Multi-scale spatial models: linking macro and micro Michael Wegener
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Multi-scale spatial models:linking macro and micro
Michael WegenerSpiekermann & Wegener, Urban and Regional ResearchDortmund, Germany
Centre for Advanced Spatial AnalysisUniversity College London09 January 2008
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Integrated land-use transport models
Today's integrated land-use transport models suffer fromseveral weaknesses:- Their classification of households and individuals is too
crude; individual lifestyles cannot be represented.- Their transport models are not activity-based and cannot
address "soft" transport policies.- Their spatial resolution is too coarse to take account of
small-scale local policies.- Forecasting environmental impacts such as air pollution,
land take and traffic noise is difficult, modelling environ-mental feedback is impossible.
- Issues of spatial equity cannot adequately be addressed.
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Microsimulation
New activity-based microsimulation models improveurban simulation models:
- Individual lifestyles can be represented, households and individuals are disaggregated to the agent level.
- Environmental impacts can be modelled with the required spatial resolution.
- Environmental feedback between environment and land use and transport can be modelled.
- Microlocations can be represented. Households affected by environmental impacts can be localised.
PROP
OLIS
& IL
UMAS
S
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However ...
To date, no full-scale microsimulation model of urban land use, transport and environment has become operational.
There are still unresolved problems regarding the inter-faces between the submodels.
The feedback between transport and location and environ-mental quality and location has not yet been implemented.
Serious problems of calibration, stability and stochastic variation have not been solved.
The computing time for existing models is calculated in terms of weeks or days, not hours.
Conc
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How much micro is enough?
Despite these problems, microsimulation modellers engage in ever more ambitious plans to further raise the complexity and spatial resolution of their models.
The common belief among most microsimulation modellers seems to be: the more micro the better.
This is the dream of the one-to-one Spitfire.
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The Spitfire
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The one-to-one model of the Spitfire
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The one-to-one Spitfire
"Simplifying assumptions are not an excrescence on model-building; they are its essence. Lewis Carroll once remarked that a map on the scale of one-to-one would serve no purpose. And the philosopher of science Russell Hanson noted that if you progressed from a five-inch balsa wood model of a Spitfire air plane to a 15-inch model without moving parts, to a half-scale model, to a full-size entirely accurate one, you would end up not with a model of a Spitfire but with a Spitfire".
Robert M. Solow (1973)
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How much micro is enough?
There seems to be little consideration of the benefits and costs of microsimulation:
- Where is microsimulation really needed?
- What is the price for microsimulation?
- Would a more aggregate model do?
For spatial planning models, the answer to these questions depends on the planning task at hand.
For instance, for modelling the impacts of transport on land use, much simpler travel models are sufficient.
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Macro or micro?
These considerations lead to a reassessment of the hypothesis that eventually all spatial modelling will be microscopic and agent-based.Co
nclu
sions
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11 Adapted from Miller et al., 1998.
Macro or micro?
??
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Conclusions (1)
Only integrated microsimulation land-use transport modelsmodels permit the modelling of - "soft" and local planning policies- individual lifestyles - environmental impacts and feedback- microlocations and spatial equity.
However, there is a price for the microscopic view in termsof data requirements and long computing times.
There are privacy concerns and ethical issues involved.
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Conclusions (2)
Under constraints of data collection and of computing time, there is for each planning problem an optimum level of conceptual, spatial and temporal resolution.
This suggests to work towards a theory of balanced multi-scale models which are as complex as necessary for the planning task at hand and as simple as possible but no simpler.
Future urban models will be modular and multi-scale in scope, space and time.
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Multi-Scale ModellingThe Dortmund Example
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Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells
Multi-level
Multi-scale
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Level 1: Regions
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Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells
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SASI Model
GDP
AccessibilityProductionfunction
Employment
Migrationfunction
PopulationIncome
Labourforce
Transportpolicy
Unemploy-ment
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The STEPs Project (2004-2006)
The EU 6th RTD Framework project STEPs (Scenarios for the Transport System and Energy Supply and their Potential Effects) developed and assessed possible scenarios for the transport system and energy supply of the future.
