2012 A Spatial Odyssey:
Common geographies and place-based stories
ABS Stats Show | 13 October 2011
Dr Kate Liley
Evidence and Modelling (EMU)
Queensland Department of Communities
A User’s perspective
The spiral view from big picture to on the ground
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the Queensland Department of Communities
Evidence based management
The use of spatial data in human service planning
Data opportunities and challenges
The future’s looking bright…
Government Context
 In March 2009, Queensland Premier Anna Bligh made significant changes to the
Queensland Government to provide better coordination and services to
Queenslanders.
 The new streamlined government is the most significant reform in almost two
decades and involves the restructure of 23 departments into 13 departments.
 The Queensland Department of Communities service areas now span:
– Housing and Homelessness services
– Child Safety, Youth and Families, Community Participation
– Disability and Community Care Services & Multicultural Affairs Queensland
– Sport and Recreation Services
– Aboriginal and Torres Strait Islander Partnerships
 Our Strategic Framework and Strategic Plan translate to prioritising activity that
aligns with the department’s vision of Fair, cohesive and vibrant Queensland
communities and our purpose in providing integrated community services that
strengthen Queensland.
Policy and Service Priorities
 Contributing to the Toward Q2: Tomorrow’s Queensland Strong, Healthy, Green,
Smart and Fair ambitions by leading the Target Delivery Plan on Volunteering and
contributing to reducing Chronic Disease, Waiting Lists, and Jobless Households
targets;
 Implementing National Agreement and Partnership commitments effectively, including
those on Affordable Housing, Disability, Mental Health and Home and Community
Care;
 Closing the Gap by working with Aboriginal and Torres Strait Islander Queenslanders
and others, supporting reconciliation and driving reform in services, infrastructure and
planning in communities and making mainstream services more responsive;
 Supporting Queensland’s children and families at risk of entering the child protection
system through earlier intervention and better access to a range of services;
 Driving policy and service reform to strengthen Queensland’s community service
system and working with the NGO sector to implement the Queensland Compact;
 Supporting Queenslanders impacted by natural disasters and other crises; and
 Delivering a substantial capital works program to support improved client services.
From the Queensland Department of Communities Strategic Framework at http://www.communities.qld.gov.au/about/our-organisation/documents/docstrategic%20framework.pdf
QLD Context: Disadvantage Profile 1991-2011
In 2011
– People who are socially included (approx 84% of
population or 3.8 million people who don’t and probably
won’t ever need our services).
– Marginal socially included (approx 10% of population or
450,000 people ‘at risk’. Target for prevention and early
intervention services).
– Socially excluded (approx 6% of population or 270,000
people at high priority need. Target for intensive support
and continuing care services).
QLD Context: Disadvantage Profile 1991-2011
 Between 1991-2011 QLD’s population increased by
more than 50%.
 As the population has grown and aged over this
period the number of socially excluded has grown
and the caseload become more complex.
 Nonetheless, Queensland has ‘held the line’ in the
sense that the proportion of socially excluded and
marginally included is virtually unchanged since 2001.
 There is indirect evidence that this profile of
disadvantage has been in place since 1991.
Clustering of Disadvantage in QLD
 Disadvantage is not distributed randomly or uniformly
across QLD;
 It clusters in particular areas;
 It concentrates among particular groups;
 These clusters and concentrations are very stable; and
 The same groups and places have been the source of most
of the departments clients for over 20 years.
Where does Evidence and Modelling
Unit (EMU) fit in?
Key functions of the Evidence and Modelling Unit (EMU)
 Deliver sound evidence, data, business modelling and a consistent
process that informs policy development, program design and service
delivery and assist in resource allocation decisions; and
 Establish, coordinate and maintain consistent procedures around land
use planning, research, evaluation and review and needs based planning.
This activity includes:
sourcing relevant internal and external data and information;
providing strategic research coordination, guidance and quality
assurance;
conducting qualitative and quantitative analysis including complex
mathematical and economic modelling; and
sharing and mobilising knowledge and resources towards continuous
improvement.
These functions are described as Evidence Based Management
What is Evidence Based Management (EBM)?
Evidence Based Management is a means for the
Queensland Department of Communities
to match human services to priority community needs.
This means that planning decisions and funding allocation
are based on evidence of need so that resources will be
allocated on an effective and efficient basis.
Key Steps in Evidence Based Management
STEP 1: CONSTANT UPDATE OF COMSIS, DEPARTMENTAL RESEARCH, EVIDENCE
GATHERING AND ANALYSIS
STEP 2a: NEEDS
IDENTIFICATION AND
ANALYSIS (DNR)
Where are we now in terms
of this population/issue?
