Urban Development Scenarios and

Probability Mapping for Greater Dublin Region

The MOLAND Model Application

Harutyun ShahumyanWhite R., Petrov L., Williams B., Convery S., Brennan M.

University College Dublinharutyun.shahumyan@ucd.ie

Recent trends: Ireland and the GDR

Between 1996 and 2006 in Ireland • Population grew by 17% from 3.6 to 4.24 million.• Numbers at work increased by 40%.• Vehicles increased by 72%. • Energy consumed by the transport sector increased by

100%. • Greenhouse gas emissions from transport increased by 88%.

The Greater Dublin Region The Greater Dublin Region experienced the biggest population experienced the biggest population

growth nationally with an increase of growth nationally with an increase of 8.3% between 2002 and 2006.8.3% between 2002 and 2006.

The Greater Dublin Region The Greater Dublin Region experienced the biggest population experienced the biggest population

growth nationally with an increase of growth nationally with an increase of 8.3% between 2002 and 2006.8.3% between 2002 and 2006.

Sources: Department of Transport, CSO

European Environment Agency (EEA) cite

Dublin’s sprawl as ‘worst-case

scenario’ of urban planning so

that newer mem

ber states such as Poland might avoid

making the sam

e mistakes…

75% of all Europeans now live in urban areas and this is expected to rise to 90% by 2020 based on current trends (EEA)

MOLAND ModelMonitoring Urban Land Cover Dynamics

• Algarve • Belgrade• Bilbao • Bratislava • Brussels • Dresden • Dublin • Helsinki • Iraklion• Milan • Munich • Nicosia

• Oporto• Palermo • Prague • Ruhr Area • Setubal• Sunderland • Tallinn• Veneto • Vienna • Dresden-Prague • Lagos

Map source: EC JRC

Study Areas

The MOLAND model was developed as part of an initiative of the EC Joint Research Centre as a response to the challenge of providing a means for assessing urban and regional development trends across Europe.

MOLANDModels at 3 coupled spatial scales

• Global levelGrowth figures for the population and the jobs are entered into the model as trend lines.

• Regional levelAllocation of the Global growth as well as the interregional migration of activities and residence based on relative attractiveness of the sub-regions (counties).

• Local levelThe detailed allocation of economic activities and people by the means of a Cellular Automata Cellular Automata based land use model.

Transport

ZonesAccessibili

tySuitabilityZonin

g

Land use map at time T

Land use map at time T+1

MICRO MODELLocal Level (426,500 cells)

Neighbourhood Rules

Socio-Economic Information

Tim

e lo

op

Global Level(Greater Dublin Region)

Land Use Transition in MOLAND Model

Time step: 1 year

Regional Level(5 counties in the Region)

MACRO MODEL

Sample MOLAND Simulation - Output

Dublin

Research on Urban Development Scenarios for the Greater Dublin Region (GDR)

• Strategic Environmental Assessment (SEA)– 4 scenarios

• Wastewater Treatment Capacity Study (WWT)

– 3 scenarios

• Environmental Impact Assessment (EIA)– 3 scenarios

General Characteristics•Simulation period: 2006 - 20262006 - 2026•Beginning from the actual land use map of 20062006 (23 classes, 200m cell size)•DifferentDifferent suitability, , zoning and transport network maps •DifferentDifferent population and employment projections

Strategic Environmental Assessment

In collaboration with the Dublin & Mid East Regional Authorities the MOLAND model was used to generate scenarios illustrating the effects of future policy directions on the GDR :

1. Baseline/Continued Trends Approach (SEA1)2. Finger Expansion of Metropolitan Footprint (SEA2)3. Consolidation of Key Towns & the City (SEA3)4. Managed Dispersal: Consolidation, sustainability and

some expansion at nodes on Transport Corridors (SEA4)

Used for Regional Planning Guidelines for the Greater Dublin Area (http://www.rpg.ie)

Simulation ResultsLand use maps of GDR in 2026

SEA 1Continued trend

SEA2Finger expansion

SEA3Consolidation of

key towns & the City

SEA 4Managed Dispersal

Residential Area Development Difference Between SEA Scenarios

SEA1

2006 & 2026

Residential areas in

2026 not in 2006 2006 not in 2026

SEA2 SEA3 SEA4

Scenarios for Wastewater Treatment Capacity Study (WWT)

1. Low Growth in population2. Medium Growth in

population3. High Growth in population

Scenarios for Environmental Impact Assessment (EIA)

1. Recession2. Compact Development3. Managed Dispersed

Residential development patterns in GDR from 2006 to 2026 under the 10 scenarios 10 scenarios from 3 different studies3 different studies.

