




































AUSTRALIAN POPULATION STUDIES  2019 | Volume 3 | Issue 1 | pages 30-39 

 

© Norman, Berrie & Exeter 2019. Published under the Creative Commons Attribution-NonCommercial licence 3.0 Australia 
(CC BY-NC 3.0 AU). Journal website: www.australianpopulationstudies.org 

 

Introductory Guide 

Calculating a deprivation index using 
census data 

Paul Norman*  University of Leeds 

Laurie Berrie  University of Leeds 

Daniel J Exeter  University of Auckland 

* Corresponding author. Email: p.d.norman@leeds.ac.uk. Address: School of Geography and Leeds 

Institute for Data Analytics, University of Leeds, Leeds LS2 9JT, United Kingdom 

Paper received 30 October 2018; accepted 4 April 2019; published 27 May 2019 

Abstract 

Background 

Deprivation indexes have widespread use in academic research and in local and national government 

applications. It is useful for people to understand their construction and to be able to calculate their 

own measures. 

Aims 

We provide an overview of the background to area based deprivation measures. We detail and 

explain a series of steps taken to calculate a deprivation index for small areas in Australia. 

Data and methods 

We use data from Australia’s 2016 Census of Population and Housing for the SA2 level of geography. 

After defining the set of variables used as inputs, we emulate the steps taken to calculate other 

census based deprivation indexes. 

Results 

The resulting scheme correlates closely with an official, but more sophisticated deprivation measure, 

suggesting that simple schemes have utility. 

Conclusions 

There are choices to be made for input variables and for some of the detail of the calculations. 

Researchers can follow the steps we describe to develop their own measures. 

 

Key words 

Area deprivation; composite measures; small areas; Census 

  

http://www.australianpopulationstudies.org/
mailto:p.d.norman@leeds.ac.uk


Australian Population Studies 3 (1) 2019 Norman, Berrie & Exeter  31 

1. Introduction 

The use of area deprivation indexes is widespread across academic research and in local and national 

government applications (Allik et al. 2016). The Australian Bureau of Statistics produce Socio-

Economic Indexes for Areas (SEIFA) using Census data, which includes the Index of Relative Socio-

economic Disadvantage (IRSD). The latest version uses 2016 Census data. 

Our aim here is to demonstrate a method of calculation to people, such as academic and local 

government researchers, who may be new to the production of composite measures. Following 

sections on generic aspects, this paper steps through the process of creating a deprivation index 

using census data for small areas (SA2s) in Australia for 2016. We take a similar approach to the 

calculation steps used in the long-established Townsend (Townsend et al. 1988) and Carstairs 

(Carstairs and Morris 1989) indexes. We compare the deprivation index calculated here with the 

SEIFA IRSD. 

We provide data files so that researchers can reproduce the deprivation index and believe the 

method used here to be accessible to quantitative population geographers new to this kind of work 

who will then be able to construct their own composite measures. These measures might focus on 

particular dimensions of deprivation or be for other purposes for which a combination of indicator 

variables are applicable. 

2. What is deprivation? 

Deprivation is one of those things which we probably think we understand until we tried to define it! 

Townsend's (1987 p. 125) definition is commonly used: “Deprivation may be defined as a state of 

observable and demonstrable disadvantage relative to the local community or the wider society or 

nation to which an individual, family or group belongs. The idea has come to be applied to conditions 

(that is, physical, environmental and social states or circumstances) rather than resources and to 

specific and not only general circumstances, and therefore can be distinguished from the concept of 

poverty”. Defining poverty itself is far from easy (Paterson and Gregory 2019). From a Eurocentric 

perspective, deprived areas are places in which concentrations of people and households with the 

relevant characteristics exist. 

3. What is a deprivation index? 

A deprivation index provides a single figure estimate of the level of deprivation for each location 

within the study area. Since deprivation is both hard to define and cannot be directly measured, the 

index is a latent construct calculated using a combination of indicator variables believed to capture 

relevant dimensions. Having a single figure per area simplifies applications that use the index 

compared to using multiple variables. The deprivation index value for any one area is usually 

expressed relative to a larger study region, often the national level (Fu et al. 2015). 

