Open Source | Robin Lovelace

Future-Proof Transport Planning: Cities Workshop

Wed, 03 Sep 2025 14:00:00 +0100

Keynote Talk

This is the keynote talk for the Sustainable Transport and Planning for Cleaner and Healthier Cities Workshop.

Robin Lovelace is Professor of Transport Data Science at the Leeds Institute for Transport Studies and Head of Data Science at Active Travel England.

Inaugural Lecture: Data Science for Future-Proof Transport Planning

Thu, 08 May 2025 16:30:00 +0100

Cycle Route Uptake and Scenario Estimation (CRUSE): An Approach for Developing Strategic Cycle Network Planning Tools

Mon, 30 Sep 2024 00:00:00 +0000

Open Source Tools for Geographic Analysis in Transport Planning

Fri, 01 Oct 2021 00:00:00 +0000

Open Science In Transportation Workshop

Thu, 21 Jan 2021 00:00:00 +0000

Today I am presenting at the Transport Research Board’s 2021 Annual meeting, aka #TRB2021. I say ‘presenting’ but in fact, the talk was a pre-record - see the video below!

Information on the talk can be found here.

It’s based on a recently published paper on ‘open access models’ which are both based on open source code and, vitally, are available for use by the public without needing specialist training (Lovelace, Parkin, and Cohen 2020).

It also links to a just-published paper in which I review a range of open source transport planning tools (Lovelace 2021).

See the interactive slides here.

And here for a link to the code, video and more!

References

Lovelace, Robin. 2021. “Open Source Tools for Geographic Analysis in Transport Planning.” Journal of Geographical Systems, January. https://doi.org/10.1007/s10109-020-00342-2.

Lovelace, Robin, John Parkin, and Tom Cohen. 2020. “Open Access Transport Models: A Leverage Point in Sustainable Transport Planning.” Transport Policy 97 (October): 47–54. https://doi.org/10.1016/j.tranpol.2020.06.015.

R vs QGIS for sustainable transport planning

Tue, 20 Jan 2015 00:00:00 +0000

The 23rd iteration of the GIS Research UK conference (#GISRUK) conference was the largest ever. 250 researchers, industry representatives and academics attended from the vibrant geospatial research communities in the UK, Europe and beyond. GISRUK has become a centrepoint for discussion of new methods, software and applications in the field. I was on the organising committee, reviewed some excellent papers for the event (a full list of these is available for download here) and attended some truly ground-breaking talks. This experience has shown that the geospatial community in the UK is strong, especially with regards to growth in open access data and open source software in the field.

This article is about one part of GISRUK and insights gleaned from it about R, QGIS and other tools for sustainable transport planning. GIS for Transport Applications (#GIS4TA for short) was a practical day-long workshop that preceded the main event. I organised the workshop and (with help from Eusebio Odiari, The Transport Geography Research Group and the Royal Geographical Society) it seems to have been a great success. More than 30 people attended, including a decent portion from transport consultancies such as Integrated Transport Planning Ltd TRP Consulting and the European Railway Association (ERA). Specifically, it is about the use of R and QGIS tools for transport planning and the potential for their adoption in academic, public and private-sector transport planning. The focus of the workshop was deliberately on open source software and sustainable transport because these are growth areas in the field that are essential for democratic and healthy transport systems compatible with the science of climate change (Tamminga, 2012). A recent report, for example, suggests we need to almost completely transition away from fossil fuels by 2050 (McGlade et al., 2015). New datasets and methods for analysing and modelling them can help get us there in the recalcitrant transport sector (Gossling, 2014).

R for transport applications

The workshop kicked-off with a short talk on ‘R and QGIS for transport applications’, which laid out some of the motivations for running the workshop outlined above. Other than a few ’early adopters’, the transport modelling community is generally conservative, based largely on mature proprietary products such as SATURN and Vissim.

The slides from this talk are available here:

Tutorial: Introduction to R and QGIS for transport applications

Routing with R

The second talk was by Nick Bearman, who provided an overview of routing in R, as well as an excellent practical tutorial.

