1. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Urban Generator introinput datasimulation results

2. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research SLEUTH model Cellular automaton based urban growth model *developed by Keith C. Clarke, University of California, Santa Barbara *written in C, running under unix, http://www.ncgia.ucsb.edu/projects/gig/ The model is initialized with four type of input data applies growth rules controlled by five growth coefficients can be calibrated to correspond recent development by using historical cross section data (input data) > individual growth coefficients for the area basic simulation unit is a growth cycle [Slope, Land cover, Exclusion, Urbanization, Transportation, and Hillshade]

3. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research SLEUTH operational principle

4. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research The model includes the following self modification procedure: Self modification

5. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research (hillshade) background urban Input Data excludedslopetransportation (landcover) for deltatron module binary/gray scale gif files

6. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Growth Coefficients Diffusion Breed Spread Slope Road Gravity Coefficient values effect how the growth rules are applied. These values are calibrated by comparing simulated land cover change with a historical data of the area.

7. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Growth Rules Spontaneous Growth (diffusion, slope)

8. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research New Spreading Center Growth (breed, slope) Growth Rules

9. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Edge Growth (spread, slope) Growth Rules

10. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Road-Influenced Growth (breed, road gravity, diffusion) Growth Rules

11. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Calibration

12. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research TUT contribution Applicability of Finnish GIS data bases Comparison of ’free’ & ’controlled’ growth land use policy scenarios Testing changes in ’total potential of growth’ of excluded layer Experimenting different representations of transportation networks

13. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki Historical data 1965 – (1975)- 1985 –(1995)– 2005 Calibrations with diffrent input data Urban area: 5 historical cross sections Excluded: ’controlled’ / ’free’ growth normalized / unnormalized Transportation: main roads as lines (binary) accessibility surface (grayscale) railroads + stations r=600m (binary) main roads + railroads + stations (binary) Predictions  2050

14. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Urban Area 196519852005 Urban areas were defined on the basis of Finnish grid format cencus data: - sum of floor area in 250x250m cells - number buildings in 250x250m cells Areas with quite low density were selected as ’urbanized areas’ in order to catch the prawl-like development. (1975)(1995)

15. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Excluded ’controlled growth’’free growth’ unnormalizednormalized

16. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Excluded 1 – Controlled Growth 196519852005 Regional plans describing probability of growth.

17. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Excluded 1 – Controlled Growth Probability of urbanization: Central functions100 Housing/ Industrial areas 80 Undefined/ special areas 60 Recreation areas 40 Protected areas 20 WaterSytems 0 Case Helsinki – Input Data Probability increases >

18. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Excluded 2 – ’Free’ Growth / unnormalized Probability of urbanization is equal everywhere – except areas of water systems.

19. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data UnnormalizedNormalized (50 % of MAX) Excluded 2 – ’Free’ growth Total potential of growth = sum of probabilities of cells to be urbanized

20. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Transportation Accessibility of road network Main roads as lines Railroads + stationsMain roads + railroads

21. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Transportation – binary step based accessibility analysis of road network. Accessibility (connectivity) analysis transformed to grayscale surface.

22. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki – Input Data Slope Slopes were generated from topographical database.

23. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Simulation Predictions Prediction maps: Probability of a cell to be urbanized. 0 … 100 % The simulations were carried out with all input data combinations.

24. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Simulation area of Helsinki region

25. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Prediction - Helsinki Input

26. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Prediction - Turku Input

27. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Predictions Helsinki 2040 ’Controlled’ Growth ’Free’ Growth accessibilityroads as linesrailroads + stationsmain roads + railroads

28. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Predictions Helsinki 2050 ’Controlled’ Growth ’Free’ Growth railroads + stationsmain roads + railroadsaccessibilityroads as lines

29. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Hki 022Hki 029Hki 030 Predictions In all three cases transportation layer: accessibility. With different ’excluded’ inputs

30. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Predictions HKI 10 unnormalized HKI 18 nomalized Transportation: Roads as lines. Comparison of unnormalized and normalized case

31. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Helsinki Predictions ’Free’ growth as ’excluded’ input Protected areas have only small probability to be urbanized also in the case of ’free’ growth.

32. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Case Helsinki Case Turku Predictions Accessibility as ’transportation’ input

33. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Predictions With different ’transportation’ inputs / Case Turku

34. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Some Conclusions Total potential of growth in ’excluded’ layer impacts to amount of growth as expected and should be taken into account when creating scenarios Representation of transportation network influenced significantly to form and focus of growth Strenght of the SLEUTH model is highly visual results Future work: tools for analyses of prediction images

35. Tampere University of TechnologySanna IltanenEDGE Laboratory for Architectural and Urban Research Thank you!