Nicolas Lachance-Bernard Geographic Information Systems Laboratory, Ecole polytechnique fédérale de Lausanne, Switzerland European Regional Science Association, 24th Summer School Modelling Cities and Urban Dynamics, July 10th 2011, Université du Luxembourg

1. Integrated land dynamics monitoring framework
Nicolas Lachance-Bernard
Geographic Information Systems Laboratory,
Ecole polytechnique fédérale de Lausanne, Switzerland
European Regional Science Association, 24th Summer School
Modelling Cities and Urban Dynamics, July 10th 2011, Université du Luxembourg
NLB / 10.07.11 / p.1
Integrated Land Dynamics Monitoring Framework

2. Plan
• Introduction
• Land dynamics monitoring framework
• Network based kernel density estimator
• Case studies
NLB / 10.07.11 / p.2
Integrated Land Dynamics Monitoring Framework

3. Planning Practice
• Public policy
– Concentrates on responding to land market demand
(Knaap 2001)
– Stronger spatio-temporal market management
(supply & demand… specially in Switzerland)
• Challenges
– Housing, commercial and activities densification
– Adaptation of planning tools: zonings, taxes, …
– Sparse data
• Time - incomplete, not updated, lack of historical depth
• Space – incomplete, insufficient resolution or scale
– Maps making not adapted to monitoring
NLB / 10.07.11 / p.3
Integrated Land Dynamics Monitoring Framework

4. Planning versus Change
• Urban planning
– Geographic information systems (GIS) since 1990s
(Nedović-Budić et al 2005)
– Interdependency of transportation systems and land uses
(Handy 2005)
– CORINE land cover, MOLAND land use, EEA Cellular Automata,
CityCoop project, SMURF, (and many others)…
• Land change science (LCS)
– Data availability, aggregation process, validation, sensitivity
(Rindfuss et al. 2004)
– Driver, Pressure, State, Impact and Response framework (DPSIR)
(Nuissl et al. 2009)
NLB / 10.07.11 / p.4
Integrated Land Dynamics Monitoring Framework

5. R&D opportunities
• Land monitoring systems
• Land covers (i.e. regional sprawl)
• Land uses (i.e. urban segregation, urban densification)
• Land markets (i.e. risk, trends)
• Development (i.e. permit emission, value capture, micro-credit)
– General indicators (comparison region to region)
– Specific indicators (following city objectives and goals)
NLB / 10.07.11 / p.5
Integrated Land Dynamics Monitoring Framework

6. This paper…
• Urban monitoring framework
– Developed for integrated land dynamics
(Transportation & Land uses)
– Looking for spatio-temporal trends, hotspots, axes, flux, …
(What? Where? Who? When? How? …)
– Illustrated by innovative indicators
(NetKDE, MCA, Jensen, Multimodal accessibility, …)
• Proof-of-concept: i.e. Density indicator (NetKDE)
– Ljubljana: biking around the city
(Where people behave?)
– Barcelona: playing with scale and comparing model
(How scale change vision?)
– Baghdad: playing with time, classification and complexity
(Which time evolution and about who?)
NLB / 10.07.11 / p.6
Integrated Land Dynamics Monitoring Framework

7. Plan
• Introduction
• Land dynamics monitoring framework
• Network based kernel density estimator
• Case studies
NLB / 10.07.11 / p.7
Integrated Land Dynamics Monitoring Framework

8. Monitoring Framework
NLB / 10.07.11 / p.8
Integrated Land Dynamics Monitoring Framework

9. Stakeholder Tier
• Concepts
– Top-Down approach
– Planning questions / needs
– Decision process oriented
– 3 levels (scale vs risk)
• Operational (local, low)
• Tactical (city, medium)
• Strategical (regional, high)
• Applications
– Dashboard, visualization system, geo-atlas, drill-down
• Current project
– Land uses and networks monitoring (Coimbra vs. Geneva),
20 years, master plan vs. actual states, 10 indicators vs. data
NLB / 10.07.11 / p.9
Integrated Land Dynamics Monitoring Framework

10. Knowledge Tier
• Concepts
– Spatio-temporal indicators
– Indicator repertories, index, systems
– Comparison of model results
– Multitemporal, multiscales
• Applications
– Aggregation and disaggregation
– Fuzzy-map comparison
– Adapted landscape metrics
• Current projects
– Temporal and aggregates analysis (Baghdad) - Casualties
– Comparison of models (Ljubljana) – Cycling infrastructures
NLB / 10.07.11 / p.10
Integrated Land Dynamics Monitoring Framework

11. Information Tier
• Concepts
– Projected - short & long term
– Actual - frequency
– Historical - “composted” / aggregated
• Applications (models)
– Time-Fixed
• GWR, KDE, NetKDE, MCA, Localization, Multimodal accessibility
– Scenarios
• UrbanSim, Cellular Automata, Multi-scale Multi-agent model
• Current projects
– NetKDE / KDE / MCA : Barcelona, Geneva, Bologna, Glasgow
(economics activities), Ljubljana (cycling behavior)
– Accessibility (Geneva), CA (Lausanne)
NLB / 10.07.11 / p.11
Integrated Land Dynamics Monitoring Framework

12. Data Tier
• Concepts
– Bottom-up approach
– Raw data
• Public and private
(government, agencies, companies)
• Volunteered geographic information
– Availability, liability, resolution, frequency
• Applications
– Spatial data infrastructure (SDI), Input translator, Topological
checker, Metadata Management
• Current projects
– SDI for EPFL (15TB of data Swisstopo, OFS, …)
– VGI in Ljubljana (cycling)
NLB / 10.07.11 / p.12
Integrated Land Dynamics Monitoring Framework

