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Visualising the energy costs of
commuting
From static graphs to online,
maps via infographics
Robin Lovelace, University o...
Motivation
• Peak oil, obesity, climate change, recession
• Energy: 'master resource', affects all
See Berners-Lee and Cla...
Thinking about energy costs of
transport
See Lovelace et al. (2011)
Here!
Sense
of future
Where we're at: regional variability
Energy use per
average one way
trip to work: Mega
Joules per trip (Etrp
in MJ)
Data s...
Train network
• 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...
Add baths and text
in image editor
Individual-level variability
Data source: National Travel Survey (2002 - 2008)
Inequalities within areas
Lovelace et al. (2013)
Going Dutch
• Scenario of high
cycling uptake
• Realistic based
on Dutch data
• 'What if' not 'it
will' approach
source: L...
Going Dutch: Energy savings from
high cycling uptake scenario
National-level comparisons
Average energy costs per one way trip to work in English regions (2001) and
Dutch provinces (20...
Going Finnish
Going Finnish: assumptions
Based on work by Finlanders Helminen,
Ristimäki, M. (2007)
Energy saving from telecommuting
results
Compare with cycling uptake
(below)
Making analysis reproducible
• Link to data: Dutch data taken from
Statistics Netherlands and English
data from Casweb
• M...
Key functions for mapping in R
x = c("ggplot2", "sp", "rgeos", "mapproj", "rgdal",
"maptools")
lapply(x, require, characte...
Making that dynamic
• Gas guzzler map - video
• Work needed here
• Ideal would be interactive
Google's Fusion Tables
• Shpescape = for
loading shp files
• As described by Dr Rae
• Pros
– Fast, user friendly
– Sensibl...
Geoserver on Amazon Web Server
• Experimented with Geoserver
• Running on Amazon's Web Services
(AWS), with 1 year free
• ...
Impact
• People seem to relate to research more
when it's in visual form
• Very good response from people in range
of othe...
Taking it further
• Geo-visualisations with 'processing'
• Flow mapping in R
• Energy use at the road level
• Comparisons ...
Conclusions
• Range of visualisation options available
now is wider than ever - take advantage!
• Each option has pros and...
Go references + questions
Berners-Lee, M., & Clark, D. (2013). The Burning Question: We can’t burn half
the world's oil, c...
'Eco-localisation'
• It's the localisation of
economic activity (North
2010; Greer 2009)
• Extent of process
depends on yo...
Visualising the energy costs of commuting
Visualising the energy costs of commuting
Visualising the energy costs of commuting
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Visualising the energy costs of commuting

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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.

Published in: Education, Technology, Business
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Visualising the energy costs of commuting

  1. 1. Visualising the energy costs of commuting From static graphs to online, maps via infographics Robin Lovelace, University of Leeds (GeoTalisman) @robinlovelace, github
  2. 2. Motivation • Peak oil, obesity, climate change, recession • Energy: 'master resource', affects all See Berners-Lee and Clarke (2013)
  3. 3. Thinking about energy costs of transport See Lovelace et al. (2011)
  4. 4. Here! Sense of future
  5. 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
  6. 6. Train network
  7. 7. • 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)
  8. 8. Add baths and text in image editor
  9. 9. Individual-level variability Data source: National Travel Survey (2002 - 2008)
  10. 10. Inequalities within areas Lovelace et al. (2013)
  11. 11. Going Dutch • Scenario of high cycling uptake • Realistic based on Dutch data • 'What if' not 'it will' approach source: London Cycling Campaign
  12. 12. Going Dutch: Energy savings from high cycling uptake scenario
  13. 13. National-level comparisons Average energy costs per one way trip to work in English regions (2001) and Dutch provinces (2010)
  14. 14. Going Finnish
  15. 15. Going Finnish: assumptions Based on work by Finlanders Helminen, Ristimäki, M. (2007)
  16. 16. Energy saving from telecommuting results
  17. 17. Compare with cycling uptake (below)
  18. 18. 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'
  19. 19. 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)) + ...
  20. 20. Making that dynamic • Gas guzzler map - video • Work needed here • Ideal would be interactive
  21. 21. 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?)
  22. 22. 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
  23. 23. 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)
  24. 24. Taking it further • Geo-visualisations with 'processing' • Flow mapping in R • Energy use at the road level • Comparisons with other energy users
  25. 25. 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
  26. 26. 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
  27. 27. '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)

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