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