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

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Person 1009 is a male aged 60, is employed, earns $594 a week and is making ends meet.They live as a couple with no dependents and privately renting a house with contents insuranceand has 0 cars. This person does not have health insurance, does not have a disability andspeaks English well. Due to the hazard impact, this person requires hospitalisation with lifethreatening injuries and their residence is completely destroyed. According to the recoveryscale, indicate how long do you think it will take for this person to recover from the followingimpact?• A destructive earthquake (1-10).....• A major flood (1-10).....• A major landslide (1-10).....• A major cyclone (1-10).....Person 1010 is a female aged 39, is employed, earns $434 a week and is making ends meet.They live as a couple with no dependents and own with no mortgage a house with houseinsuranceand has 1 car. This person does not have health insurance, does not have a disabilityand speaks English well. Due to the hazard impact, this person requires hospitalisation withlife threatening injuries and their residence is moderately damaged. According to the recoveryscale, indicate how long do you think it will take for this person to recover from the followingimpact?• A destructive earthquake (1-10).....• A major flood (1-10).....• A major landslide (1-10).....• A major cyclone (1-10).....75
Appendix BDecision Tree MethodologyIdentifying attributes that are perceived to make a person vulnerable is considered here tobe a ‘Knowledge Discovery in Databases’ exercise. Knowledge discovery is defined as ‘thenon-trivial extraction of implicit, unknown, and potentially useful information from data’ [27]and refers to techniques developed to transform large quantities of data into useful and understandableinformation [68]. One such technique for extracting useful knowledge from data isthrough data mining techniques, which refer to a class of algorithms used to identify patternsin data. The primary purpose of data mining is predictive modelling, a task which seeks toestablish a functional relationship between two sets of attributes in a database in order to predictvalues of one of the sets based on values of the other. Usually, one variable is selectedas the target or response variable and the resultant predictive model can be used to establishthe outcome of the target given any combination of the predicting attribute values. When theresponse variable is categorical, that is there are a fixed set of outcomes, one speaks of a classificationproblem. When it is continuous, there are an infinite number of outcomes, usually areal number, the problem is said to be a regression problem, where one wants to predict a realnumber.Decision tree induction is one of the most widely used Knowledge Discovery predictivetechniques because it;• Can handle both discrete (a value which has a finite number of distinct values, suchas the number of cars owned, whether or not someone has house or health insurance)and continuous data (a value which has an infinite number of possible values within aninterval, such as income, age and injuries)• Makes no assumption as to the shape or form of the distribution of the underlying data,• Is fast and efficient,• Is robust with regards to missing values and noisy data,• Is straightforward to interpret and comprehend,• Can provide an indication of attributes that are most important for prediction or classification,and• Is nonparametric: Decision trees do not require additional information (such as a functionalform) besides that already contained in the data set.The major weakness of decision tree induction (as well as other nonparametric methods)is that it is prone to errors in classification if many classes are involved or if there is a relativelysmall training data set.76
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G E O S C I E N C E A U S T R A L I
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Department of Industry, Tourism and
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3.4.3 Therelativeimportanceofthe15i
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AcknowledgmentsWe would like to tha
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in order to predict the vulnerabili
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BuildingVulnerability Model• Stru
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live in, while others relate direct
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Objectives of the studyThe research
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Chapter 1Indicator SelectionLiterat
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Indicators Mexico City San Francisc
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1.2.2 Study 2: Cities ProjectThe Ci
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anking the different administrative
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Number Indicator References1 Age [6
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Chapter 2Risk Perception Questionna
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have challenged the psychometric pa
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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 and 58: available at the SLA level for use
Page 59 and 60: • 4. Synthetic socio-economic dat
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: Person 1005 is a female aged 25, is
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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