4. Which variables should be used as inputs? 

As Noble et al. (2006 p. 172) point out, if a researcher is devising their own index, there should first 

be a clear theoretical foundation underpinning the notion of small area deprivation, which may then 



32 Norman, Berrie & Exeter  Australian Population Studies 3 (1) 2019 

be operationalised as an index. Deprivation indexes have their roots in census-based work in the UK, 

first by Holtermann (1975) and subsequently by Townsend (1987) and others. We cannot turn back 

the clock, but speculate that there may not have been the same motivation to construct deprivation 

indexes had the UK, unlike many other countries, included an income question in the census (Dorling 

1999; Boyle and Dorling 2004). Thus, deprivation indexes in the UK use ‘proxy’ indicators of income 

and other dimensions of social and/or material hardship. Unemployment is ubiquitously included in 

schemes but there is variation in further variables used as inputs (see Senior 2002). In countries such 

as Australia, Canada and New Zealand, where income-related questions are asked, measures of 

individual and/or household income are components of their deprivation indexes. 

The researcher needs to obtain variables which best represent particular aspects of deprivation to 

ensure the index is fit for purpose. The set of chosen variables should complement each other so that 

the index is ‘greater than the sum of its parts’. The availability of potential variables for inclusion will 

be affected by decisions on the geographic unit used (below). Being based on data from a census, 

schemes are cross-sectional (though see the paper’s concluding section). To an extent there will be 

overlap of the facets chosen to proxy aspects of deprivation and likely to be strong inter-

relationships. Since the purpose is to identify multiple deprivation this collinearity may not be an 

issue. However, an index calculated in the manner described here would tend to have a few key 

variables. A more multi-dimensional approach would need a technique such as Principal Components 

Analysis (noted again below). 

5. Which geography should be used? 

Most deprivation indexes are devised for small areas (sub local government). As above, since the 

roots of schemes lie in the UK and the census has been the main source of data, then census 

geographies have tended to be used. Initially, within England and Wales, the most common 

geographical units used were the electoral wards (Mackenzie et al. 1998), while the Carstairs index, 

originally developed for Scotland, used Postal Sectors. These geographies were typically chosen in 

order to ensure that the count of people in each ward/sector was greater than 100, thus minimising 

the amount of missing / suppressed data. 

People are often motivated to develop an index for the smallest possible areas, to aid regeneration 

targeting, for example. If the aim is to relate to geographical health variations, then a geography 

which represents people’s day-to-day lives might be more suitable. Ideally, the researcher would 

choose the geography which best suited the relevant situation. In reality, researchers may well not 

have the freedom of choice of geography they might wish for, as the smallest unit for which the 

desired indicators are available may also dictate the level of analysis. It is important to note that all 

results will be affected by the specification of the zones which are used (Openshaw and Taylor 1981; 

Flowerdew et al. 2008). 

6. A small area deprivation index for Australia 

We now continue with the specifics involved with developing a small area deprivation index for 

Australia. Norman et al. (2016) calculated a measure of changing deprivation using data from the 

2001 and 2011 Censuses in Australia. We will incorporate similar variables here for a cross-sectional 



Australian Population Studies 3 (1) 2019 Norman, Berrie & Exeter  33 

deprivation index for 2016. The geography will be the Statistical Areas Level 2 (SA2s). Whilst not the 

smallest census geography, the SA2s are local level areal units for which detailed Australian Bureau 

of Statistics data are released, including tables for geographic areas by a variety of person and 

household attributes. SA2s generally have populations between 3,000 and 25,000 persons with an 

average population of around 10,000 (ABS 2017). 

