visualising the energy costs of commuting
DESCRIPTION
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.TRANSCRIPT
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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
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Motivation• Peak oil, obesity, climate change, recession• Energy: 'master resource', affects all
See Berners-Lee and Clarke (2013)
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Thinking about energy costs of transport
See Lovelace et al. (2011)
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Here!Sense
of future
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Where we're at: regional variabilityEnergy use per average one way trip to work: Mega Joules per trip (Etrp in MJ)
Data source: 2001 Census
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Train network
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• 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)
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Add baths and textin image editor
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Individual-level variability
Data source: National Travel Survey (2002 - 2008)
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Inequalities within areas
Lovelace et al. (2013)
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Going Dutch
• Scenario of high cycling uptake
• Realistic based on Dutch data
• 'What if' not 'it will' approach
source: London Cycling Campaign
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Going Dutch: Energy savings from high cycling uptake scenario
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National-level comparisons
Average energy costs per one way trip to work in English regions (2001) and Dutch provinces (2010)
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Going Finnish
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Going Finnish: assumptions
Based on work by Finlanders Helminen, Ristimäki, M. (2007)
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Energy saving from telecommuting results
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Compare with cycling uptake (below)
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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'
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Key functions for mapping in Rx = 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)) + ...
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Making that dynamic
• Gas guzzler map - video
• Work needed here
• Ideal would be interactive
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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?)
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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
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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)
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Taking it further
• Geo-visualisations with 'processing'
• Flow mapping in R
• Energy use at the road level
• Comparisons with other energy users
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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
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Go references + questionsBerners-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 BooksHelminen, 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
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'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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