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

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1 Network based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana Nicolas Lachance-Bernard 1,Timothée Produit 1, Biba Tominc 2, Matej Nikšič 2, and Barbara Goličnik Marušić 2 1 Laboratory of Geographic Information Systems, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland 2 Urban Planning Institute of the Republic of Slovenia, 1000 Ljubljana, Slovenia Abstract. 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. Keywords: Network based kernel density estimation (NetKDE), Volunteered geographic information (VGI), GPS Tracking, Urban planning, Urban design, Public participation, Bicycle, Cycling. 1 Introduction 1.1 Cycling and Urban Planning In contemporary urban planning paradigms, cycling is promoted as one of the most appropriate ways of urban mobility, from transportation and public health researchers, and planners [27]. Cycling is environmentally friendly, its facilities require less space and the activity itself have positive impacts on health [26]. The post-modern cities are facing traffic congestions, air and noise pollution, consequences of car-oriented traffic planning. Urban mobility based on cycling results in reduced traveling time as well in increased social interactions while cyclists use public space [35]. Previous studies have stated the importance of cycling facilities provision, as bicycle paths and lanes, for the development of this transportation mode around multiple cities and countries [7,10,24,23]. From the mid-1970s, cycling facilities have expended greatly in two of the most cycling B. Murgante et al. (Eds.): ICCSA 2011, Part II, LNCS 6783, pp , c Springer-Verlag Berlin Heidelberg 2011

2 NetKDE for Cycling Facilities Optimal Location Applied to Ljubljana 137 countries. In Germany, the 12,911km of cycle paths and lanes in 1976 expended into 31,236km in In The Netherlands, the cycling network doubled from 9,282km in 1978 to 18,948km in The main result from these investments is a complete and integrated cycling network system that covers most of the cyclist s trips [23]. Researchers have completed exhaustive reviews of factors that have been influencing cycling behavior and route choice [13,25]. Based on stated preference surveys, others have shown the effects of facility discontinuities [15], of route attributes [28] and of top motivators for commuting by bicycle [38]. Although the advantages of cycling seem obvious, cycling needs encouragement in order to take place in urban environments - both in terms of promotion of cycling as a life style as well as in terms of providing appropriate physical conditions for cycling. If the cycling facilities are provided at the right places and along the right corridors [18,39], and designed in an appropriate manner [34], people will more likely decide to use them on daily bases. So far, urban and transportation planners have developed some tools and methods in order to locate the cycling facilities: by linking major origins and destinations using shortest distances, by providing alternative paths by their character (isolated from motorized traffic via greenways, along main streets where urban functions are concentrated), etc. Such approaches were good enough for societies where lifestyles and consequent use patterns were homogeneous and predictable. However, with the individualization of lifestyles, they became insufficiency, at least if the main goal of planning cycling facilities is to fulfil the actual/future needs of people. 1.2 Challenges and Needs for Optimal Location of Cycling Facilities The rise of portable, lightweight, unobtrusive and low-cost GPS tracking devices [32] made gathering real data on actual cycling trips possible on a daily bases and at a wider scale. In order to propose the relevant interventions for cycling facilities, planners and stakeholders needs to have insights of current and future cyclist s behaviors. The current main question is where people are already cycling. Until today, few studies have published papers on the usage of GPS and geographic information systems (GIS). In the 1990s, only one revealed preference survey geo-coded commute trips made by cycling [2]. Dill and Gliebe have investigated cyclist s choices in function of the type of cycling facilities [8]. A paper from Winter et al. is currently under revision and is looking to determine how urban form affects mode choice [39]. Jensen et al. have tracked flows of cyclist using Lyon s shared bicycle system database [14]. The first route choice model for cyclists was proposed by Menghini et al. [17]. Based on the recent availability of on-going and long-duration GPS observations, this study explores the flux of cyclists in the city. They concluded the importance of direct and marked route, and that policy, which aims to increase the amount and length of cycling, will have to provide direct, preferably marked, paths between origins and destinations of the travelers. Accordingly, the advantages of the new GPS tracking technologies shall be tested and new visual analytic and decision making approaches shall be implemented into a regular planning practice. The implementation of

