This was my presentation for the Free Open Source Software for Geo (FOSS4G) conference, held in Nottingham, 2013. It shows the images I've made to try and make my work accessible to everyone.

1. Visualising the energy costs of
commuting
From static graphs to online,
maps via infographics
Robin Lovelace, University of Leeds
(GeoTalisman)
@robinlovelace, github

2. Motivation
• Peak oil, obesity, climate change, recession
• Energy: 'master resource', affects all
See Berners-Lee and Clarke (2013)

3. Thinking about energy costs of
transport
See Lovelace et al. (2011)

4. Here!
Sense
of future

5. Where we're at: regional variability
Energy use per
average one way
trip to work: Mega
Joules per trip (Etrp
in MJ)
Data source: 2001 Census

7. Train network

8. • Cut continous variable in
bath-sized chunks:
la$Baths <- cut(la$ET/
(la$all.all * 3.6 * 5),
breaks=c(0,1,2,3)
)
One bath = 5 kWh = 3.6 * 5 MJ
(MacKay, 2009)

9. Add baths and text
in image editor

10. Individual-level variability
Data source: National Travel Survey (2002 - 2008)

11. Inequalities within areas
Lovelace et al. (2013)

12. Going Dutch
• Scenario of high
cycling uptake
• Realistic based
on Dutch data
• 'What if' not 'it
will' approach
source: London Cycling Campaign

13. Going Dutch: Energy savings from
high cycling uptake scenario

14. National-level comparisons
Average energy costs per one way trip to work in English regions (2001) and
Dutch provinces (2010)

15. Going Finnish

16. Going Finnish: assumptions
Based on work by Finlanders Helminen,
Ristimäki, M. (2007)

17. Energy saving from telecommuting
results

18. Compare with cycling uptake
(below)

19. Making analysis reproducible
• Link to data: Dutch data taken from
Statistics Netherlands and English
data from Casweb
• Most analysis + visualisation in R
• Result reproducible: RPubs
documents + uploaded .zip folder
• RMarkdown runs code 'live'

20. Key functions for mapping in R
x = c("ggplot2", "sp", "rgeos", "mapproj", "rgdal",
"maptools")
lapply(x, require, character.only = T)
gors <- readOGR(".", layer = "GOR_st121")
fgor <- fortify(gors, region = "ZONE_LABEL")
fgor <- merge(fgor, gors@data[, c(1, 2, 3, 8, ncol(gors@data))],
by.x = "id", by.y = ZONE_LABEL")
p <- ggplot(data = fgor, aes(x = long/1000, y =
lat/1000))
p + geom_polygon(data = fgor, aes(x = long/1000, y =
lat/1000, fill = ET/all.all,
group = group)) + ...

22. Making that dynamic
• Gas guzzler map - video
• Work needed here
• Ideal would be interactive

23. Google's Fusion Tables
• Shpescape = for
loading shp files
• As described by Dr Rae
• Pros
– Fast, user friendly
– Sensible presets
– no need for servers
• Cons
– Not flexible
– Data ownership (NSA?)

24. Geoserver on Amazon Web Server
• Experimented with Geoserver
• Running on Amazon's Web Services
(AWS), with 1 year free
• Upload shapefiles, server side (Geoserver
interface) + client side (OpenLayers) edits
• Not currently set-up
• Pros: Flexibility, control of information,
massively scalable (geodb)
• Cons: Tricky, time consuming and some
cost

25. Impact
• People seem to relate to research more
when it's in visual form
• Very good response from people in range
of other disciplines
• Still struggling to engage 'policy makers'
• Increased accessibility and potential
'impact' (in context of REF)

26. Taking it further
• Geo-visualisations with 'processing'
• Flow mapping in R
• Energy use at the road level
• Comparisons with other energy users

27. Conclusions
• Range of visualisation options available
now is wider than ever - take advantage!
• Each option has pros and cons - decision
should be context-specific
• Advantages of moving beyond static
graphs and maps, esp. in age of 'big data'
• Don't get caught up in the details, focus on
message

28. Go references + questions
Berners-Lee, M., & Clark, D. (2013). The Burning Question: We can’t burn half
the world's oil, coal and gas. So how do we quit? Profile Books
Helminen, V., & Ristimäki, M. (2007). Relationships between commuting
distance, frequency and telework in Finland. Journal of Transport
Geography
Lovelace, R., Ballas, D., & Watson, M. (2013). A spatial microsimulation
approach for the analysis of commuter patterns: from individual to regional
levels. Journal of Transport Geography
Lovelace, R., Beck, S. B. M. B. M., Watson, M., & Wild, A. (2011). Assessing
the energy implications of replacing car trips with bicycle trips in Sheffield,
UK. Energy Policy
Email: R . Lovelace @ Leeds . ac . uk

29. 'Eco-localisation'
• It's the localisation of
economic activity (North
2010; Greer 2009)
• Extent of process
depends on your
perspective
• Tried to model it...
• But some things are best
not quantified (and so
says Vaclav Smil)