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Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
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Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications

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Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications …

Urban Development Scenarios and Probability Mapping for Greater Dublin Region: The MOLAND Model Applications
Harutyun Shahumyan, Laura Petrov, Brendan Williams, Sheila Convery,
Michael Brennan - University College Dublin Urban Institute Ireland
Roger White - Memorial University of Newfoundland Canada

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  • Within the scope of the Urban Environment Project implemented in UCD Urban Institute Ireland (UII) several scenarios have been developed for the Greater Dublin Region and simulated by the MOLAND model in recent years. Initially simple scenarios were tested where only one or two elements were changed in each scenario. Then, based on the lessons learnt, more realistic and complex scenarios were simulated with key input from different thematic groups, researchers and officials. Some of these scenarios resulted in practical application, forming an important part of the latest review of the Regional Planning Guidelines (RPG) published in 2010.
  • The MOLAND model was used to simulate the spatial distribution of new urban development using three population projections for the GDR and to examine how this could impact planned future wastewater treatment capacity and defined catchment areas. The scenarios were based on the Irish Central Statistics Office’s (CSO) regional population projections for 2011-2026 . In addition to the Baseline scenario described above (SEA1), three other scenarios of GDR urban development were produced by early-stage and senior scientists and policy-makers during a summer school and workshop in 2009 held at UII, Dublin. The aim of this exercise was to bridge the scientific and stakeholder communities in order to collaborate around spatial models and produce future land use maps which should have a clear and accepted interpretation, be robust, statistically validated and respond to policy interventions. By using five driving forces (population, economic trends, urbanisation, transport and overall trends), the following qualitative scenarios were realised using the MOLAND model:
  • All ten scenarios described above were simulated by the MOLAND model running from 2006 to 2026. Though most of the parameters in the model were not changed, there were specific modifications in the input maps of suitability, zoning and the transport network as well as in the projected population and employment numbers. As a result the land use maps of 2026 generated by the model for different scenarios vary substantially. However, the aims of the described three studies are convergent, generating scenarios for future policy directions and urban development (e.g. with impact on future wastewater treatment), and linking the scientific and stakeholders community and therefore, the maps retrieved can be used complementarily. Figure 2 shows a comparison of the maps for each scenario. Specifically, the residential areas of the simulated maps in 2026 are compared with the actual residential areas in 2006. The comparison maps confirm that the GDR can have substantially different development patterns depending on the decisions made. Specifically, in some scenarios we have large urban development in the north of Dublin County (WWT2, WWT3); while in other scenarios urban sprawl is directed to the west of Dublin (SEA2, SEA3, EIA2). In some cases a few hinterland towns are developed broadly (SEA3, SEA4, EIA2); while in other cases more dispersed development in the hinterlands is observed (WWT1, WWT2, SEA1, EIA3). It is also noticeable that the most development occurs in the scenario with the highest projected population (WWT3); while the least development and even shrinkage of residential areas occur in the case of the recession scenario (EIA1).
  • The statistics show that most of the development takes place in Dublin County; and the maximum is reached in scenario WWT3. The minimum and maximum possible increase of residential areas if any one of the scenarios occurs was also calculated. Thus, compared with the residential areas as they were in 2006, the maximum increase of total residential areas by 2026 could be about 128% (WWT3) and the minimum increase could be about 65% (EIA1). The average estimated increase from all ten scenarios is about 87%.
  • Figure 4 shows five statistics (minimum, first quartile, median, third quartile, and maximum) as well as outliners for residential areas in hectares by county. Here again it is noticeable, that for all scenarios the most residential areas are developed in Dublin County. In addition, the residential areas in scenario WWT3 are substantially greater than in any other scenario in almost all counties. Therefore, it appears as an outlier in the boxplot (marked as ‘o’). Similarly, residential areas in 2006 are substantially smaller than in 2026 for any scenario, making them outliers too (marked as ‘*’).
  • Figures 5 and 6 show the industrial areas in 2026 for each scenario in each county compared with 2006 actual areas. For industrial areas the maximum increase by 2026 is 101% in the EIA2 scenario while the minimum increase is 11% in the EIA1 scenario. The average estimated increase is 47%.
  • In the case of the EIA2 scenario, Dublin and Kildare get significantly more industrial areas compared with other scenarios and counties (outliers in the boxplot). Similar analyses were done for commercial and service areas in the GDR.
