Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana


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ICCSA 2011 Santander, Spain, June

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  • Example: solar panel policies in Switzerland … linked to a particular neighborhood, city, region?
  • Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana

    1. 1. Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana<br />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<br />The International Conference on Computational Science and its Applications – Cities, Technologies and Planning,June 2011, Santander, Spain<br />
    2. 2. Plan<br />Introduction<br />Conceptual background<br />Methodology<br />Ljubljana case study<br />
    3. 3. Plan<br />Introduction<br />Cycling and Urban Planning<br />Challenges and Needs for Optimal Location of Cycling Facilities<br />Conceptual background<br />Methodology<br />Ljubljana case study<br />
    4. 4. Cycling and Urban Planning<br />Cycling?<br />Promoted as one of the most appropriate ways of urban mobility<br />Environmentally friendly, require less space, impacts on health<br />Planning?<br />Importance of cycling facilities provision for cycling development<br />Germany: 12,911km (1976)  31,236km (1996)<br />The Netherlands: 9,282km (1978)  18,948km (1996)<br />Statedpreferencesurveys: Facilitiesdiscontinuities, route attributes<br />Goal?<br />Cyclingfacilities: Right places (O-D), right corridors (Flux)<br />
    5. 5. Optimal Location of CyclingFacilities<br />Opportunities<br />GPS: Portable, lightweight, unobtrusive and low-cost<br />Planners: Insights of current and future behaviors (monitoring)<br />Paststudies<br />Aultman et al. 1997 – Bicycle commuter routes and GIS<br />Dill and Gliebe 2003 – Bicycle and facilities in USA<br />Jensen et al. 2010 – Speed and paths of shared bicycle in Lyon<br />Menghini et al. 2010 – Route choice of cyclists in Zurich<br />Winters et al. 2011 – Motivators and deterrents of bicycling<br />
    6. 6. Optimal Location of CyclingFacilities<br />Challenges and Needs<br />GPS tracking visual presentation: data volume<br />Direct usage of GPS data in the planning practice: lack of methods<br />GVI: free enriched geographic data sources (i.e. OSM)<br />
    7. 7. Plan<br />Introduction<br />Conceptual background<br />Examples of Current GPS Tracking Projects<br />Ljubljana Investigation Background<br />Kerned Density Estimation (KDE)<br />Network Based Kernel Density Estimation (NetKDE)<br />Methodology<br />Ljubljana case study<br />
    8. 8. Examples of Current GPS TrackingProjects<br />San Francisco (USA) – Smart phones<br />Weeklyprizedraw<br />“Developing”facilitiesinstead of “building”them<br />Copenhagen (Denmark) – Web-based GIS portal<br />3,000 trips mappedby citizen VISUM model<br />COWI A/S GPS tracking: before / afterfacilitiesimprovements<br />Barcelona (Spain) – Qualitative / Quantitative<br />Bici_Nprojectrent-a-cycles video/audio<br />Data transfert from station to central DB for furtheranalysis<br />
    9. 9. Ljubljana Investigation Background<br />Statedpreferences (2008)<br />Web-based portal Geae+<br />Cyclist description, trip information<br />Digitalization of trip <br />GPS track transfert fromenableddevice<br />Low-Tech: Paperover mapdrawing<br />Revealedpreferences(2010)<br />GPS trackingdevice<br />User friendly, low-cost, accurate<br />Data transfert by technicians<br />Broader investigation<br />