In the project five urban/regional models were applied to forecast the long-term economic, social and environmental impacts of different scenarios of fuel price increases and different combinations of infrastructure, technology and demand regulation policies.
Here the model results for the urban region of Dortmund, Germany, will be presented.
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The STEPs Project: Scenarios
The project developed a set of scenarios assuming different rates of energy price increases with three sets of policies:
Fuel price increase
+1% p.a. +4% p.a. +7% p.a.
Do-nothing A-1 B-1 C-1
Business as usual A0 B0 C0
Infrastructure & technology A1 B1 C1
Demand regulation A2 B2 C2
All policies A3 B3 C3A-1 Reference Scenario
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DortmundDortmund
Dortmund
Dortmund
Cologne
Hagen
Düsseldorf
Münster
EssenDuisburg Bochum
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Economic impacts for the Dortmund region
According to the SASI model, the fuel price increases and related policies of the scenarios have significant negative impacts on the economy of the Dortmund urban region:
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Level 2: Zones
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Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells
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Internal zones
External zones
Dortmund
Hamm
Hagen
Bochum
Study area
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26E
n v i r o n m e n t
r b a nU
Urban systems
Networks
Travel
Population
Workplaces
Land use
Housing
Very fast
Fast
Slow
Very slow
Very slow
Goods transport
Employment
Speed of change
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Dortmundmodel
Microsimulation
from SASI model
Populationhouseholds
Nonresidentialbuildings
Residentialbuildings
EmploymentWorkplaces
Housingmarket
Transportmarket
Market fornonresidential
buildings
Land market
Labourmarket
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Travel distance per capita per day (km)
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Share of walking and cycling trips (%)
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Share of public transport trips (%)
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Share of car trips (%)
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Average trip speed (km/h)
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Car fuel consumption per car trip per traveller (l)
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CO2 emission by transport per capita per day (kg)
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Level 3: Raster Cells
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Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells
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The ILUMASS Project (2001-2006)
The project ILUMASS (Integrated Land-Use Modelling and Transport Systems Simulation) embedded a microscopic dynamic simulation model of urban traffic flows into a comprehensive model system incorporating both changes of land use and the resulting changes in transport demand as well as their environmental impacts.
For testing the land use submodels, the transport and environmental submodels were replaced by the aggregate transport model of the IRPUD model and simpler envir-onmental impact models (= reduced ILUMASS model).
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ReducedILUMASSmodel
Microsimulation
from SASI model
Microsimulation
from SASI model
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0 <
20 <
40 <
60 <
80 <
100 <
120 <
140 <
160 <
180 <
<
20 E/ha
40 E/ha
60 E/ha
80 E/ha
100 E/ha
120 E/ha
140 E/ha
160 E/ha
180 E/ha
200 E/ha
200 E/ha
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Firms and householdsStart
End
Another household?
Yes
No
Select a household
Demographic events- Ageing- Death- Birth- Marriage/cohabitation- Divorce/separation- Persons leave household- Persons move together
Economic events- Education- Place of work- Employment status- Income- Mobility budget of household
Update lists- Persons- Households- Dwellings- Firms
Start
End
Anotherfirm?
Yes
No
Select a firm
Select alternative location
Accept?No No
1-10 11
Yes
Update lists- Firms- Nonresidential floorspace- Employed persons
Check satisfaction with present location
Satisfied?
No
Yes
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Dwellings
Another project?
End
No
Update lists- Persons- Households- Dwellings- Construction
Yes
Select zone and microlocation
Select type and num-ber of dwellings
Upgrading
Select zone and microlocation
Select type and num-ber of dwellings
Demolition and result-ing moves
Select zone and microlocation
Select type and num-ber of dwellings
Construction
Start
Expected demand- Demolition- Upgrading- Construction
Select project- Demolition- Upgrading- Construction
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MovesStart
End
Another dwelling?
