preparing nations, cities, organisations and their people spatial vulnerability assessment using...
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Preparing Nations, Cities, Organisations and their People
Spatial Vulnerability Assessment Using Dasymetrics and Multi-Attribute Value Functions
Paul KailiponiDuncan ShawAston Business SchoolAston CRISIS Centre
www.astoncrisis.com
Preparing Nations, Cities, Organisations and their People
Presentation Outline
• Spatial decision analysis– Decision theory process using spatial data– Spatial location as unit identifier
• Limitations to spatial data in decision analysis– Arbitrary polygon aggregation– Assumption of homogenous distribution
• Combining Dasymetrics with Multi-Attribute Value Functions• Working Case Study – UK
– Flood vulnerability assessment– Sensitivity Analysis
• Generalization beyond emergency vulnerability assessments
Preparing Nations, Cities, Organisations and their People
Spatial Decision Theoretic
• Decision Theory ranking problem Choose (1) (2)
• Literature using multi-criteria spatial data to rank geographic features– Hazardous vehicle transport (Erkut & Verter 1995; Verter 2001, 2008)– Community development (Ghosh 2008)– Site suitability of evacuation shelters (Kar & Hodgson 2008)– Environmental justice (Maantay 2009)– Flood vulnerability (DEFRA/EA, 2006)– Loss estimates (Hazus MR4, 2009)
• Common Features– Unit identification based on spatial location– Use of census data as aggregation zones – Multiple criteria– Combine and Compare
c C
1 2max ( , ,..., )icc f x x x
Preparing Nations, Cities, Organisations and their People
Spatial Data & Decision Analysis
• Use of census data as aggregation zones• Polygon aggregation of population data• Reduce variation in population between aggregation zones• Arbitrary Zone creation (Malcezewski, 2000)
– US Census tract/blocks– UK Output areas
• Assumption of homogenous data spread
(3)
• Not unique to census data
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Preparing Nations, Cities, Organisations and their People
Spatial Data & Decision Analysis
• Unit identification based on spatial location– Unique unit identifier in statistical analysis– Non-commensurate spatial data– Comparison method for layered data
Preparing Nations, Cities, Organisations and their People
Spatial Data & Decision Analysis
• Multiple criteria analysis– Combining multiple attributes– Non-comparable attributes– Normalizations vs. Multi-attribute value functions
• Normalization
(4)
(5)
• Value Function (6)
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Preparing Nations, Cities, Organisations and their People
Combination methods
• Weighted Linear Combination (WLC)– Linear preferences of attributes (normalization
method)– Data independence between ( ) assumed (7)
(8)
• Multi-Attribute Value Functions– Verification of attribute independence– Additive functions similar to WLC– Multiplicative function for attribute dependence
(9)
ix
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Preparing Nations, Cities, Organisations and their People
Dasymetrics – Comparison Methods
• Apportionment• Ancillary Data
– Land-use mapping– Ground cover maps– City-level zoning– Settlement area zoning
• Advantages to Dasymetrics– Possible with both raster and polygon data– Explicit computational method– Allows variation in data redistribution & weighting
(population data)
Preparing Nations, Cities, Organisations and their People
Dasymetrics and Decision Theory
• Represents a method to analyse spatial data within decision theory
• Assumption of homogenous spread
• (4)
• Provides a unique identifier to (Holloway)
(5),
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Preparing Nations, Cities, Organisations and their People
UK Case Study – Flood Vulnerability
• Environmental Agency (EA) Guidance• Multi-criteria vulnerability (Mileti 1999, Cutter
2000)• Evacuation Vulnerability Factors
1. Hazard data – Flood depth levels2. Social data – Aged populations (60+) and
population with disability
• Identify areas of where the population may need additional evacuation resources due to vulnerability to flooding
Preparing Nations, Cities, Organisations and their People
Functional form verification
• Comparison methods– Normalization– Value Functions– Dasymetric vs. Homogenous distribution
• Combination method– Verification of data independence– Simple regression shows no interdependence
between aged (60+) and disabled population (sig. 0.255)
– Further expert elicitation through interview process– Equal weighting of factors (w = 0.33)
Preparing Nations, Cities, Organisations and their People
Results (Visualisation)
• Normalized factors, non-dasymetric
Preparing Nations, Cities, Organisations and their People
Results (Visualisation)
• Normalized, Dasymetric
Preparing Nations, Cities, Organisations and their People
Results (Visualisation)
• Value Function, dasymetric
Preparing Nations, Cities, Organisations and their People
Spatial data error term
• Aggregated unit error term– Measure of appropriateness of homogenous distribution– Habitable area
• Post Dasymetric cell error– Approx. 60% per – Difference between Dasymetric & Normalized map
statistically significant (p < 0.001)
Ward Level Total Population Error 1
Mablethrope Cen. 2086 0.045515
Mablethorpe East 2059 0.06164
Mablethorpe North 2125 0.045079
Sutton - Sea North 2161 0.079861
Sutton -Sea South 2226 0.108091
Trustthorpe 2411 0.335995
ic
Preparing Nations, Cities, Organisations and their People
Discussion & Generalisation• Compare spatial decision theoretic methods for risk assessment• Assumption of homogenous distribution can limit analysis
accuracy due to:1. Arbitrary nature of population data aggregation2. Low-density areas3. Need for areal interpolation (dasymetrics)
• Decision Theory contribution1. Substantive improvement to spatial risk assessment2. Explicit spatial error terms for aggregated polygon data
• Generalisation– Any multi-criteria spatial problem– Most useful for population data analysis