    10. 10. KDE vs. NetKDE<br />Kernel Density Estimator (KDE*)<br />Operates in euclidean space<br />Weights events by their radial distances from grid centroid<br />Network Based Kernel Density Estimator (NetKDE*)<br />Operates in a network constrained space<br />Weights events by the distance from grid centroid measured along this network<br />* Density estimation function + Epanechnikow kernel function <br />NetKDE and KDE (2009-2011) by TimothéeProduit, Nicolas Lachance-Bernard, Loic Gasser, Dr. StephaneJoost, Prof. Sergio Porta, EmanueleStrano<br />
    11. 11. KDE vs. NetKDE<br />KDE<br />NetKDE<br />
    12. 12. KDE vs. NetKDE<br />KDE<br />NetKDE<br />
    13. 13. Plan<br />Introduction<br />Conceptual background<br />Methodology<br />GPS Tracking<br />Network and Grids<br />Low Resolution KDE, High Resolution NetKDE<br />Ljubljana case study<br />
    14. 14. GPS Tracking<br />Device<br />Sport tracker QSTARZ BT-Q1300s<br />62 x 38 x 7 mm, 10m accuracy<br />One button (On/Off), mini USB port<br />KML, GPX, CVS<br />Tracking: 5 seconds, 15h autonomy<br />Data <br />CSV  SHP (WGS84)  Merge  Projection (UTM33N) [Manifold]<br />
    15. 15. Network and Grids<br />Open Street Map Network<br />Source: Cloudmadewebsite<br />SHP (WGS84)  10km GPS Buffer  Projection (UTM33N)  Places digitalization  Highwaydeleted[Manifold]<br />Topology (0.5m connecting/merging) + attributescleaning[ESRI ArcGIS model builder]<br />Grids<br />100m: Lowresolution multi-bandwidths KDE<br />20m: High resolutionspecific-bandwidthsNetKDE[IDRISI]<br />
    16. 16. Plan<br />Introduction<br />Conceptual background<br />Methodology<br />Ljubljana case study<br />Resources, Data and Calculations<br />Low Resolution Grid KDE Results<br />High Resolution Grid NetKDE Results<br />Discussion<br />
    17. 17. Resources<br />Software / Hardware<br />Postgres/PostGIS/Python/QuantumGIS<br />Windows XP 64<br />Intel® Core™2 Quad CPU Q950 @ 3.GHz 7.83GB of RAM<br />
    18. 18. Data and Calculations<br />Lowresolution KDE 100m  425km213,630 segments, 42,342 gridpoints, 442,260 GPS points<br />KDE bandwidths [200m, 2500m] 24 X 100m steps(2-3h) <br />High NetKDE/KDE 20m  20km28,114 segments, 314,250 gridpoints, 423,748 GPS points<br />NetKDEbandwidths 60m (17h), 100m (19h), 200m (24h), 400m (27h)<br />KDE bandwidths [40m, 100m] 7 X 10m steps [200m, 1000m] 9 X 100m steps (total 18h)<br />
    19. 19. KDE results100m grid<br />Bandwidths:<br /> A)300m B)500m C)1000m<br /> D)2000m<br />*Decilesdistribution<br />
    20. 20. KDE results20m grid<br />Bandwidths:<br /> A)60m B)100m C)200m<br /> D)400m<br />*Decilesdistribution<br />
    21. 21. NetKDEresults20m grid<br />Bandwidths:<br /> A)60m B)100m C)200m<br /> D)400m<br />*Decilesdistribution<br />
    22. 22. Discussion<br />NetKDE 20m (Visual analytics)<br />3:1 ratio - Shows flux corridors (a)<br />5:1 ratio - Smooths corridors only (b)<br />10:1 ratio - Highlights axis and intersections (c)<br />20:1 ratio - Shows cyclist’s main area presence and main axis<br />
    23. 23. Discussion<br />Researchunderrapidevolution…<br />3rd algorithm: Calculationoptimization 90-95% (10h network-indexing, 5 min. for eachsteps)<br />Currentwork on Barcelona, Ljubljana, Geneva, Glasgow, Baghdad<br />Professional uses: Architects, Planners, Criminologs, Biologists<br />Actualprojects…<br />Spatio-temporal and statisticalanalysis<br />Fuzzy-mapcomparison (time, model, resolution, bandwidth)<br />TestingAdaptedLandscapemetrics<br />TestingHPC for calculation and subsequentanalysis<br />Prototyping the integration of NetKDE, KDE, MCA, … into SDI<br />
    24. 24. Thankyou!<br />