6.1. Choose a set of indicator variables 

Table 1 lists the variables we use. These have been chosen to represent a range of deprivation 

dimensions: unemployment (Haynes et al. 1996); low income (D’Ambrosio and Frick 2007); lack of 

good English (Bertotti et al. 2012); low educational achievement (Wilkinson and Pickett 2007) and; 

lone parents (Santana 2002). These are here as examples and each can be substituted or there can 

be additional variables should a researcher wish to use different indicators. 

Table 1: Input variables from the 2016 Census in Australia (SA2) 

2016 Census table number & title Variable input to index 

G04 ‘Age by Sex’ Count of persons 

G13 ‘Language Spoken at Home by Proficiency in 
Spoken English Language by Sex’ 

% Persons who do not speak English well 

G16 ‘Highest Year of School Completed by Age by 
Sex’ 

% Persons not completing school to year 10  

G29 ‘Total Household Income (Weekly) by 
Household Composition’ 

% Households with income less than $1,500 (median 
income is $1,438) 

G39 ‘Dwelling Structure by Household Composition 
and Family Composition’  

% Dwellings with lone parent households  

G43 ‘Labour Force Status by Age by Sex’ % Persons unemployed 

Source: Australian Bureau of Statistics 

Note: The variables expressed as a percentage are Numerator / Denominator x 100. 

The variables listed in Table 1 have been obtained from the table number stated. The numerators 

and denominators are sourced from the same tables so that appropriate percentages can be 

calculated. The inputs used for these are included in this article’s online resources (noted at the end 

of the paper). The percentages represent variables whereby the higher the percentage, the greater 

the level of the deprivation. The polarity of the variables needs to be consistent.  

6.2. Excluding areas with very small populations 

In 2016 there were 2,301 SA2s in Australia but since some have very small populations, we only 

include those with 200 or more persons so the deprivation index developed is for 2,169 SA2s. 

Researchers could combine an area with a small population with an adjacent area so as not to lose 

data. 

6.3. Transforming and standardising the input variables 

Many social variables have skewed distributions and researchers may choose to transform input 

variables to (near) normal distributions. The Townsend index, for example, log transforms the 



34 Norman, Berrie & Exeter  Australian Population Studies 3 (1) 2019 

percentages of unemployment and overcrowding as these are positively skewed. The need for, and 

methods of transformations, are subject to debate (Gilthorpe 1995; Senior 2002). For simplicity, we 

do not transform variables here. 

There is more of a consensus on the need to standardise variables. Underpinning the need for this 

step is that each of the input variables will have differences in what are inherently large and small 

values. For example, at the SA2 level, the percentage of persons not completing school to year 10 

varies between 4.02% and 70.42% with a mean of 20.00, whereas the percentage of lone parents 

varies between 2.16% and 36.05% with a mean of 10.96. There is a need to have variables on a 

comparable scale. 

The most common method (as used in both the Townsend and Carstairs schemes) is to standardise 

using Z-scores. The inputs to the Z-score calculation for the SA2 variables are the percentages of each 

variable. The resulting Z-scores for all variables have a mean of zero and a standard deviation of 1 so 

they are now comparable. Higher positive Z-scores are higher levels of the indicator and more 

negative values are lower levels. Z-scores are calculated using the formula: 

Z-score = (Observation - Mean) / Standard Deviation 

Alternatively, the ‘range standardisation’ calculation can be used (Lucy and Burns 2017). Range 

standardisation rescales observations for a variable to lie between 0 and 1. 

6.4. Combining the standardised input variables 

The aim is to have a single figure deprivation index and a regular approach is to sum the Z-scores of 

the input variables. A simple ‘additive’, unweighted approach is used in the Townsend and Carstairs 

indexes so that all variables contribute equally. The Jarman index (1983) applies weights to 

differentially change the impact of inputs when they are summed. In reality, the weights applied by 

Jarman more likely reflect GP workload rather than a variable’s ability to better measure deprivation 

(Senior 2002).  

