The following data has now been added to the Community Insight Australia tool. It includes size and ages of Indigenous population, early childhood capabilities from the Australian Early Development Census (AEDC), breakdowns of industries of work for males and females, fields people are qualified in, median house price and weekly rent, internal migration (moving house), people who don’t speak English at home and a whole heap of health indicators from the Australian Institute of Health and Welfare (AIHW).

Population

Population aged 0-14

Population aged 65+

Median age of persons

Population density (persons/km2)

Population of working age (15-64 years)

Couple or one-parent families with children under 15 and/or dependent students

Population projection estimates 2017

Population projection estimates 2018

Population projection estimates 2019

Population projection estimates 2020

Population projection estimates 2021

Population projection estimates 2022

Population projection estimates 2023

Population projection estimates 2024

Population projection estimates 2025

Population projection estimates 2026

Population projection estimates 2027

Indigenous population

Indigenous females

Indigenous females aged 0 to 4 years

Indigenous females aged 10 to 14 years

Indigenous females aged 15 to 19 years

Indigenous females aged 20 to 24 years

Indigenous females aged 25 to 29 years

Indigenous females aged 30 to 34 years

Indigenous females aged 35 to 39 years

Indigenous females aged 40 to 44 years

Indigenous females aged 45 to 49 years

Indigenous females aged 5 to 9 years

Indigenous females aged 50 to 54 years

Indigenous females aged 55 to 59 years

Indigenous females aged 60 to 64 years

Indigenous females aged 65+ years

Indigenous males

Indigenous males aged 0 to 4 years

Indigenous males aged 10 to 14 years

Indigenous males aged 15 to 19 years

Indigenous males aged 20 to 24 years

Indigenous males aged 25 to 29 years

Indigenous males aged 30 to 34 years

Indigenous males aged 35 to 39 years

Indigenous males aged 40 to 44 years

Indigenous males aged 45 to 49 years

Indigenous males aged 5 to 9 years

Indigenous males aged 50 to 54 years

Indigenous males aged 55 to 59 years

Indigenous males aged 60 to 64 years

Indigenous males aged 65+ years

Indigenous people

Indigenous people aged 0 to 4 years

Indigenous people aged 10 to 14 years

Indigenous people aged 15 to 19 years

Indigenous people aged 20 to 24 years

Indigenous people aged 25 to 29 years

Indigenous people aged 30 to 34 years

Indigenous people aged 35 to 39 years

Indigenous people aged 40 to 44 years

Indigenous people aged 45 to 49 years

Indigenous people aged 5 to 9 years

Indigenous people aged 50 to 54 years

Indigenous people aged 55 to 59 years

Indigenous people aged 60 to 64 years

Indigenous people aged 65+ years

Health and Disabilities

Standardised death rate (deaths per 1,000 population)

Total fertility rate (births per woman)

Total number of registered births

Infant and young child mortality rate (< 5 years)

Infant mortality rate (< 1 year)

Life expectancy at birth for females

Life expectancy at birth for males

Rate of deaths from potentially treatable conditions per 100,000 people

Rate of female deaths from potentially treatable conditions per 100,000 people

Rate of male deaths from potentially treatable conditions per 100,000 people

Rate of potentially avoidable deaths per 100,000 people

Rate of potentially avoidable female deaths per 100,000 people

Rate of potentially avoidable male deaths per 100,000 people

Rate of potentially preventable deaths per 100,000 people

Rate of potentially preventable female deaths per 100,000 people

Rate of potentially preventable male deaths per 100,000 people

Percentage of frequent gp attenders

Percentage of frequent gp attenders (age-standardised)

Percentage of girls turning 15 years who were fully immunised against Human Papillomavirus (HPV)

Percentage of live births that were of low birth weight

Percentage of live births that were of low birth weight, Aboriginal and Torres Strait Islander women

Percentage of people who saw an allied health professional or nurse

Percentage of very high gp attenders

Percentage of very high gp attenders (age-standardised)

Percentage of women who gave birth and had at least one antenatal visit in the first trimester of pregnancy

Percentage of women who gave birth and had at least one antenatal visit in the first trimester of pregnancy, Aboriginal and Torres Strait Islander women

Percentage of women who gave birth and smoked during pregnancy

Percentage of women who gave birth and smoked during pregnancy, Aboriginal and Torres Strait Islander women

Percentage of Aboriginal and Torres Strait Islander children aged 1 year who were fully immunised