  • The MOLAND model helps us to understand trends we are interested in and provides predictions of future land use changes. But in reality it offers not predictions of the future, but perspectives or alternative possible futures [18]. Indeed, each time the model runs it gives different predictions, both because of random elements and bifurcations inherent in the dynamics of the model. Therefore, the proper way to view the output of the model is probabilistically. To do this, a simulation should be run a sufficient number of times and a map of all the output possibilities produced [19]. Of course some possible outcomes will be very similar, and some can be quite different.
  • Probability mapping of a single scenario is used often in urban modelling practice. It is an effective approach to assist decision makers to understand the most likely development patterns of a particular scenario. However, if there are several scenarios, it is often difficult to justify the preference of a particular one. A solution can be a combined probability map of several 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. For example, in the case of three 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. The weighting factors themselves constitute a higher level scenario.
  • For illustration purposes the WWT scenarios described above were used for combined probability mapping. These scenarios are especially appropriate because of their similarity and simplicity. In particular, the WWT scenarios vary only by population projections. Using the methodology described in the previous section we have created a composite probability map using the three WWT scenarios. Specifically, each of the WWT scenarios was run 10 times, resulting in 30 land use maps of the GDR for 2026. Based on [11] and discussions with several researchers and officials, the following weights were defined for the WWT scenarios: 0.2 (WWT1), 0.5 (WWT2), 0.3 (WWT3). The residential development probability map was generated from the weighted sum of the 30 land use raster maps in ArcGIS using the specified weights. The result is shown in the right image of Figure 7, which represents the likelihood of residential sprawl in the GDR in 2026 given the assumption that three WWT scenarios have the specified likelihoods (weights). The maps in Figure 7 show that in the case of combined scenarios, the probability of residential development is decreased in some areas. More spatial statistical analyses of these maps is presented below. Probability maps show that most areas around Dublin are relatively predictable in terms of future urban land use: either they are likely to be developed or they are unlikely to be developed. However, if one land use class is equally likely as another of being present, there is a high degree of uncertainty related to the modelled class transition. Thus, many areas are not easily predictable (e.g. areas presented by the middle colours from the legend scheme). These areas are approximately equally likely to be developed or not and therefore the model is not capable of predicting accurately what may happen. In spite of this, for planners and decision makers these results still contain useful information (as these areas are capable of change and being influenced to change in various ways). It is useful to know, in a spatially explicit sense, where the probabilities of certain land use transitions are intermediate, because in these areas the future land uses can be influenced by small interventions in the present. In contrast, in the highly predictable area, major efforts would have to be made to alter the future land use patterns.
  • Figure 8 presents the areas in hectares by counties where there is no likelihood of residential development for scenario WWT3 as well as for all three WWT scenarios combined. Total area in each county with no development is larger in case of WWT combined scenarios compared with a single WWT3 scenario. This is the result of the variation of population projections used in the scenarios. While WWT3 reflects the population high growth scenario, WWT combined scenario includes weighted scenarios with lower population growth. Therefore, in case of combined scenarios we obtain less residential development than in WWT3.
  • More interesting are the areas with some likelihood of becoming residential. Figure 9 presents the areas with 10% to100% probability of residential development in the GDR. It should be noted that the numbers with 100% development include also residential areas already existing in 2006 the start year of the simulations (marked by ‘o’ in the figure). This shows that the vast majority of areas with 100% predictability are areas which already were residential in 2006 and in case of combined probability mapping new development with 100% probability is essentially smaller than in the case of WTT3 scenario. Indeed, uncertainty is higher in the combined probability mapping. Combining three different scenarios includes some scenario-specific assumptions, making the results more general. Therefore, the combined scenario probability map in general has less area where the model predicts residential development by 2026.
  • But whether it makes sense to combine scenarios depends also on the point of view — i.e. on the user of the probability map. For example, to combine the output from three different planning scenarios, corresponding to different land use zoning schemes 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. But from the standpoint of a developer, who can't know what policy will be adopted in the future, or to what degree a policy, if adopted, will be enforced, 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.
  • Transcript