STEP 2b: REGIONAL
VALIDATION OF NEEDS
ANALYSIS
What is the regional
experience?
STEP 3: SERVICE SYSTEM
ANALYSIS
Includes Service System Analysis (Mapping
of need against service locations, service
capacity, catchments, service continuum
and community capacity)
STEP 5: IMPLEMENTATION,
MONITORING AND REVIEW
STEP 4: SYNTHESIS OF THE DATA (QUANT AND QUAL) TO
SUPPORT DEVELOPMENT OF OPTIONS FOR IMPROVEMENT
(Towards a systematic approach to maximising client benefit)
Disadvantage-need-risk ranking (DNR)
 An ordinal and indicative index calculated for each statistical local area (SLA) in
Queensland.
 The DNR rank is an output of internal analyses is a composite of:
 Disadvantage: the Socio-Economic Index for Areas (SEIFA) Disadvantage;
 Sentinel Indicators of Need such as births to mothers aged 15–19 years, and/or
child protection notifications in the case of identifying vulnerable children and
families; and
 Risk is based on the proportion of the target population within the general
population.
 Localities with a lower DNR rank are identified as having a higher need for support
services than localities with higher DNR ranks. However, the ranks do not represent an
absolute comparison of need (i.e., a locality with a lower DNR rank than another
locality is not necessarily more ‘needy’).
 This ranking provides a first indication of need within relevant populations that can be
checked against the local knowledge of regional service staff.
The sector has access to the DNR ranks through COMSIS as a means of accessing internal
analyses based on small area protected data.
COMSIS
 COMSIS is the Community Services Information System and is availably
publicly at https://statistics.oesr.qld.gov.au/comsis;
 is built and maintained by the Office of Economic and Statistical Research
(OESR – a portfolio office of Queensland Treasury) for the Department of
Communities;
 is a key tool in establishing a core set of common standards and
information across Queensland for identifying disadvantage need and risk;
 ABS data (needs focus) presented at SLA, LGA and DoC region level and
includes specific administrative data from other agencies;
 COMSIS delivers regional profiles and access to automatically updated
data from over 200 datasets for localities and population groups;
 is a key part of Evidence Based Management; and
 is available to the sector.
(see Queensland Compact at http://210.247.155.209/department/about/corporate-plans/queensland-compact/)
Evidence Based Management
EMU’s business decision support tools – EBM v2.0 toolkit
EBM Component
Output
EBM components
Organisational
Knowledge
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Research Theory
Research Synthesis
Strategic Research
Applied Research
Data – internal & external
Information & knowledge
sharing
Applied and targeted
evidence to inform decisions
about what works and why
Need-Demand
Analysis
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Existing & projected demand
for services
Service need (based on needs
assessment information)
Unsatisfied demand
Statistical & trend analysis
Market segmentation
Service utilisation rates and
projections (including barriers/
preconditions to service use)
Evidence showing the present
and future demand for
services (eg client group,
quantum, location, needs) to
inform strategic, tactical and
operational planning
Business Analytics
and Modelling
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Economic analysis & modelling
o Cost efficiency
o Cost effectiveness
o Cost benefit analysis
o Sensitivity analysis
· Financial modelling
· Resource allocation modelling
· Other quantitative analysis
method
Evidence showing whether
new or existing interventions
can meet identified outcomes
in the most cost effective and
efficient way.
Supply Analysis
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Spatial (GIS) analysis
Statistical & trend analysis
Service System effectiveness
Community Capacity
Assessment
Network pathways analysis
Organisational networks
Performance data, information
and benchmarking
Reviews & Evaluations
Evidence showing the present
and potential service system
and community capacity,
including productivity and
performance, to respond to
need
EMU – Tools, Processes, Guidelines
Across service and business areas following an agreed ‘standards-based’ approach and a core set of standards and principles
A ‘Mosaic’ approach
A Mosaic approach is the fusion/fission of art and science (or qualitative and
quantitative) using many small elements to make a picture that tells a story about an at
risk population or specific issue…but how do we make sense of the evidence?
‘I refer the Minister to Column YZ, Row 386...
the figure speaks for itself!’
Spatially enabling government (SEG)
The term spatial enablement of government (SEG) has appeared
relatively recently and mainly in Australia although it is starting to be
used elsewhere. There is no agreement yet on a definition of SEG,
however, spatial practitioners and researchers generally see it as the
deployment of spatial technology across government in a way that
enables government to make better decisions and become more
efficient. In the words of Williamson (2009) the enabling science,
technology and infrastructure provided by spatial information is
transforming the way governments do business.