Percentage of Aboriginal and Torres Strait Islander children aged 2 years who were fully immunised

Percentage of Aboriginal and Torres Strait Islander children aged 5 years who were fully immunised

Percentage of adults who are obese

Percentage of adults who are overweight

Percentage of adults who are overweight or obese

Percentage of adults who felt they waited longer than acceptable to get an appointment with a GP

Percentage of adults who smoke daily

Percentage of children aged 1 year who were fully immunised

Percentage of children aged 2 years who were fully immunised

Percentage of children aged 5 years who were fully immunised

Expenditure on specialist attendances per person

Australian Early Development Census

Percentage of children developmentally vulnerable on one or more domains

Percentage of children developmentally vulnerable on two or more domains

Percentage of children developmentally at risk on the communication skills and general knowledge domain

Percentage of children developmentally at risk on the emotional maturity domain

Percentage of children developmentally at risk on the language and cognitive skills (school-based) domain

Percentage of children developmentally at risk on the physical health and wellbeing domain

Percentage of children developmentally at risk on the social competence domain

Percentage of children developmentally on track on the communication skills and general knowledge domain

Percentage of children developmentally on track on the emotional maturity domain

Percentage of children developmentally on track on the language and cognitive skills (school-based) domain

Percentage of children developmentally on track on the physical health and wellbeing domain

Percentage of children developmentally on track on the social competence domain

Percentage of children developmentally vulnerable on the communication skills and general knowledge domain

Percentage of children developmentally vulnerable on the emotional maturity domain

Percentage of children developmentally vulnerable on the language and cognitive skills (school-based) domain

Percentage of children developmentally vulnerable on the physical health and wellbeing domain

Percentage of children developmentally vulnerable on the social competence domain

Work and Earnings

Businesses with 5 or more employees

Labour force unemployment estimates

Occupations and Industries of Employment

  

These two maps look at employees of the manufacturing industry and show a difference between areas of highest concentration of males and areas of high concentration of females.

Females employed in accommodation and food services industry

Females employed in administrative and support services industry

Females employed in agriculture forestry and fishing industry

Females employed in arts and recreation services industry

Females employed in construction industry

Females employed in education and training industry

Females employed in electricity gas water and waste industry

Females employed in financial and insurance services industry

Females employed in health care and social assistance industry

Females employed in information media and telecommunications industry

Females employed in manufacturing industry

Females employed in mining industry

Females employed in other services industry

Females employed in professional scientific and technical services industry

Females employed in public administration and safety industry

Females employed in rental hiring and real estate services industry

Females employed in retail trade industry

Females employed in transport postal and warehousing industry

Females employed in wholesale trade industry

Males employed in accommodation and food services industry

Males employed in administrative and support services industry

Males employed in agriculture forestry and fishing industry

Males employed in arts and recreation services industry

Males employed in construction industry

Males employed in education and training industry

Males employed in electricity gas water and waste industry

Males employed in financial and insurance services industry

Males employed in health care and social assistance industry

Males employed in information media and telecommunications industry

Males employed in manufacturing industry

Males employed in mining industry

Males employed in other services industry

Males employed in professional scientific and technical services industry

Males employed in public administration and safety industry

Males employed in rental hiring and real estate services industry

Males employed in retail trade industry

Males employed in transport postal and warehousing industry

Males employed in wholesale trade industry

Education and Training

Non-School Qualification: Field of Study Agriculture Environmental and Related Studies

Non-School Qualification: Field of Study Architecture and Building

Non-School Qualification: Field of Study Creative Arts

Non-School Qualification: Field of Study Education

Non-School Qualification: Field of Study Engineering and Related Technologies

Non-School Qualification: Field of Study Food Hospitality and Personal Services

Non-School Qualification: Field of Study Health

Non-School Qualification: Field of Study Information Technology

Non-School Qualification: Field of Study Management and Commerce

Non-School Qualification: Field of Study Mixed Field Programmes

Non-School Qualification: Field of Study Natural and Physical Sciences

Non-School Qualification: Field of Study Society and Culture

Housing

Average household size

Average number of persons per bedroom

Lived at same address 1 year ago

Same usual address 1 year ago as in 2011

Same usual address 5 year ago as in 2011

Median family income (weekly)

Median house sale price

Median household income (weekly)

Median mortgage repayment (monthly)

Median personal income (weekly)

Median rent payment (weekly)

People living in a different usual address 5 years ago (in a different SA2)

People living in a different usual address 5 years ago (in the same SA2)

People living overseas 5 years ago

Migration and Culture

Internal Migration – Arrivals

Internal Migration – Departures

Internal Migration – Net

Females who speak language other than English at home

Males who speak language other than English at home

People who speak language other than English at home

What is data linkage?

