visualising the energy costs of commuting

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 variabilityEnergy 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 textin 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 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)) + ...

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 + 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

'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)