Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana Nicolas Lachance-Bernard, Timothée Produit - Ecole polytechnique fédérale de Lausanne Biba Tominc, Matej Niksic, Barbara Golicnik Marusic - Urban Planning Institute of the Republic of Slovenia

1. Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana Nicolas Lachance-Bernard1, Timothée Produit1, BibaTominc2, Matej Nikšič2, Barbara Goličnik21 Geographic Information SystemsLaboratory, Ecole polytechnique fédérale de Lausanne2Urban Planning Institute of the Republic of Slovenia, Ljubjlana The International Conference on Computational Science and its Applications – Cities, Technologies and Planning,June 2011, Santander, Spain

2. Plan Introduction Conceptual background Methodology Ljubljana case study

3. Plan Introduction Cycling and Urban Planning Challenges and Needs for Optimal Location of Cycling Facilities Conceptual background Methodology Ljubljana case study

4. Cycling and Urban Planning Cycling? Promoted as one of the most appropriate ways of urban mobility Environmentally friendly, require less space, impacts on health Planning? Importance of cycling facilities provision for cycling development Germany: 12,911km (1976)  31,236km (1996) The Netherlands: 9,282km (1978)  18,948km (1996) Statedpreferencesurveys: Facilitiesdiscontinuities, route attributes Goal? Cyclingfacilities: Right places (O-D), right corridors (Flux)

5. Optimal Location of CyclingFacilities Opportunities GPS: Portable, lightweight, unobtrusive and low-cost Planners: Insights of current and future behaviors (monitoring) Paststudies Aultman et al. 1997 – Bicycle commuter routes and GIS Dill and Gliebe 2003 – Bicycle and facilities in USA Jensen et al. 2010 – Speed and paths of shared bicycle in Lyon Menghini et al. 2010 – Route choice of cyclists in Zurich Winters et al. 2011 – Motivators and deterrents of bicycling

6. Optimal Location of CyclingFacilities Challenges and Needs GPS tracking visual presentation: data volume Direct usage of GPS data in the planning practice: lack of methods GVI: free enriched geographic data sources (i.e. OSM)

7. Plan Introduction Conceptual background Examples of Current GPS Tracking Projects Ljubljana Investigation Background Kerned Density Estimation (KDE) Network Based Kernel Density Estimation (NetKDE) Methodology Ljubljana case study

8. Examples of Current GPS TrackingProjects San Francisco (USA) – Smart phones Weeklyprizedraw “Developing”facilitiesinstead of “building”them Copenhagen (Denmark) – Web-based GIS portal 3,000 trips mappedby citizen VISUM model COWI A/S GPS tracking: before / afterfacilitiesimprovements Barcelona (Spain) – Qualitative / Quantitative Bici_Nprojectrent-a-cycles video/audio Data transfert from station to central DB for furtheranalysis

9. Ljubljana Investigation Background Statedpreferences (2008) Web-based portal Geae+ Cyclist description, trip information Digitalization of trip GPS track transfert fromenableddevice Low-Tech: Paperover mapdrawing Revealedpreferences(2010) GPS trackingdevice User friendly, low-cost, accurate Data transfert by technicians Broader investigation

10. KDE vs. NetKDE Kernel Density Estimator (KDE*) Operates in euclidean space Weights events by their radial distances from grid centroid Network Based Kernel Density Estimator (NetKDE*) Operates in a network constrained space Weights events by the distance from grid centroid measured along this network * Density estimation function + Epanechnikow kernel function NetKDE and KDE (2009-2011) by TimothéeProduit, Nicolas Lachance-Bernard, Loic Gasser, Dr. StephaneJoost, Prof. Sergio Porta, EmanueleStrano

11. KDE vs. NetKDE KDE NetKDE

12. KDE vs. NetKDE KDE NetKDE

13. Plan Introduction Conceptual background Methodology GPS Tracking Network and Grids Low Resolution KDE, High Resolution NetKDE Ljubljana case study

14. GPS Tracking Device Sport tracker QSTARZ BT-Q1300s 62 x 38 x 7 mm, 10m accuracy One button (On/Off), mini USB port KML, GPX, CVS Tracking: 5 seconds, 15h autonomy Data CSV  SHP (WGS84)  Merge  Projection (UTM33N) [Manifold]

15. Network and Grids Open Street Map Network Source: Cloudmadewebsite SHP (WGS84)  10km GPS Buffer  Projection (UTM33N)  Places digitalization  Highwaydeleted[Manifold] Topology (0.5m connecting/merging) + attributescleaning[ESRI ArcGIS model builder] Grids 100m: Lowresolution multi-bandwidths KDE 20m: High resolutionspecific-bandwidthsNetKDE[IDRISI]

16. Plan Introduction Conceptual background Methodology Ljubljana case study Resources, Data and Calculations Low Resolution Grid KDE Results High Resolution Grid NetKDE Results Discussion

17. Resources Software / Hardware Postgres/PostGIS/Python/QuantumGIS Windows XP 64 Intel® Core™2 Quad CPU Q950 @ 3.GHz 7.83GB of RAM

18. Data and Calculations Lowresolution KDE 100m  425km213,630 segments, 42,342 gridpoints, 442,260 GPS points KDE bandwidths [200m, 2500m] 24 X 100m steps(2-3h) High NetKDE/KDE 20m  20km28,114 segments, 314,250 gridpoints, 423,748 GPS points NetKDEbandwidths 60m (17h), 100m (19h), 200m (24h), 400m (27h) KDE bandwidths [40m, 100m] 7 X 10m steps [200m, 1000m] 9 X 100m steps (total 18h)

19. KDE results100m grid Bandwidths: A)300m B)500m C)1000m D)2000m *Decilesdistribution

20. KDE results20m grid Bandwidths: A)60m B)100m C)200m D)400m *Decilesdistribution

21. NetKDEresults20m grid Bandwidths: A)60m B)100m C)200m D)400m *Decilesdistribution

22. Discussion NetKDE 20m (Visual analytics) 3:1 ratio - Shows flux corridors (a) 5:1 ratio - Smooths corridors only (b) 10:1 ratio - Highlights axis and intersections (c) 20:1 ratio - Shows cyclist’s main area presence and main axis

23. Discussion Researchunderrapidevolution… 3rd algorithm: Calculationoptimization 90-95% (10h network-indexing, 5 min. for eachsteps) Currentwork on Barcelona, Ljubljana, Geneva, Glasgow, Baghdad Professional uses: Architects, Planners, Criminologs, Biologists Actualprojects… Spatio-temporal and statisticalanalysis Fuzzy-mapcomparison (time, model, resolution, bandwidth) TestingAdaptedLandscapemetrics TestingHPC for calculation and subsequentanalysis Prototyping the integration of NetKDE, KDE, MCA, … into SDI

24. Thankyou!