Data linkage is the action of bringing together data that relate to the same individual, family, place or event. The resulting datasets reveal rich information that can be used for research, service planning, delivery and evaluation. For example, if data related to each person’s driving licences was linked to their public hospital admissions data, we could see whether people who are caught speeding are more likely to be admitted to hospital with heart problems over their lifetime. Data linkage development and centres that perform data linkage are rooted in health research, but are spreading across other service areas. Australia is one of the world leaders in data linkage, along with Canada and the UK, and has set an exceptionally high standard for the process and the use of its outputs.

Data linkage depends on the collection of data and produces rich datasets for the analysis of data, but neither collection nor analysis of data are within the definition of data linkage.

The data linkage process is designed to minimise the risk of anyone having access to identifying information (e.g. names and addresses) and linked data at the same time. The process usually seeks to avoid a situation where a massive dataset exists with everyone’s identifying information as well as information from more than one source.

Why do we need data linkage?

Many people assume that their state and federal governments have a massive database with information about everyone on it. This just isn’t the case. Every government department has a separate data system or even several. This means that when you enrol your child at school, they can’t just check your name and date of birth from when you applied for your driving licence. It can become repetitive giving each government agency the same information over and over again, but departments are not allowed to simply share people’s information with each other.

But if we do link data we can answer really difficult questions and even save lives. For example, if we link birth records to prescription records we can see whether certain drugs may be harmful if taken during pregnancy or childhood. This is important because many drugs haven’t been evaluated for pregnant women and children.^[1] Transport NSW linked its crash data to a number of other agencies to better understand the factors related to serious injuries and therefore improve road safety.^[2]

The Bureau of Crime Statistics and Research in NSW provides information to the public and policy makers all over the world. It is an exception to most government agencies as it continuously links Police data with data from the courts and corrections systems.^[3]

The primary purpose for developing linked datasets might be for research, service planning, delivery or evaluation. There are, however, secondary benefits to these datasets. For example, data linkage to evaluate the provision of health services to juvenile offenders will also provide valuable information that allows public servants to understand the health needs of juvenile offenders, so that they can plan to better address these needs and track changes over time. Other benefits include increased collaboration between agencies, improved analytical skills in the public service and new datasets that may be de-identified and provided for public access.

Is Community Insight Australia data linkage?

Community Insight Australia is technically data linkage as it links data by location, but our data is not linked by individual person, which is what most people mean when they refer to data linkage. So you can’t see how many divorced single parents born in Greece are on a disability pension. You can only define an area and see the proportion of divorcees, proportion of single parents, proportion of people born in Greece and proportion of people on a disability pension (see below).

Excerpt from Community Insight Australia dashboard. Figures are percentages of the population.

	Divorced	One parent family	Born in Greece	Disability Support Pension
Adelaide City	7.7	12	0.4	5.9
Gold Coast combined	10	17.3	0.1	4.7
Gold Coast North	10.4	18.6	0.1	4.9
Greater Hobart	8.1	17.6	0.2	6.4
Sutherland Shire	6.2	14.9	0	1.5

 Data sources: ABS census 2011, DSS payments data December 2015.

Why are people worried about data linkage?

One of the biggest challenges in making data available is ensuring that no person or organisation is likely to be identified in the data. Recently people were worried about their census data being linked to other datasets held by the government. Data linkage has risks:

1. Due to the increased richness of information relating to an individual during data linkage, it is more likely that a person can be identified from the new dataset than from the original datasets. For this reason, researchers working on linked data usually do so within secure environments (e.g. the SAX Institute’s Secure Unified Research Environment), which means that they cannot print or download the data, and their movements are tracked by secure systems.
2. Most data linkage processes ensure that personal information and linked data are never held by the same person (see example from CHeReL below). This means that even if data is hacked or leaked, the possibility of people and their information being identified is very low.
3. Current data linkage processes in Australia are very risk-averse and contain multiple safeguards. However unlikely, it is possible that there may be future changes to ABS legislation and other laws and processes to allow the creation and storage of linked datasets that include identifying information.