Land use statistics: Residential areasResidential areas

Increase by 2026: Min 65% (EIA1) - Max 128% (WWT3); Average: 87%

Land use statistics: Residential areasResidential areas

In allall scenarios the most residential areas are developed in Dublin County.Dublin County.

Land use statistics: Industrial areasIndustrial areas

Increase by 2026: Min 11% (EIA1) – Max 101% (EIA2); Average 47%

Land use statistics: Industrial areasIndustrial areas

In allall scenarios the most residential areas are developed in Dublin County.Dublin County.

Probability Mapping: Single Scenario

• Each time the model runs a scenario it gives different predictions, both because of random elements and bifurcations inherent in the dynamics of the model.

• Rather than showing a single land use map for a scenario as the prediction for 2026, a series of probability maps were developed one for each land use class.

• To do this, a scenario simulation was run numerous times (e.g. using Monte-Carlo methodology). Then the output maps were combined using ArcGIS Spatial Analyst Raster Calculator.

Probability Mapping: Multiple Scenarios

• What if there are several scenarios?• What if it difficult to justify the preference of a particular one?

• A solution can be a composite probability map of multiple different scenarios. • In principle a composite probability map generated from the output of several

different appropriate scenarios is not qualitatively different from a probability map representing the effect of the stochastic perturbations within a single scenario.– E.g.: In the case of 3 growth scenarios (low, medium, and high) the combined

probability map of urbanisation is essentially equivalent to a map generated from model runs in which the growth rate parameter varies stochastically.

• In any case some scenarios are more likely than others, and so the composite probability map should be constructed by weighting the various scenarios by their estimated likelihoods.

WWT scenarios appropriate because of their similarity and simplicity.

10 Iterations for each scenario

Weights:0.2 (WWT1) 0.5 (WWT2) 0.3 (WWT3)

Likelihood of residential sprawl in GDR by 2026: Assuming that 3 scenarios have the specified likelihoods (weights)

Areas which will have no residential development by 2026 in any of considered scenario

Areas by their likelihood of becoming residential by 2026 for WWT3 and for 3 WWT scenarios combined

• Combining the output from different planning scenarios, corresponding to different land use zoning or transportation policies, would make no sense from the point of view of the planner, who would be using the model to examine the consequences of alternative policies with the aim of choosing one of them.

• However, from the standpoint of a developer, who can't know what policy will be adopted in the future combining the scenarios is reasonable because the combined probability map would represent the uncertainty of the future land use environment given the information available to the developer.

Conclusion

• The exploration of the 3 different research outputs proved that the analysis of complementary scenarios can be used by planners and decision makers for getting a better insight of a region development.

• The MOLAND model may be usefully applied in exploring the spatial distribution of land uses under a range of scenarios.

• Scenario comparison and the probability mapping allow estimates to be produced of the likelihood of certain land use transitions. They provide a valuable tool to describe predicted land cover change and its uncertainty.

Conclusion

Thank Youwww.uep.ie

The Urban Environment Project is generously sponsored by the Irish Environmental Protection Agency as part of the ERTDI programme which is funded through the National Development Plan. 2005-CD-U1-M1 “Decision support tools for managing urban environment in Ireland’

All work undertaken on the MOLAND model, for the Greater Dublin Region is subject to the license conditions of the software developers, Research Institute Knowledge Systems b.v. (RIKS b.v.) and the data set owners, DG JRC under license no. JRC.BWL.30715.

The authors would like to thank the Dublin and Mid East Regional Authorities for their cooperation in providing data and the context for the waste water facility case study. We would also like to acknowledge the assistance of the Urban Environment Project team.