Liveability aspirations and realities: Implementation of urban policies designed to create healthy cities in Australia

Journal Pre-proof Liveability aspirations and realities: Implementation of urban policies designed to create healthy cities in Australia Melanie Lowe, Jonathan Arundel, Paula Hooper, Julianna Rozek, Carl Higgs, Rebecca Roberts, Billie Giles-Corti PII:

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https://doi.org/10.1016/j.socscimed.2019.112713

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Social Science & Medicine

Received Date: 3 August 2019 Revised Date:

3 November 2019

Accepted Date: 1 December 2019

Please cite this article as: Lowe, M., Arundel, J., Hooper, P., Rozek, J., Higgs, C., Roberts, R., Giles-Corti, B., Liveability aspirations and realities: Implementation of urban policies designed to create healthy cities in Australia, Social Science & Medicine (2020), doi: https://doi.org/10.1016/ j.socscimed.2019.112713. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Manuscript title: Liveability aspirations and realities: Implementation of urban policies designed to create healthy cities in Australia

Authors: Melanie Lowea, Jonathan Arundelb, Paula Hooperc, Julianna Rozekb, Carl Higgsb, Rebecca Robertsb, Billie Giles-Cortib

Affiliations: a

School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia b Healthy Liveable Cities Group, Centre for Urban Research, RMIT University, Melbourne, Victoria, Australia c Australian Urban Design Research Centre, School of Design, The University of Western Australia, Perth, Western Australia, Australia

Corresponding author: Dr Melanie Lowe Lecturer in Public Health, School of Behavioural and Health Sciences, Australian Catholic University Level 2, Daniel Mannix Building, 17-29 Young Street, Fitzroy VIC 3065, Australia T: +61(0)399533837 E: [email protected] Post: Locked Bag 4115, Fitzroy VIC 3065, Australia

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Liveability aspirations and realities: Implementation of urban policies designed

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to create healthy cities in Australia

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Abstract

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Creating healthy, liveable cities is a common policy aspiration globally. However, little

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research has explored the capacity of urban policies to deliver this aspiration, or levels of

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policy implementation. This study aimed to develop policy-relevant indicators, to detect

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within- and between-city inequities in the implementation of Australian state government

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policy targets related to urban liveability. Seventy-three government policies were reviewed

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across Australia’s four largest cities to identify measurable spatial policies that contribute to

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creating healthy, liveable neighbourhoods. Spatial indicators based on these policies were

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developed to assess and map levels of policy implementation at the metropolitan and sub-

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metropolitan level. Measurable spatial policies were identified for only three out of seven

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policy domains: walkability, transit access, and public open space. While there was

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significant variation between cities, policies were often inconsistent with evidence about how

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to achieve liveability. No Australian city performed well on all liveability domains. Even

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modest policy targets were often not achieved, and there were significant spatial inequities in

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policy implementation. With few exceptions, people living in outer suburbs had poorer access

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to amenities than inner-city residents. This study demonstrates the benefits and challenges of

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measuring urban policy implementation. Evidence-informed targets are needed in urban,

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transport and infrastructure policies designed to create healthy, liveable cities, to enable

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levels of (and inequities in) policy implementation to be assessed. Consistent standards for

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government spatial data would enable development of comparable indicators and cities to be

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directly compared.

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Keywords: healthy cities; liveability; walkability; indicators; policy implementation;

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geographic inequities; spatial analysis

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1. Introduction

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The co-benefits of urban liveability for the economy, social inclusion, environmental

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sustainability and public health are now well recognised by urban policymakers

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internationally (United Nations, 2016). Yet liveability is rarely defined in policy documents

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or the academic literature. A liveable community has been defined as one that is ‘safe,

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attractive, socially cohesive and inclusive, and environmentally sustainable; with affordable

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and diverse housing linked by convenient public transport, walking and cycling infrastructure

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to employment, education, public open space, local shops, health and community services,

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and leisure and cultural opportunities’ (Lowe et al., 2015, p.138). Designing liveable cities

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will both promote the health and wellbeing of residents (World Health Organization, 2016),

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and help nations achieve the United Nations (UN) Sustainable Development Goals (United

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Nations, 2016; United Nations General Assembly, 2015). Urban liveability is shaped by the

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policies and practices of many sectors. In this study, seven policy domains relevant to urban

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liveability (Badland et al., 2015) were analysed with regard to levels of policy

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implementation in four Australian capital cities: walkability, transit access, public open

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space, housing affordability, employment, food environments and alcohol environments.

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1.1 How do domains of liveability influence health?

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Increasing walking is a global priority given the health benefits of physical activity (Giles-

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Corti et al., 2016; World Health Organization & UN Habitat, 2016). Urban design, land use

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and transport planning can create walkable neighbourhoods, which are the building blocks of

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liveable cities (Hooper et al., 2015). Walkable, pedestrian-friendly neighbourhoods have

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higher residential densities and street connectivity, mixed land uses and high-quality

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pedestrian infrastructure (Giles-Corti et al., 2016). They encourage transport walking by

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creating shorter and more convenient walking routes between homes and local destinations

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such as retail shops, essential infrastructure and services (Giles-Corti et al., 2016; Sallis et al.,

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2016).

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Access to high quality transit is influenced by both land use and transport planning. Shorter

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distances to transit stops increases transport-related walking and reduces motor vehicle use

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(Frank et al., 2004; Rachele et al., 2015). Private motor vehicle traffic increases the risk of

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traffic-related injuries (Ewing et al., 2003), and reduces air quality (Samet, 2011) . However,

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transit access is also an underlying social determinant of health, determining the accessibility

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of employment and services (Kjellstrom & Hinde, 2007).

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Urban design codes often specify the amount and proximity of public open space. Access to

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high-quality public open space such as parks or sporting fields creates convivial and

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attractive environments (Villanueva et al., 2015), encouraging recreational physical activity

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(Giles-Corti et al., 2015a; Sugiyama et al., 2015) and improving mental health (Francis et al.,

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2012; The Healthy Built Environments Program, 2012). Urban greening also helps reduce the

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urban heat island effect and protects biodiversity (Davern et al., 2016).

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A host of policies influence housing affordability including land use planning, building

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regulations, social housing and financial policies. Housing affordability and the quality,

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location and density of housing are all health equity issues (Commission on Social

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Determinants of Health, 2008). High housing costs relative to income for low-income groups,

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and a higher proportion of households renting are associated with poorer self-rated health

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(Badland et al., 2017). Further, less affordable housing is associated with greater

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dissatisfaction, and more residents feeling unsafe (Badland et al., 2017). 3

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The availability of local employment is shaped by land use planning and economic

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development policies (Badland et al., 2016a; Badland et al., 2014). Employment location

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influences access to local job opportunities, the length of daily commutes, transport mode

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choice, and time spent driving (Frank et al., 2004), all of which are associated with

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population health and wellbeing (Giles-Corti et al., 2016).