For deprivation in Australia, we sum the five sets of Z-scores unweighted to thereby have a 

deprivation index for the SA2 geography. Once summed, in terms of interpretation, more positive 

index scores reflect higher levels of deprivation and more negative values are lower levels of 

deprivation. Since the indicators aim to capture deprivation, areas found to lack deprivation should 

not be described as relatively wealthy or affluent. 

An alternative method of combining a set of indicator variables is to use Principal Components 

Analysis. The New Zealand Index of Deprivation for 2013 used nine census-derived variables of 

deprivation and the weights of the first principal components are used to combine the nine variables 

into an overall deprivation score (Atkinson et al. 2014). The SEIFA / IRSD in Australia also uses 

Principal Components Analysis (ABS 2018a). 

6.5. Categorising into quantiles 

Index scores themselves have great utility, but many applications use the scores categorised into 

quantiles. It is common to use quintiles or deciles whereby the distribution is divided into fifths or 

tenths. Often this is based on the number of areas or sometimes on the count of persons so that 



Australian Population Studies 3 (1) 2019 Norman, Berrie & Exeter  35 

there would be an equal number of areas or persons in each category. The impact of differently 

defined categorisations has had little coverage in the literature (compared with discussions of 

transformation and standardisation). The use of ‘population weighted’ quantiles has become 

common and is what we adopt here. The quantile cut-offs partition the deprivation distribution into 

categories with (near) equal numbers of people in each (Table 2). 

Table 2: Counts of persons in each deprivation quantile 

Deciles Persons Percent   Quintiles Persons Percent 

Q1 (least deprived) 2,337,625 10.01   Q1 (least deprived) 4,671,384 20.01 

Q2 2,333,759 9.99      

Q3 2,333,735 9.99   Q2 4,668,398 19.99 

Q4 2,334,663 10.00      

Q5 2,336,028 10.00   Q3 4,672,799 20.01 

Q6 2,336,771 10.01     

Q7 2,327,819 9.97   Q4 4,660,545 19.96 

Q8 2,332,726 9.99   

   Q9 2,332,560 9.99   Q5 (most deprived) 4,677,180 20.03 

Q10 (most deprived) 2,344,620 10.04   

   Total 23,350,306 100.00   Total 23,350,306 100.00 

Source: Based on Australian Bureau of Statistics data 

7. Comparison of the deprivation index with an existing scheme 

The Australian Bureau of Statistics produce Socio-Economic Indexes for Areas (SEIFA), which includes 

the Index of Relative Socio-economic Disadvantage (IRSD). The most recent version uses 2016 Census 

data. Note that there is a difference in polarity from the census measure we have developed here, so 

a low IRSD score indicates relatively greater disadvantage whereas a high score indicates a relative 

lack of disadvantage (ABS 2018b). The deprivation index developed in our analysis correlates -0.933 

with the IRSD suggesting that the two schemes identify similar patterns of (non-) deprivation. A 

cross-tabulation of deciles from both schemes also demonstrates fair similarity in the categorisation 

of areas (Table 3). 

Table 3: Cross-tabulation of the deprivation index and SEIFA IRSD quantiles 

  
Deprivation Index 

 

 
Deciles 

Q1 
(least) 

Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 
(most) 

Total 

SE
IF

A
 IR

SD
 

Q1 (most) 0 0 0 0 0 0 0 1 46 169 216 

Q2 0 0 0 0 0 1 25 49 111 31 217 

Q3 0 0 0 0 7 24 52 90 40 5 218 

Q4 0 0 0 9 25 52 67 43 21 1 218 

Q5 0 0 3 20 53 75 40 25 2 1 219 

Q6 0 0 11 63 70 49 18 6 0 0 217 

Q7 2 13 70 71 41 15 4 0 0 1 217 

Q8 4 65 93 34 15 4 1 0 0 0 216 

Q9 48 112 47 7 2 0 1 1 1 0 219 

Q10 (least) 177 34 1 0 0 0 0 0 0 0 212 

 
Total 231 224 225 204 213 220 208 215 221 208 2169 

Source: Based on Australian Bureau of Statistics data 



36 Norman, Berrie & Exeter  Australian Population Studies 3 (1) 2019 

8. Conclusion 

We have developed a deprivation index for the Australian SA2 geography using five indicators 

derived from the country’s 2016 Census. We have taken similar steps to those used to calculate the 

