Rasterbaseret modellering som beslutningsstøtte- værktøj til rumlig planlægning
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”Tilpasning mod mere bæredygtig udvikling i urbaniserede regioner er af stigende betydning i de fleste dele af verden. Ved at benytte rasterbaseret modellering af arealanvendelse kan der skabes viden om, hvordan planlægning af byområder kan anvendes som et redskab til sikre en højere grad af bæredygtighed. Der fokuseres her på anvendelsen af avancerede videnskabelige modeller, der bliver brugt til at illustrere, hvordan forskellige scenarier for fremtidig byudvikling vil udvikle sig, og hvilke konsekvenser disse vil have for den fremtidige arealanvendelse. Fokus er på tre fremtidige udviklings-scenarier fra EU FP7 projektet PASHMINA (Paradigm Shifts And Innovative Approaches), hvor der foreslås forskellige fremtidige strategier. De anvendte metoder varierer fra forskellige arealanvendelses-simuleringer med ’Land Use Change Impact Analysis' (LUCIA) Cellular automata modellen og afledte GIS analyser, alle med det mål at fremhæver konsekvenserne af de fremtidige planlægnings scenarier. Resultaterne viser, hvordan rumlig planlægning kan udnytte avancerede GIS teknikker til at opnå værdifuld indsigt i resultaterne af forskellige planlægnings strategier. Resultaterne illustrerer de potentielle forbedringer på bæredygtighed, der kunne opnås, hvis planlægningspraksis skiftede væk fra de nuværende traditioner. Blandt andet er faktorer såsom by- nærhed, offentlig transport tilgængelighed og job tæthed blevet evalueret i arbejdet med scenarierne. Endvidere er metoder til analyse af trafik-virkninger og befolkningstæthed blevet anvendt, som også illustrere de potentielle konsekvenser ved at nytænke det urbane landskab gennem planlægnings-strategi.”
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Rasterbaseret modellering som beslutningsstøtte- værktøj til rumlig planlægning
- 1. Rasterbaseret modellering som beslutningsstøtte- værktøj til rumlig planlægning Morten Fuglsang, Grontmij
- 2. Indhold 1. Baggrunden for phd’en 2. Hypoteser og forskningsspørgsmål 3. Metoder 4. Resultater 5. Konklusion
- 3. Baggrund 1. Målet med studiet var at demonstrere hvordan anvendelsen af avanceret GIS modellering kan støtte den rumlige planlægning, ved at berenge konsekvens- vurderinger af foreskellige planlægnings-strategier. Studier var financieret af: – PASHMINA projektet (Paradigm Shifts And Innovative Approaches) – BLAST projektet(Bringing Land And Sea Together)
- 4. Overordnede arbejdsopgaver • Projektet havde tre faser: 1. Modellering af scenarier frem mod 2040 2. Udvikling af supplerende modeller og værktøjer 3. Analyse af bæredygtighed
- 5. Forskningsmål • Analyser og beskriv drivere af urbanisering i studieområdet for at validere LUCIA modellen • Omsæt ‘historierne’ fra PASHMINA til planlægningsscenarier • På baggrund af disse scenarier, skabe arealanvendelseskort for 2040 • Belyse konsekvenserne af scenarierne gennem analyse af bæredygtigheden
- 7. Studie-området • I PASHMINA var Sjælland og Lolland Falster udpeget som studie-områder
- 8. Geografiske datasæt • Baseret på et litteraturstudie, de mest almideligt anvendte datatyper var analyseret • En lang række af disse data blev derefter produceret, for potentielt at indgå i analysen: 1. Nærhedsanalyser 2. Tæthedsanalyser 3. Tilgængelighedsanalyser 4. Analyser på værdi og boligmasse
- 9. Tilgængelighed til offentlig transport • Baseret på en ‘cell- crossing-time’ cost raster metode • Benytter constraints I netværket for at modellere stop og stationer
- 10. Tilgængelighed i bil • Benytter en klassifikation af 175 byer baseret på befolkningstallet • Ved at benytte service- area algoritmer bestemmes oplandet til den enkelte by
- 11. Analyse af indikatorer • For hver indikator blev en grundig følsomhedsanalyse foretaget • Samtidigt blev afstandsforholdet til bymæssig bebyggelse belyst for alle datasæt
- 13. LUCIA modellen • Beregner areal ændringer pba. behov • Bruger arealanvendelseskort og befolkning som hoved-input
- 15. Analyse af scenarie- konsekvenser • Scenarierne blev testet imod en række bæredygtighedsmål • Derudover blev resultaterne af scenarierne brugt som input til andre modeller, for ydereliger at kunne analysere deres konskekvenser
- 16. Fragmentering • De alternative scenarier resulterede i en noget tættere bystruktur
- 17. Adgangen til offentlig transport Effekten af at adgangen til offentig transport er nøgle-element i æble scenariet er tydelig
- 18. Tilgængeligheden til offentlig transport
- 19. Nærhed til jobs Begge de alternative scenarier placer byudviklingen tættere på arbejdspladser
- 20. Øvrige modelleringer Tre supplerende modelleringsopgaver blev udført: • Hotspot forudsigelse • To add-ons til LUCIA blev udviklet – et til trafik og et til befolkningstæthed blev foreslået
- 21. Hotspot forudsigelser • Baseret på en kombination af scenarierne, blev hotspots beregnet • Udpeger områder der sandsynligtvist bliver til byområder lige meget hvilke planlægningsstrategier der bliver besluttet
- 22. Befolkningsfordeling • Baseret på dasymetric mapping blev en befolkningsmodel konstrueret. • Benytter kommunale værdier som input • Er dog ikke en selv- balancerende model
- 23. Resultat eksempler • En rasterbaseret trafik-dustributionsmodel blev foreslået • Bruger cellens placering sammen med statistiske parametre til at forudsige trafik-konsekvenser af nye byområder
- 24. Konklusion • LUCIA modellen blev tilfredsstillende valideret for observationsperioden. • Resultaterne viser en konsekvensanalyse af forskellige planlægningsstrategier. • Det er blevet vist, hvordan alternative planlægningsstrategier kan have gavnlig effekt I forhold til bæredygtigheden.