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The local food environment describes the type, availability and accessibility of food options

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(Murphy et al., 2017), and influences dietary choices. Unhealthy diets are a leading cause of

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chronic disease globally (Institute for Health Metrics and Evaluation, 2013). Access to

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supermarkets is associated with higher consumption of fruit and vegetables (The Healthy

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Built Environments Program, 2012); and the relative density of healthy to unhealthy food

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outlets influences food choices (Mason et al., 2013). Further, nearby food stores may

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encourage shopping trips using active transport (Stafford et al., 2007).

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The alcohol environment also affects health, with higher densities of alcohol outlets

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associated with harmful consumption of alcohol (Foster et al., 2017b) and alcohol-related

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violence (Livingstone, 2011). Disadvantaged areas have been found to have more alcohol

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outlets (Foster et al., 2017a) and greater associated harms (Badland et al., 2016b).

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1.2 Creating liveable cities in Australia

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By international standards, Australian cities are very liveable (Economist Intelligence Unit,

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2015; Holden & Scerri, 2013). However, inequitable distribution of infrastructure and

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services is well recognised. Affordable housing developments located in outer-suburban areas

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lack access to local essential infrastructure, jobs and services, resulting in car dependency and

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long commutes, while amenity-rich inner-city areas experience traffic congestion and high

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housing costs (Arundel et al., 2017; Dodson & Sipe, 2008; McCrea & Walters, 2012;

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Victorian Auditor-General, 2013). 4

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Creating healthy, liveable communities requires integrated planning across multiple sectors

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and levels of government. In Australia, city planning responsibilities are shared across three

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tiers of government (federal, state/territory and local) and the private and not-for-profit

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sectors. State government policy is the focus of this research as it plays a leading role in

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metropolitan-wide planning (Williams & Maginn, 2012). State governments produce

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metropolitan strategic plans for capital cities, provide the legislative framework for local

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government planning schemes, and deliver major infrastructure that determines urban

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liveability: roads, transit, schools and hospitals (Williams & Maginn, 2012).

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1.3 Evaluating policies that create liveable cities

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Progress towards creating healthy cities can be assessed through monitoring of evidence-

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informed policy and its implementation (Hooper et al., 2014). The identification of inequities

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within and between cities is increasingly important in Australia given projected urban

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population growth (Australian Bureau of Statistics, 2012; Giles-Corti et al., 2016). However,

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urban indicators linked to policies are rarely developed and applied (including for Australian

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cities) (Lowe et al., 2015), and there is little research assessing urban policy implementation.

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Thus, this study aimed to identify and evaluate measurable state government policies related

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to liveability; and assess and map their level of implementation in Australia’s four largest

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cities (Brisbane, Sydney, Melbourne and Perth), to examine spatial inequities within cities.

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2. Methods

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2.1 Policy review

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This study involved a multi-disciplinary research team participating in three federally-funded

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research programs. Advisory groups comprising policymakers and practitioners from all

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levels of government and the private sector provided input into the study design and methods,

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to ensure their relevance to policy and practice. They also assisted in dissemination of

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findings.

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Government websites and electronic search engines were searched between 2014 and 2016 to

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identify current policies and legislation pertaining to walkability, transit access, public open

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space, housing affordability, employment, food environments and alcohol environments for

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the state governments of New South Wales, Queensland, Victoria and Western Australia; and

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their respective capital cities Sydney, Brisbane, Melbourne and Perth. ‘Current’ policies were

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those available on government websites at the time of the search and not classified as

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superseded or archived.

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Relevant policy documents were reviewed to identify spatial policy standards and targets.

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Eligibility for inclusion was based on whether policy standards/targets could be measured

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using available geographic information systems (GIS) spatial data. Where policy targets were

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phrased ambiguously, a literal interpretation was applied. For example, ‘most’ residences

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having access to infrastructure was interpreted as ≥50%, and a policy without a specific target

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was interpreted as applying to all residences (e.g. a target of 100% of dwellings having access

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to infrastructure). Where policies included unmeasurable qualifiers (e.g. ‘safe’ walking

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distance), we included the measurable component of the policy (i.e. ‘walking distance’) in the

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analysis.

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2.2 Spatial analysis

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Once measurable state policies were identified, their implementation was calculated and

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mapped. In many cases, the policy-derived indicators were different for each city, so we were

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unable to directly compare planning outcomes between cities.

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A variety of data sources were used to construct each spatial measure. Address (PSMA

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Australia Limited, 2012b), cadastre (PSMA Australia Limited, 2012a) and street network 6

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(PSMA Australia Limited, 2012c) data were sourced from the Australian government-owned

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mapping company, which compiles data provided by planning agencies in each state. City,

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local government, suburb and statistical area boundaries were sourced from the Australian

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Bureau of Statistics (ABS) (2010) (see Australian Bureau of Statistics (2016) for a full

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description of the administrative boundaries). Destination data (e.g. transit stops, public open

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space and supermarkets) were sourced or compiled from commercial providers, government

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agencies, desktop auditing and/or directly from the internet.

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The study focussed on metropolitan urban settings. As such, for each city we identified the

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‘urban’ extent of the metropolitan region Statistical Divisions classified as either ‘Major

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Urban’ (geographical areas with population clusters of 100,000 or more) or ‘Other Urban’

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(population clusters of 1000–99,999) from the Sections of State (Australian Bureau of

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Statistics, 2010). Within each city's urban boundaries, we identified small census areas (Mesh

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Blocks) with a dwelling count greater than zero. Finally, within the residential areas we

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identified addresses (PSMA Australia Limited, 2012b). The unique locations of address

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points served as proxy locations for residential lots. We adopted ABS eligibility criteria and

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excluded residential lots located in statistical areas (SA1s) for which the ABS Index of

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Relative Socio-economic Disadvantage (Pink, 2013) had not been calculated.

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Distance to destinations (e.g., transit stops, public open space) was estimated using street

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network analysis, rather than Euclidean ‘as the crow flies’ distance. A pedestrian-accessible

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street network dataset suitable for network analysis was developed by removing non-

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pedestrian roads such as freeways.

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Measures of access (e.g. transit access) were computed for each residential lot within the

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study area using Origin-Destination cost matrices, while area-level statistics of net and gross

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dwelling density were calculated using small area census data. Results were aggregated as

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summary statistics at the suburb, local government area (LGA) and city-wide scale (e.g.

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percentage of residences within 400m of a bus stop). Suburb-level averages were mapped,

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with non-residential areas masked to avoid bias in the visual interpretation of the data. This is

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important for maps of an entire city, as larger urban fringe suburbs comprising only a small

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proportion of residential land otherwise dominate the map. Results were mapped as indicators

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using QGIS (QGIS Development Team, 2019), and will be available to policymakers,

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planners and the general community through an online platform built on open-source

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software to be launched in 2020 (reference removed for peer-review).

