network based kernel density estimation for cycling facilities optimal location applied to ljubljana

NLB / 28.06.11 / p.1NetKDE 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, Biba Tominc2, Matej Nikšič2, Barbara Goličnik2

1 Geographic Information Systems Laboratory, Ecole polytechnique fédérale de Lausanne

2 Urban 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)

– Stated preference surveys: Facilities discontinuities, route attributes

• Goal?

– Cycling facilities: Right places (O-D), right corridors (Flux)


Optimal Location of Cycling Facilities

• Opportunities

– GPS: Portable, lightweight, unobtrusive and low-cost

– Planners: Insights of current and future behaviors (monitoring)

• Past studies

– 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 Cycling Facilities

• 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 Tracking Projects

• San Francisco (USA) – Smart phones

– Weekly prize draw

– “Developing” facilities instead of “building” them

• Copenhagen (Denmark) – Web-based GIS portal

– 3,000 trips mapped by citizen VISUM model

– COWI A/S GPS tracking: before / after facilities improvements

• Barcelona (Spain) – Qualitative / Quantitative

– Bici_N project rent-a-cycles video/audio

– Data transfert from station to central DB for further analysis


Ljubljana Investigation Background

• Stated preferences (2008)

– Web-based portal Geae+

• Cyclist description, trip information

• Digitalization of trip

• GPS track transfert from enabled device

– Low-Tech: Paper over map drawing

• Revealed preferences (2010)

– GPS tracking device

• 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 Timothée Produit, Nicolas Lachance-Bernard, Loic Gasser, Dr. Stephane Joost, Prof. Sergio Porta, Emanuele Strano


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: Cloudmade website

– SHP (WGS84) 10km GPS Buffer Projection (UTM33N) Places digitalization Highway deleted[Manifold]

– Topology (0.5m connecting/merging) + attributes cleaning[ESRI ArcGIS model builder]

• Grids

– 100m: Low resolution multi-bandwidths KDE

– 20m: High resolution specific-bandwidths NetKDE[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

• Low resolution KDE 100m 425km2

13,630 segments, 42,342 gridpoints, 442,260 GPS pointsKDE bandwidths

[200m, 2500m] 24 X 100m steps (2-3h)

• High NetKDE/KDE 20m 20km2

8,114 segments, 314,250 gridpoints, 423,748 GPS pointsNetKDE bandwidths

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 results

100m grid

Bandwidths:

A)300m

B)500m

C)1000m

D)2000m

*Deciles distribution


KDE results

20m grid

Bandwidths:

A)60m

B)100m

C)200m

D)400m



NetKDE

results

20m 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 - Smoothscorridors only (b)

– 10:1 ratio - Highlights axis and intersections (c)

– 20:1 ratio - Shows cyclist’smain area presence and main axis


Discussion

• Research under rapid evolution…

– 3rd algorithm: Calculation optimization 90-95% (10h network-indexing, 5 min. for each steps)

– Current work on Barcelona, Ljubljana, Geneva, Glasgow, Baghdad

– Professional uses: Architects, Planners, Criminologs, Biologists

• Actual projects…

– Spatio-temporal and statistical analysis

– Fuzzy-map comparison (time, model, resolution, bandwidth)

– Testing Adapted Landscape metrics

– Testing HPC for calculation and subsequent analysis

– Prototyping the integration of NetKDE, KDE, MCA, … into SDI


Thank you!

network based kernel density estimation for cycling facilities optimal location applied to ljubljana

Technology

cycling developmentgermany

accuratedata transfert

insights of current

planning practice

urban planningchallenges

urban planningcycling

bicycle commuter routes

euclidean spaceweights