Townsend and Carstairs indexes by calculating percentages of the variables, which are then 

standardised and summed (unweighted) to form a single figure deprivation index. We categorise the 

deprivation scores into deciles and quintiles. The similarity between an index produced in this 

relatively simple way with the more sophisticated SEIFA IRSD is consistent with previous findings in 

Australia (Norman et al. 2016). 

Researchers wishing to develop their own indexes and make choices on geographic scale and input 

variables should be able to follow the same steps. It could be that an input to an existing scheme is 

regarded by others as an outcome of deprivation so there is a need for an index without that 

variable. The IMD for England includes a health indicator so there is risk of a self-fulfilling prophecy if 

relationships between deprivation and health are investigated (Adams and White 2004). Exeter et al. 

(2017) overcame this by providing a version of the IMD for New Zealand which removes a domain 

(i.e. IMD no Health) and redistributes the weights for the remaining six domains to reduce issues 

associated with data circularity. A composite index for a different application can be developed using 

relevant inputs. See, for example, Congdon (2004) on an index of social fragmentation and Lucy and 

Burns (2017) on an index of loneliness. 

Two aspects will be relevant to people wanting to build on this work relating to alternative data 

sources or extending from cross-sectional measures to those which capture changing deprivation. A 

variety of deprivation schemes now use administrative data sources (e.g. Noble et al. 2006; Exeter et 

al. 2017). With a move away from reliance on census data, the same approach as we use here can 

have administrative data inputs. A scheme for England (Ajebon and Norman 2016) using a small 

number of administrative variables as inputs relates closely to both a census derived measure as well 

as the more sophisticated, multiple domain IMDs (Noble et al. 2006) now in common usage. 

Census-based deprivation schemes are specific to the period of census data collection and 

administrative schemes for the year they are released (but may be based on inputs for different time 

points). All these are cross-sectional. However, areas change over time in their characteristics so 

increasingly researchers are interested in capturing this. Examples of methods and applications can 

be found in Norman (2010), Exeter et al. (2011; 2019), Norman et al. (2016), Pearce et al. (2016), 

Norman (2016), Green et al. (2017), Norman and Darlington-Pollock (2017) and Shackleton et al. 

(2018). 

9. Resources 

We provide an Excel file of the raw input variables plus commented files of SPSS syntax, Stata do and 

R script so that people can reproduce the process we have described above. A working knowledge of 

these packages is needed. The files can be accessed via: http://dx.doi.org/10.17632/k957gctr9d.1. 

  

http://dx.doi.org/10.17632/k957gctr9d.1


Australian Population Studies 3 (1) 2019 Norman, Berrie & Exeter  37 

Key messages 

 This introductory guide explains the background concepts relating to area deprivation and to data 

and geography choices. 

 The calculation of a deprivation index is stepped through and explained. 

 Researchers are provided with a set of resources through which to reproduce the process. 

 Researchers are encouraged to develop their own small area indexes. 

Acknowledgements 

We are grateful for the feedback from two anonymous referees. 

References 

Allik M, Brown D, Dundas R and Leyland A H (2016) Developing a new small-area measure of 
deprivation using 2001 and 2011 census data from Scotland. Health & Place 39: 122-130. 

ABS (2017) Australian Statistical Geography Standard (ASGS). 
http://www.abs.gov.au/websitedbs/D3310114.nsf/home/Australian+Statistical+Geography+St
andard+(ASGS). Accessed 17 May 2018. 