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3. Results

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The policy search yielded 73 state government policy documents across the four cities

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studied. Spatial standards were found mainly in land use planning regulations and urban

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design codes or guidelines. Some sector-specific strategic policies were also sufficiently

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detailed to enable spatial indicators to be developed and mapped.

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Measurable spatial policies were identified for only three of the seven liveability domains:

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walkability, transit access, and public open space. No measurable state government spatial

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policies were identified for local employment, housing affordability, or alcohol

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environments. Only Victoria had a measurable spatial policy for access to activity centres

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with a supermarket, and as this emphasised mixed land use over access to healthy food, this

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policy is discussed under walkability, rather than the food environment. Hence, the following

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results focus on walkability, transit access, public open space, presenting identified policy

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targets, spatial indicators and policy implementation findings.

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3.1 Walkability

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3.1.1 Selected policy targets and indicators for walkability

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As discussed above, walkable neighbourhoods have higher housing densities, a diversity of

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land uses, and connected street networks. State policy standards for Perth, Melbourne,

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Sydney and Brisbane all stipulated suburban residential development density targets (see

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Table 1). However, the policy ambition was low, with 15 dwellings per hectare the common

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target for Melbourne (Growth Areas Authority, 2013), Sydney (Department of Planning and

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Infrastructure, 2013; Department of Urban Affairs and Planning, 2001) and suburban

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development in Brisbane (Department of Infrastructure Local Government and Planning,

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2016). With a target of 26 dwellings per hectare (WA Planning Commission & Department

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of Planning, 2015), Perth’s policy ambition was more in line with levels of density found to

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encourage walking, and enable better transit service provision (Boulange et al., 2017; Gunn et

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al., 2017). Similarly, Brisbane’s target for ‘urban areas’ (not defined) was 30 dwellings per

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hectare (Department of Infrastructure Local Government and Planning, 2016).

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INSERT TABLE 1 HERE

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Density indicators were created using both net and gross density, as policies did not always

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distinguish between the two. Gross density is the number of dwellings in a particular region

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(e.g. suburb) divided by the total area size in hectares, including non-residential land. Net

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density is the number of dwellings within the residential parts of a region divided by total

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residential area (Brisbane City Council & Queensland Government, 2011). Residential

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classification was undertaken using small area Mesh Block geometries. For regions that are

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entirely residential, net density will be the same as gross density.

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Access to destinations provides reasons to walk (Hooper et al., 2015). Only Victorian and

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Western Australian governments had specific, spatially measurable policies for access to

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destinations (see Table 1). The Victorian Government set a target that 80-90% of residences

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in Melbourne should be within walking distance of activity centres with a supermarket 9

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(Growth Areas Authority, 2013) and Western Australia had a policy related to walkable

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catchments in Perth: ‘most’ or a ‘substantial majority’ of residences should be within walking

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distance of an activity centre (Department of Planning Lands and Heritage, 2016).

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Perth, Melbourne and Brisbane also had policy guidelines on the length and width of street

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blocks to support street connectivity, but no specific targets (see Table 1) (Department of

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Environment Land Water and Planning, 2016; Department of Infrastructure Local

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Government and Planning, 2016; WA Planning Commission, 2009).

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3.1.2 Walkability policy implementation findings

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Table 1 summarises the findings for policy-based walkability indicators and their

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implementation across the four cities.

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Irrespective of whether measured as gross or net density, dwelling densities were low. With

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suburb averages for gross density ranging from 3.96 to 7.25 dwellings per hectare,

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Melbourne, Perth and Brisbane were all well below their respective state suburban density

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targets (15–26 dwellings per hectare). Sydney was closest to meeting its density policy target

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of 15 dwellings per hectare, with average suburb-level densities ranging from 11.13 (gross) to

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18.25 (net). Indeed, applying the more lenient net density measure, 37% of Sydney’s suburbs

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achieved the density target. Perth’s more ambitious target of 26 dwellings per hectare was

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only achieved in 2% of suburbs; although this policy was only adopted in 2015. Similarly,

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only 2% of Brisbane suburbs achieved Queensland’s urban target of 30 dwellings per hectare,

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and only 12% met its suburban target of 15 dwellings per hectare.

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The spatial distribution of net dwelling density is shown in the upper panel of Figure 1. The

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lower panel shows compliance with state policies, using 15 dwellings per hectare as the cut-

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off. Sydney was the only city where high densities extended beyond the inner city, with a

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larger number of suburbs - particularly south of the city - complying with or exceeding this

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policy standard.

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For walkable access to destinations, only 40% of residences in Melbourne were within 1 km

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of an activity centre with a supermarket, which was below the 80% policy target. Figure 2

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shows the level and geographical distribution of activity centre access in Melbourne. The

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right-hand panel shows that very few suburbs and only three LGAs met the state

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government’s target, most of which were in the inner city or inner-north. Meanwhile, only

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10% of residences in Perth were within 400 m of a secondary or district centre, or within 200

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m of a neighbourhood centre, which is well below the policy target (see Table 1).

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INSERT FIGURES 1 AND 2 HERE

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In terms of street connectivity policies, around 71% of Perth residential street blocks had a

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perimeter of less than 720 m, while in Melbourne it was 65%. In Brisbane, 43% of street

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blocks were less than 560 m in perimeter (see Table 1).

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3.2 Transit access

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3.2.1 Selected policy targets and indicators for transit access

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Transit access policies in all four cities aimed for a significant proportion of (or all)

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residences to have access to transit within walking distance, with shorter distance

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requirements for bus stops (400 m) and tram stops (600 m) than for train stations (800 m)

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(see Table 2). However, the policy ambition for the proportion of residences with nearby

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access varied markedly between states: Perth’s target was that at least 60% of residences have

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access to transit within walking distance (WA Planning Commission, 2009), compared with a

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90% target in Brisbane (Department of Infrastructure Local Government and Planning, 2016)

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and 95% in Melbourne (Department of Environment Land Water and Planning, 2016).

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New South Wales had the most ambitious transit target. Its Integrated Public Transport

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Service Planning Guidelines (Transport for New South Wales, 2013, 2016) contained very

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detailed requirements for service frequencies by type of service and time-of-day. These

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could not be mapped using available data, hence we mapped the policy standard from the

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2001 Integrated Transport and Land Use Guidelines (still available at the time of analysis)

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(Department of Urban Affairs and Planning, 2001), which required 100% of households to be

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within walking distance of bus stops (400 m) serviced every 30 minutes and train stations

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(800 m) serviced every 15 minutes (see Table 2) (Department of Urban Affairs and Planning,

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2001). This combined measure of transit proximity and service frequency has been found to

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be a stronger predictor of transport walking than proximity alone (Rachele et al., 2018).