The practical demonstrated 2 ways of routing in R:

Using ggmap. The following code was used to navigate to the event!

from <- 'Leeds station, New Station Street, Leeds LS1 5DL, United Kingdom'
to <- 'LS2 9JT'
route_df <- route(from, to, structure = 'route', mode = 'walking')

qmap('Merrion Centre', zoom = 15) +
 geom_path(
 aes(x = lon, y = lat), colour = 'red', size = 1.5,
 data = route_df, lineend = 'round')

The package I created, stplanr, to get routes optimised for cyclists (see transport-workshop.Rmd for a working version):

rquiet <- gLines2CyclePath(l = rlines, plan = "quietest")
plot(rquiet[1,]) # route from Leeds station to Leeds University (North - South)
plot(rquiet[2,]) # route from Leeds to Manchester!

Tutorial: Route analysis using R

Large gps datasets with PostGIS

The most technical session involved using R to query huge datasets storing GPS data containing 100,000+ rows. Amazingly, Richard and Adrian Ellison set up a remotely accessible database instance from their laptop which participants queried via RPostgreSQL. Their session information can be seen here:

github.com/richardellison/GIS4TA_GPS

A hackathon

Finally there was a miniature hackathon organised by Godwin Yeboah. Participants made progress in better understanding the travel patterns of cyclists using real data. The hackathon notes can be found here:

github.com/spatialscientist/GIS4TA2015

Summary

GIS is a field of knowledge that has a huge amount to offer transport planners and researchers, especially regarding new and open source software tools that can effectively generate, process and analyse transport-related data. R is well-suited to fill this research gap and has a wide range of tools to help. Packages such as ggmap (Kahle and Wickham 2013), RPostgreSQL and the new stplanr have great potential to help plan the transport systems of the future. QGIS is also increasingly attractive for transport applications, with it inbuilt support for PGRouting, flow analysis and a friendly user interface that many will be used to.

Photos taken during the hackathon are testament to its role as a forum for not only learning but also debate about the future of GIS in transport. These can be seen here:

flickr.com/photos/97888609@N02/sets/72157652012715766

Hearing feedback from users new to R using it to solve transport problems provided an insight into how it compares to traditional tools. The removal of ‘glass ceilings’ imposed by restrictive licenses or the need to buy ‘add-on’ features was one comment, but that applies equally to QGIS and other FOSS4G offerings. The steep learning curve of R seems to still be an issue compared with QGIS, although this is becoming less of an issue with the evolution of RStudio as an GUI for R. In conclusion, both R and QGIS are coming of age as tools in the transport planner’s ‘war cabinet’. The latest evidence unequivocally shows the impact of transport decisions on obesity, environmental degradation and quality of life. So it is time, surely, to harness this new open source software to ‘save the world’!

Acknowledgements

Thanks to the Consumer Data Research Centre and the Royal Geographical society for subsidising the event. Thanks to all the participants and especially the demonstrators Godwin, Nick, Adrian and Richard for making it happen.

References

Gössling, Stefan, and Scott Cohen. 2014. “Why sustainable transport policies will fail: EU climate policy in the light of transport taboos.” Journal of Transport Geography 39 (July). Elsevier Ltd: 197–207. doi:10.1016/j.jtrangeo.2014.07.010.

Kahle, D, and Hadley Wickham. 2013. “ggmap: Spatial Visualization with ggplot2.” The R Journal 5: 144–61. citeseerx.ist.psu.edu.

McGlade, Christophe, and Paul Ekins. 2015. “The geographical distribution of fossil fuels unused when limiting global warming to 2 °C.” Nature 517 (7533). Nature Publishing Group: 187–90. doi:10.1038/nature14016.

Tamminga, Guus, Marc Miska, Edgar Santos, Hans van Lint, Arturo Nakasone, Helmut Prendinger, and Serge Hoogendoorn. 2012. “Design of Open Source Framework for Traffic and Travel Simulation.” Transportation Research Record: Journal of the Transportation Research Board 2291 (-1): 44–52. doi:10.3141/2291-06.

Clipping spatial data in R

Tue, 29 Jul 2014 00:00:00 +0000

This miniature vignette shows how to clip spatial data based on different spatial objects in R and a ‘bounding box’. Spatial overlays are common in GIS applications and R users are fortunate that the clipping and spatial subsetting functions are mature and fairly fast. We’ll also write a new function called gClip(), that will make clipping by bounding boxes easier.

