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Robin Lovelace, profile picture
Uploaded byRobin Lovelace
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Visualising the energy costs of commuting

The document discusses the visualization of energy costs associated with commuting, advocating for dynamic mapping and infographics over static representations. It highlights regional variability in energy use and presents scenarios for increased cycling and telecommuting in the UK and the Netherlands. The author emphasizes the importance of making analysis reproducible and engaging policymakers through effective visual communication.

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Visualising the energy costs of commuting From static graphs to online, maps via infographics Robin Lovelace, University of Leeds (GeoTalisman) @robinlovelace, github
Motivation • Peak oil, obesity, climate change, recession • Energy: 'master resource', affects all See Berners-Lee and Clarke (2013)
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 source: 2001 Census
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 = 5 kWh = 3.6 * 5 MJ (MacKay, 2009)
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: London Cycling Campaign
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 (2010)
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 • Most analysis + visualisation in R • Result reproducible: RPubs documents + uploaded .zip folder • RMarkdown runs code 'live'
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)) + ...
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 – Sensible presets – no need for servers • Cons – Not flexible – Data ownership (NSA?)
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
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)
Taking it further • Geo-visualisations with 'processing' • Flow mapping in R • Energy use at the road level • Comparisons with other energy users
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
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
'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)
Visualising the energy costs of commuting