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INSERT TABLE 2 HERE

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3.2.2 Transit access policy implementation findings

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Table 2 shows that Perth appeared to meet its transit access target. Overall, approximately

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64% of Perth residences were within 400 m of a bus stop, or 800 m of a train station,

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exceeding the state government’s relatively modest target of 60%. More favorably, almost

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70% of residences in Melbourne were within 400 m of a bus stop, 600 m of a tram stop or

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800 m of a train station, but this fell short of the state’s more ambitious target of 95%.

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Similarly, although 61% of residences in Brisbane were within 400 m of a transit stop, this

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fell short of its target of 90%. Fewer Sydney residences (38%) achieved the state’s transit

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access policy, which more ambitiously combined both frequency of, and proximity to transit

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services.

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Analysis at the suburb level highlighted significant spatial inequities in implementation of

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transit policies across cities. In general, inner-city suburbs had better access to transit, with

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declining access in outer-suburban areas (see Figures 3-6). As shown in Figure 3, residences

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in Perth’s outer-north and outer-east have the poorest transit access. Meanwhile, only 14% of

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Melbourne suburbs, 13% of Brisbane suburbs and nine inner-city Sydney suburbs (2%) met

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their respective state policy targets (see Table 2 and Figures 4-6).

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INSERT FIGURES 3-6 HERE

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3.3 Public open space

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3.3.1 Selected policy targets and indicators for public open space

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State policy documents mention both ‘parks’ and ‘public open space’, with varying

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definitions, and sometimes complex and unmeasurable requirements. Thus, policy targets for

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either parks or open space were included; and our indicators measured proximity to publicly-

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accessible open spaces used for a range of active and passive recreation. Conservation and

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bushland areas and spaces with restricted public access, such as school grounds and golf

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courses, were excluded.

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Measurable public open space access targets were identified for all four cities (see Table 3).

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In most states, policies required small public open spaces to be accessible within a short

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walking distance, and larger spaces at increasing distances. Melbourne was the only city

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without a specified hierarchy of public open space provision, with a single requirement for

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parks of any size to be within 400 m of residences (Department of Environment Land Water

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and Planning, 2016). In contrast, at the 400 m distance, the public open space size

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requirement for Brisbane and Sydney was larger than 0.5 hectares, and for Perth it was 0.4-

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1.0 hectares. Larger public open spaces were required within 2 km of residences in Sydney

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(>2 hectares) and Perth (>5 hectares), and within 2.5 km of residences in Brisbane (>5

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hectares).

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INSERT TABLE 3 HERE

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Policy ambition for the proportion of residences with accessible public open spaces within

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specified distances varied markedly between cities (see Table 3). The target was for 95% of

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residences in Melbourne (Department of Environment Land Water and Planning, 2016) and

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90% in Brisbane (Department of Infrastructure Local Government and Planning, 2016).

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Policies in both Perth and Sydney more modestly required most residences (interpreted as

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≥50%) to have access to public open spaces of various sizes (Department of Sport and

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Recreation, 2012; NSW Government, 2010). Perth also had a relatively new requirement that

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all residences (100%) have access to (any size) public open space within 300 m (WA

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Planning Commission & Department of Planning, 2015).

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3.3.2 Public open space policy implementation findings

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Table 3 summarises the findings of the policy-derived public open space implementation

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indicators for each city. Overall, 82% of residences across Melbourne met the Victorian

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Government’s target of being within 400 m of public open space. This was short of its 95%

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target, which only 12% of Melbourne’s suburbs met.

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For the other three states, there was one comparable indicator across cities: public open space

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of at least 0.4 hectares within 400 m. Overall, 65% of residences and 8% of suburbs in the

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City of Brisbane LGA (which covers most of greater Brisbane’s land area) had this level of

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access, which was short of the 90% target. In Perth and Sydney where the policy target was

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≥50%, 40% and 59% of residences had this level of access, respectively. However, when

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larger public open spaces were considered, Perth, Sydney and the City of Brisbane all

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exceeded their respective targets. Indeed, 89% of Perth residences were within 800 m of a

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public open space between 1-5 hectares, and 76% were within 2 km of a public open space

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larger than 5 hectares; while 99% of Sydney residences were within 2 km of a public open

14

329

space greater than 2 hectares. Similarly, 99% of City of Brisbane residences met the standard

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of being within 2.5 km of a public open space larger than 5 hectares.

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Figures 7-12 show the spatial distribution (and policy compliance by suburb) of access to

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public open spaces. In Melbourne, many suburbs had good access to public open space

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except for south-east areas of the inner-city, and more outer-suburbs met the state policy

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target. Compared with Melbourne, maps of Perth and Sydney show greater suburb-level

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compliance with their relatively modest state policy targets, and also highlight much greater

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policy implementation for larger compared with smaller public open spaces. Brisbane

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suburbs that complied with the policy target for public open space >0.5 hectares were

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scattered across the city area.

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INSERT FIGURES 7-12 HERE

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4. Discussion

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Achieving the UN Sustainable Development Goals, particularly Goal 11, requires evidence-

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informed and well-implemented urban policy that creates healthy, liveable and sustainable

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communities in all cities (United Nations, 2016; United Nations General Assembly, 2015).

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Policy targets and related indicators are widely recognised as having an important role to play

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in measuring and monitoring cities’ progress towards achieving these goals (Giles-Corti et

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al., 2016; United Nations, 2016). Spatial indicators and benchmarking can provide vital

347

feedback to city planners trying to manage and run complex urban systems (Kitchin et al.,

348

2015). However, there is little research exploring the capacity of existing urban policies to

349

equitably deliver healthy cities, nor assessing their level of implementation. For example, a

350

National Cities Performance Framework published by the Australian Government (2017),

351

includes a set of indicators for assessing cities. However, these indicators, and others such as

352

the Economist Intelligence Unit’s (2015) Liveability Index are measured at the city-scale, and

15

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obscure within-city inequities, providing little guidance on priority areas for intervention.

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There is also a lack of liveability indicators linked to policies, or explicitly measuring

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liveability from a health determinants perspective (Badland et al., 2014; Lowe et al., 2015).

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Thus, this research sought to fill a gap in the literature and policy environment by developing

357

spatial liveability indicators based on state government policies in four Australian cities, and

358

mapping levels of policy implementation. By highlighting specific policy limitations and

359

implementation gaps, this research could assist policymakers to strengthen policy targets and

360

reduce geospatial inequities in delivery of infrastructure and urban design that supports

361

health.

362

Although liveability is a common policy aspiration in Australia, state governments appeared

363

to lack an integrated policy framework to deliver liveable cities (Sallis et al., 2016). No

364

specific, measurable spatial policy standards were identified for key determinants of urban

365

health and liveability: local employment, housing affordability, or alcohol environments.