3 138 N. Lachance-Bernard et al. such usages of GPS tracking devices also means a more active participation of citizens into the urban planning process. Large datasets, from GPS tracking experiments, show some presentation difficulties because of their volume. New approaches need to be developed for relatively direct usage of GPS data in the planning practice. In parallel, new and impressive development of free enriched geographic data sources (e.g. Open Street Map) give opportunities for network based analysis. The innovative analysis approaches should use these very large datasets with few aggregations and modifications and be available within reasonable laps of time using normal desktop computer. This paper proposes an innovative usage of these GPS tracks coupled with network data freely available. Mainly based on open source database (PostgreSQL/PostGIS) and open source GIS software (Quantum GIS), the methodology is implemented in Python. For the proofing of the prototype and because of lack of time, diverse GIS software (ArcGIS, Manifold and IDRISI) are used: to create multi-resolution grids, to prepare the GPS tracking data and to clean the topology of the street network. The proposed methodology is divided in three steps. The first step gathers the data with the GPS tracking devices and from the Open Street Map (OSM) network database. The second and the third steps use spatial smoothing techniques to process the raw data into information understandable by non-mathematicians and non-gis users [31]. The first smoothing technique used is the Kernel Density Estimation (KDE). The goal of this KDE is to produce a global multiscale view (low resolution investigation) of the phenomenon within short computation time. On the other hand, the patterns of points resulting from cycling are constrained by the network. In response to the network constrained nature of the cyclist s mobility, a second and more precise smoothing technique used: the Network based Kernel Density Estimation (NetKDE). This technique have been developed and used by Produit et al. [22] to study the spatial distribution of Barcelona s economic activities. This second technique proves to give an extended vision for high resolution investigations of phenomenon in urban context. Results from KDE and NetKDE could be adapted to produce continuous surfaces as suggested by [33]. These continuous surfaces of densities could be later used for decision making based on multi-criteria analysis [16]. Planners could assess using MCA the optimal location for development of bicycle lanes by coupling criteria made from the density surfaces to functional criteria (connecting relevant destination, topography, level of service), social criteria (user age, gender, social status, purpose of the journey), technical criteria (urban morphology, climate condition, soil characteristics, urban design) and juridical criteria (land ownership). This paper is divided in three parts. Firstly, a general conceptual background about cyclist tracking, KDE and NetKDE are presented. Secondly, the methodology to use GPS tracking with KDE and NetKDE is explained. Finally, interesting results from the Ljubljana case study proof of concept are discussed.

4 NetKDE for Cycling Facilities Optimal Location Applied to Ljubljana Conceptual Background 2.1 Examples of Current GPS Tracking Projects GPS tracking of urban cyclists is used in various cities. San Francisco (USA), Copenhagen (Denmark) and Barcelona (Spain) are relevant examples that could be highlighted. In 2009, the San Francisco County Transportation Authority (SFCTA) started to collect urban cycling data trough smart phones 1.Toencourage the citizens participation, SFCTA launched a weekly prize draw (50$) for those who entered a cycle track via CycleTracks mobile application. Based on these data, the city planners will choose where to develop facilities instead of building them where street are flat, where there is room or where they thought bicycle lanes should be, said Billy Charlton, Deputy Director of Technology Services at the SFCTA 2. Goličnik et al. interviewed Troels Andersen from Cycling Embassy of Denmark on how GPS is used in the process of designing the cycle facilities in their country, one of the most advanced cycling environments [12]. In the City of Odense, citizens mapped 3,000 cycling trips via a web-based GIS portal 3.The data later were used to construct cycling traffic model in VISUM, an multimodal analysis software integrating all modes of transportation. In Copenhagen and its surroundings, COWI A/S Danish branch is currently tracking cyclists with GPS devices and is collecting data about the conditions before and after improvements of facilities. In Århus, pupils are using GPS devices as part of a competition where they are supposed to cycle all together the distance of around the world in 80 days. In Barcelona, the Bici N project is collecting qualitative and quantitative data about cyclists 4. Some bicycles of the rent-a-cycles are equipped by a video camera (with audio) and a GPS tracking device, collecting data about cyclist paths and habits. The data then are transmitted from the station to a central database and prepared for further analysis. 2.2 Ljubljana Investigation Background In order to identify current Ljubljana s cyclist behavior, several approaches were implemented. The main objective of this work was to identify an efficient method based on a reliable data collection platform/process. At the time of the beginning of the project in 2008, it was impossible to provide cyclists with accurate enabled GPS tracking mobile phones or with available GPS devices on the Slovenian market at reasonable price. Resulting from this context, two main approaches have been tested for gathering data on cyclists and their habits. The first could 1 (accessed Feb ). 2 (accessed Feb ). 3 (accessed 21. Feb. 2011). 4 (accessed Feb ).