    • 1. Urban Development Scenarios and Probability Mapping for Greater Dublin Region The MOLAND Model Application Harutyun Shahumyan White R., Petrov L., Williams B., Convery S., Brennan M. University College Dublin [email_address]
    • 2. 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 experienced the biggest population growth nationally with an increase of 8.3% between 2002 and 2006. Sources: Department of Transport, CSO
    • 3. European Environment Agency (EEA) cite Dublin’s sprawl as ‘worst-case scenario’ of urban planning so that newer member states such as Poland might avoid making the same 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)
    • 4. MOLAND Model Monitoring 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.
    • 5. MOLAND Models at 3 coupled spatial scales
      • Global level
      • Growth figures for the population and the jobs are entered into the model as trend lines.
      • Regional level
      • Allocation of the Global growth as well as the interregional migration of activities and residence based on relative attractiveness of the sub-regions (counties) .
      • Local level
      • The detailed allocation of economic activities and people by the means of a Cellular Automata based land use model.
    • 6. MICRO MODEL Local Level (426,500 cells) Time loop Global Level (Greater Dublin Region)
        • Land Use Transition in
      • MOLAND Model
      Time step: 1 year Regional Level (5 counties in the Region) MACRO MODEL Transport Zones Accessibility Suitability Zoning Land use map at time T Land use map at time T+1 Neighbourhood Rules Socio-Economic Information
    • 7. Sample MOLAND Simulation - Output Dublin
    • 8.
        • Strategic Environmental Assessment (SEA)
        • 4 scenarios
      • Wastewater Treatment Capacity Study (WWT)
        • 3 scenarios
        • Environmental Impact Assessment (EIA)
        • 3 scenarios
        • General Characteristics
        • Simulation period: 2006 - 2026
        • Beginning from the actual land use map of 2006 (23 classes, 200m cell size)
        • Different suitability , zoning and transport network maps
        • Different population and employment projections
      Research on Urban Development Scenarios for the Greater Dublin Region (GDR)
    • 9. 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 :
        • Baseline/Continued Trends Approach (SEA1)
        • Finger Expansion of Metropolitan Footprint (SEA2)
        • Consolidation of Key Towns & the City (SEA3)
        • 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 )
    • 10. Simulation Results Land use maps of GDR in 2026 SEA 1 Continued trend SEA2 Finger expansion SEA3 Consolidation of key towns & the City SEA 4 Managed Dispersal
    • 11. Residential Area Development Difference Between SEA Scenarios SEA1 2006 & 2026 Residential areas in 2026 not in 2006 2006 not in 2026 SEA2 SEA3 SEA4
    • 12.
      • Scenarios for Wastewater Treatment Capacity Study (WWT)
      • Low Growth in population
      • Medium Growth in population
      • High Growth in population
      • Scenarios for Environmental Impact Assessment (EIA)
      • Recession
      • Compact Development
      • Managed Dispersed
    • 13. Residential development patterns in GDR from 2006 to 2026 under the 10 scenarios from 3 different studies .
    • 14. Land use statistics: Residential areas
      • Increase by 2026: Min 65% (EIA1) - Max 128% (WWT3); Average: 87%
    • 15. Land use statistics: Residential areas
      • In all scenarios the most residential areas are developed in Dublin County.
    • 16. Land use statistics: Industrial areas
      • Increase by 2026: Min 11% (EIA1) – Max 101% (EIA2); Average 47%
    • 17. Land use statistics: Industrial areas
      • In all scenarios the most residential areas are developed in Dublin County.
    • 18. 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.
    • 19. 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.
    • 20.
      • 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)
    • 21. Areas which will have no residential development by 2026 in any of considered scenario
    • 22. Areas by their likelihood of becoming residential by 2026 for WWT3 and for 3 WWT scenarios combined
    • 23.
      • 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
    • 24.
      • 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
    • 25. Thank You www.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.

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