An example of a data linkage process – from CHeReL

The Centre for Health Record Linkage (CHeReL) is helps researchers, planners and policy makers access linked health data about people in the NSW and ACT.^[4] We describe the process they use to link data below. You can see how the process is constructed so that no one person or agency has access to both datasets as well as identifying information.

1. Approval

The researcher applies to CHeReL for specific data from specific custodians to be linked. Use of linked data through CHeReL requires three approvals, from:

• CHeReL
• the custodians of all data collections used in a linkage study
• a human research ethics committee

Before releasing any data, the NSW Ministry of Health requests a signed confidentiality agreement from the researchers.  Confidentiality agreement templates are available on the Ministry’s website at: http://www.health.nsw.gov.au/policies/pd/2012/PD2012_051.html

2. Linking

Data custodians split each dataset into the information that identifies a person and the information about their condition or history. They provide only the identifiers for each to CHeReL. Identifiers are information fields that can be matched, like name, birthday, address as well as government-generated information like medicare number.

3. CHeReL links the identifiers (using a master linkage key) and creates a Project Person Number (PPN) for each person in the datasets. They send the PPNs for each person’s identifiers back to the data custodians.

4. The data custodians attach the PPN to each person’s information in the original dataset (e.g. medical history, educational qualifications, traffic offences). The PPN replaces the identifiers. The data custodians provide the datasets + PPN to the researcher.

5. The researcher uses the PPN to link the datasets and create one big linked dataset. They receive no identifiers. The linked dataset is not available to CHeReL or the data custodians, only the researcher.

[1] http://www.abc.net.au/worldtoday/content/2013/s3739795.htm

[2] http://roadsafety.transport.nsw.gov.au/statistics/serious-injuries.html

[3] http://crimetool.bocsar.nsw.gov.au/bocsar/

[4] http://www.cherel.org.au/

Australian censuses usually pass without too much fuss and produce one of the most trustworthy sets of official statistics in the world. But the 2016 Census has seen data collection in the news, on talk shows, trending on twitter. So what’s the problem? Well statisticians aren’t always the best communicators. That’s why Community Insight Australia exists – because there’s a huge gap between the raw data and what most people easily understand. For this census, the Australian Bureau of Statistics changed the rules to keep people’s names and addresses for at least four years. And they failed to predict then effectively respond to the confusion and fear that many Australians felt when they learned of this change.

We have learnt from studying confidence in vaccines^[1], that when people have real concerns and we throw information at them, they are even less likely to change their mind because they feel they’re not being listened to. So before we spew forth information about data linkage and all its potential, let’s take a look at the concerns^[*].

And these concerns were realised. The census website was shut down due to attacks from hackers. Although it doesn’t appear these hackers accessed any information.

That’s pretty scary, so why aren’t we scared too?

Well it’s a lot easier to trust people you’ve met, and we’ve met a lot of people who work at the Australian Bureau of Statistics (ABS). Like most people who know a lot about the ABS^[2], we have a great deal of trust in them. They’re like you’d expect – most of them very clever, quiet, thoughtful. They dedicate their lives to collecting data and presenting it in a way that maintains confidentiality.  Shutting down the census website is consistent with that – it’s a public relations nightmare, but it put data security first. We’ve also spent the last year trying to get information from a lot of different government agencies, and even though we’re not asking for individual names and addresses, we’re hearing ‘No’ a lot because of data confidentiality. While we’d like them to be a bit more flexible, we couldn’t have more confidence in the integrity of public data custodians around Australia. And finally, we’ve done some work on the processes of data linkage. Current data linkage processes do not combine more than one dataset and personal information at the same time, so even if the data was hacked or leaked, it would be difficult to identify you. We’ll write a bit more about that next time.

But for now, as we wonder what will become of this stalled 2016 Census, we understand why people are worried about providing their names and addresses. If we didn’t capture your concerns, please add them in the comments below.

[1] BBC, 2015, What’s behind the ‘anti-vax’ movement? http://www.bbc.com/news/health-33774181

[2] ABS, 2010, Trust in ABS and ABS Statistics: A survey of specialist users and the general community http://www.nss.gov.au/nss/home.nsf/0/553dde916ab833e9ca2577b50072bf2f/$FILE/Trust%20in%20ABS%20and%20ABS%20Statistics.pdf

[*] If you have a concern that we haven’t captured, please let us know in the comments below.