ABS (2018a) Technical Paper Socio-Economic Indexes for Areas (SEIFA) 2016. Catalogue No. 
2033.0.55.001. Canberra: ABS. 
http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/756EE3DBEFA869EFCA258259000B
A746/$File/SEIFA%202016%20Technical%20Paper.pdf. Accessed 22 March 2019. 

ABS (2018b) Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 
2016. Catalogue No. 2033.0.55.001. Canberra: ABS. 
http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001. Accessed 22 March 2019. 

Adams J and White M (2006) Removing the health domain from the Index of Multiple Deprivation 
2004 - effect on measured inequalities in census measures of health. Journal of Public Health 
28(4): 379-383. 

Ajebon M and Norman P (2016) Beyond the census: a spatial analysis of health and deprivation in 
England. GeoJournal 81(3): 395-410. 

Atkinson J, Salmond C and Crampton P (2014) NZDep2013 Index of Deprivation. University of Otago 
(Wellington) 
https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.h
tml. Accessed 27 September 2018. 

Bertotti M, Adams-Eaton F, Sheridan K and Renton A (2012) Key barriers to community cohesion: 
views from residents of 20 London deprived neighbourhoods GeoJournal 77(2): 223-234. 

Boyle P and Dorling D (2004) The 2001 UK census: remarkable resource or bygone legacy of the 
‘pencil and paper era’? Area 36(2): 101-110. 

Carstairs V and Morris R (1989) Deprivation: explaining differences in mortality between Scotland, 
England and Wales. British Medical Journal 299(6704): 886-889. 

Congdon P (2004) Commentary: Contextual effects: index construction and technique. International 
Journal of Epidemiology 33(4): 741-742. 

D’Ambrosio C and Frick J R (2007). Income satisfaction and relative deprivation: An empirical link. 
Social Indicators Research 81(3): 497-519. 

Dorling D (1999) Who’s afraid of income inequality? Environment & Planning A 31(4): 571-574. 

http://www.abs.gov.au/websitedbs/D3310114.nsf/home/Australian+Statistical+Geography+Standard+(ASGS)
http://www.abs.gov.au/websitedbs/D3310114.nsf/home/Australian+Statistical+Geography+Standard+(ASGS)
http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/756EE3DBEFA869EFCA258259000BA746/$File/SEIFA%202016%20Technical%20Paper.pdf
http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/756EE3DBEFA869EFCA258259000BA746/$File/SEIFA%202016%20Technical%20Paper.pdf
http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001
https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html
https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html


38 Norman, Berrie & Exeter  Australian Population Studies 3 (1) 2019 

Exeter D J, Boyle P J and Norman P (2011) Deprivation (im)mobility and cause-specific premature 
mortality in Scotland. Social Science & Medicine 72(3): 389-397. 

Exeter D J, Zhao J, Crengle S, Lee A and Browne M (2017) The New Zealand Indices of Multiple 
Deprivation (IMD): A new suite of indicators for social and health research in Aotearoa, New 
Zealand. PLoS ONE 12(8): e0181260. 

Exeter D J, Feng Z, Zhao J, Cavadino A and Norman P (2019) The geographic harmonisation of 
Scotland’s small area census data, 1981 to 2011. Health & Place 57: 22-26.  

Flowerdew R, Manley D J and Sabel S E (2008) Neighbourhood effects on health: does it matter 
where you draw the boundaries? Social Science & Medicine 66(5): 1241-1255.  

Fu M, Exeter D J and Anderson A (2015) “So, is that your ‘relative’ or mine?” A political-ecological 
critique of census-based area deprivation indices. Social Science & Medicine 142: 27-36.  

Gilthorpe M (1995) The importance of normalisation in the construction of deprivation indices. 
Journal of Epidemiology and Community Health 49(S2): S45-S50. 

Green M A, Arcaya M and Subramanian S V (2017) Using internal migration to estimate the causal 
effect of neighbourhood socioeconomic context on health: a longitudinal analysis (England, 
1995-2008). Annals of the Association of American Geographers 107(6): 1266-1278. 

