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Modelling Cycling:
Potential Cycling & Potential Benefits
James Woodcock1, Alvaro Ullrich1, Robin Lovelace2
1CEDAR MRC Epidemiology Unit, 2University of Leeds
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Summary of talk
• James
• Introducing CEDAR
• Introducing DfT National Propensity to Cycle Tool
• Robin
• PhD Spatial Micro Simulation
• Subsequent projects
• Alvaro
• Cambridge(shire) project
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CEDAR, MRC Epidemiology Unit
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• Woodcock J, Tainio M, Cheshire J, O’Brien O, Goodman A. Health effects of the London bicycle sharing system: health impact modelling study. BMJ 2014;348
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Associations between
exposure to takeaway
food outlets, takeaway
food consumption, and
body weight in
Cambridgeshire, UK:
population based, cross
sectional study
BMJ 2014; 348 doi:
http://dx.doi.org/10.1136
/bmj.g146 Burgoine T,
Forouhi, Griffin,
Wareham, Monsivais
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DfT: Provision of Research Programme into Cycling: Propensity to Cycle Tool
• Stage 1: Jan 2015 until June 2015
• Prototype model
• Stage 2?: June 2015 - ?
• National Propensity to Cycle Tool with health & carbon
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Stage 1
• Evidence Review
• Interventions
• Which people, which trips
• Impact on inequalities
• Statistical analysis
• Who cycles & for which trips: England & Netherlands?
• Estimates need for creating Propensity to Cycle model
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Stage 1
• Modelling Health & Carbon benefits of switching trips to cycling:
• Two models/ two approaches: London & England
• Prototype model for three cities
• Scoping Report: “How to build a National Propensity to Cycle model”
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Why a Propensity to Cycle Tool?
• Where to prioritise cycling investment?
• City by city
• Street by street
• Potential in terms of
• Cycling
• Health
• Carbon
• Inequalities
• Consider separately factors relating to
• Characteristics of trips
• Characteristics of people
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All Cycling Trips are not the Same?
• Which trips are cycled?
• Who cycles?
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Carbon: Cumulative % of Distance by Trip Length
0
0.1
0.2
0.3
0.4
0.5
0.6
0.70.2
5 1 2 3 4 5 6 7 8 9
10
11
12
13
14
15
16
17
18
19
20
Cu
mu
lati
ve %
of
tota
l d
ista
nce (
so
lid
li
nes) /
% o
f d
ista
nce b
y c
ar (
dash
ed
li
nes)
Distance (miles)
London
SW Rural
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Distance Decay Odds Cycling
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
<0.5 0.5 to
<1.5
1.5 to
<2.5
2.5 to
<3.5
3.5 to
<4.5
4.5 to
<5.5
5.5 to
<6.5
6.5 to
<9.5
9.5 to
<12.5
12.5 to
<15.5
15.5 to
<20.5
Female age 16-59 Female age 60+ Male age 16-59 Male age 60+
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Harms to males
Benefits to males
Harms to females
Benefits to females
Males Females
-2500
-2000
-1500
-1000
-500
0
500
Ch
an
ge
in
DA
LY
s
Age group Age group
15-2930-4445-5960-6970-79 80+ 15-2930-4445-5960-6970-79 80+
-3000
Health Trade-offs of Cycling: Central London
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Definitions
• Current level of cycling (CLC) number who regularly cycle work or leisure OR rate (trips/ week)
• Potential level of cycling (PLC) expected rate of cycling in an area or between origin-destination pairs (under certain assumptions).
• PLC is affected by
• Overall level of OR shift to cycling in the wider area
• Trip distances (see distance decay, below),
• Socio-demographics (and its influence on distance decay)
• Transport network (e.g. circuity and cycle infrastructure)
• Hilliness
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Definitions
• Extra cycling potential (ECP) the number of additional trips or cyclists that would be expected in a given scenario.
• Distance decay relates distance of a trip to the probability (or odds) of it being made by a specific mode (e.g. by bicycle) with respect to explanatory variables such as the person's socio-demographic group and the hilliness.
• Circuity is the actual length of a trip along the transport network compared with the straight-line (Euclidean) distance.
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Spatial Microsimulation
• Generating individual level data (usually at a small area level) starting from aggregate data
• Robin integrating with individual level dataset (usually national or regional)
• Alvaro hypothetical individual dataset- not real data
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Modelling cycling uptake at individual, local and national levels
Robin Lovelace (University of Leeds)
Presented at the University of Cambridge
18th February 2015
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Research interests
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Current research: Twitter to calibrate SIM
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Scenarios of cycling: national
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Spatial Microsimulation
• Two definitions of spatial microsimulation
– A method for combining individual-level data with aggregate-level data
– An approach to policy evaluation and analysis
• Generating spatial microdata
– Deterministic method (IPF)
– Probabilistic (combinatorial optimisation)
• Uses of spatial microdata
– Input into ABMs
– Analysis of sub-regional issues
– Basis for 'what if' scenarios
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Applications: 1 - Smoking rate
Tomintz et al (2008). The
geography of smoking in Leeds: estimating individual smoking rates and the implications for the location of stop smoking
services. Area, 40(3), 341–353. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1111/j.1475-
4762.2008.00837.x/full
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2. Health Behaviours
Lovelace, R. (2014). Introducing spatial microsimulation with R: a practical. National Centre for Research Methods, 08(14). Retrieved from
http://eprints.ncrm.ac.uk/3348/
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Spatial microsimulation with FMF
The Flexible Modelling Framework is a free and open source Java program. It can be downloaded from https://github.com/MassAtLeeds/FMF
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Spatial microsimulation with R
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What is ‘spatial microsimulation’?
