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Adapting to the consequences of climate change in urbanised regions are of increasing importance in most parts of the world. This PhD study contributes with methods and knowledge regarding how planning of urban areas can be used as a tool to reduce impacts from transportation. The research focus on the development of land use and transportation models, that will be used to illustrate how different paths of future urban development can reduce transportation demands and thereby the related impacts on the climate and environment.
The demands for transportation are generated from a many different human activities. Modelling where people locate themselves in urban areas in relation to the placement of their opportunities (works, shopping etc.), is important part of describing how the impacts from transportation will evolve. The work conducted focus on three future pathways from the PASHMINA project, where different future development strategies are proposed. The methodologies applied vary from different GIS analysis, to land use change simulations conducted with in the ‘Land Use Change Impact Analysis’ (LUCIA) Cellular automata model, highlighting the consequences of future planning strategies
The results from this PhD study shows of how spatial planning can utilize advanced GIS techniques to gain valuable insight into the outcomes of different planning strategies. The results illustrate the potential improvements on sustainability that could be achieved, if planning practice where to change from the current patterns. Amongst others, factors such as urban proximity, public transportation accessibility and job proximity have been evaluated in the work with the scenarios. Furthermore well as methodologies for traffic impact analysis and population density measures that have been suggested, all demonstrating that the potential gains from rethinking the urban landscape through planning strategy.

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Forsvar

  1. 1. Supporting sustainable urban development through spatial planning by cell-based modelling Ph.d. defence by Morten Fuglsang, April 29 2014
  2. 2. Agenda 1. Brief introduction 2. The projects financing the phd study 3. The hypothesis and objectives 4. Methodology 5. Results 6. Conclusions 2
  3. 3. Introduction • Adapting towards more sustainable development in urbanised regions are of increasing importance • The aim of this study was to show how spatial planning can utilize advanced GIS techniques to gain valuable insight into the outcomes of different planning strategies 1
  4. 4. Financing projects • The project was financed by two EU projects: 1. The PASHMINA project (Paradigm Shifts And Innovative Approaches) 2. The BLAST project (Bringing Land And Sea Together) 3
  5. 5. Overall structure of the work • Three phases of the project: 1. The modelling of the scenarios towards 2040 2. Development of supplementary models and tools 3. Analysis of sustainability 4
  6. 6. The main hypothesis • Spatial analysis and modelling of land-use change can be applied to strengthen spatial planning by creating insight into the expected outcome of plans and thereby contribute to sustainable development 5
  7. 7. Research objectives • Analysing and describing drivers of land- use change in the case region, aiming at calibration and validation of the land-use simulations with the LUCIA model. • Analysing how the narratives from the PASHMINA project can be translated into land-use change projections expressing the structural changes from the scenarios. 6
  8. 8. Research objectives • Producing a set of spatial maps predicting the land- use change by 2040 under the drivers described in the scenario narratives. • Highlighting the consequences of the PASHMINA scenarios in terms of sustainability through the development of GIS analysis and discussing the outcome in relation to spatial planning. 7
  9. 9. The Pashmina scenarios 8
  10. 10. Case region • For the study, the region of Zealand and Lolland Falster was used as case region 9
  11. 11. Spatial datasets • Based on a literature study, the common datasets were analyzed. • A series of these datasets were created to potentially being used in the model including: 1. Urban density 2. Proximity 3. Accessibility 4. Value 10
  12. 12. Public transportation accessibility • Based on a ‘cell- crossing-time’ cost raster framework • Added constraints to the network to use stations 11
  13. 13. Car accessibility indicator • Uses a classification of 175 centers according to population • Using service areas, the hinterland of each center was determined 12
  14. 14. Analysis of indicators • For each indicator, an analysis of sensitivity was conducted on key parameters • Furthermore, the distance relationship to urban development was analyzed 13
  15. 15. Elements from the narrative 14
  16. 16. Interpretation into planning 15
  17. 17. The LUCIA model • Calculates land use demand from factors • Uses land use maps and population development as main data inputs 16
  18. 18. Modelling assumptions • Use existing administrative boundaries • Population estimates from DST are depicting shifts from rural to urban • No landscape protection measures included • LUCIA’s prediction of land use requirements are adequate 17
  19. 19. Configuration of drivers 18
  20. 20. Validation • Validation period from 1990 to 2002 was selected • Results were analyzed using Kappa statstics and fragmentation Fragmentation Mean Std. Dev. Observed 3,9 1,25 Modelled 3,5 1,92 19
  21. 21. Scenario results 20
  22. 22. Land-use impacts • Based on the CORINE classification, the results were analyzed for land-use conversions. 21
  23. 23. Analysis of scenario consequences • The scenarios were tested against a series of sustainability-measures • Furthermore, the scenario-results were used as inputs to other models to further access the implications of the proposed strategies 22
  24. 24. Fragmentation • The alternative scenarios produce a more compact urban structure 23
  25. 25. Public transportation accessibility The effect of the public transportation accessibility used in the apple scenario is evident 24
  26. 26. Public transportation accessibility 25
  27. 27. Job proximity Both alternative scenarios locate urban development closer to the jobs 26
  28. 28. Modelling tasks Three further analysis tasks were conducted: • A hotspot prediction mapping • Two add-ons for the LUCIA model – one for traffic and one for population were suggested 27
  29. 29. Hotspot predictions • Based on a combination of the scenario results, hotspots were calculated • Highlighted areas that are likely to urbanize no matter what planning practices are used 28
  30. 30. Population distribution • Using a dasymetric mapping approach, a model was constructed • Uses regional parameters for distribution functions • Not, however, a self- balanced model 29
  31. 31. Traffic prediction model • A raster based traffic distribution model was proposed • Uses location in space combined with statistics to predict traffic consequences of new urban developments 30
  32. 32. Result examples • The model produces result maps showing how the traffic load is going to evolve 31
  33. 33. Impacts • The Apple scenario shows improvements in traffic generated for all three case areas • Used to analyze the scenarios, it is clear that the model have difficulties with the orange scenario 32
  34. 34. Conclusions • The LUCIA model has been satisfactory validated for the observation period • The main spatial drivers of the observed land- use change in the case region are a combination of accessibility to urban centres and main roads, combined with property values 33
  35. 35. Perspectives • The scenarios were created in a subjective process – changes in the drivers affect the results • Focus on protection and preservation might have been included 34
  36. 36. Conclusions - continued • The results show how different the planning paths are from creations of very different urban landscapes • It has been shown that the impacts from the alternative planning paths can provide positive results in relation to urban sustainability 35
  37. 37. Perspectives • Hard to find studies to compare the sustainability analysis to • Work with the population and traffic prediction add-ons could and should be continued 36
  38. 38. Conclusions - continued • With the increase in computational power and advances in spatial data and models, municipality planners will be able to use scientific models, in order to predict the outcome of their plans. • The knowledge requirements may not be met presently, but with strong candidates educating from the universities, the skill levels of the GIS departments are rising 37
  39. 39. Conclusions - final • Therefore, models such as the LUCIA CA model will definitely become possible tools for the planners to create visualisations of their plans to ensure as high a degree of sustainable development as possible for the future… 38
  40. 40. Thank you for your attention 39

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