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Impact of policy on urban growth Gargi Chaudhuri Postdoctoral Fellow Resilience and Adaptive Management Group University of Alaska Anchorage.

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Presentation on theme: "Impact of policy on urban growth Gargi Chaudhuri Postdoctoral Fellow Resilience and Adaptive Management Group University of Alaska Anchorage."— Presentation transcript:

1 Impact of policy on urban growth Gargi Chaudhuri Postdoctoral Fellow Resilience and Adaptive Management Group University of Alaska Anchorage

2 Background Simulate policy effects on urban growth via CA model Border represents a transitional zone, a region of flow and connection between two socio-economic systems Policy is broadly defined as the combination of the political decisions, political history and situations which have directly affected the land use and socio-economic activities on the both sides of the border Policy makers are the land managers and political leaders who affect how land is used at this very local level (Reid et al. 2006) Background Objectives Study Area Methodology Data Results Conclusion

3 Objective To find out the impact of policy on the pattern of urban growth in a trans-border region Courtesy:

4 4 4 ItalySlovenia Italy Slovenia International Border Source: Google Maps

5

6 Historical Background Initially under the Austro-Hungarian empire 1915: Taken over by Italy 1943 – 1945: Taken over by Germany Completely destroyed and rebuilt during World War II 1945 – 1947: Acquired by Yugoslavia 1947: Old city of Gorizia was divided and western part of the city went back to Italy and eastern portion remained in Yugoslavia (Nova Gorica) 1992: Became part of Slovenia after partition of Yugoslavia. 2004: Slovenia became part of EU Spatial Development Strategy of Slovenia (2004) implemented to foster agglomerated growth of the region 2007: Became part of Eurozone

7 SLEUTH

8 S L E U T H Non-Weighted Roads Weighted Roads

9 Non-Weighted Roads Weighted Roads S L E U T H

10 S L E U T H Non-Weighted Roads Weighted Roads

11 11

12 Sc 1 Sc 2 Sc 3

13 2040 Predicted Images 2040 Predicted Images with Weighted Road data 2040 Predicted Images with Non-weighted Road data

14 14 Non-weighted road Weighted road Scenario 1 Non-weighted road Weighted road Scenario 2 Scenario 3 Weighted road Non-weighted road

15 15 Kappa coefficient Map comparison technique which is computed from a confusion matrix derived from cell-by-cell comparison of the observed map and the predicted map Kappa = khisto * kloc khisto similarity of the quantitative results, based upon the total number of cells taken in by each class kloc compares the actual success space to the expected success rate relative to the maximum success space given that the total number of cells of each category does not change < 0.00 = poor; 0.00 – 0.20 = slight; 0.21 – 0.40 = fair; 0.41 – 0.60 = moderate; 0.61 – 0.80 = substantial; 0.81 – 1.00 = almost perfect. (Landis and Koch, 1977)

16 Number of patches determines the level of fragmentation Perimeter-area fractal dimension (PAFRAC) determines the complexity of patch shape (1PAFRAC2) The contagion index is an overall measure of the extent to which the landscape patches are dispersed or clumped

17 At the local level there are no significant differences between urban growth with weighted roads and non- weighted roads Scenario 3 is more accurate than Scenario 2 and 1 Nova Gorica still retains its dispersed urban growth pattern Scenario 2 and 1 have almost same level of accuracy Overall, the territorial cohesion hasnt affected the urban growth of both the cities EU and local integration policies havent been successful in fostering an integrated regional growth of the region 17

18 Thank you!! University of California Transportation Center (UCTC) Dissertation Research Grant Dr. Andrea Faverratti, University of Trieste Dr. Federico Martellozzo, McGill University Acknowledgements


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