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Generating spatial microdataSubtitle
Algorithm assigns weight to each individual
Original implementation in pure R
Now use faster ipfppackage (C)
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Where do people travel?
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Input: work-time population
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Input: MSOA flow data
• Breakdown of flow by destination MSOA and mode of travel - published 25th July 2014
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Assignment to travel network
• Next stage: allocate flows to roads/paths
• New software available to do this
– Google/CycleStreets API
– PG Routing
– ggmap/igraph/R
• Evaluation of local policies
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'What if' scenarios
• A 'snapshot' scenario of a future state
• 'What-if peoples’ willingness to cycle doubled for every trip distance?'
• ‘What if people cycled further?'
• 'What if male-female differences in cycling reduce?'
• ‘What if new cyclists have different needs than existing cyclists?
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Future work
Lovelace, R., Ballas, D., & Watson, M. (2014). A spatial microsimulation approach for the analysis of commuter patterns: fromindividual to regional levels. Journal of Transport Geography, 34(0), 282–296.
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Cambridgeshire:
Commuting Microsimulation
Alvaro Ullrich
CEDAR, MRC Epi Unit
Institute of Public Health
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• Goal: accurate picture of city
commuting trips (residents +
inflow)
• 1st attempt to use microsim
• Sources: Census aggregates
2011
• IPF method (deterministic)
(incursions on probabilities)
• Tools: R – Data analysis – SQL
Databases-ArcGIS
Cambridge model: Objectives
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Cambridge model: Overview
4 constraints(.csv)
Flows (by MSOA)
Census 2011
public data
ind.csv
IPF(deterministic)+
4 populations combined
Categories combined
(+filtering)
[Route allocation –
ABM –Analysis]
[Probability
allocation]
Translate to
Map…
[Synthetic Population]
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Cambridge model: Spatial level of detail
• 13 MSOAs (~5,000 people /each)
• Population weighted centroids
MSOA centroids apart ~1km vs. LSOA centroids <500 m
ACCURACY LIMIT
• 69 LSOAs (~1,000 people /each)
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Cambridge model: choosing the variables
Flows by MSOA
What variables? [Age]- [Gender]- [Mode]- …. at LSOA/MSOA level
…. BUT: correlated, i.e. crosstabbed !! [Age ~ Mode] - [Mode~Distance] - [Gender~Mode]
Mode categ. (11)
Cambr. MSOA (13)
‘1 constraint, 1 var’
‘1 crosstab var’
Mode-Age categ. (11x 6)
Cambr. MSOA (13)
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• Challenge: getting crosstab + MSOA/LSOA (‘more info, less detail’)
Assumption: corr. hold at MSOA/LSOA level
Allocate individuals by MSOA (multinomial distr.)
• The Lego-IKEA problem:
IPF finds ‘best’ correlation (Math)
IRL: Multiple solutions
Availability of variables
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Census Flows (added end 2014)
Flows by MSOA
Flows [age]-[gender]-[mode], MSOA to MSOA
Distance variable: Euclidean, added using ArcGIS (exact)
Option: Route length adjustment (LSOA)
Population weighted centroids (.shp)
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Target Flows. Linked populations
City level flows: interflow - outflow- inflow - other
… although 4 Census populations:
1. interflow
2. outflow
3. inflow
Cambridge CC
4. other
Population (as per Census) #
I. Live UK, work Cambridge 85K
II. Live Cambridge, work UK 50K
III. Live Cambridge, work Cambridge 35 K
IV. Live Cambridge, work Other cat. 10 K
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Processing populations
Total Working in city (I. LA_WC + IV. Other): ~94K
Residents working (II. LC_WC + IV. LC_WOth): ~60K
Daily Residents Outflow (II.+IV1,2 – III): ~17K
Daily Inflow (Total – WCity): ~51K
Get final combined populations (SQL language + ddbb):
Census dataset: flow data = 1 origin, 1 destination
translate
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Results: Synthetic Population
• Results: 1 data file. Next: clustering using Mach.Learning.
• Next: better mapping & Visualisation
• Check vs. real data: CC cordon data, transport aggregates…
Natural groups SP file
Data Protection: ‘How real is a ‘Synthetic Population?’
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Results: some examples
Barnwell > Addenbrooke’s trips (mode) Mode distribution by MSOA (core vs periphery)
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Thanks for listening!
• Any questions?
• Contacts:
• Contact: [email protected]
• @robinlovelace
• slides: robinlovelace.net
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ACKNOWLEDGEMENT
This work was undertaken by the Centre for Diet and Activity Research (CEDAR), a
UKCRC Public Health Research Centre of Excellence.
Funding from Cancer Research UK, the British Heart Foundation, the Economic and
Social Research Council, the Medical Research Council, the National Institute for
Health Research, and the Wellcome Trust, under the auspices of the UK Clinical
Research Collaboration, is gratefully acknowledged.