Loading the data

To start with let’s load some data. I’m working in the root directory of the Creating-maps-in-R github repository, so there are some spatial datasets available to play with:

setwd("../")
library(rgdal)
stations <- readOGR("data", "lnd-stns")
## OGR data source with driver: ESRI Shapefile
## Source: "data", layer: "lnd-stns"
## with 2532 features and 27 fields
## Feature type: wkbPoint with 2 dimensions
zones <- readOGR("data", "london_sport")
## OGR data source with driver: ESRI Shapefile
## Source: "data", layer: "london_sport"
## with 33 features and 4 fields
## Feature type: wkbPolygon with 2 dimensions

The wonder of spatial subsetting in R

Now, it’s easy to subset spatial data in R, using the same incredibly concise square bracket [] notation as R uses for non spatial data. To re-confirm how this works on non-spatial data, here’s a mini example:

M <- matrix(1:10, ncol = 5)
M[2, 3:5]
## [1] 6 8 10

The above code creates a matrix with 5 columns and 2 rows: the [2, 3:5] part takes the subset of M corresponding to 3rd to 5th columns in the second row.

Subsetting spatial data works in exactly the same way: note that zones are far more extensive than the stations points. (We have to change the projection of stations before plotting, so the objects are on the same coordinate reference system.)

stations <- spTransform(stations, CRS(proj4string(zones))) # transform CRS
plot(zones)
points(stations)

So how do we select only points that are are within the zones of London? Believe it or not, it’s as simple as subsetting the matrix M above. This is an amazingly concise and convenient way of spatial subsetting that was added to R at some point between versions 1 and 2 of Applied Spatial Data Analysis with R. In the earlier (2008) book, one had to use overlay(x, y) just to get the selection, and then another line of code was required to actually make the subset. Now things are much simpler. As Bivand et al. put it in the latest edtion (p. 131), “the selection syntax for features was extended such that it understands:”

stations_subset <- stations[zones, ]

This is so amazing and intuitive, whoever invented it should be given a medal!! Despite this simplicity, it seems many R users who I’ve taught spatial functions to are unaware of []’s spatial extension. So spread the word (and if anyone knows of the history of this innovation, please let us know). Now we have a sample of all stations zones: progress.

plot(zones)
points(stations_subset)

Clipping by a bounding box

But what if we want to clip the polygons data, based on a bounding box? To start with, let’s look at and modify the existing bounding box for the zones, making it half the size:

b <- bbox(zones)
b[1, ] <- (b[1, ] - mean(b[1, ])) * 0.5 + mean(b[1, ])
b[2, ] <- (b[2, ] - mean(b[2, ])) * 0.5 + mean(b[2, ])
b <- bbox(t(b))
plot(zones, xlim = b[1, ], ylim = b[2, ])

Now, to clip this area, we can use a custom function, which I’ve called gClip, following the rgeos function naming convention (this was inspired by an online answer that didn’t work for me):

library(raster)
library(rgeos)
## rgeos version: 0.3-5, (SVN revision 447)
## GEOS runtime version: 3.4.2-CAPI-1.8.2 r3921
## Polygon checking: TRUE
gClip <- function(shp, bb){
if(class(bb) == "matrix") b_poly <- as(extent(as.vector(t(bb))), "SpatialPolygons")
else b_poly <- as(extent(bb), "SpatialPolygons")
gIntersection(shp, b_poly, byid = T)
}
zones_clipped <- gClip(zones, b)
## Warning: spgeom1 and spgeom2 have different proj4 strings
plot(zones_clipped)

Note that due to the if statements in gClip’s body, it can handle almost any spatial data input, and still work. Let’s clip to the borough of Westminster, one of London’s better known boroughs:

westminster <- zones[grep("West", zones$name),]
zones_clipped_w <- gClip(zones, westminster)
## Warning: spgeom1 and spgeom2 have different proj4 strings
plot(zones_clipped_w); plot(westminster, col = "red", add = T)

Conclusion

There are many more spatial tips available from the Introduction to visualising spatial data in R tutorial that James Cheshire and I are maintaining. The source code of this post can also be viewed online as just one of a series of vignettes to showcase some of R’s impressive spatial capabilities.