Haynes R, Gale S, Lovett A and Bentham G (1996) Unemployment rate as an updatable health needs 
indicator for small areas. Journal of Public Health 18(1): 27-32. 

Holtermann S (1975) Areas of urban deprivation in Great Britain: an analysis of 1971 Census data. 
Social Trends 6: 33-45. 

Jarman B (1983) Identification of underprivileged areas. British Medical Journal 286: 1705-1709. 

Lucy L and Burns L (2017) Devising a composite index to analyze and model loneliness and related 
health risks in the United Kingdom. Gerontology and Geriatric Medicine 3: 1-10. 

Mackenzie I F, Nelder R, Maconachie M and Radford G (1998) My ward is more deprived than yours. 
Journal of Public Health Medicine 20(2): 186-190. 

Noble M, Wright G, Smith G and Dibben C (2006) Measuring multiple deprivation at the small-area 
level. Environment and Planning A 38(1): 169-185. 

Norman P (2010) Identifying change over time in small area socio-economic deprivation. Applied 
Spatial Analysis and Policy 3(2-3): 107-138. 

Norman P (2016) The changing geography of deprivation in Britain, 1971 to 2011 and beyond. In 
Champion T and Falkingham J (eds.) Population Change in the United Kingdom. London: 
Rowman and Littlefield; 193-214. 

Norman P and Darlington-Pollock F (2017) The changing geography of deprivation in Great Britain: 
exploiting small area census data, 1971 to 2011. In Stillwell J (ed.) Routledge Handbook of 
Census Resources, Methods and Applications. Abingdon, Oxon: Routledge; 404-420. 

Norman P, Charles-Edwards E and Wilson T (2016) Relationships between population change, 
deprivation change and health change at small area level: Australia 2001-2011. In Wilson T, 
Charles-Edwards E and Bell M (eds.) Demography for Planning and Policy: Australian Case 
Studies. Cham: Springer; 197-214. 

Openshaw S and Taylor P J (1981) The modifiable areal unit problem. In Wrigley N and Bennett R J 
(eds.) Quantitative Geography: A British View. London: Routledge and Kegan Paul; 335-350. 

Paterson L L and Gregory I N (2019) Defining and measuring poverty. In Representations of Poverty 
and Place. Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-319-93503-4_1  

Pearce J, Shortt N, Rind E and Mitchell R (2016) Life course, green space and health: incorporating 
place into life course epidemiology. International Journal of Environmental Research in Public 
Health 13(3): 331. 

Santana P (2002) Poverty, social exclusion and health in Portugal. Social Science & Medicine 55(1): 
33-45. 

https://www.sciencedirect.com/science/article/pii/S0277953607006272#!
https://www.sciencedirect.com/science/article/pii/S0277953607006272#!
https://www.sciencedirect.com/science/article/pii/S0277953607006272#!
https://www.sciencedirect.com/science/article/pii/S0277953615300526#!
https://www.sciencedirect.com/science/article/pii/S0277953615300526#!
https://www.sciencedirect.com/science/article/pii/S0277953615300526#!
https://doi.org/10.1007/978-3-319-93503-4_1


Australian Population Studies 3 (1) 2019 Norman, Berrie & Exeter  39 

Senior M L (2002) Deprivation indicators. In Rees P, Martin D and Williamson P (eds.) The Census 
Data System. Chichester: John Wiley; 123-137. 

Shackleton N, Darlington-Pollock F, Norman P, Jackson R and Exeter D (2018) Longitudinal 
deprivation trajectories and risk of cardiovascular disease in New Zealand. Health and Place 
53: 34-42. 

Townsend P (1987) Deprivation. Journal of Social Policy 16(2): 125-146. 

Townsend P, Phillimore P and Beattie A (1988) Health and Deprivation: Inequality and the North. 
Andover: Croom Helm. 

Wilkinson R G and Pickett K E (2007). The problems of relative deprivation: why some societies do 
better than others. Social Science & Medicine 65(9): 1965-1978. 

 


