1. Network Based Kernel Density Estimator
for Urban Dynamics Analysis
Nicolas Lachance-Bernard, Timothée Produit, Loïc Gasser,
Stéphane Joost and François Golay
Geographic Information Systems Laboratory,
Ecole polytechnique fédérale de Lausanne
Environmental Engineering Institute Green Days 2011
September 8th – 9th 2011, Champoussin, Switzerland
NLB / 12.09.11 / p.1
Network Based Kernel Density Estimator for Urban Dynamics Analysis

2. Content
• Context and definitions
• Barcelona: Conceptualization and computation
• Ljubljana: Volunteered Geographic Information (VGI)
and GPS tracking
• Geneva: Visualization and clustering
• Baghdad: Spatio-temporal multi-dimension analysis
• Further research
Cover picture: Photograph NLB, New city of Belval, Luxembourg, 2011
NLB / 12.09.11 / p.2
Network Based Kernel Density Estimator for Urban Dynamics Analysis

3. Context
• Problematic
– Fact
Spatial events location influenced by environment forms
– Principal need
Adapting density estimation for graph constrained space
– Goal
Spatio-temporal monitoring of urban dynamics
• Challenges
1. To use very large datasets
2. To decrease processing times compared to out-of-the-box
3. To develop multi-scale approaches
4. To develop visualization methods
NLB / 12.09.11 / p.3
Network Based Kernel Density Estimator for Urban Dynamics Analysis

4. Network based Kernel Density Estimator
KDE NetKDE
Source: Produit et Lachance-Bernard 2009, 2010
NLB / 12.09.11 / p.4
Network Based Kernel Density Estimator for Urban Dynamics Analysis

5. Case study data 2009-2011
Barcolona Ljubljana Geneva Baghdad
(2009/2010) (May 2011) (June 2011) (June 2011)
Network (Agency) 20 km2 (OSM) (Agency) (OSM)
Segments 11,200 8,100 10,800 66,600
Events Eco. Activities Cyclist GPS Eco. Activities War records
Locations 166,300 314,200 15,000 93,100
Types 1 1 12 16 + Weights
Grid 200m 100m 100m 20m 200m 50m
50m 20m 10m -
Gridpoints 160,000 310,000 300,000
Buildings - - 70,000 -
Bandwidths [20m,6000m] [40m,1000m]
-
KDE (Steps) (20m) (20m)
Bandwidths [100m,1000m] 60m 100m 500m
NetKDE (Steps) (100m) 200m 400m
9 computers 1 computer 1 computer 1 computer
Processing time
500h 18h + 77h 15h & 20h &
KDE + NetKDE
5min/class 5min/class
NLB / 12.09.11 / p.5
Network Based Kernel Density Estimator for Urban Dynamics Analysis

6. Barcelona: Conceptualization and computation
• Comparing NetKDE and KDE approaches
– Model effects?
– Grid resolution effects?
– Bandwidth effects?
• Data and Parameters
– Retail and service activities (166,311 events)
– Street network (11,222 segments)
– Grids 200m, 100m, 50m, 20m, 10m (+160,000 gridpoints)
– Bandwidths: NetKDE [100m, 1000m], KDE [20m, 6000m]
NLB / 12.09.11 / p.6
Network Based Kernel Density Estimator for Urban Dynamics Analysis

7. Barcelona case: Model & Bandwidth variations (200m)
KDE 400m KDE 600m KDE 800m KDE 1000m
NetKDE 400m NetKDE 600m NetKDE 800m NetKDE 1000m
Low density High density Not calculated
NLB / 12.09.11 / p.7
Network Based Kernel Density Estimator for Urban Dynamics Analysis

8. Barcelona case: Grid resolution variations (200m)
H
KDE grid: 200m, band.: 500m NetKDE grid: 200m, band.: 500m
L
KDE grid: 50m, band.: 500m NetKDE grid: 50m, band.: 500m
NLB / 12.09.11 / p.8
Network Based Kernel Density Estimator for Urban Dynamics Analysis