366

Only Victoria had a measurable policy related to the food environment. These policy gaps

367

may create challenges for integrated city planning and accountable policy implementation.

368

Measurable spatial policies were found for walkability, transit access and public open space

369

in all cities included in the review. However, policies varied markedly across the four cities

370

in terms of their targets and ambition, and the urban characteristics covered. When policy

371

implementation was mapped, in many cases we found significant implementation gaps, with

372

spatial inequities within each city. In general, people living in outer and middle suburbs had

373

poorer access to infrastructure and amenities that determine health, compared with inner-city

374

residents.

375

In many cases, policies were inconsistent with evidence on how to create healthy cities.

376

While most metropolitan strategic plans include general aims to create walkable, liveable 30-

377

minute (Department of Planning and Environment, 2014) or 20-minute cities (Department of 16

378

Environment Land Water and Planning, 2017; Department of Transport Planning and Local

379

Infrastructure, 2014), specified targets for walkability, transit access and public open space

380

provision did not support these aspirations. For example, targets for transit access in Perth

381

and public open space access in Sydney were very modest. Most housing density targets were

382

also unambitious and fell short of levels required to achieve walkable communities (Boulange

383

et al., 2017; Gunn et al., 2017); and even these modest targets were poorly implemented

384

across the Australian cities studied.

385

Policy variation may reflect a lack of agreement or different interpretations among

386

policymakers about what is required to create liveable neighbourhoods. It is possible that

387

decision-makers lack access to the type of evidence needed to create evidence-informed

388

policy (Giles-Corti et al., 2015b; Pratt et al., 2016; Sallis et al., 2016). Moreover, urban

389

research is often piecemeal and context-specific, and does not encapsulate the complexity of

390

urban systems (Batty, 2012; Vojnovic, 2014). It can also be difficult to interpret by non-

391

academic audiences, and not policy- and practice-relevant (Pratt et al., 2016). Therefore,

392

greater efforts are required by researchers to synthesize and disseminate policy-relevant

393

evidence to inform practice, with indicators potentially having an important role to play.

394

There is also a need for greater commitment amongst policymakers to access and use

395

evidence to inform policy (Sallis et al., 2016), and to ensure its implementation on-the-

396

ground.

397

Consistent, evidence-informed policy standards and targets for all urban liveability domains

398

could help avoid between- and within-city inequities in access to amenities. Although harder

399

to achieve, more ambitious policies are required to ensure that governments continue striving

400

to maintain and improve liveability across entire cities. Short-, medium- and long-term

401

targets could be established, with implementation progress and community impacts

402

monitored over time. Policy standards for infrastructure and services should consider both 17

403

optimal proximity and quality (e.g. frequency of services for transit, and size and quality of

404

public open space) (Francis et al., 2012; Giles-Corti et al., 2005; Mavoa et al., 2016;

405

Sugiyama et al., 2015).

406

Importantly, unambitious targets for suburban housing development need to be reconsidered.

407

Policy requiring 15 dwellings per hectare in some Australian cities results in insufficient

408

people to warrant investing in high quality transit, local shops and services, and perpetuates

409

low density suburban sprawl and inactive and unsustainable lifestyles (Watts et al., 2015).

410

Higher housing densities are essential for achieving more compact development, and

411

facilitate better access to transit and physical and social infrastructure (Giles-Corti et al.,

412

2012), both of which contribute to creating walkable neighbourhoods. This study, along with

413

others (Hooper et al., 2014), also highlights the need for much greater emphasis on closing

414

policy-implementation gaps.

415

4.1 Limitations

416

4.1.1 Policy review limitations

417

Some policies contained ambiguous policy standards that could not be measured. For

418

example, it was difficult to accurately interpret and measure policies specifying ‘safe’

419

walking routes or access to ‘potential or future’ bus stops. This highlights the need for urban

420

policies to include transparent, specific, spatial and measurable targets, to enable monitoring

421

and to ensure accountability (Lowe et al., 2015).

422

In addition, many of the policy targets were adopted after established areas were developed

423

and could be considered unfair benchmarks. Nevertheless, we found that the most recently

424

established urban fringe developments were often less likely to meet policy targets, despite

425

being built under contemporary guidelines. This is consistent with previous findings of

18

426

infrastructure deficits in rapidly growing greenfield developments, with long delays in

427

government infrastructure and service provision relative to the pace of housing development

428

(Lowe et al., 2018; Whitzman & Ryan, 2014). It highlights a significant policy-

429

implementation gap observed in outer suburban developments and reinforces the need to

430

benchmark and monitor levels of policy implementation on-the-ground (Hooper et al., 2014).

431

The policy environment can change quickly, and since commencement of this study, new

432

policies have been developed. For example, Melbourne’s revised metropolitan strategic plan

433

now aims to reach 20 dwellings per hectare ‘in the future’, replacing its previous target

434

measured in this study, of 15 dwellings per hectare in growth areas (Department of

435

Environment Land Water and Planning, 2017). Although an improvement, it is not evidence-

436

based, with recent research showing that densities of around 25-29 dwellings per hectare are

437

required to encourage walking, cycling and transit and reduce driving (Boulange et al., 2017;

438

Gunn et al., 2017).

439

4.1.2 Spatial data and indicator limitations

440

Several methodological challenges were faced when calculating liveability indicators. The

441

accuracy and utility of urban indicators depends on data availability, quality, indicator

442

selection, and analysis (Kitchin et al., 2015; Leach et al., 2017). Lack of consistent available

443

data proved challenging when measuring policy implementation in Australia. Even when

444

high quality data were available, there was significant state variation in standards for

445

collecting and attributing spatial data. For example, there was no national public open space

446

dataset available. Datasets were therefore collected from different sources in each state with

447

varying quality, compilation dates and categorisation of open space types. Wider adoption of

448

nation-wide standards for spatial data would ensure the availability of comparable datasets.

19

449

The calculated walkable distances to destinations using the street network omitting non-

450

pedestrian roads is an improvement on use of 'as the crow flies' distances. However, this may

451

not consistently account for access barriers, such as busy roads lacking pedestrian crossings.

452

Further, data on footpath location and quality were not available nationally, but are important

453

to consider when modelling pedestrian accessibility.

454

Another study limitation was the use of 2011 ABS data, which pre-dated the policy review.

455

The 2016 ABS data were unavailable at the time of this study. We were unable to establish

456

which areas of each city were developed under which policies, so we applied analysis

457

uniformly across all cities at one time-point. Land use and urban development patterns may

458

have changed since 2011 in line with more recent policy, so our indicators may not accurately

459

reflect current levels of policy implementation.