5 140 N. Lachance-Bernard et al. be classified as an approach based on stated preferences and the second as an approach based on revealed preferences [17,29]. The first approach gathers cycling data using a web-based GIS portal 5, Geae+, to collect daily routes. The web-based GIS portal offers either 3D or 2D virtual environments in which cyclists digitalize their own cycle-tracks on the map (Fig 1). The web-based GIS portal records cyclist description (e.g. age, social status, etc.) and trip information (e.g. purpose). Later on, the web-based GIS portal was updated to give cyclists the functionality for transferring GPS tracks directly from GPS devices. To be able to reach less computer and GPS skilled cyclists an alternative approach was offered. This is a low tech approach to share cyclist daily routes. Using transparent paper lying over a city map, cyclists were drawing their daily routes. These drawings were later geo-localized into a GIS database by the researchers. Fig. 1. Web-based GIS portal and Geae+ interface (a: Drawing; b: 3D view; c: 2D View) Later on, in 2010, a second approach was developed and integrated to the web-based GIS portal. Indeed at this time appropriate GPS tracking device for gathering empirical data were available in Slovenia. The selected GPS tracking device was user-friendly, low-cost and accurate enough, to start a broader investigation. This paper is based on these data. 2.3 Kernel Density Estimation KDE is a statistical process for spatial smoothing and interpolation [37]. It have been used recently in urban studies for crime spatial analysis [1], park visitor activity analysis [20], urban area delimitation [4] and economic activities spatial distribution [21]. Borruso [4] showed that KDE could better represent spatial phenomenon. For the methodology presented in this paper, KDE plays a central role to have a multiscale low resolution view of the phenomenon. KDE use Euclidean space and is based on two parameters: the bandwidth and the weighting function (Fig 2). This second parameters is less critical [11]. Inspired from the work of [6], to reduce stakeholder bias, it is suggested to always use 5 (accessed Feb ).

6 NetKDE for Cycling Facilities Optimal Location Applied to Ljubljana 141 multiple bandwidths to create concurrent views of the phenomenon. With x j being a location vector and x 1...x n the location vectors of the n events, the intensity estimation f(x j )inx j is: f KDE,h (x j )= 1 h 2 K( d ij h ) (1) i=1 d ij = x i x j is the Euclidean distance between the grid point x j and the event n i, h being the bandwidth. Actually, several kernel functions are implemented in different GIS. The quadratic or Epanechnikov kernel function have been implemented: 1 K(t i )={ 3π (1 t2 i )2 if t 2 i < 1 (2) 0otherwise with t i = d ij /h. The value at each point of the grid j at a distance d ij of the event n i is obtained from the sum of the individual kernel functions (K(x i )) of the points belonging the bandwidth h. n Fig. 2. KDE and NetKDE Kernel functions 2.4 Network Based Kernel Density Estimation Batty noted that current GIS methods prevent Euclidean space from being distorted by road network constraints [3]. Answering that need, some researchers have developed network constrained approach. Borruso suggested a network constrained density indicator, called Network Density Estimation (NDE) [5]. He concluded that there is no strong difference between KDE and NDE; however, NDE seems more proficient to highlight linear clusters. For cycling facilities development, our approach is to find these clusters. In counterpart, NDE approach doesn t use a distance weighting function as KDE. SANET, another approach, was developed by Okabe with two unbiased Kernel functions that are calculating density values attributed to edges [19]. This indicator has no unit and it refers to linear density index rather than spatial density index. For NetKDE, the points of the phenomenon (being activities, GPS tracking points, etc.) are projected onto the network edges. Instead of using Euclidean bandwidth, NetKDE uses bandwidth measured along the network to produce the continuous surface of density (Fig 2). Dijkstra s shortest path tree (SPT) algorithm selects all accessible network edges from the analysis surface grid points

