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Modelling home to school travel for state pupils in England, 2008-2011, GISRUK 2014

nickbearman
April 18, 2014
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Modelling home to school travel for state pupils in England, 2008-2011, GISRUK 2014

Presented at GISRUK2014, University of Glasgow on 18th April 2014.
Discussion of work on home to school travel project for state school children in England, 2008-2011 using R, Routino and pgRouting

nickbearman

April 18, 2014
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  1. Dr Nick Bearman, CGeog (GIS)
    Department of Geography and Planning
    Modelling home to school travel for
    state pupils in England, 2008-2011
    @nickbearmanuk
    http://www.fotolog.com/luckyshot/41568423/
    https://twitter.com/Peter_Tennant/status/444404118330044416

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  2. Overview
    • Home to school travel
    • Data, technical challenges
    & modeling routes
    • Primary to Secondary
    transition

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  3. Home to School Travel
    • About 7.5m school aged children in England
    • (Most) have to travel from home to school
    D Sharon Pruitt http://www.flickr.com/photos/pinksherbet/234942843/ http://www.flickr.com/photos/bike/8560715649/in/photostream/
    http://en.wikipedia.org/wiki/File:Alpine_Travel_bus_DVG517_(YMB_517W)_1981_Bristol_VRT_SL3_EC
    W,_10_July_2006.jpg

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  4. • What influences choice?
    – Distance
    – Pupil age
    – Road infrastructure
    – Family factors
    • Why is it important?
    – Active Transport
    – CO2
    emissions / air pollution
    – Congestion
    http://chestercycling.files.wordpress.com/2011/0
    2/cimg2369.jpg
    http://static2.stuff.co.nz/1333093574/087/6670087.jpg

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  5. Data sources
    • Pupil Level Annual School Census (2008-2011)
    – Pupil home postcode
    – “Usual” mode of travel (11 options)
    – Data for each year
    – State schools only (Independent schools ~ 7%)
    • Edubase - school information
    • CO2
    emissions by travel mode

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  6. Traditional estimation technique
    • Euclidean distance
    • Average emissions
    values for mode of
    transport
    • Straight lines will
    typically underestimate
    true distances
    • No sensitivity to
    different vehicle types

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  7. Technical challenges
    • Large data sets
    • Multiple routing modes
    • Long run times
    • Confidential data
    Arne Hückelheim (author) http://en.wikipedia.org/wiki/File:SunsetTracksCrop.JPG

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  8. Routing: School census
    • 5m school children with records for 2008-2011
    3.8m (75.9%)
    • with complete mode data
    • Not outliers
    – Tukey outlier (Tukey, 1977)
    – weighted by mode (m), year (a), local authority (g)
    – not too far from station
    – journey not too long

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  9. Routing: Mode of travel
    • Home: L11 4SH School: L11 0BP Mode: WLK
    • Street network
    • Non-street network
    C. G. P. Grey http://en.wikipedia.org/wiki/File:Citadis_dublin.jpg

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  10. • Street network - OpenStreetMap & Routino
    – Walk, Cycle, Car (+ Car Share), Taxi
    – Bus (public, school, unknown)
    Text file:
    • Distance
    • Route

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  11. • Non-street network - pgRouting
    – Train, Tram (Metro / Light Rail), Tube
    pgRouting

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  12. Home: L11 4SH School: L11 0BP Mode: WLK
    • R then called Routino or pgRouting
    • Process was repeated for each school child
    • And for each year (2008-2011)
    – If either Mode, Start postcode or End postcode
    were different
    • Processing time ~8.5 days in total

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  13. Why these programs?
    • Routino
    – Easy way of getting street based routes
    • pgRouting
    – Routes for custom networks
    • R - Open Source
    – can handle big data (o/n running)
    • OS X & R
    – good command line interface
    – stable (?)

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  14. Big Data and the Cloud
    • Run time an issue
    • But confidential data
    • Cloud solutions complex for permissions
    • “Where is the data stored?”
    • Keeping locally is a solution
    http://www.ft.com/cms/s/0/e2672ccc-349f-11e2-8986-
    00144feabdc0.html#axzz2xAmBRk6u

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  15. Primary to Secondary mode choice
    • Big change primary to secondary
    – longer distances
    – Bus travel
    – limited average change in mode after this
    http://pixabay.com/en/boy-girl-hand-in-hand-kids-school-
    160168/

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  16. View Slide

  17. View Slide

  18. Maj. of BUS is DSB
    Secondary: Walk to 2.75km (1.75m), then Bus (+ Car)
    Primary: Walk to 1.25km (0.75m), then Car (+ Bus) Maj. of NON is WLK
    Distance

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  19. CO2
    Emissions
    • Have routes for all school traffic
    • Can calculate the CO2
    impact of school traffic
    • Can model altering the % of pupils who use
    active travel

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  20. Conclusion
    • Home to school travel is important
    • Can model individual routes nationally
    • Large processing can be done locally
    • Limitations
    • Future work

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  21. Questions ?
    "Piled Higher and Deeper" by Jorge Cham
    www.phdcomics.com
    http://phdcomics.com/comics/archive.php?comicid=1692

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