460

We developed indicators for only four Australian cities. Similar methods could be used to

461

derive liveability indicators for other cities in Australia and internationally. In addition, while

462

we focussed on policy-derived, quantitative spatial indicators, other types of indicators could

463

complement these, to provide a more holistic city performance assessment (Leach et al.,

464

2017). For example, to directly compare cities, consistent liveability indicators are needed

465

rather than policy-derived indicators that differ by city. Hence, reported elsewhere (reference

466

removed for peer-review), we also created health-related liveability indicators. Further, non-

467

spatial indicators and subjective assessments of residents’ experiences can inform urban

468

policy development (Kent et al., 2017; Leach et al., 2017; Pacione, 2003).

469

5. Conclusion

470

This study demonstrates a method for deriving and visualising policy-relevant liveability

471

indicators.

472

Australian city performs well on all underlying domains that will create healthy, liveable

Despite policy rhetoric championing urban liveability, we found that no

20

473

neighbourhoods. Policy targets for walkability, transit access and public open space were

474

often not met at the metropolitan and/or suburb level; and there were significant spatial

475

inequities in the implementation of policies within each city. With few exceptions, people

476

living in outer and many middle suburbs, were significantly less well served by urban

477

policies, than residents of inner-city suburbs, highlighting significant city planning inequities.

478

Creating healthy, liveable cities that are equitable for all residents requires evidence-

479

informed, integrated transport, land use and infrastructure planning (Lowe et al., 2018). We

480

showed that policy-derived liveability indicators can be useful for benchmarking and

481

monitoring progress, determining priorities and targeting interventions to reduce geographic

482

inequities. They could also provide an early warning system of unintended consequences of

483

policies and identify policies requiring adjustment. Short-, medium-, and long-term policy

484

targets may support implementation of more ambitious, evidence-informed policy. Spatial

485

data standards are also needed to ensure that indicator measurement is consistent and

486

comparable. Without evidence-informed planning and policy implementation, good

487

intentions will not be realised and within- and between-city inequities will continue to grow.

21

488

6. References

489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535

Arundel, J., Lowe, M., Hooper, P., Roberts, R., Rozek, J., Higgs, C., et al. (2017). Creating liveable cities in Australia: Mapping urban policy implementation and evidence-based national liveability indicators. Melbourne: RMIT University. Australian Bureau of Statistics. (2010). Australian Statistical Geography Standard (ASGS): Volume 1 - main structure and Greater Capital City Statistical Areas, July 2011. Canberra, ACT. Australian Bureau of Statistics. (2012). 2011 Census of Population and Housing, customised data report. Sydney, NSW: Australian Bureau of Statistics. Australian Bureau of Statistics. (2016). ABS structures. Australian Bureau of Statistics. Australian Government. (2017). Smart cities plan: National Cities Performance Framework. Final report. Canberra, ACT: Australian Government. Badland, H., Davern, M., Villanueva, K., Mavoa, S., Milner, A., Roberts, R., et al. (2016a). Conceptualising and measuring spatial indicators of employment through a liveability lens. Social Indicators Research, 127, 565-576. Badland, H., Foster, S., Bentley, R., Higgs, C., Roberts, R., Pettit, C., et al. (2017). Examining associations between area-level spatial measures of housing with selected health and wellbeing behaviours and outcomes in an urban context. Health & Place, 43, 17-24. Badland, H., Mavoa, S., Livingston, M., David, S., & Giles-Corti, B. (2016b). Testing spatial measures of alcohol outlet density with self-rated health in the Australian context: Implications for policy and practice. Drug and Alcohol Review, 35, 298-306. Badland, H., Roberts, R., Butterworth, I., & Giles-Corti, B. (2015). How liveable is Melbourne? Conceptualising and testing urban liveability indicators: Progress to date. Melbourne, Victoria: The University of Melbourne. Badland, H., Whitzman, C., Lowe, M., Davern, M., Aye, L., Butterworth, I., et al. (2014). Urban liveability: Emerging lessons from Australia for exploring the potential for indicators to measure the social determinants of health. Social Science and Medicine, 111, 64-73. Batty, M. (2012). Building a science of cities. Cities, 29, S9-S16. Boulange, C., Gunn, L., Giles-Corti, B., Pettit , C., & Badland, H. (2017). Examining associations between urban design attributes and transport mode choices for walking, cycling, public transport and private motor vehicle trips. Journal of Transport & Health, 6, 155-166. Brisbane City Council, & Queensland Government. (2011). Residential form handbook. Brisbane, Australia: Queensland Government. Commission on Social Determinants of Health. (2008). Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the Commission on Social Determinants of Health. Geneva: World Health Organization. Davern, M., Farrar, A., Kenda, D., & Giles-Corti, B. (2016). Quality green space supporting health, wellbeing and biodiversity: A literature review. Adelaide, South Australia: Heart Foundation of Australia. Department of Environment Land Water and Planning. (2016). Victorian planning provisions. Melbourne, Victoria: Victorian Government. Department of Environment Land Water and Planning. (2017). Plan Melbourne 2017-2050. Melbourne, Victoria: Victorian Government. Department of Infrastructure Local Government and Planning. (2016). Priority Development Area guidelines and practice notes. Brisbane, Queensland: Queensland Government.