7 142 N. Lachance-Bernard et al. [9]. These grid points have been previously projected on the network edges. This creates a non-uniform space of analysis for each grid points, compared to circle space with KDE. The most interesting bandwidths are chosen from the fast multiscale KDE approach results to create high resolution information of the phenomenon spatial distribution. For comparison purpose of the results, NetKDE should use the same and adapted KDE function. Thus, the NetKDE of points is calculated by using: K net (t net,i )={ 1 3π (1 t2 net,i )2 if t 2 net,i < 1 0otherwise (3) with t net,i = d net,ij /h and d net,ij is the distance between the grid point x j and the event n i measured along the network. Then, the NetKDE value in grid point x j is: f NetKDE,h (x j )= 1 h 2 K net ( d net,ij h ) (4) i=1 n is the number of events on the SPT for the bandwidth h. n 3 Methodology 3.1 GPS Tracking and Data Preparation Empirical data on urban cycling was collected by GPS tracking device. This process is realized with the GPS sport tracker device QSTARZ, modelbt- Q1300s (Fig 3a) which measures only 62 x 38 x 7 millimeters. It is powered by high durability battery and use highly sensitive 66-channels GPS receiver with 10 meters accuracy. Only one button (On/Off) and warning lights are available to the user. Mini USB port gives access to functions of the device via computer and is also used to recharge the device. Enclosed computer software enables GPS device setting (type of movement, recorded time or distance intervals, etc.), live data reviewing and data transfer in different formats (KML, GPX, CVS, etc.). For cycling, interval of five seconds is used after empirical pilot tests. For these selected settings, the battery keeps on tracking over 15 hours. Researchers monitor and review the collected data using an interactive map based on Google Maps API (Fig 3b). Specific queries are configured to search within cyclist attributes (gender, age group, social status) and trip attributes (purpose of the journey). The collected data is transferred using comma-separated values (CSV) files. Each cyclist tracking produce one CSV file that keeps all tracked trip points and linked information. Then, using Manifold GIS 6, the CSV files are merged in one ESRI SHP file (Fig 3c). The geographic coordinate system and projection (CS&P) of the SHP file is modified from Lat/Long World Geodetic System 1984 (WGS84), used by the tracking device to Universal Transverse Mercator zone 33 north (UTM33N), corresponding to Slovenia. 6 Manifold release bit mode.

8 NetKDE for Cycling Facilities Optimal Location Applied to Ljubljana 143 Fig. 3. Tracking and visualization (a: QSTARZ device; b: Google Maps interactive visualization; c: GPS data in Manifold) 3.2 Open Street Map Network Preparation The OSM network of Ljubljana was downloaded from the website Cloudmade. com 7. Using Manifold, the street network is extracted for an area covering a buffer of 10km around the GPS tracking data. This street network is composed of 13,630 segments. The extracted network CS&P is projected from WGS84 to UTM33N. Experiences from other empirical investigations for Swiss cities using OSM network have shown the importance of updating the connectivity of public open place of the network (e.g. Geneva and Winterthur). For Ljubljana case study, only one place needed an update. This is a small but important step of the methodology, since places in OSM are most of the time digitalized as polygon (area), not as network segments. Since the study is about cycling in the urban area, all the network segments corresponding to highway are deleted using segment attributes. Finally, a semi-automated ESRI ArcGIS 8 application (model builder) ensures that the network segments attributes fit the needs for NetKDE processing. First, it connects the segments together according a 0.5m distance tolerance. Then, it simplifies the segment in order to delete useless vertices with a 0.5m tolerance. 3.3 Multi-resolution Grids Preparation Five grids of different resolutions were prepared using IDRISI 9 in UTM33N. Two low resolution grids (200m and 100m) have been created for the KDE approach. These coarse grids are needed for fast resolution of multiscale bandwidths KDE. They provide global results of the phenomenon distribution and ease the bandwidth specification for NetKDE. For the NetKDE, three high resolution grids (50m, 20m and 10m) have been created. 7 Slovenia.shapefiles.zip (8.8Megs) accessed & downloaded Dec ESRI ArcGIS release IDRISI release Taiga.

