1. Preparing Nations, Cities, Organisations and their People Spatial Vulnerability Assessment Using Dasymetrics and Multi-Attribute Value Functions Paul Kailiponi Duncan Shaw Aston Business School Aston CRISIS Centre www.astoncrisis.com

2. 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

3. 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

4. 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

5. 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

6. 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)

7. 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)

8. 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)

9. Preparing Nations, Cities, Organisations and their People Dasymetrics, explained

10. 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)

11. 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 levels 2.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

12. Preparing Nations, Cities, Organisations and their People Factor maps (polygon aged)

13. Preparing Nations, Cities, Organisations and their People Factor Map (dasymetric)

14. Preparing Nations, Cities, Organisations and their People Factor map (Flood hieght)

15. 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)

16. Preparing Nations, Cities, Organisations and their People Results (Visualisation) Normalized factors, non-dasymetric

17. Preparing Nations, Cities, Organisations and their People Results (Visualisation) Normalized, Dasymetric

18. Preparing Nations, Cities, Organisations and their People Results (Visualisation) Value Function, dasymetric

19. 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 LevelTotal PopulationError 1 Mablethrope Cen.20860.045515 Mablethorpe East20590.06164 Mablethorpe North21250.045079 Sutton - Sea North21610.079861 Sutton -Sea South22260.108091 Trustthorpe24110.335995

20. 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 aggregation 2.Low-density areas 3.Need for areal interpolation (dasymetrics) Decision Theory contribution 1.Substantive improvement to spatial risk assessment 2.Explicit spatial error terms for aggregated polygon data Generalisation –Any multi-criteria spatial problem –Most useful for population data analysis

21. Preparing Nations, Cities, Organisations and their People Questions Comments