22

536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584

Department of Planning and Environment. (2014). A plan for growing Sydney. Sydney, NSW: NSW Government. Department of Planning and Infrastructure. (2013). Housing choice and affordability in growth areas: dwelling density guide. Sydney, NSW: NSW Government. Department of Planning Lands and Heritage. (2016). State planning framework: state planning policies. Perth, Western Australia: Government of Western Australia. Department of Sport and Recreation. (2012). Classification framework for public open space. Perth, Western Australia: Government of Western Australia. Department of Transport Planning and Local Infrastructure. (2014). Plan Melbourne: Metropolitan planning strategy 2014. Melbourne, Victoria: Victorian Government. Department of Urban Affairs and Planning. (2001). Integrated transport and land use: Improving transport choice - guidelines for planning and development. Sydney, NSW: NSW Government. Dodson, J., & Sipe, N. (2008). Unsettling suburbia: The new landscape of oil and morgage vulnerability in Australian cities, Urban Research Program, Research Paper No. 17. Brisbane, Queensland: Griffith University. Economist Intelligence Unit. (2015). A summary of the liveability ranking and overview. London: Economist Intelligence Unit. Ewing, R., Schieber, R., & Zegeer, C. (2003). Urban sprawl as a risk factor in motor vehicle occupant and pedestrian fatalities. American Journal of Public Health, 93, 1541-1545. Foster, S., Hooper, P., Knuiman, M., Trapp, G., & Wood, L. (2017a). Does alcohol outlet density differ by area-level disadvantage in metropolitan Perth? Drug Alcohol Rev, 36, 701-708. Foster, S., Trapp, G., Hooper, P., Oddy, W.H., Wood, L., & Knuiman, M. (2017b). Liquor landscapes: Does access to alcohol outlets influence alcohol consumption in young adults? Health & Place, 45, 17-23. Francis, J., Wood, L., Knuiman, M., & Giles-Corti, B. (2012). Quality or quantity? Exploring the relationship between public open space attributes and mental health in Perth, Western Australia. Social Science and Medicine, 74, 1570-1577. Frank, L., Andresen, M., & Schmid, T. (2004). Obesity relationships with community design, physical activity, and time spent in cars. American Journal of Preventive Medicine, 27, 87-96. Giles-Corti, B., Broomhall, M.H., Knuiman, M., Collins, C., Douglas, K., Ng, K., et al. (2005). Increasing walking: How important is distance to, attractiveness, and size of public open space? American Journal of Preventive Medicine, 28, 169-176. Giles-Corti, B., Foster, S., Koohsari, M.J., Francis, J., & Hooper, P. (2015a). The influence of urban design and planning on physical activity. In H. Barton, S. Thompson, S. Burgess, & M. Grant (Eds.), The Routledge handbook of planning for health and wellbeing: Shaping a sustainable and healthy future. Oxon, UK: Routledge. Giles-Corti, B., Ryan, K., & Foster, S. (2012). Increasing density in Australia: Maximising the benefits and minimising the harm. National Heart Foundation of Australia. Giles-Corti, B., Sallis, J.F., Sugiyama, T., Frank, L.D., Lowe, M., & Owen, N. (2015b). Translating active living research into policy and practice: One important pathway to chronic disease prevention. Journal of Public Health Policy, 36, 231-243. Giles-Corti, B., Vernez-Moudon, A., Reis, R., Turrell, G., Dannenberg, A.L., Badland, H., et al. (2016). City planning and population health: A global challenge. The Lancet, 388, 2912-2924. Growth Areas Authority. (2013). Precinct Structure Planning guidelines. Melbourne. Victoria: Growth Areas Authority, Victorian Government.

23

585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634

Gunn, L.D., Mavoa, S., Boulangé, C., Hooper, P., Kavanagh, A., & Giles-Corti, B. (2017). Designing healthy communities: creating evidence on metrics for built environment features associated with walkable neighbourhood activity centres. International Journal of Behavioral Nutrition and Physical Activity, 14, 164. Holden, M., & Scerri, A. (2013). More than this: Liveable Melbourne meets liveable Vancouver. Cities, 31, 444-453. Hooper, P., Giles-Corti, B., & Knuiman, M. (2014). Evaluating the implementation and active living impacts of a state government planning policy designed to create walkable neighborhoods in Perth, Western Australia. American Journal of Health Promotion, 28, S5-S18. Hooper, P., Knuiman, M., Foster, S., & Giles-Corti, B. (2015). The building blocks of a 'liveable neighbourhood': Identifying the key performance indicators for walking of an operational planning policy in Perth, Western Australia. Health & Place, 36, 173183. Institute for Health Metrics and Evaluation. (2013). GBD profile: Australia. Global burden of diseases, injuries and risk factors study 2010. Seattle, WA: IHME. Kent, J.L., Ma, L., & Mulley, C. (2017). The objective and perceived built environment: What matters for happiness? Cities & Health, 1, 59-71. Kitchin, R., Lauriault, T.P., & McArdle, G. (2015). Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science, 2, 6-28. Kjellstrom, T., & Hinde, S. (2007). Car culture, transport policy, and public health. In I. Kawachi, & S. Wamala (Eds.), Globalisation and health. New York, NY: Oxford University Press. Leach, J.M., Lee, S.E., Hunt, D.V.L., & Rogers, C.D.F. (2017). Improving city-scale measures of livable sustainability: A study of urban measurement and assessment through application to the city of Birmingham, UK. Cities, 71, 80-87. Livingstone, M. (2011). Alcohol outlet density and harm: Comparing the impacts on violence and chronic harms. Drug and Alcohol Review, 30, 515-523. Lowe, M., Whitzman, C., Badland, H., Davern, M., Aye, L., Hes, D., et al. (2015). Planning healthy, liveable and sustainable cities: How can indicators inform policy? Urban Policy and Research, 33, 131–144. Lowe, M., Whitzman, C., & Giles-Corti, B. (2018). Health-promoting spatial planning: Approaches for strengthening urban policy integration. Planning Theory & Practice, 19, 180-197. Mason, K.E., Bentley, R.J., & Kavanagh, A.M. (2013). Fruit and vegetable purchasing and the relative density of healthy and unhealthy food stores: Evidence from an Australian multilevel study. Journal of Epidemiology and Community Health, 67, 231-236. Mavoa, S., Badland, H., Lernihan, V., Boruff, B., Pettit, C., Astell-Burt, T., et al. (2016). The Australian national liveability study final report: development of policy-relevant liveability indicators relating to health and wellbeing and recommendations for dissemination. Melbourne, Victoria: McCaughey VicHealth Community Wellbeing Unit. McCrea, R., & Walters, P. (2012). Impacts of urban consolidation on urban liveability: Comparing an inner and outer suburb in Brisbane, Australia. Housing, Theory and Society, 29, 190-206. Murphy, M., Badland, H., Koohsari, M.J., Astell-Burt, T., Trapp, G., Villanueva, K., et al. (2017). Indicators of a health-promoting local food environment: a conceptual framework to inform urban planning policy and practice. Health Promotion Journal of Australia, 28, 82. 24

