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

This paper presents a methodology that use volunteered geographic information (VGI), cyclist GPS tracking and Open Street Map network, with network based kernel density estimation. It investigates optimal location for cycle paths and lanes development. Recently completed research provides cycling data for Ljubljana, Slovenia. It was conducted over two years and was commissioned by the Municipality of Ljubljana. The methodology combines and adapts these VGI data and is mainly based on open source software. It handles large datasets with multiscale perspectives. This methodology should help planners to find and to develop suitable facility locations corresponding to current user behaviors.

[1]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[2]  J. Sallis,et al.  Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures , 2003, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[3]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[4]  M. Brauer,et al.  Built Environment Influences on Healthy Transportation Choices: Bicycling versus Driving , 2010, Journal of urban health.

[5]  Jennifer Dill,et al.  Understanding and Measuring Bicycling Behavior: a Focus on Travel Time and Route Choice , 2008 .

[6]  Atsuyuki Okabe,et al.  A kernel density estimation method for networks, its computational method and a GIS‐based tool , 2009, Int. J. Geogr. Inf. Sci..

[7]  Frank O. Ostermann,et al.  Digital representation of park use and visual analysis of visitor activities , 2010, Comput. Environ. Urban Syst..

[8]  N. Shoval The GPS Revolution in Spatial Research , 2008 .

[9]  Kay W. Axhausen,et al.  Route choice of cyclists in Zurich , 2010 .

[10]  Ralph Buehler,et al.  Making Cycling Irresistible: Lessons from The Netherlands, Denmark and Germany , 2008 .

[11]  V. Latora,et al.  Street Centrality and Densities of Retail and Services in Bologna, Italy , 2009 .

[12]  Chris Brunsdon,et al.  Estimating probability surfaces for geographical point data: an adaptive kernel algorithm , 1995 .

[13]  M. Maguire The Crime Reduction Programme in England and Wales , 2004 .

[14]  Fahui Wang Quantitative methods and applications in GIS , 2006 .

[15]  L. Dijkstra,et al.  Promoting safe walking and cycling to improve public health: lessons from The Netherlands and Germany. , 2003, American journal of public health.

[16]  Produit Timothée,et al.  A network based kernel density estimator applied to barcelona economic activities , 2010, ICCSA 2010.

[17]  John E Abraham,et al.  Influences on bicycle use , 2007 .

[18]  Lawrence D. Frank,et al.  Active transportation and physical activity: opportunities for collaboration on transportation and public health research , 2004 .

[19]  Jennifer Dill,et al.  Bicycle Commuting and Facilities in Major U.S. Cities: If You Build Them, Commuters Will Use Them , 2003 .

[20]  Jacek Malczewski,et al.  GIS‐based multicriteria decision analysis: a survey of the literature , 2006, Int. J. Geogr. Inf. Sci..

[21]  V. E. Daniel,et al.  Determinants of bicycle use: do municipal policies matter? , 2004 .

[22]  K. Teschke,et al.  Motivators and deterrents of bicycling: comparing influences on decisions to ride , 2011 .

[23]  Arthur Christian Nelson,et al.  If You Build Them, Commuters Will Use Them: Association Between Bicycle Facilities and Bicycle Commuting , 1997 .

[24]  David J. Unwin,et al.  Defining and Delineating the Central Areas of Towns for Statistical Monitoring Using Continuous Surface Representations , 2000, Trans. GIS.

[25]  Chandra R. Bhat,et al.  An analysis of bicycle route choice preferences in Texas, US , 2009 .

[26]  D. Levinson,et al.  TRAILS, LANES, OR TRAFFIC: VALUING BICYCLE FACILITIES WITH AN ADAPTIVE STATED PREFERENCE SURVEY , 2007 .

[27]  G. Rose,et al.  Promoting transportation cycling for women: the role of bicycle infrastructure. , 2008, Preventive medicine.

[28]  Lisa Aultman-Hall,et al.  Analysis of Bicycle Commuter Routes Using Geographic Information Systems: Implications for Bicycle Planning , 1997 .

[29]  S. C. Van der Spek,et al.  Urbanism on Track: Application of tracking technologies in urbanism , 2008 .

[30]  Peter R. Stopher,et al.  The Travel Survey Toolkit: Where to From Here? , 2009 .

[31]  Céline Robardet,et al.  Characterizing the speed and paths of shared bicycles in Lyon , 2010, ArXiv.

[32]  P. Diggle,et al.  Spatial point pattern analysis and its application in geographical epidemiology , 1996 .

[33]  Kevin J. Krizek,et al.  What is at the end of the road? Understanding discontinuities of on-street bicycle lanes in urban settings , 2005 .

[34]  Giuseppe Borruso,et al.  Network Density and the Delimitation of Urban Areas , 2003, Trans. GIS.

[35]  Michael Batty,et al.  Network Geography: Relations, Interactions, Scaling and Spatial Processes in GIS , 2003 .

[36]  Giuseppe Borruso,et al.  Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space , 2008, Trans. GIS.

[37]  David Taniar,et al.  Computational Science and Its Applications - ICCSA 2010, International Conference, Fukuoka, Japan, March 23-26, 2010, Proceedings, Part I , 2010, ICCSA.