9 144 N. Lachance-Bernard et al. 3.4 Low Resolution Visual Overview with KDE This step of the methodology produces first overviews of the phenomenon density distribution. Using 200m grid, multiple KDE bandwidths is calculated to assess the computation time needed for the 100m grid KDE (150,500 grid points). From this computer work load first insight, a buffer of 3,000m around the GPS tracking points is used to select grid points for the 100m grid KDE. This buffer selects 42,342 grid points to compute KDE of 442,260 GPS tracking points. The buffer also extracts 9,574 network segments for later use with NetKDE. The multiple bandwidths varied between 200m and 2,500m with 100m steps (24 iterations). The calculations for 100m grid take approximately 2-3 hours. 3.5 High Resolution Visual Analysis Based on NetKDE The goal of the high resolution NetKDE is to produce a detailed view of the density distribution for later uses in decision making (e.g. visual analysis and MCA). The NetKDE uses the high resolution 20m grid (3,762,500 grid points). Other grid resolutions are put aside for three main arguments: previous empirical experiences [22], GPS tracking device precision and small work load needed during low resolution grid KDE. To reduce the computing time, grid points are extracted for the city center combine to 200m buffer selection surrounding GPS tracked points. This selection extracts 8,114 segments (84.8 percent of the low resolution network segments), 423,748 GPS tracking points (95.8 percent of all tracked points) and 314,250 grid points (8.4 percent of the low resolution area). From the previous low resolution grid KDE insights, specific bandwidths are chosen: 60m, 100m, 200m and 400m. NetKDE computations take respectively around 17 hours (60m), 19 hours (100m), 24 hours (200m) and 27 hours (400m). KDE have also been computed for the 20m grid using bandwidths from 40m to 100m with 10m steps, and from 100m to 1,000m with 100m steps (16 iterations). This KDE calculation has taken approximately 18 hours. 3.6 KDE and NetKDE Software Resources KDE and NetKDE use the same software. The GPS tracking SHP file, the OSM network SHP file and multiple resolution grid SHP files are imported to PostgreSQL/PostGIS relational database management system (RDMS) using Shape2pgsql application. Spatial objects recovery and other KDE/NetKDE calculations use Python scripts coupled with Egenix MX base and Psycopg2 API 10. Visualization of the results are realized with Quantum GIS 11 directly connected to the PostgreSQL/PostGIS database server using: transparency, grid point symbols adapted to viewing scale, personalized color-blend for decile normalization and SQL queries to exclude grid points that with null or zero density value. All the calculations have been completed using Intel(R) Core(TM)2 Quad CPU, 10 PostgreSQL release / PostGIS release / Shape2pgsql release / Python release 2.1 / Egenix MX base release / Psycopg2 API release Quantum GIS release Copiapo.

10 NetKDE for Cycling Facilities Optimal Location Applied to Ljubljana 145 Fig. 4. KDE results 100m grid (Bandwidths: a-300m; b-500m; c-1000m; d-2000m) 3.00GHz 7.83GB of RAM on Microsoft Windows XP Prof. x64 Service Pack 2. 4 Ljubljana Case Study For this paper, we applied the methodology on the city of Ljubljana. The study covers rectangular area of approximately 425km2 for the low resolution KDE and 20km2 for the high resolution NetKDE. The first step acquires and prepares the GPS tracking data, the OSM network and the multi-resolution grids. The second applies the KDE approach on the GPS tracking data using low resolution grids. The third step applies the NetKDE approach on GPS tracking and OSM network data of the city center, using high resolution grid. In order to compare KDE and NetKDE results, KDE was applied on the same city center data. All result use the same color blend for the deciles. The next sections present the most interesting results from both approaches and compare NetKDE to KDE results for the high resolution approach. 4.1 Low Resolution Grid KDE Results The Figure 4 presents the low resolution grid KDE results using four different bandwidths: 300m, 500m, 1,000m and 2,000m. For this particular case, some

