Thank you very much for the possibility to participate in the SSS8

1. SSS8 - Chile 2012Time use and movement behaviour of young people in citiesThe application of GPS tracking in tracing movement pattern of young people for a week in AalborgThe Aalborg Case • Thank you very much for the possibility to participate in the SSS8 • Thank you for the critique to our paper/presentation • Thank youfor placing the presentation in this session • A special thank you to Akkelies Van Ness, Delft University of Technology, Who introduced us to the magnifying world of Space Syntax Analyses • And to Margarita Greene Z. and José Reyes S. from the SSS8 Organizing Committee for answering our e-mails

2. Diverse Urban Spaces • Our research profile • Research group located at Aalborg University’s Department of Architecture, Design and Media Technology in Denmark 6 members • Employees with various academic backgrounds, ph.d.’s, post doc., architects, surveyors, sociologists, student assistants etc. • Our research work • Studies of mobility among different population groups across varying scales in both indoor and outdoor environments • Examples of our research • Analyses of how citizens/humans in an urban environment make use of over parks / plazas / central city areas e.g. – GIS/GPS mapping of the everyday movement and time consumption patterns of 300 high school students (2007), 200 Bicyclists (2011), and 400 individual respondents in around 100 families (2011)

3. Motivation for applying Space Syntax on GPS data • All our projects – especially our flagship project ”Diverse Urban Spaces” – result in very rich GPS data sources describing actual movement and behaviour, compare to others types of datasamples • Akkelies Van Ness, Delft University of Technology, provided a series of Space Syntax analyses of Aalborg City’s road and path network • Two data sources depicting the infrastructure (the space syntax data and the GPS data) • Comparison in order to evaluate theory versus practice and as such the quality of the Space Syntax method • Ratti, C. 2004b, "Space syntax: some inconsistencies", Environment and Planning B: Planning and Design, vol. 31, pp. 487-499.

4. Diverse Urban Spaces in a nutshell • A grand research projectinvolving over 300 youngpeopleattendinghighschool or equevalentlevel of education in Aalborg Denmark whoweretracked in 7 days • Research objective: To studyhow the city og Aalborg is used by thisparticular segment of respondents • Technical setupwastwofold: • Each respondent had to carry a hand-heldGPS-receiver throughout a week • Aftereachsurveyday, the respondent wastasked with filling out a trip diary • Motivation for continuous participation wasdaily and weekly lotteries with cash prizes

5. Technical setup Data cleansing And preparation GIS analyses Case Data gathering Server / database

6. Example of output analysis - Accumulated time consumptionCentral city area in Aalborg, DenmarkUrban life ?? • In the Aalborg Case we used a subsample of data from 169 statistical verified respondents tracked 24 hours in 7 days from the 300 respondents

7. Overall - Space syntax analyses • Conducted by Akkelies Van Ness, DelftUniversity of Technology • Different analyses usingvaryingsettings • High metrical radius highlights maintransportationcorridors • Low metrical radius highlights local city centres • Output stems with reality (with exceptions) for Aalborg, cf. next slide • Main transportationcorridors Vesterbro, Hobrovej, Sønderbro, Østre Allé gethighlighted • Pedestrianareas in Aalborg and Nørresundby gethighlighted • Somenoise in both analyses

8. Overall - Space syntax analyses • Local integration – low metrical radius • Global integration – high metrical radius • Map uses in the analyses

9. Time consumption of woman. Time consumption of men.

10. Put the twotogether • The analyses depicting the global integration was chosen for the comparison • The reason for using this analysis is that the dataset containing mobility and time consumption involves the entire urban area • Time accumulation Maps - The largest time was spent in the various shopping areas in Aalborg in the central city area. Secondly, some high amount of time was spent on the various main routes leading through and between urban areas. • Womanspent more time in central shopping areas

11. Overall - Comparison method • Founded on an assumption of positive linear correlation between a road segment’s rank as classified by the space syntax analysis and the accumulated time consumption on streets created by the respondents in the Diverse Urban Spaces project • ”Home-spun” procedure involving3 steps

12. Comparisonmethod, step 1 • 10 categories of time consumptionlevelsarecalculated, corresponding to the 10 spacesyntaxclassificationlevels. • Time consumptioncategoriesareyielded as quantilevaluesbased on the time consumption data

13. Overall - Comparisonmethod, step 2 • Each observation of accumulated time whichspatiallyintersects a given road segment is selected • A meanvalue of accumulated time consumption for the road segment is calculatedbased on the values of the selected observations

14. Comparisonmethod, step 3 • The meanvalue (µ) is evaluatedagainst the quantilevaluescorresponding to the thespacesyntaxclassification • If µ matches the quantilevalues, coherence is attained

15. Overall Men/Women - Results, strictcoherencerule • Men • Women

16. Results, strictcoherencerule • Generally onlycoherencealong the maintransportationcorridors • There is coherence between time spent and space syntax classification, when the mean value of the selected squares is compared with the expected time consumption for the polyline based on the classification. The expected value is derived by yielding 9 quantile values (q) which divide the time consumption registration dataset into 10 approximately evenly distributed groups.

17. Results, loosecoherencerule • Men • Women

18. Results, loosecoherencerule • A greaterdegree of coherence in suburban areas as well as some parts of the city centres in addition to the maintransportationcorridors • A close-up view of the mainpedestrianareas shows lack of coherencebetweenspacesyntaxclassification and time consumption • The coherence rule is loosened slightly in the sense that coherence is achieved if the mean value resides within a buffer of ± 1 quantile of the expected time consumption value. I.e., coherence is reached for the space syntax class 5 at q4 < µ < q7 instead of q5 < µ < q6.

19. Still lackof coherence in the mainpedestrian in the central City areas for men and woman

20. Results, overall statistics

21. Evaluation and conclusions • High school students in Aalborg alsotravelfrequentlyalongmain - transportationcorridors • There is nocoherencebetweenspacesyntaxclassification and actual time spendure in the mainpedestrianareas in the central City area.Mostlikelybecause the usedSpace Syntaxanalysis in this cases uses a highmetrical radius whichdoesn’t highlight road segments with a highlocalintegrationbut thiswillchangewhenusing a more loosecoherencerule and maybelow metrical radius in the Space Syntaxanalysis. • The lack of coherence is a natural consequence when the space syntax analysis is executed with a high metrical radius. As such, the northernmost shopping districts have a classification which is too low to attain coherence with the massive time consumption registered in these areas. If the space syntax classification is mapped on top of the time consumption dataset, this assumption becomes more reliable.

22. Whatcanbe done to adjust Space Syntax to GPS data • One of the points of criticism of the space syntax method was a lack of the metrical properties in their analyses (Ratti 2004). Now it is incorporated in the calculations. As research results show, the geometrical and topological distances correspond with the pedestrian and vehicle flow rates and the location pattern of shops more than the metrical distances. However, when applying metrical radiuses in the angular and axial analyses, some striking results can be seen. • Streets with high integration values with a high metrical radius tend to be the potential routes for through movement. Conversely, streets with high integration values with a low metrical radius tend to be potential meeting places for the neighbourhood. When comparing these two analyses with one another, the most vital urban areas tend to be where streets have high integration values with both high and low metrical radiuses. (van Nes, Berghauser-Pont, Maschoodi, 2011).

23. Furtherwork • Conduct the comparison using a better scientific founded method such as Linear Regression instead of the “homespun” method • Conduct the comparison for centre areas using the Space Syntax analysis with a low metrical radius – will most likely lead to a higher comparison rate • Conduct the comparison based on amount of trips and not accumulated time • Definewhat urban life is ..

24. Thankyou for your attention Any questions?