Holling, P. (2009).Understanding spatial enablement of government. Proceedings of the Surveying & Spatial Sciences Institute Biennial
International Conference, Adelaide 2009, Surveying & Spatial Sciences Institute, pp. 65-73.
The Mappers Toolbox
viewing /exploring data
creating and editing data (the dataset is extended/ modified)
Storing data
conflation (integrating datasets from different sources)
transforming data (into different representations resulting in new
representation/format of the same data)
querying (resulting in a selection from the dataset)
analysing (resulting in a new dataset, with new information
obtained from the original dataset)
 create maps
Layering of :
 basic map of Queensland by region and
small area (currently SLA and SA2 level
forthcoming)
 thematic layer (such as SEIFA IRSD
disadvantage or DNR rank for a
program area)
 catchment area (custom boundary)
 service system points
 dot density of target population other
relevant services
 other relevant features such as
transport networks or water
Case Study 1: Need-Demand post disaster
Question: How many potential additional clients are services
likely to see post disaster in the short to medium term?
- Modelled on place-based data using assumptions about
severity of impact and SEIFA IRSD at CCD level
- Households identified through Rapid Damage Assessment
data provided by Department of Community Services
SEIFA IRSD at CCD Level
Roads
Services (inc. Child Safety)
Services (inc. Child Safety, HACC and Disability Providers)
Additional clients for the medium term
Additional clients for the short term
All Additional clients up to the medium term
All data
Case Study 2:
Feasibility testing of cross sector catchment data
Developing catchment data to identify opportunities for
integrated service provision across the North Coast Region
with the Action Network Team (includes, Education Queensland,
Queensland Health, Queensland Police Service, Boystown, Mission
Australia, Department of Human Services)
- Modelled on shared target population demand for
services (at risk persons 0-5 years and persons 6-17 years)
- Supporting the sector to identify the benefits of
developing catchment data
The power of spatially enabled data
GIS methods have been used nationally and internationally
to better understand the relationship between communities
and services (not just health services). There is a strong
argument that social catchment areas are more valuable,
useful and relevant as a base geography to plan and
understand services, rather than artificial administrative
boundaries that do not necessarily relate to the way
communities connect with each other.
http://www.anu.edu.au/aphcri/Hub_Research/GIS_REPORT_FINAL.pdf
North Coast Region
At Risk population
Catchments
Catchments and At Risk population
Catchment/At Risk population/Services
DNR
Recent QLD developments
 The Queensland Regionalisation Strategy is currently under
public consultation and includes:
Strategic direction 4 – fostering partnerships at the local,
state and national levels to promote coordination and drive
local leadership; and
Common regional boundaries across government to
provide greater consistency across planning, programming,
reporting and service delivery
http://waytogrow.qld.gov.au/queensland-regionalisation-strategy
Recent QLD developments
Agreement between key agencies to develop the conditions for a
common spatial understanding across government (DLGP, OESR,
ABS, DoC). Department of Communities is feasibility testing:
Adoption of ABS ASGS SA2 as the basic geography for
performance-administrative data collection and reporting;
Agreement to link data to place (geocoding);
Agreement to define broader administrative boundaries as
aggregates of SA2s; and
Data sharing on a place basis.
This common understanding will form a locality based evidence
base for whole-of-government policy and service development
delivering red-tape reductions and savings (data measured once for
multiple applications)
Opportunities include:
Challenges include:
 Queensland Regionalisation Strategy
 Differing levels of maturity across
government in the availability and use of
 Results from Census 2011
spatial information:
 Standards based spatial sources and examples
 Speed of access, data quality, data
of better practice:
sharing arrangements
– ABS, OESR, DERM, DLGP, Queensland
 Closing the loop – turning data into
Health
evidence and creating audience
– Information Queensland
understanding of analytical results:
(http://intranet.iq.govnet.qld.gov.au/inde
 Differing skillsets across government
x.html)
and the sector as consumers of data
– Queensland Government Information
and GIS outputs (not what does it
Service
mean, but rather what are the
http://dds.information.qld.gov.au/dds/
options for implementation)
– Other agency data (e.g. RDA data from
 …but the future’s looking bright
DCS)
2012 A Spatial Odyssey:
Common geographies and place-based stories
ABS Stats Show | 13 October 2011
Dr Kate Liley
Manager | Evidence and Modelling (EMU)
Queensland Department of Communities
Level 1, 61 Mary Street, PO Box 806, Qld 4001
E: Kate.Liley@Communities.qld.gov.au | T: 3224 5588 | M: 0467 811 806
Tomorrow's Queensland: strong, green, smart, healthy and fair www.towardQ2.qld.gov.au