Yes
No
Select dwelling- Area- Type
Select household- Immigration- New household - Forced move- Move
Accept?No No
Update lists- Households: - with dwelling - without dwelling- Dwellings - occupied - vacant
1-5
Housing supply
Yes
Start
End
Anotherhousehold?
Yes
No
Select household- Outmigration- Immigration- New household- Move
Select dwelling- Area- Type
Accept?No No1-5 6
Yes
Housing demand
Update lists- Households: - with dwelling - without dwelling- Dwellings - occupied - vacant
6
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Environmental Impacts and Feedback
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Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells
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Environmental feedback PROPOLIS ILUMASS
Aggregate land-use transport model
Zonal data
Aggregate land-use transport model
Zonal environmental impact model
Aggregate land-use transport model
Zonal data
Aggregate land-use transport model
Spatial disaggregation
Zonal data
Microsimulationland-use transportmodel
Disaggregateenvironmental impact model
Disaggregateenvironmental impact model
No spatial disaggregation
Spatial disaggregationof output
Spatial disaggregationof input
Few impactsLimited feedback
All impactsLimited feedback
All impactsAll feedbacks
Spatial disaggregation
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46 Net residential density (Raster)
Net
resi
dent
ial d
ensi
ty (Z
one)
Zone v. RasterDensity
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47 Percent open space (Raster)
Per
cent
ope
n sp
ace
(Zon
e)
Zone v. RasterOpen space
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48 Mean No2 exposure (Raster)
Mea
n N
o 2 e
mis
sion
(Zon
e)
Zone v. RasterAir pollution
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49 Mean traffic noise exposure (Raster)
Mea
n tra
ffic
nois
e (Z
one)
Zone v. RasterTraffic noise
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Reduced ILUMASSmodel
Microsimulation
from SASI model
Microsimulation
from SASI model
Environmental impacts
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Typical Model Run
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Model dimensions
1.2 million households 2.6 million persons 1.2 million dwellings 80,000 firms 92,000 industrial sites
8,400 public transport links 848 public transport lines 13,000 road links
246/54 internal/external zones 209,000 raster cells
30 simulation periods (years)
90 minutes computing time
Conc
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Model parameters
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Micro dataMicro data
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Travel flows
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Public transportflowsTravel flows
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Travel flowsTravel flows
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Link loadsLink loadsLink loads
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Link loadsLink loadsLink loadsPublic transportspeed
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Link loadsLink loadsLink loadsPublic transportspeed
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Air quality NO2
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Traffic noisedB(A)
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FirmsFirmsFirmsFirmsFirmsFirms
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HouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholds
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HouseholdsDwellingsHouseholdsDwellingsHouseholdsDwellingsHouseholdsDwellings
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Vacant dwellings
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HouseholdsMovesMovesMovesMovesMoves
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Compression of micro data
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Aggregationto zones
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Micro dataMicro data
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Simulationcompleted
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More informationMoeckel, R., Schwarze, B., Spiekermann, K., Wegener, M. (2007): Simulating interactions between land use, transport and environment. Proceedings of the 11th World Conference on Transport Research. Berkeley, CA: University of California at Berkeley.
Wagner, P., Wegener, M. (2007): Urban land use, transport and environmental models: Experiences with an integrated microscopic approach. disP 43(170):45–56.
Wegener, M. (1998): The IRPUD Model: Overview. http://www. raumplanung.uni-dortmund.de/irpud/pro/mod/mod_e.htm.
Wegener, M. (2007): After the Oil Age: Do we have to rebuild our cities? Presentation at the SOLUTIONS Conference, University College London, 11-12 July 2007. http://www.suburbansolutions. ac.uk/sitemapdocs.aspx.
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