preparing nations, cities, organisations and their people spatial vulnerability assessment using...

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


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


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


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

,i

a i

xe

a



• Unit identification based on spatial location– Unique unit identifier in statistical analysis– Non-commensurate spatial data– Comparison method for layered data



• Multiple criteria analysis– Combining multiple attributes– Non-comparable attributes– Normalizations vs. Multi-attribute value functions

• Normalization

(4)

(5)

• Value Function (6)

max

ii

xn

x

0 1in

/( ) ix Ri iv x k e


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

i i ic w x1iw

1

1 ( ) [ ( ) 1]n

i i i ii

wV x wwv x


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)


Dasymetrics, explained


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),

( * )a

a it

NR

EeA

,i

a i

xe

a

ic


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


Factor maps (polygon aged)


Factor Map (dasymetric)


Factor map (Flood hieght)


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)


Results (Visualisation)

• Normalized factors, non-dasymetric



• Normalized, Dasymetric



• Value Function, dasymetric


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


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


•Questions•Comments

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