13. Plan
• Introduction
• Land dynamics monitoring framework
• Network based kernel density estimator
• Case studies
NLB / 10.07.11 / p.13
Integrated Land Dynamics Monitoring Framework

14. 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
• Objectives
– Handling large datasets in opensource framework/applications
*NetKDE and KDE (2009-2011) by Timothée Produit, Nicolas Lachance-Bernard,
Loic Gasser, Dr. Stephane Joost, Prof. François Golay, Prof. Sergio Porta, Emanuele Strano
NLB / 10.07.11 / p.14
Integrated Land Dynamics Monitoring Framework

15. KDE vs. NetKDE
KDE NetKDE
Source: Produit 2009
NLB / 10.07.11 / p.15
Integrated Land Dynamics Monitoring Framework

16. KDE vs. NetKDE
KDE NetKDE
Source: Produit and Lachance-Bernard, 2010
NLB / 10.07.11 / p.16
Integrated Land Dynamics Monitoring Framework

17. Plan
• Introduction
• Land dynamics monitoring framework
• Network based kernel density estimator
• Case studies
NLB / 10.07.11 / p.17
Integrated Land Dynamics Monitoring Framework

18. Data Tier: Ljubljana and VGI
• Low resolution KDE 100m  425km2
13,630 segments, 42,342 gridpoints, 442,260 GPS points
KDE bandwidths
[200m, 2500m] 24 X 100m steps (2-3h)
• High resolution NetKDE/KDE 20m  20km2
8,114 segments, 314,250 gridpoints, 423,748 GPS points
NetKDE 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)
NLB / 10.07.11 / p.18
Integrated Land Dynamics Monitoring Framework

19. NetKDE (Left) KDE (Right) results 20m grid (Bandwidths: a-60m; b-100m; c-200m; d-400m)
Data Tier: Ljubljana and VGI
NLB / 10.07.11 / p.19
Integrated Land Dynamics Monitoring Framework

20. Data Tier: Ljubljana and VGI
NetKDE (Left)
KDE (Right)
Results 20m grid
(Bandwidths: 400m)
*Deciles distribution
NLB / 10.07.11 / p.20
Integrated Land Dynamics Monitoring Framework

21. KDE results
20m grid
Bandwidths:
A)60m
B)100m
C)200m
D)400m
*Deciles distribution
NLB / 10.07.11 / p.21
Integrated Land Dynamics Monitoring Framework

22. NetKDE
results
20m grid
Bandwidths:
A)60m
B)100m
C)200m
D)400m
*Deciles distribution
NLB / 10.07.11 / p.22
Integrated Land Dynamics Monitoring Framework

23. Information Tier: Barcelona KDE vs. NetKDE
Data 2002 Treatments
(Agencia de Ecologia Urbana)
• Multiple grids
• Retail and service activities 10m, 20m, 50m, 100m, 200m
166,311 entities • Multiple density models
• Street network NetKDE, KDE
11,222 segments • Multiple bandwidths
NetKDE [100m, 1000m]
KDE [20m, 6000m]
• Duration: +500 hours
with 9 computers
NLB / 10.07.11 / p.23
Integrated Land Dynamics Monitoring Framework

24. Barcelona Activities KDE/NetKDE bandwidth variations (200m grid)
KDE 400m KDE 600m KDE 800m KDE 1000m
NetKDE 400m NetKDE 600m NetKDE 800m NetKDE 1000m
Low density High density Not calculated
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Integrated Land Dynamics Monitoring Framework

25. Barcelona Activities KDE/NetKDE grid scale variations
H
KDE grid: 200m, band.: 500m NetKDE grid: 200m, band.: 500m
L
KDE grid: 50m, band.: 500m NetKDE grid: 50m, band.: 500m
NLB / 10.07.11 / p.25
Integrated Land Dynamics Monitoring Framework

26. Barcelona Activities KDE/NetKDE high resolution grid variations
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 / 10.07.11 / p.26
Integrated Land Dynamics Monitoring Framework

27. Knowledge Tier: Spatio-temporal evolution Baghdad
• Events – Data journalism war death casualties Baghdad 2004-2009
Coalition Iraqi
Civilians Insurgents
Forces Forces
Attack 86 1,167 1,627 3,775
Direct Fire 482 4,270 4,766 6,807
Indirect Fire 192 284 2,087 1,040
IED Explosion 2,107 5,990 20,228 3,455
Murder 9 2,169 32,563 73
Total (93,157) 2,876 13,880 61,251 15,150
• Network
Open Street Map (OSM) 66,648 segments
• Grid
200m resolution 22,644 gridpoints / 50m resolution 362,304 gridpoints
NLB / 10.07.11 / p.27
Integrated Land Dynamics Monitoring Framework

28. Distribution of religious groups in Baghdad
Source: Loic Gasser, 2011
NLB / 10.07.11 / p.28
Integrated Land Dynamics Monitoring Framework

29. Spatio-temporal KDE-NetKDE*
• Proposed by Demsar and Virrantaus (2010)
based on space-time cube by Hägerstrand
• 3D KDE function
3D Epanechnikov kernel function
* Developped by Loic Gasser, Timothée Produit and Nicolas Lachance-Bernard
NLB / 10.07.11 / p.29
Integrated Land Dynamics Monitoring Framework

30. Baghdad IED explosions KDE
January 2004 - December 2009
• 2,652 events - 1,030 km2 - 76 steps, 1500m KDE
• 1 day equivalent to 10 meters
Source: Loic Gasser, 2011
NLB / 10.07.11 / p.30
Integrated Land Dynamics Monitoring Framework

31. Discussion
• Research under rapid evolution…
– 3rd-4th algorithm: Calculation optimization 90-95%
– 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
NLB / 10.07.11 / p.31
Integrated Land Dynamics Monitoring Framework