11 146 N. Lachance-Bernard et al. hypotheses have been suggested and investigated later using NetKDE and KDE at high resolution. Hyp.1) A ratio of bandwidth to grid resolution around 3:1 produce information on corridors (linear clusters). Hyp.2) A ratio around 5:1 produces insights about highly concentrated corridors and keeps low decile class stable. Hyp.3) A ratio equal or greater than 10:1 produces insights about global axes and global concentration-dispersion of the phenomenon. 4.2 High Resolution Grid NetKDE Results The Figure 5 presents high resolution grid NetKDE results using four different bandwidths: 60m, 100m, 200m and 400m. NetKDE produces a visual information with a higher degree of precision and information. It is also possible to visually confirm (qualitatively) some of the previously suggested hypotheses. The first hypothesis is confirmed using 60m bandwidth (3:1 ratio). The visual results with this bandwidth show corridors, particularly in the old city center where the street network is really dense. The second hypothesis is refuted using 100m bandwidth (5:1 ratio). Using 100m bandwidth does not change visually the results compared to 60m bandwidth except that there s probably a better smoothing of the corridors. With this bandwidth, the old city center seems to become a unique zone with high density of GPS points, reflecting the high connectivity of the area (greater SPT). The third hypothesis is confirmed using both 200m (10:1 ratio) and 400m (20:1 ratio) bandwidths. The 200m bandwidth increases the visibility of major axis along the network. Also, major intersections Fig. 5. NetKDE results 20m grid (Bandwidths: a-60m; b-100m; c-200m; d-400m)

12 NetKDE for Cycling Facilities Optimal Location Applied to Ljubljana 147 Fig. 6. KDE results 20m grid (Bandwidths: a-60m; b-100m; c-200m; d-400m) used in multiple directions appear. Using 400m bandwidth produces local and dispersed densities along the network, and a large central uniform zone. Finally, the border limits of the phenomenon high density within the city center appears with this last bandwidth. Comparing Figure 5 results to Figure 6 enlights the enriched information produced with NetKDE compared to KDE. The Figure 6showsmoresmoothed density, but for each bandwidth size some information are missing. At 60m bandwidth, the density calculated with KDE miss the detailed view for the core of the city center. At 100m bandwidth, the density calculated with KDE merges some of the most important corridors visible with the NetKDE. At 200m bandwidth, the high density of points located in the core of the city center make all surrounding corridors changing decile class. Also the KDE is missing to reveal information about major intersections. At 400m bandwidth, the complexity of the dispersion of the phenomenon is completely hidden with KDE. Only five major axes are shown instead of more complex distribution of density. 5 Discussion In the last decade the application of tracking technologies has developed substantially in transportation science and social science, yet in the scientific field of urbanism and spatial planning has failed to make a significant step [36]. As Shoval has noted: Advanced tracking technologies could do much to facilitate

