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

Ac36cbdeb128eb88c6bce0ddff38a030?s=47 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

Ac36cbdeb128eb88c6bce0ddff38a030?s=128

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
  2. Overview • Home to school travel • Data, technical challenges

    & modeling routes • Primary to Secondary transition
  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
  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
  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
  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
  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
  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
  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
  10. • Street network - OpenStreetMap & Routino – Walk, Cycle,

    Car (+ Car Share), Taxi – Bus (public, school, unknown) Text file: • Distance • Route
  11. • Non-street network - pgRouting – Train, Tram (Metro /

    Light Rail), Tube pgRouting
  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
  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 (?)
  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
  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/
  16. None
  17. None
  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
  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
  20. Conclusion • Home to school travel is important • Can

    model individual routes nationally • Large processing can be done locally • Limitations • Future work
  21. Questions ? "Piled Higher and Deeper" by Jorge Cham www.phdcomics.com

    http://phdcomics.com/comics/archive.php?comicid=1692