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Paul Kailiponi Duncan Shaw Aston Business School Aston CRISIS Centre

Spatial Vulnerability Assessment Using Dasymetrics and Multi-Attribute Value Functions. Paul Kailiponi Duncan Shaw Aston Business School Aston CRISIS Centre. www.astoncrisis.com. Presentation Outline. Spatial decision analysis Decision theory process using spatial data

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Paul Kailiponi Duncan Shaw Aston Business School Aston CRISIS Centre

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  1. Spatial Vulnerability Assessment Using Dasymetrics and Multi-Attribute Value Functions Paul KailiponiDuncan ShawAston Business SchoolAston CRISIS Centre www.astoncrisis.com

  2. 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. 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. 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. 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. 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. 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. 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. Dasymetrics, explained

  10. 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. UK Case Study – Flood Vulnerability • Environmental Agency (EA) Guidance • Multi-criteria vulnerability (Mileti 1999, Cutter 2000) • Evacuation Vulnerability Factors • Hazard data – Flood depth levels • 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. Factor maps (polygon aged)

  13. Factor Map (dasymetric)

  14. Factor map (Flood hieght)

  15. 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. Results (Visualisation) • Normalized factors, non-dasymetric

  17. Results (Visualisation) • Normalized, Dasymetric

  18. Results (Visualisation) • Value Function, dasymetric

  19. 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)

  20. Discussion & Generalisation • Compare spatial decision theoretic methods for risk assessment • Assumption of homogenous distribution can limit analysis accuracy due to: • Arbitrary nature of population data aggregation • Low-density areas • Need for areal interpolation (dasymetrics) • Decision Theory contribution • Substantive improvement to spatial risk assessment • Explicit spatial error terms for aggregated polygon data • Generalisation • Any multi-criteria spatial problem • Most useful for population data analysis

  21. Questions • Comments

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