9. Barcelona: Model variations very high resolution grid (10m)
H
KDE grid: 10m, band.: 500m (ZOOM) NetKDE grid: 10m, band.: 500m (ZOOM)
L
KDE grid: 10m, band.: 500m NetKDE grid: 10m, band.: 500m
NLB / 12.09.11 / p.9
Network Based Kernel Density Estimator for Urban Dynamics Analysis

10. Barcelona: Conceptualization and computation
• Calculation
– 9 computers / +500 hours
• Conclusion (2009-2010)
Important further R&D needs
– Multi-scale, multi-resolution, comparison/clustering methods
– Optimization of NetKDE/KDE algorithms
NLB / 12.09.11 / p.10
Network Based Kernel Density Estimator for Urban Dynamics Analysis

11. Ljubljana: VGI and GPS tracking density
• Criteria for urban planning decision making
– Where to build infrastructures considering current behaviors?
– Which are the most important locations of use?
– Are VGI data reliable?
• Data and Parameters
– GPS tracking (314,250 points)
– OpenStreetMap network (8,114 segments)
– Grids 100m, 20m  20km2 (310,000 gridpoints)
– Bandwidths: NetKDE 60m, 100m, 200m, 400m
KDE [40m, 1000m]
NLB / 12.09.11 / p.11
Network Based Kernel Density Estimator for Urban Dynamics Analysis

12. NetKDE (Left) KDE (Right) results 20m grid (Bandwidths: a-60m; b-100m; c-200m; d-400m)
Data Tier: Ljubljana and VGI
NLB / 12.09.11 / p.12
Network Based Kernel Density Estimator for Urban Dynamics Analysis

13. KDE results
20m grid
Bandwidths:
A)60m
B)100m
C)200m
D)400m
*Deciles distribution
NLB / 12.09.11 / p.13
Network Based Kernel Density Estimator for Urban Dynamics Analysis

14. NetKDE
results
20m grid
Bandwidths:
A)60m
B)100m
C)200m
D)400m
*Deciles distribution
NLB / 12.09.11 / p.14
Network Based Kernel Density Estimator for Urban Dynamics Analysis

15. Ljubljana: VGI and GPS tracking density
• Calculation
– 1 computer / NetKDE 77 hours (- 50%) KDE 18 hours (- 80%)
• Conclusion (2011)
Ready to be used by urban planning professional
– Infrastructure development:
• Level of use corridor
• Most active intersection
• Constrained area or behaviour
NLB / 12.09.11 / p.15
Network Based Kernel Density Estimator for Urban Dynamics Analysis

16. Geneva: Visualization and clustering
• Spatial cluster in the city
– Where are located the activities?
– Is there a hierarchy or structure present between activities?
– What is the best way to represent urban densities?
• Data and Parameters
NOT Disclosed
NLB / 12.09.11 / p.16
Network Based Kernel Density Estimator for Urban Dynamics Analysis

17. Geneva: Visualization and clustering
• Calculation
NOT Disclosed
• Conclusion (June 2011)
New approaches to understand cities
– Planning: City and economic development, urban sprawl
– Tech: New visualization, fastest algorithms, density clustering
methods
NLB / 12.09.11 / p.17
Network Based Kernel Density Estimator for Urban Dynamics Analysis

18. Baghdad: Spatio-temporal multi-dimension analysis
NOT Disclosed
NLB / 12.09.11 / p.18
Network Based Kernel Density Estimator for Urban Dynamics Analysis

19. Baghdad: Spatio-temporal multi-dimension analysis
NetKDE 800 NetKDE 1400
NLB / 12.09.11 / p.19
Network Based Kernel Density Estimator for Urban Dynamics Analysis

20. Conclusion
• Spatio-temporal monitoring of urban dynamics
– Developed methodology for analysis of land uses constrained by
transportation network
– Looking for spatio-temporal trends, hotspots, axes, flux, …
(What? Where? Who? When? How? …)
• Proof-of-concept
– Barcelona: playing with scale and model
How does scale change vision?
– Ljubljana: biking around the city
Where are people most active?
– Geneva: choosing the right place to develop
Where are the density and the diversity?
– Baghdad: playing with time and classification
When and how do event density patterns change?
NLB / 12.09.11 / p.20
Network Based Kernel Density Estimator for Urban Dynamics Analysis