

d[,1] # the first column

## [1] 1 2 3

d$x # the first column

## [1] 1 2 3

d [1] # the first column, as a data frame

##   x
## 1 1
## 2 2
## 3 3

Recently the data frame has been extended:

library("tibble")
dt = tibble(x = 1:3, y = c("A", "B", "C"))
dt

## # A tibble: 3 x 2
##       x y    
##   <int> <chr>
## 1     1 A    
## 2     2 B    
## 3     3 C

It comes down to efficiency and usability

• When printed, the tibble diff reports class
• Character vectors are not coerced into factors
• When printing a tibble diff to screen, only the first ten rows are displayed

Like tibbles, has advantages over historic ways of doing things

• Type stability (data frame in, data frame out)
• Consistent functions - functions not [ do everything
• Piping make complex operations easy

ghg_ems %>%
  filter(!grepl("World|Europe", Country)) %>% 
  group_by(Country) %>% 
  summarise(Mean = mean(Transportation),
            Growth = diff(range(Transportation))) %>%
  top_n(3, Growth) %>%
  arrange(desc(Growth))

# dplyr must be loaded with
library(dplyr)

wb_ineq %>% 
  filter(grepl("g", Country)) %>%
  group_by(Year) %>%
  summarise(gini = mean(gini, na.rm  = TRUE)) %>%
  arrange(desc(gini)) %>%
  top_n(n = 5)

vs

top_n(
  arrange(
    summarise(
      group_by(
        filter(wb_ineq, grepl("g", Country)),
        Year),
      gini = mean(gini, na.rm  = TRUE)),
    desc(gini)),
  n = 5)

Only 1 way to do it, making life simpler:

select(dt, x) # select columns

## # A tibble: 3 x 1
##       x
##   <int>
## 1     1
## 2     2
## 3     3

slice(dt, 2) # 'slice' rows

## # A tibble: 1 x 2
##       x y    
##   <int> <chr>
## 1     2 B

• We'll download and visualise some transport data

u_pct = "https://github.com/npct/pct-data/raw/master/west-yorkshire/l.Rds"
if(!file.exists("l.Rds"))
  download.file(u_pct, "l.Rds")
library(stplanr)
l = readRDS("l.Rds")
plot(l)

sel_walk = l$foot > 9
l_walk = l[sel_walk,]
plot(l)
plot(l_walk, add = T, col = "red")

library(dplyr) # for next slide...

l_walk1 = l %>% filter(All > 10) # fails

library(sf)

## Linking to GEOS 3.5.1, GDAL 2.2.2, proj.4 4.9.2

l_sf = st_as_sf(l)
plot(l_sf[6])

l_walk2 = l_sf %>% 
  filter(foot > 9)
plot(l_sf[6])
plot(l_walk2, add = T)

## Warning in plot.sf(l_walk2, add = T): ignoring all but the first attribute

## Warning in classInt::classIntervals(na.omit(values), min(nbreaks, n.unq), :
## n same as number of different finite values\neach different finite value is
## a separate class

l_sf$distsf = as.numeric(st_length(l_sf))
l_drive_short2 = l_sf %>% 
  filter(distsf < 1000) %>% 
  filter(car_driver > foot)

library(tmap)
tmap_mode("view")

## tmap mode set to interactive viewing

qtm(l_drive_short2)

It is clear that geographical research can have large policy impacts.

• That researchers can act to maximise the social benefit of the research
• That involves getting the evidence out to as many people as possible
• And using open source, accessible tools - the 'science' in GDS?

But many questions remain:

• Where to draw the line between impartial research and campaigning?
• To what extent should researchers open-sourcing their work defend against commercial exploitation?

• What can you do to maximise the social benefits arising from your work?

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