- 26. Hvordan kan mit arbejde med geodata skabe værdi? I tilfælde af den forskningsbaserede anvendelse af geodata som mit arbejde er eksempel på, så skabes værdien gennem den viden og de resultater som bliver produceret Hvorfor skaber det værdi? Værdien skabes ved at kombinere grunddata modeller for at belyse konsekvenser og resultater der kan indgå som element i beslutningsprocesser og strategiske overvejelser – i dette tilfælde i planlægningen. For hvem? Planlæggere, beslutningstagere og forskere med interesse for konsekvensvurdering af planlægning Rasterbaseret modellering som beslutningsstøtte- værktøj til rumlig planlægning – Morten Fuglsang, Grontmij
Editor's Notes
- First i will shortly present the two projects that financed this phd study, to present how the tasks from the two projects shaped the study. Second i will present the main hypothesis ond objectives from the study, to frame the work and results that i am going to present. Third and fourt i will present the methodologies that i have used, and the results obtained from the different tasks within the study. Finally fitht, i will present the main conclusions of the study.
- My Phd was constructed as a combination of two EU projects: From AU Pashmina project From AAU the BLAST project In practice i was part time at both universities. From Pashmina the scenarios and the work with land use change modelling. From BLAST the traffic prediction mapping. An indicator for traffic development in realtion to climate change impacts was desired, and therefor a supplement to LUCIA was suggested, to create this indicator.
- The project was organised in three phases: Configuration and calibration of the LUCIA model to create the land use predictions. (main part) Creation of supplemnatary models and tools to be used to analyse the scenario results. Finally the scenarios are analysed for sustainability based on both the core LUCIA results, as well as the supplementary tools.
- Under the main hypothesis, a series of research questions was formed. Analyse drivers, and calibrate the LUCIA model Translate the scenarios from the PASHMINA project into scenarios in the model Produce result maps highlight the concequences by analysing the results, using different measures of sustainability.
- In the PASHMINA project, four scenarios was proposed, based on an analysis of scenarios from other projects, and a survey amongst different experts. The four scenarios was the pear, the apple, the orange and the potatoe. Pear… Apple… Orange… The potatoe scenario was in many of the modelling tasks in the project discarded, since it was agreed, that the models would not be able to make meaningfull predictions in this collaps situation. These three three scenarios was the backbone of the work conducted in this study. For all of the scenarios, a comprehensive narrative was written, that describe the world as it would evolve in the future under their circumstances.
- For the study, a case region comprising of Sealand and Lolland falster was selected - This was the case region that was written into the PASHMINA proposal, so i had limited influence on this choice. The case region comprise of the metropolitan area of copenhagen, and the hinterland. The case region are on an overall level expected to have a population increase in the comming decades, however with great variations on municipality level.
- In order to make a toolbox of spatial data, a study of litterature was conducted, analysing the most commenly applied. These data would potentially be used to impliment the scenarios. Based on this study, a list of potential datasets was created, and based on the availability of data, the datasets was then created. The list of datasets that was created include measures of urban density, proximity to motorway junctions, urban areas, stations etc., accessibility measures, and a property value indicators.
- To illustrate public transportation accessibility, a raster based calculation was proposed. Uses the networks from service providers, with information of travel speed. The concept was, that in vector based model, the netowork can be accessed at all points – our network only on stations. Therefor a system was created, that applied constrints to the network, so that the stations was only entry to the network. Based on a cost path analysis for centres, the traveltimes are combined, highlighting where we have high and low accessibility.
- Indicator based on the ‘acceptable commuting time’ concept – how longe are peopple willing to travel to work. A centre with few jobs have smaller attraction than major centres. Based on data from DST 175 centres was classified with an time attribute. Using service area calculations in a network anaylsis, the interland for all centres was established. The results from each centres was assigned the value 1, converted to raster and summarized. The cell value depictes the number of centres reacheable from each cell within acceptable commuting time.