Update

There was a lively discussion of this post when it was first published in 2016. Although I’ve switched to a new commenting system, the comments, which include more history and a link to the commit that added spatial subsetting to the sp package, can be found here: https://disqus.com/home/discussion/robinlovelace/clipping_spatial_data_in_r/

Coxcomb plots and 'spiecharts' in R

Fri, 27 Dec 2013 00:00:00 +0000

After switching to a new site I decided to revive some old posts. I found this one that was written back 7 years ago (in January 2021 when this update was written), back in December 2013. The results of the book are now published in the book Low Impact Living: A Field Guide to Ecological, Affordable Community Building (Chatterton, 2015, for more info on the Lilac project in particular and cohousing in general see lilac.coop/resources/). I was amazed to find that, with some tweaks, the ggplot2 code still ran.

I was contacted recently by a housing organisation who wanted an attractive visualisation of their finances, arranged in a circular form. Because there were two 4 continuous variables to include, all of which were proportions of each other, the client suggested a plot similar to a pie chart, but with each segment extending out a different radius from the segment. I realised later that what I had been asked to make was a modified coxcomb plot, invented by Florence Nightingale to represent statistics on cause of death during the Crimean War. In fact, I had been asked to make a “spie chart.” This post demonstrates, for the first time to my knowledge, how it can be done using ggplot2. A reproducible example of this, including sample data input, can be found on the project’s github repository: https://github.com/Robinlovelace/lilacPlot . Please fork and attribute as appropriate!

Reading and looking at the data

This is the original dataset I was given:

u <- "https://github.com/Robinlovelace/lilacPlot/raw/master/F2.csv"
f <- read.csv(u)
knitr::kable(f[1:3, ])

H	Value	Value.P	Allocation	Deposit	Captial	Debt	Cap	Contribution	Repayments
q	163827	0.065	0.979	16382	147445	0	2457.405	1287.24	0.00
a	165994	0.066	1.022	16599	5488	138847	2489.910	208.02	208.02
z	159425	0.063	0.933	15943	76632	63601	2391.375	995.46	995.46

Without worrying too much about the details, the basics of the dataset are as follows:

One observation per row, these will later be bars on the box plot
Two components of data - captital and revenue
Different orders of magnitude: some data is in absolute monetary terms, some in percentages

Base on the above points, a prerequisite was to create preliminary plots and manipulate the data so it would better fit in a coxcomb plot.

The first stage, however, is to demonstrate how the addition of coord_polar to a barchart can conver it into a pie chart:

library(ggplot2)
(p <- ggplot(f, aes(x = H, y = Allocation)) + geom_bar(color = "black", stat = "identity",
width = 1))

plot of chunk unnamed-chunk-2

p + coord_polar()

The above example works well, but notice that all the bars are of equal widths. What we want is to be proportional to a value (variable “Value”) of each observation. To do this we use the age-old function cumsum, as described in an answer to a stackexchange question.

w <- f$Value
pos <- 0.5 * (cumsum(w) + cumsum(c(0, w[-length(w)])))
(p <- ggplot(f, aes(x = pos)) + geom_bar(aes(y = Allocation), width = w, color = "black",
stat = "identity"))

p + coord_polar(theta = "x") + scale_x_continuous(labels = f$H, breaks = pos)

Finally a spie chart has been created. After that revelation, it was essentially about adding the ‘bells and whistles’, including a 10% line to represent how much more or less than their share each observation was paying.

Adding the 10 %

f$Deposit/f$Value

## [1] 0.09999573 0.09999759 0.10000314 0.09999837 0.10000120 0.10000000
## [7] 0.10000311 0.10000085 0.10000511 0.10000356 0.09999676 0.09999700
## [13] 0.09999812 0.10000511 0.10000085 0.10000240 0.10000000 0.10000694
## [19] 0.09999901 0.09999883

# add 10% in there
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allocation), width = w, color = "black", stat = "identity") +
geom_bar(aes(x = pos, y = 0.1), width = w, color = "black", stat = "identity",
fill = "green") + coord_polar()

# make proportional to area
f$Allo <- sqrt(f$Allocation)
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "black", stat = "identity") +
geom_bar(aes(x = pos, y = sqrt(0.1), width = w), color = "black", stat = "identity",
fill = "green") + coord_polar()

