New data added

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

Dec update - Perth - median age of persons.png

 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

dec-update-se-aus-indigenous-people

 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 DisabilitiesDec update - Brisbane and surrounds- life expectancy at birth for males.png

 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

Dec update - southern perth region- AEDC developmentally vulnerable on two or more domains.png

 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

Dec update - East of Adelaide- businesses with 5 or more employees.png

 Businesses with 5 or more employees
 Labour force unemployment estimates

Occupations and Industries of Employment

dec-update-sydney-to-newie-males-employed-in-manufacturing  dec-update-sydney-to-newie-females-employed-in-manufacturing

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 TrainingDec update - East Coast - health qualifications.png

 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

Dec update - Melbourne - median rental payment.png

 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

Dec update - South Canberra - People who speak a language other than English at home.png

 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

Data Linkage

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

  1. 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.

Data linkage 2.png

  1. 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.

data linkage 3.png

  1. 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.

data linkage 4.png

  1. 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.

data linkage 5.png

[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/

Submission to the Productivity Commission: data availability and use

The Data Availability and Use Inquiry

The Productivity Commission is carrying out a 12-month public inquiry to improve the availability and use of public and private sector data (http://www.pc.gov.au/inquiries/current/data-access) and is required to:

  • look at the benefits and costs of making public and private datasets more available
  • examine options for collection, sharing and release of data
  • identify ways consumers can use and benefit from access to data, particularly data about themselves
  • consider how to preserve individual privacy and control over data use.

Community Insight Australia made a submission that focused on the value of small area data. We described the datasets of high value to our subscribers as:

  • Aggregated by small area: it is not necessary to know the addresses of individuals to make evidence-based decisions for communities
  • Granular: the smaller the granularity, the higher the value for decision making
  • Easily accessible: if datasets have a hyperlink, they can checked by anyone who wants to verify their origin and accuracy
  • Related to the most vulnerable people: the more vulnerable the person, the more they benefit from providers better serving them.

We made several recommendations that we believe will enhance the availability and use of data in Australia. Our recommendations are based on our own experiences over the last two years.

Recommendations

1. Promote small area datasets as a good way to release data while maintaining confidentiality

Custodians are rightly concerned with maintaining the privacy of the individuals that are the subject of their data. Many datasets are a collection of information pertaining to individuals, such that each person has a number of fields of information relating to them. One of these fields is often their address. We have found that data custodians are reluctant to release datasets that contain address information, or if they do release it, it’s aggregated into such large areas (like states) that its spatial value is lost. One way to solve this problem, and still maintain confidentiality, is to aggregate addresses into small geographical areas.

CIA snapshot - Brisbane- unemployed and looking for work blue.png

For many government, community and commercial organisations, information aggregated into small areas can be used to make their work much more efficient and effective. As technology to process, display and analyse small area datasets advances, it becomes quicker and easier to use this information to add value to important decisions. Aggregating fields of information by grouping individuals who live or work in small geographical areas can protect the identity of those individuals. A minimum field (e.g. <15) can be set for small counts to protect the identification of individuals.

Custodians should consider small area aggregation as an option for their datasets and be able to access resources to help them publish in this manner.

2.  Identify and clearly signpost the people and resources within government that can educate data custodians about how to release data

We have found that we are educating data custodians about how to release data in a way that is valuable to external organisations, but maintains the confidentiality of the data subjects. It would be great if this role existed inside government. It took us over a year to connect with the regional dataset team in the ABS, who can help data custodians with their releases, but may not have the resources to do so. As well as having resources to assist with data release, custodians need to be able to locate those resources easily. Contacts and resources for data custodians should be clearly signposted in obvious places like data.gov.au and its state equivalents. These resources should include clear and accurate information on legislation and policy around data release and should be public, so that internal and external agencies can have informed conversations.

3.  Identify and clearly signpost the people within government who can approve or advise on data release for custodians

One of the most significant barriers to data release is the lack of clarity around governing legislation and policy. This is compounded by a cultural reluctance to share information. Agencies are risk averse around releasing data and lack confidence in what might happen to their programs and clients as a result of the data being used. A government authority that is familiar with the legislation and policy and can advise on release would solve this issue. This proposed authority would have a different role from that of the open data policy teams who advocate for the release of more data. The proposed authority would need to understand the minutiae of the relevant laws and policies, and the technical processes of organizing and releasing data that can help custodians to comply.

