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

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

Plan
Introduction
Conceptual background
Methodology
Ljubljana case study

Plan
Introduction
Cycling and Urban Planning
Challenges and Needs for Optimal Location of Cycling Facilities
Methodology

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)

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

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)

Plan
Introduction
Examples of Current GPS Tracking Projects
Ljubljana Investigation Background
Kerned Density Estimation (KDE)
Network Based Kernel Density Estimation (NetKDE)
Methodology

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

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

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

KDE vs. NetKDE
KDE
NetKDE

Plan
Introduction
Methodology
GPS Tracking
Network and Grids
Low Resolution KDE, High Resolution NetKDE

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]

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]

Plan
Introduction
Methodology
Resources, Data and Calculations
Low Resolution Grid KDE Results
High Resolution Grid NetKDE Results
Discussion

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

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)

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

KDE results20m grid
Bandwidths:
A)60m B)100m C)200m
D)400m

NetKDEresults20m grid
Bandwidths:
A)60m B)100m C)200m
D)400m

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

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

Thankyou!

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

by Nicolas Lachance-Bernard on Jun 27, 2011

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