impact of policy on urban growth gargi chaudhuri postdoctoral fellow resilience and adaptive...
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Impact of policy on urban growth
Gargi ChaudhuriPostdoctoral Fellow
Resilience and Adaptive Management GroupUniversity of Alaska Anchorage
AAG Annual Meeting 2012: SLEUTH Symposium
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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
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Objective•To find out the impact of policy on the
pattern of urban growth in a trans-border region
Courtesy: http://www.eea.europa.eu/articles/analysing-and-managing-urban-growth
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Gorizia, Italy and Nova Gorica, Slovenia
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4
Italy Slovenia
Gori
zia,
Italy
an
d N
ova G
ori
ca,
Slo
venia
Italy
Slovenia
Inte
rnat
iona
l Bor
der
Inte
rnati
onal B
ord
er
Source: Google Maps
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Nova Gorica, SLOVENIA
Gorizia, ITALY
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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
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SLEUTH
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Dat
a: S
cen
ario
11985 1991 1999 20041969 1998
S
L
E
U
T
H
Non-Weighted Roads
Weighted Roads
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Dat
a: S
cen
ario
2
Non-Weighted Roads
Weighted Roads
1985 1991 1999 20041969 1998
S
L
E
U
T
H
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Dat
a: S
cen
ario
3
1985 1991 1999 20041969 1998
S
L
E
U
T
H
Non-Weighted Roads
Weighted Roads
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Sc 1
Sc 2
Sc 3
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2040 Predicted Images 2040 Predicted Images with Weighted Road
data
2040 Predicted Images with Non-weighted Road data
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Non-weighted roadWeighted road
Scenario 1
Non-weighted road
Weighted road
Scenario 2
Scenario 3
Weighted road
Non-weighted road
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• 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)
Validation
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Spatial Metrics• Number of patches determines the level of fragmentation
• Perimeter-area fractal dimension (PAFRAC) determines the complexity of patch shape (1≤PAFRAC≤2)
• The contagion index is an overall measure of the extent to which the landscape patches are dispersed or clumped
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Conclusion• 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 hasn’t affected the urban growth of both the cities
• EU and local integration policies haven’t been successful in fostering an integrated regional growth of the region
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Thank you!!
University of California Transportation Center (UCTC) Dissertation Research
Grant
Dr. Andrea Faverratti, University of Trieste
Dr. Federico Martellozzo, McGill University
Acknowledgements