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Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
Perugia Voiron
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Perugia Voiron

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Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

Published in: Economy & Finance, Technology
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  • Du fait même de la définition de l’INSEE, sont exclus du recensement tous les logements appartenant à une personne âgée placée dans une institution, les logements en attente de règlement de succession
  • Transcript

    • 1. A Spatio-morphological Modelling for Spread Predicting Christine Voiron – Canicio UMR ESPACE - University of Nice Sophia Antipolis / CNRS
    • 2. The aim of the modelling <ul><li>The aim </li></ul><ul><ul><li>Predicting the broad outlines of a built-up areas extension </li></ul></ul><ul><ul><li>Providing decision makers with a tool which allows them to explore spatial consequences of different urbanisation policies </li></ul></ul><ul><li>The challenge </li></ul><ul><li>Finding a compromise between the level of generalisation and the level of accuracy </li></ul>
    • 3. Outline <ul><li>What is a spatio-morphological modelling ? </li></ul><ul><li>A model to predict the built-up areas spread in the coastal region of Languedoc (Southern France) </li></ul><ul><li>The stages of the modelling : </li></ul><ul><li>1) determining the spatial rules of the model </li></ul><ul><li>2) simulating in a retrospective way the progressive extension of </li></ul><ul><li>the built-up areas </li></ul><ul><li>3) validating the model. </li></ul><ul><li>4) using the validated model to simulate the future extension of built-up areas. </li></ul>
    • 4. What is a spatio-morphological modelling ? <ul><li>A spatial modelling performed by image processing and algorithms of Mathematical Morphology. MM is suited to spread models and to propagation simulations. </li></ul><ul><li>In this application, the model is deterministic, it assumes that the spatial spread depends on both distance and morphology of the built-up areas. </li></ul><ul><li>The spreading process essentially complies with elementary rules of distance to the built-up areas which are supposed to explain the major part of the spread. </li></ul>
    • 5. Stage 1: determining the spatial rules
    • 6. Stage 1: determining the spatial rules 98% of the new built-up areas have sprawled from the already built-up zones <ul><li>The spatial diffusion mode is that of « expansion diffusion » </li></ul><ul><li>Since 2000, the French law only permits new constructions which are contiguous to already built-up zones. </li></ul>
    • 7. Stage 1: determining the spatial rules <ul><li>Spatial rules : </li></ul><ul><li>The new built-up areas can spread from the existing built-up elements only. </li></ul><ul><li>The spread is forbidden wherever protected natural zones exist. </li></ul><ul><li>The extension of built-up areas occurs by the progressive connection of the nearest elements. </li></ul><ul><li>These connections are performed by using operators of image analysis. </li></ul>
    • 8. <ul><li>The spread process is performing by using 3 basic operations of Mathematical Morphology : the dilation , the erosion and the closing </li></ul><ul><li>A dilation corresponds to a thickening process of a given size </li></ul><ul><li>An erosion corresponds to a thining process </li></ul>Stage 2: simulating the spread by using image processing dilation size 1 erosion size 1
    • 9. <ul><li>A closing is a dilation of a given size + an erosion of the same size </li></ul>Stage 2: simulating the spread by using image processing The result of a closing is : i) the clustering of parts in the set under study ii) the hole filling action The spread process will be performed by using conditional closings of increasing size = + a closing size 2
    • 10. <ul><li>We deal with bmp images: </li></ul>Stage 2 : simulating the spread by using image processing
    • 11. Y Coefficient simil Image A1: Built-up areas in 1977 Image B: closing of size i I = i+ 1 simil is higher ? Elimination of points falling into sea, ponds, protected zones Image A2: observed built-up areas in 1990 Image B: result of closing simil (A2,B) = surface of intersection (A2,B) / surface of union (A2,B) Y Matching Flow chart of the spread modelling by image processing N The ctiterion for stopping the spread is the value of simil. The matching is performed for all probable sizes of closing, one takes that which maximizes simil
    • 12. <ul><li>« The predictive models are not expected to be accurate at the pixel scale but they are expected to predict the approximative shapes and locations of the phenomenon » (Power, Simms and White, 2001) </li></ul><ul><ul><li>1) The evaluation of similarity takes into account margins of error of : </li></ul></ul><ul><li>1 pixel (37 m) </li></ul><ul><li>2 pixels (74 m) </li></ul><ul><li>3 pixels (117 m) </li></ul><ul><li>by dilating the new predicted surfaces by 1, 2 or 3 pixels successively, before performing the intersection with the new observed surfaces. </li></ul><ul><ul><li>The visual comparison can suggest the need of local calibrations </li></ul></ul>Stage 3: validating the outputs
    • 13. Application: basic model simil calculated on the new built-up areas = 0.257 Results of the model 71% 65% 58% 48.5% Margin of error 3 pixels Margin of error 2 pixels Margin of error 1 pixel Pixel agreement
    • 14. Improving the model Subset 1: « attractive zones »: Stage 1: new spread process Subset 2: rest of the built-up areas Stage 2: protocol used to the 1st model
    • 15. Application: Improved model simil calculated on the new built-up areas = 0.41 Results of the model 77% 72% 65% 58% Improved model 71% 65% 58% 48.5% Basic model Margin of error 3 pixels Margin of error 2 pixels Margin of error 1 pixel Pixel agreement
    • 16. Stage 4: simulating the future extension of built-up areas This improved model is applied to seek the broad outlines of the future built-up surfaces, up to 2010 2 spread rates have been tested 6 % 3 %
    • 17. Summary <ul><li>Modelling by image processing is rich in potential. MM is well adapted to spatial analysis, especially to spatial propagation simulations. </li></ul><ul><li>This model has been performed to measure how much the urban spread depends on elementary rules of distance and morphology. </li></ul><ul><li>The results give the broad outlines of the future urbanisation to be discussed with the local autorithies. </li></ul><ul><li>New spatial rules can be added to take into account the topography and the road network of the region under study. </li></ul><ul><li>This model is deterministic. We are working on a randomisation of the urban spread by combining both « dilation » operation and Poisson points diffusion. </li></ul><ul><li>In other applications as risks the prediction is based on probabilistic approaches and simulations with random spread models. </li></ul>
    • 18. Thank you for your attention

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