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

# add capital
capital <- (f$Captial + f$Deposit)/(f$Value) * f$Allocation
capital <- sqrt(capital)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "black", stat = "identity") +
geom_bar(aes(x = pos, y = capital, width = w), color = "black", stat = "identity",
fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1), width = w), color = "black",
stat = "identity", fill = "green") + coord_polar() + scale_x_continuous(labels = f$H,
breaks = pos)

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

# add ablines
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40",
stat = "identity", fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1),
width = w), color = "grey40", stat = "identity", fill = "green") + geom_abline(intercept = 1,
slope = 0, linetype = 2) + geom_abline(intercept = sqrt(1.1), slope = 0,
linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3)

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

plot of chunk unnamed-chunk-4

# calculate vertical ablines of divisions
v1 <- 0.51 * f$Value[1]
v2 <- cumsum(f$Value)[17] + f$Value[18] * 0.31
v3 <- cumsum(f$Value)[17] + f$Value[18] * 0.64
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey") +
geom_vline(x = v1, linetype = 5, xintercept = 0) +
geom_vline(x = v2, linetype = 5, xintercept = 0) +
geom_vline(x = v3, linetype = 5, xintercept = 0) +
coord_polar()

## Warning: Ignoring unknown aesthetics: width

## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x

# putting it all together
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40",
stat = "identity", fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1),
width = w), color = "grey40", stat = "identity", fill = "green") + geom_abline(intercept = 1,
slope = 0, linetype = 2) + geom_abline(intercept = sqrt(1.1), slope = 0,
linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3) +
geom_vline(x = v1, linetype = 5, xintercept = 0) +
geom_vline(x = v2, linetype = 5, xintercept = 0) +
geom_vline(x = v3, linetype = 5, xintercept = 0) +
coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) +
theme_classic()

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x

The above looks great, but ideally, for an ‘infographic’ feel, it would have no annoying axes clogging up the visuals. This was done by creating an entirely new ggpot theme.

Create theme with no axes

theme_infog <- theme_classic() + theme(axis.line = element_blank(), axis.title = element_blank(),
axis.ticks = element_blank(), axis.text.y = element_blank())
last_plot() + theme_infog

Creating a ring

To add the revenue element to the graph is not a task to be taken likely. This was how I tackled the problem, by creating a tall, variable-width bar chart first, and later adding the original spie chart after:

f$Cap.r <- f$Cap/mean(f$Cap) * 0.1 + 1.2
f$Cont.r <- f$Contribution/mean(f$Cap) * 0.1 + 1.2
f$Rep.r <- f$Cont.r + f$Repayments/mean(f$Cap) * 0.1
f$H <- c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n",
"o", "p", "q", "r", "s", "t")
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey")

## Warning: Ignoring unknown aesthetics: width

# we need the axes to be bigger for starters - try 1.3 to 1.5
p + geom_bar(aes(x = pos, y = Cap.r, width = w), color = "grey40", stat = "identity",
fill = "white") + geom_bar(aes(x = pos, y = Rep.r, width = w), color = "grey40",
stat = "identity", fill = "grey80") + geom_bar(aes(x = pos, y = Cont.r,
width = w), color = "grey40", stat = "identity", fill = "grey30") + geom_bar(aes(x = pos,
y = 1.196, width = w), color = "white", stat = "identity", fill = "white")

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

last_plot() + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40",
stat = "identity", fill = "grey80") + geom_bar(aes(x = pos, y = capital,
width = w), color = "grey40", stat = "identity", fill = "grey30") + geom_bar(aes(x = pos,
y = sqrt(0.1), width = w), color = "grey40", stat = "identity", fill = "black") +
geom_abline(intercept = 1, slope = 0, linetype = 5) + geom_abline(intercept = sqrt(1.1),
slope = 0, linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0,
linetype = 3) + coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) +
theme_infog

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

Just inner

After all that it was decided it looked nicer with only the inner ring anyway. Here is the finished product:

p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "grey80") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40",
stat = "identity", fill = "grey30") + geom_bar(aes(x = pos, y = sqrt(0.1),
width = w), color = "grey40", stat = "identity", fill = "black") + geom_abline(intercept = 1,
slope = 0, linetype = 5) + geom_abline(intercept = sqrt(1.1), slope = 0,
linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3) +
coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) + theme_infog

## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width

ggsave("just-inner.png", width = 7, height = 7, dpi = 800)