GEOG3025

Multivariate neighbourhood indicators

GEOG3025

Multivariate neighbourhood indicators

• Lecture overview:Objectives of lecture

Introductory questions

Multivariate deprivation

Deprivation indicators

Calculation of indicators

Lecture summary

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Objectives• Understand the concept of multiple

material deprivation• Understand the principle of

multivariate deprivation indicators• Be able to calculate and interpret

general-purpose indicators

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Introductory questions…

What makes an area “deprived”?

How might you measure social deprivation?

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Multiple deprivation

• Recognition that deprivation is a multifaceted phenomenon (Black Report, 1980)

• Particular focus on health care applications

• Measurement requires combination of multiple variables – multivariate indicators

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Research areas

• Material deprivation as cause of health inequality

• Deprivation and mortality• Deprivation and educational

achievement• Use in resourcing public services

(e.g. standard spending assessment)

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Deprivaton or exclusion?

• Multiple deprivation– Derived directly from census data– Derived from census and administrative

data

• Social exclusion– Incorporating concepts of exclusion from

normal activities of citizens e.g. consumption, savings, production, political social (Burchardt et al. 2002)

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Multivariate deprivation…

Remoteness, elderly population?

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Poor transportation, unemployment, lone parents?

Overcrowding, unskilled jobs?

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Census-based deprivation indicators

• Jarman– Deprviation payments to GPs 1980s, 90s

• Carstairs– Health-related, esp. Scotland

• Townsend– Health-based, widely used

• Department of the Environment

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Census deprivation index construction: issues

• Data source choice• Variable choice• To combine or not?• To standardise or not?• To transform or not?• To weight or not?

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Two distributions…

Number of areas

Percentage

Not owner occupied

Unemployed

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Transform and/or standardise?

Standardise:adjust for spread

Transform: adjust for shape

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Example: Townsend score

• Input variables– No car– More than one person per room– Not owner occupied– Unemployed

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Townsend calculation

• Express variables as percentages• Log transformation of

unemployment and overcrowding values (+ 1)

• Z score standardisation of all variables

• Sum the standardised scores with equal weighting

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Townsend score for single area

Variable Percent Log Z

No car

Overcr.

Not own

Unemp.

Score

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Townsend score for single area

Variable Percent Log Score

No car 17

Overcr. 6

Not own 45

Unemp. 9

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Townsend score for single area

Variable Percent Log Z

No car 17 -

Overcr. 6 0.84

Not own 45 -

Unemp. 9 1

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Townsend score for single area

Variable Percent Log Z

No car 17 - 0.9

Overcr. 6 0.84 1.5

Not own 45 - 2.3

Unemp. 9 1 1.1

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Townsend score for single area

Variable Percent Log Z

No car 17 - 0.9

Overcr. 6 0.84 1.5

Not own 45 - 2.3

Unemp. 9 1 1.1

Score 5.8

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Deprivation mapping

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Non-census/hybrid indicators• Index of local conditions• Welsh Jarman• Index of local deprivation• Index of multiple deprivation• Combining census and non-census

variables:– House condition, income support, derelict

land, low GCSE grades, house insurance weights, etc.

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IMD 2000• Index of multiple

deprivation• 6 ‘domains’:

– Income– Employment– Health and disablity– Education, skills and training– Housing– Geographical access to

services

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MAUP/ecological fallacy

• Levels of measured deprivation dependent on area boundaries

• Large areas can conceal small areas (‘pockets’ of deprivation)

• Area-level deprivation indicators will misrepresent many individuals and households within the areas

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Structural problems…

• Different regions have different deprivation structures

• Claims of London-bias may just represent population figures

• Urban-rural deprivation differences• Welsh Jarman – additional

variables

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Assignment• Select variable set and study region• CASWEB retrieval (including

denominators)• Run Geodep spreadsheet• Import dataset• Define denominators• Design indicator• Run/re-run• Interpret scores

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Lecture summary

• Problems with specification and design• Data limitations – especially early

versions• Particularly relevant to general-

purpose indicators• Best to use purpose-specific indicators• Key review reference: Senior (2002) in

Census Data System