4.  Include someone with technical skills in open data policy teams

We have found that those in government advocating for open data and those in government who are custodians of data are not connected to each other, and that we are playing this connecting role from outside government. To date, the role of open data policy teams has focused on advocacy while the needs of data custodians in terms of making their data open are quite technical. Open data teams are doing a great job and we’re seeing a lot of great policy released, but in order to implement these policies, the teams need to include or work closely with those with the requisite technical skills. Open data policy teams need to include people with the capability to actually help custodians open their data. Over time, this should include a cross-over of technical processes and knowledge between custodians and open date policy teams so that the process of opening data becomes more efficient and less costly.

5.  Work with local governments to retain data in smaller areas

In several states, local governments are merging to create larger jurisdictions. We run the risk of losing richness in data categorised by local government area as part of this process. As the mergers progress, collectors and custodians should make efforts to maintain as much as possible the smaller data collection areas in their new systems. This data would be able to be aggregated to the new Local Government Area, but could still be split into the previous smaller councils for analysis.

productivity-commission-council-merger-picture(https://www.strongercouncils.nsw.gov.au/)

6.  Make it easier to request datasets

It is currently very difficult to find the appropriate person within a government department to contact about a dataset and then figure out the correct process to request the release of that dataset. Most of the processes available assume that the data is being requested for a one-off use and ask us to detail the intended use of the data before it is released. We have found that there is usually no process for requesting that datasets be released publicly. It would be more efficient and transparent to have a single point of contact in each government department that can direct traffic to the right custodian or a publicly available list of data custodians with a description of their data.  We envisage this role to be analogous to a freedom of information officer.

7.  Encourage custodians to remove barriers such as logins to see particular datasets

Some custodians require users to log in to access their datasets, for example Transport for NSW. This means that when a data point is referenced, there are several more steps to checking its source, which means people are less likely to do it. To encourage evidence-based decisions, we need to allow decision-makers to quickly view raw data.

transport-for-nsw

(https://opendata.transport.nsw.gov.au/)

8.  Create a process to standardise the definitions of state data

The collections of the productivity commission have created standardisation for a lot of state data.

Commencing a dialogue about standardizing other data collections would be the first step in a long process of using the same definitions, collection timeframes and geographical categories across all state data.

Comparable state datasets would enable linkage projects, help states learn from each other and also make life easier for organisations that work across state borders.

Users of data

When public data is made available, its value is often limited by the assumption that its audience is individual community members making personal decisions. The My School website is a good example of this (myschool.edu.au). While this in an important audience, it’s not the only one. At the other end of the scale, Transport for NSW (https://opendata.transport.nsw.gov.au/) has assumed that the primary audience for their data is app developers, which creates barriers for other users to access their datasets.

To maximise the value of data, it needs to be available or adaptable to the broader range of audiences that want to use it. Any publisher of data should check that it’s easy to find and download their raw data, before tailoring it to a specific audience.

We take raw data and make it meaningful to organisations making location-based decisions for people in need. Users of our data platform include government departments and not-for-profits. These organisations are important consumers of public data and use it to make valuable decisions for services in our communities.

It’s interesting that the scope of this Productivity Commission inquiry looks to “provide examples of public sector datasets that would provide high value to:

  • the public sector,
  • research sector,
  • academics and
  • the community.”

Missing from the list are not-for-profits and commercial businesses, organisations that are increasingly delivering government services. These organisations could vastly improve their service planning and delivery if they had better access to public sector datasets.

Uses of data

Examples of decisions that could be improved with better access to spatial data

Sara– proposing a parenting support program

Sara is proposing a new parenting support program in response to a WA Government tender. The government wants to target young families by providing weekly support and connections to local services. Sara’s organisation has a strong presence in Belmont, East Vic Park, Kelmscott and Morley, so she defines each of the areas in Community Insight Australia.

productivity commission slide 3.pngShe can see that, of the four areas, East Vic Park has the highest percentage of children under 5. She uses the heat map to find out that there are 1010 children under 5.

productivity commission slide 4.png

productivity commission slide 5.pngShe also discovers that there are 616 households that do not own a car.

She designs her program to cater to the community of East Vic Park and builds the cost of a mini-bus and children’s car seats into her budget.

Sara can do all of this using data from the census, but she wishes she had access to child protection information for each of the areas under consideration so that she could target the area where a parenting support program could most help families with young children at risk.

