Quantifying Social Vulnerability: A methodology for ... - EarthByte

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Figure 4.2: The aggregated Census Districts across 3 Statistical Local Areas used in the study. The casestudy area includes southern Wanneroo and Swan Local Governemnts and is part of the larger GeoscienceAustralia Perth Cities Project study area.49
• 4. Synthetic socio-economic data The resulting synthetic data for each CensusDistrict has over 70 fields (i.e. all data variables for each of the 15 selectedindicators). Each field name indicates the variable and each field in the syntheticdata contains the number of households or reference people in that variable inthat Census District.4.3 Synthetic estimates for the Perth Case studyThe risk perception questionnaire describes an individual in terms of particular personalcharacteristics, but there is no information on the characteristics of other membersof the household (apart from whether a partner or dependents exist). Hence theunit of analysis is persons, but not all persons in the household. Therefore the syntheticdata must replicate these persons and those characteristics in order for the decisionrules to be appropriately applied to determine the small area geographic distribution ofvulnerability.In this application, it was decided to use the HES Household Reference person(a person chosen according to certain criteria articulated by the ABS) as a surrogatefor the randomly generated hypothetical individual. The household reference personis defined in the ABS HES technical paper [54] as one of the usual residents aged 15years and over that meet one or more of the following criteria:• partner in a registered or de facto marriage• lone parent with dependent child(ren)• person with the highest income• the eldest person.Table 4.1 shows the ratio of males to females for each family composition class.From this, we can deduce that the characteristics of household reference people areunlikely to match those of a randomly generated person. For example, the householdreference person is male in over 70% of cases, while one parent families are leadby a woman in nearly 90% of cases. The sex distribution of household referencepeople is less skewed in group and lone person households. This choice of unit ofanalysis means that the synthetic results are for the number of households or householdreference people in each Census District as a surrogate for the number of hypotheticalindividuals in each Census District.4.3.1 Matching questionnaire variablesOften HES data has more detail than the questionnaire’s classes of interest, so HESclasses were merged in order to match the classes. HES surrogates for English skills,disability and debt and savings were agreed as there were no exact matches. Table 4.2shows the variables selected from the 1998/99 HES which best match the vulnerabilityindicators. The third column in this table shows the classes used for each vulnerabilityindicator.50
Page 1 and 2:
G E O S C I E N C E A U S T R A L I
Page 3 and 4:
Department of Industry, Tourism and
Page 5 and 6:
3.4.3 Therelativeimportanceofthe15i
Page 7 and 8: AcknowledgmentsWe would like to tha
Page 9 and 10: in order to predict the vulnerabili
Page 11 and 12: BuildingVulnerability Model• Stru
Page 13 and 14: live in, while others relate direct
Page 15 and 16: Objectives of the studyThe research
Page 17 and 18: Chapter 1Indicator SelectionLiterat
Page 19 and 20: Indicators Mexico City San Francisc
Page 21 and 22: 1.2.2 Study 2: Cities ProjectThe Ci
Page 23 and 24: anking the different administrative
Page 25 and 26: Number Indicator References1 Age [6
Page 27 and 28: Chapter 2Risk Perception Questionna
Page 29 and 30: have challenged the psychometric pa
Page 31 and 32: Figure 2.2: Indicator 2: Income. We
Page 33 and 34: Figure 2.6: Indicator 6: English Sk
Page 35 and 36: Figure 2.10: Indicator 11: Debt and
Page 37 and 38: Indicator Classification Restrictio
Page 39 and 40: Experienced a hazard? Expert Non-Ex
Page 41 and 42: Chapter 3Decision Tree Analysis1. I
Page 43 and 44: Figure 3.1: Eight attributes with b
Page 45 and 46: P 2P 3P 4P 1P 5P 2 P 4P 3 P 5P 1(a)
Page 47 and 48: 38Rule Expression1 Injuries > 3.52
Page 49 and 50: Rule 4• An injury that requires h
Page 51 and 52: • Either owns their house or rent
Page 53 and 54: • Experts did rank ability to rec
Page 55 and 56: Chapter 4Synthetic Estimation1. Ind
Page 57: available at the SLA level for use
Page 61 and 62: Classes Residence Damage Descriptio
Page 63 and 64: increases from 49% (when damage is
Page 65 and 66: 56Figure 4.4: Scenario 2. This scen
Page 71 and 72: Percentage of CD in highly vulnerab
Page 73 and 74: 64Figure 4.11: Percentage of vulner
Page 75 and 76: Chapter 5DiscussionThis report has
Page 77 and 78: 68Figure 5.1: The Community Vulnera
Page 79 and 80: impact (including recovery time) wo
Page 81 and 82: Appendix ARisk Perception Questionn
Page 83 and 84: Person 1005 is a female aged 25, is
Page 85 and 86: Appendix BDecision Tree Methodology
Page 87 and 88: ule, which progressively refines th
Page 89 and 90: a subset will contain instances fro
Page 91 and 92: CharacteristicAgeTotal Individual I
Page 93 and 94: Appendix DValidationThe decision ru
Page 95 and 96: Bibliography[1] L. Anderson-Berry.
Page 97 and 98: [27] W. J. Frawley, G. Piatetsky-Sh
Page 99 and 100: [54] Australian Bureau of Statistic
Page 101: tenure, 83transportation, 27vulnera
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