- The datasets was analysed for sensitivity in the GIS analysis – testing parameter values – for the car asseccibility for instance, different values was assigned to the centres to test the output result, and se how the results vary. The indicators was also tested against observed land use changes from 1990 – 2002., to analyse if there are a general trend in the placement of urban areas in relation to the data values. If the observed urban development was spread evenly over the data, then the dataset have limited use. If hovever there are a clear trend in the placement, the dataset can be used in a prediction of new urban areas. Finally alle the indicators was analysed statistically to highlight the differneces in mean, std. Deviations etc.
- Here are the overview on the implimentation of the interpretations of the scenarios. Pear – no zones, road accessibility, proximity low Apple – developement on the fingerplan concepts, public transportation accessibility, proximmity is increased Orange – recreational accessibility, urban density and proximity is the key parameters. These setups where then to be incorporated into the LUCIA model – which i will just shortly present.
- The LUCIA model is a so called cellular automata model. Cellular automata is a mathematical methodology for cell based representation of data, where each individual cell is evaluated based on its value, and according to its neighbours. The LUCIA models basic operations can be seen on this figure (go through it) The factors included in the model are used in a multicriteria expression, assigning weights to factors in the calculation. Based on this calculation, the model predicts the most suitable candidates for land use change, and assignes them to urban in t+1 The model is then capable of producing future land use maps – year by year, determining how the urban landscape is going to develop based on the input data..
- Here we have the results from each of the scenarios. (1-3)It might be difficult to see the differnces in the maps, but if we look at Slagelse, we can se that there is in fact some major differences in the results. (4) If we combine the three results op, the overall pattern can be seen.
- Then the results from the scenarios was tested against a series of measures: First the actual scenario output was analysed And second the scenarios results was used as input to other model tools, to create extended results for the scenario predictions.
- For the three scenarios, the fragementation analysis was conducted for the 2040 result maps, analysing the areas that was urbanized It can be seen, that both the apple and orange scenario have a great effect on reducing fragmentation, making the urban landscape less scattered. It is commonly agreed, that if the the urban landscape is more compact and less scattered, the transportation work will be reduced – which benefits the environment.
- We dont know how the public trasnportation landscape is going to develope towards 2040, so it is hard to predict how the accessibility is going to develop Therefor we can only conduct analysis of this parameter against the current accessibility It can be seen, how the effect of the fingerplan and the closeness to stations priciples that was used in the creation of the apple scenario have a possitive effect on the potential accessibility to public transportation – which ofcourse was expected due to the driver configuration. The Orange scenario which uses the recreational indicator as one of the main drivers produce a poor result here, since the public transportation was not in focus, due to the nature of the scenarios dictating new forms of work and travel habbits.
- If the public transportation accessbility is analysed for each municipality, the improvements from the pear to the apple scenario can be highlighted.4 It can be seen, that in especially the southern and wester parts of the region, inrpovements can be achived, in creating an urban surface with closer linking to the public transportation network.
- Comparing the three scenarios to the job proximity indicators, it can be seen, how the two alternative scenarioes perform in locating urban development in areas which has a better local connection to jobs. By locating the development in areas with higher job densities should theoretically result in lower transportation amounts It can be seen, that the increas in job proximity is quite good for both the alternative scenarios, both showing an increase above 10% Therefor on this parameter as well, the two alternative scenarios would theoretically produce positive effects in relation to travel to and from work
- A series of further modelling tasks was conducted, where three was included in the thesis. Based on the scenarios a series of hotspot maps was calculated As addons to LUCIA two models was sugessted, calculating traffic and population density
- For the hotspot predictions the three rasters was combined into one, with the cell values idicating how many scenarios they where urbanized in (0-3) Then a hotspot analysis was conducted with different scale, to highlight where the urbanization is going to happen across the scenarios. This identifyes the areas where high degree of urbanization is going to happen – no matter what the planning focus is going to be.
- The first proposed LUCIA addon was a population distribution measure. Based on a dassymetric mapping approach where the population density in the different land use classes in 2006 are extrapolated to 2004 population maps are created for the three scenarios. The model uses regional estimation of weights, menaning that it is determined for each municiplaity – as the startpoint for the analysis. However due to time constriaints, the model was never coded to be self balancing, to produce the best population prediction based on the 2040 population, but was only based on the weights determined initially. The results here from køge shows how the pear scenrio comapres to the apple and orange. It can be seen, that the distribution of population changes alot between the scenarios, which directly could be used for estimating capacity planning.
- The results from the model shows, how the traffic development is going to evolve year by year based on the outputs from LUCIA. Here examples from Køge shows how differently the traffic development is going to evolve in the pear and apple scenario. It can be seen, that the traffic is much more spread out in the pear scenarios, where as it is more concentrated in the apple.
- Based on the criteria for implementation and validation that can be found in the literature, the model has been successfully validated for the observation period. The goal was not a pixel by pixel exact replication of the observed changes, but more a pattern that resembles in terms of fragmentation, and overall urban placement. 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 value. These where the drivers that was used to create the validation of the baseline – meaning that with this set of drivers, the urban development can be discribed as it was observed from 1990 - 2002