Ari – evaluating the tender

Ari works for the WA Government and is evaluating the responses to the tender for parenting support programs mentioned in the above example. He makes a shortlist of the top proposals, and then defines the areas each proposes to work in. He downloads a report on each of these areas and judges each tender on whether it meets the needs of the community proposed. The parenting program is part of a government-wide push to intervene earlier with children who develop anti-social and criminal behaviour. He wishes he could easily map the home suburbs of the children appearing in children’s court, so that he could target communities where youth crime is a particular problem. If he’d had access to that data as he wrote the tender, he could have asked organisations to propose programs in those areas from the start.

Jane – Buying new properties for community housing

Jane is on the assets management team of a community housing organisation. They are doing a strategic review of properties they own, trying to decide where to sell and where to buy. She creates ‘neighbourhoods’ by drawing around the areas they currently own in and areas they are considering investing in. She uses the dashboard to compare the economic descriptors of people in these communities and their median house prices, which informs the team’s discussion on the trade-offs between where they can afford to buy and where their tenants will thrive. Part of the work of the organisation is to support homeless people into housing. Jane wishes they had access to the areas where specialist homelessness services are in greatest demand, so that they could look for properties that didn’t ask people to relocate too far away from their support networks.

Additional example of high value datasets for the public sector, researchers, community organisations and individuals

The 2016 Census – what is everyone so worried about?

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.

Census picture

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.

Tech Talk with Malcolm Turnbull

We chatted with Malcolm Turnbull about how we’re making public data accessible! Check it out:

Malcolm Turnbull Tech Talk

”Community Insight Australia are seeking to use, based on a UK model, a lot of this open data that the government has put out there on data.gov.au …in a way that is going to be valuable for social service agencies, for businesses, for not-for-profits, to be able to better understand the communities they are serving and manage their service delivery.” Malcolm Turnbull, August 2015

Australian Government Big Data Public- Private Partnership with Community Insight Australia

ProBono Australia published the following article, “Partnership to Open Up Data for Social Enterprises”.

New Sydney-based social enterprise, Community Insight Australia will partner with the Federal Government to help social purpose organisations access data.

“There’s a lot of data openly available that’s not being used to target and design services for those in need,”  Community Insight Australia founder, social impact investor and analyst, Emma Tomkinson said.

“Spreadsheets really put people off using data, so we’ve replaced the spreadsheet with a map.

“Community Insight Australia will be a translation of Community Insight in the UK, which was nominated for an innovation award by the Open Data Institute.”

Tomkinson said Australian social purpose organisations will be able to define an area to analyse and then tailor their services to the needs of people in that area.

“The new web-tool will incorporate over 50 key data releases by all levels of Australian Government, including Federal, State and local councils, for example, datasets from the Australian Bureau of Statistics,” she said.

“Community Insight Australia is working in partnership with the Federal Department of Communications to identify useful and relevant datasets for inclusion in the tool.

“The Department is providing in-kind support through facilitating a User Group with representatives from other Federal Government agencies to identify appropriate data and, where possible, make the data openly available through data.gov.au.”

Tomkinson said the Community Insight Australia initiative is an example of how Government data can be used and re-used by organisations that serve Australian communities.

She pointed to a speech on open Government data to the Locate 15 Conference in March 2015, where the Minister for Communications, Malcolm Turnbull said, “Governments hold an extraordinary amount of unique data, collected directly and indirectly in the course of doing our job. It is there. We have it. But there is no point in keeping all that data stored away. It needs to be accessed, analysed, understood, used and reused.”

Also behind the new social enterprise is Brigitte Poletto, a winner of the University of NSW 2014 Start-Up Games.

“Poletto is one of a new generation of skilled young entrepreneurs who are emerging from University with an appetite for running their own businesses,” Tomkinson said.

“It’s really exciting to be working directly with the custodians of Government data. This is a great opportunity for us to make key data more accessible,” Poletto said.

“We want to help people planning new services. If you can find out before you start how many families don’t speak English at home and how many households don’t own a car, you can deliver a more tailored service from day one.”

Tomkinson said Community Insight Australia is currently securing funding to build the tool via a 3-way match; $50,000 institutional loan, $50,000 from ‘angel’ co-investors and $50,000 in pre-subscriptions.

“Community Insight Australia is seeking community organisations, businesses and Government agencies to pre-subscribe as ‘Foundation Partners’.”

Contact Community Insight Australia via jaime@communityinsightaustralia.org.