13 148 N. Lachance-Bernard et al. and indeed improve empirical research in the field of urban studies [30]. This paper have presented a methodology for smoothing large network constrained datasets collected by GPS devices and/or web-based GIS portals for urban analyses. Travel speed, safety and comfort are the most important factors for choosing bicycle for urban mobility. Planners should therefore undertake endeavors to make bicycle paths and lanes network easily accessible and to take into account optimal distances between activities and services offered along it. The bicycle network density is therefore an important factor. From this point of view, NetKDE analysis represents a promising tool. Firstly, it smoothes the collected data to a more understandable information for planners. Secondly, it offers opportunity to confront relevant questions such as which places are more likely to be used by certain cyclist-user-groups, which places are more or less often visited by cyclists, etc. Layers of different information related (e.g. purpose of cycling, age group of cyclists and land use analysis) can support urban planners and designers in decision making for the development of user friendly cities. Represented approaches for gathering empirical data on urban cycling and further analysis can support urban planning process when designing the comprehensive cycling strategy on a general city level as well on a detailed site-related level. Acknowledgments. The research about cyclist s behavior presented in this paper was financed by the Municipality of Ljubljana. This research was conducted by dr. Barbara Goličnik Marušić and supported by Biba Tominc, dr. Matej Nikšič, mag. Luka Mladenovič and Igor Bizjak. The authors are grateful to the COST Action TU0602 Land Management for Urban Dynamics for the shortterm scientific mission grant given for further development of KDE/NetKDE applications and methodology at University of Strathclyde in The authors recognize researchers who have participated to the elaboration/discussion of the KDE/NetKDE methodology: at Ecole polytechnique fédérale de Lausanne (CH) -prof. François Golay, dr. Stéphane Joost and Mélina Wist; at University of Strathclyde, Glasgow (UK) - prof. Sergio Porta and Emanuele Strano; at University of Rome La Sapienza (IT) - Lorenzo Quaglietta. References 1. Anselin, L., Cohen, J., Cook, D., Gorr, W., Tita, G.: Spatial analyses of crime. Criminal Justice 4, (2000) 2. Aultman-Hall, L., Hall, F.L., Baetz, B.B.: Analysis of bicycle commuter routes using geographic information systems: implications for bicycle planning. Transportation Research Record 1578, (1997) 3. Batty, M.: Network Geography: Relations, Interactions, Scaling and Spatial Processes in GIS. Re-presenting GIS, (2005) 4. Borruso, G.: Network Density and the Delimitation of Urban Areas. Transactions in GIS 7, (2003) 5. Borruso, G.: Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space. Transactions in GIS 12, (2008)

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15 150 N. Lachance-Bernard et al. 24. Pucher, J., Dijkstra, P.: Promoting safe walking and cycling to improve public health: lessons from the Netherlands and Germany. American Journal of Public Health 93(9), (2003) 25. Rietveld, P., Daniel, V.: Determinants of bicycle use: do municipal policies matter? Transportation Research Part A-Policy and Practice 38(7), (2004) 26. Saelens, B.E., Sallis, J.F., Lawrence, D.F.: Environmental Correlates of Walking and Cycling: Findings From the Transportation, Urban Design, and Planning Literatures. Annales of Behavioural Medicine 25(2), (2003) 27. Sallis, J.F., Frank, L.D., Saelens, B.E., Krafts, K.E.: Active transportation and physical activity: opportunities for collaboration on transportation and public health research. Transportation Research Part A-Policy and Practice 38(4), (2004) 28. Sener, I., Eluru, N., Bhat, C.: An analysis of bicycle route choice preferences. Transportation 36(5), (2009) 29. Sener, I.N., Eluru, N., Bhat, C.R.: An analysis of bicycle route choice preferences using a web-based survey to examine bicycle facilities. Working Paper, Department of Civil Engineering, University of Texas, Austin (2008) 30. Shoval, N.: The GPS Revolution in Spatial Research. In: van Schaick, J., van der Spek, S.C. (eds.) Urbanism on Track, Application of Tracking Technologies in Urbanism. IOS Press, Amsterdam (2008) 31. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC (1986) 32. Stopher, P.R.: The travel survey toolkit: where to from here? Paper Presented at the 8th International Conference on Survey Methods in Transport, Annecy (2008) 33. Thurstain-Goodwin, M., Unwin, D.: Defining and Delineating the Central Areas of Towns for Statistical Monitoring Using Continuous Surface Representations. Transactions in GIS 4, (2000) 34. Tilahun, N.Y., Levinson, D.M., Krizek, K.J.: Trails, lanes, or traffic: Valuing bicycle facilities with an adaptive stated preference survey. Transportation Research Part A-Policy and Practice 41(4), (2007) 35. Urry, J.: Travelling times. European Journal of Communication 21, (2006) 36. van Schaick, J., van der Spek, S.C. (eds.): Urbanism on Track, Application of Tracking Technologies in Urbanism. IOS Press, Amsterdam (2008) 37. Wang, F.: Quantitative methods and applications in GIS. CRC Press, Boca Raton (2006) 38. Winters, M., Brauer, M., Setton, E., Teschke, K.: Built Environment Influences on Healthy Transportation Choices: Bicycling versus Driving. Journal of Urban Health 87, (2010) 39. Winters, M., Davidson, G., Kao, D., Teschke, K.: Motivators and deterrents of bicycling: comparing influences on decisions to ride. Transportation 38(1), (2011)

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