635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683

NSW Government. (2010). Recreation and open space guidelines for local government. Sydney, NSW: NSW Government. Pacione, M. (2003). Urban environmental quality and human wellbeing—a social geographical perspective. Landscape and Urban Planning, 65, 19-30. Pink, B. (2013). Socio-Economic Indexes for Areas (SEIFA) - 2011. Belconnen, ACT: Australian Bureau of Statistics. Pratt, M., Salvo, D., Cavill, N., Giles-Corti, B., McCue, P., Reis, R.S., et al. (2016). An international perspective on the nexus of physical activity research and policy. Environment and Behavior, 48, 37-54. PSMA Australia Limited. (2012a). CadLite. Canberra, ACT: Callpoint Spatial Pty Ltd. PSMA Australia Limited. (2012b). G-NAF: the Geocoded National Address File. Canberra, ACT: Callpoint Spatial Pty Ltd. PSMA Australia Limited. (2012c). Transport and Topography. Canberra, ACT: Callpoint Spatial Pty Ltd. QGIS Development Team. (2019). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Rachele, J.N., Kavanagh, A.M., Badland, H., Giles-Corti, B., Washington, S., & Turrell, G. (2015). Associations between individual socioeconomic position, neighbourhood disadvantage and transport mode: baseline results from the HABITAT multilevel study. Journal of Epidemiology and Community Health, 69, 1217-1223. Rachele, J.N., Learnihan, V., Badland, H.M., Mavoa, S., Turrell, G., & Giles-Corti, B. (2018). Are measures derived from land use and transport policies associated with walking for transport? Journal of Physical Activity and Health, 15, 13-21. Sallis, J.F., Bull, F., Burdett, R., Frank, L.D., Griffiths, P., Giles-Corti, B., et al. (2016). Use of science to guide city planning policy and practice: how to achieve healthy and sustainable future cities. The Lancet, 388, 2936-2947. Samet, J. (2011). Community design and air quality. In A. Dannenberg, H. Frumkin, & R. Jackson (Eds.), Making healthy places: Designing and building for health, well-being, and sustainability. Washington, DC: Island Press. Stafford, M., Cummins, S., Ellaway, A., Sacker, A., Wiggins, R., & Macintyre, S. (2007). Pathways to obesity: identifying local, modifiable determinants of physical activity and diet. Social Science and Medicine, 65, 1882-1897. Sugiyama, T., Gunn, L.D., Christian, H., Francis, J., Foster, S., Hooper, P., et al. (2015). Quality of public open spaces and recreational walking. American Journal of Public Health, 105, 2490-2495. The Healthy Built Environments Program. (2012). Healthy built environments: A review of the literature. Fact sheets. Sydney, NSW: HBEP, Cities Futures Research Centre, University of New South Wales. Transport for New South Wales. (2013). Integrated public transport service planning guidelines: Sydney metropolitan area. Sydney, NSW: Transport for New South Wales, NSW Government. Transport for New South Wales. (2016). Integrated public transport service planning guidelines: Outer metropolitan area. Sydney, NSW: Transport for New South Wales, NSW Government. United Nations. (2016). Resolution adopted by the General Assembly on 23 December 2016: New Urban Agenda. United Nations. United Nations General Assembly. (2015). Resolution adopted by the General Assembly: Transforming our world: the 2030 agenda for sustainable development A/RES/70/1. New York: United Nations.

25

684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710

Victorian Auditor-General. (2013). Developing transport infrastructure and services for population growth areas. Melbourne, Victoria: Victorian Auditor-General's Office. Villanueva, K., Badland, H., Hooper, P., Koohsari, M.J., Mavoa, S., Davern, M., et al. (2015). Developing indicators of public open space to promote health and wellbeing in communities. Applied Geography, 57, 112-119. Vojnovic, I. (2014). Urban sustainability: Research, politics, policy and practice. Cities, 41, S30-S44. WA Planning Commission. (2009). Liveable neighbourboods: A Western Australian Government sustainable cities initiative. Perth, Western Australia: WA Planning Commission. WA Planning Commission, & Department of Planning. (2015). Liveable neighbourboods: Draft 2015. Perth, Western Australia: WA Planning Commission. Watts, N., Adger, W.N., Agnolucci, P., Blackstock, J., Byass, P., Cai, W., et al. (2015). Health and climate change: policy responses to protect public health. Lancet, 386, 1861-1914. Whitzman, C., & Ryan, C. (2014). A vision for metropolitan Melbourne. In C. Whitzman, B. Gleeson, & A. Sheko (Eds.), Melbourne: What next?, Research monograph no. 1. Parkville, Victoria: Melbourne Sustainable Society Institute, The University of Melbourne. Williams, P., & Maginn, P. (2012). Planning and governance. In S. Thompson, & P. Maginn (Eds.), Planning Australia: An overview of urban and regional planning. Melbourne, Victoria: Cambridge University Press. World Health Organization. (2016). Shanghai Declaration on promoting health in the 2030 Agenda for Sustainable Development. WHO 9th Global Conference on Health Promotion. Shanghai: WHO. World Health Organization, & UN Habitat. (2016). Global report on urban health: equitable healthier cities for sustainable development. Italy: WHO.

26

711

Figure captions

712 713

Figure 1. Average net dwelling density (by suburb) in dwellings per hectare (top); suburbs achieving this level of policy implementation (bottom)

714 715

Figure 2. Melbourne: percentage of residences (by suburb) within 1 km of an activity centre with a supermarket (left); suburbs that comply with the state policy (right)

716 717

Figure 3. Perth: percentage of residences (by suburb) within 400 m of a bus stop, or 800 m of a railway station (left); suburbs that comply with the state policy (right)

718 719 720

Figure 4. Melbourne: percentage of residences (by suburb) within 400 m of a bus stop, 600 m of a tram stop, or 800 m of a train station (left); suburbs that comply with the state policy (right)

721 722

Figure 5. Brisbane: percentage of residences (by suburb) within 400 m of a transit stop (left); suburbs that comply with the state policy (right)

723 724 725

Figure 6. Sydney: percentage of residences (by suburb) within 400 m of a bus stop with a service every 30 minutes or within 800 m of a train with a service every 15 minutes (left); suburbs that comply with the state policy (right)

726 727

Figure 7. Melbourne: percentage of residences (by suburb) within 400 m of public open space (left); suburbs that comply with the state policy (right)

728 729

Figure 8. Perth: percentage of residences (by suburb) within 400 m of public open space 0.4– 1.0 hectare in size (left); suburbs that comply with the state policy (right)

730 731

Figure 9. Perth: percentage of residences (by suburb) within 800 m of public open space 1–5 hectare in size (left); suburbs that comply with the state policy (right)

732 733

Figure 10. Sydney: percentage of residences (by suburb) within 400 m of public open space larger than 0.5 hectare (left); suburbs that comply with the state policy (right)

734 735

Figure 11. Sydney: percentage of residences (by suburb) within 2 km of public open space larger than 2 hectare (left); suburbs that comply with the state policy (right)

736 737 738

Figure 12. Brisbane City Council: percentage of residences (by suburb) within 400 m of public open space larger than 0.5 hectare (left); suburbs that comply with the state policy (right)

27

739

Table 1: Policy implementation indicators for walkability

City

Policy implementation indicator % of street blocks with a perimeter of less than 720 m (120–240 m long and 60–120 m wide)

Melbourne

LGA average

64.71

59.00

64.19

80–90

39.78

38.73

44.45

15

2.08 (gross) 8.72 (net)

7.25 (gross) 13.04 (net)

7.45 (gross) 13.83 (net)

11% (n = 403) 23% (net) (n = 470)

–

70.53

64.80

71.01

–

–

9.97

9.72

15.49

–

Dwellings per hectare

26

1.63 (gross) 4.92 (net)

6.03 (gross) 9.00 (net)

5.36 (gross) 9.71 (net)

% of street blocks with a perimeter of less than 560 m (100–200 m long and 40–80 m wide)

–

43.34

38.51

40.55

2% (net) (n = 349) –

% of residential lots within 1 km of an activity centre (supermarket)

% of street blocks with a perimeter of less than 720 m (less than 240 m long and less than 120 m wide) % of residential lots within 400 m of a secondary or district centre, or 200 m of a neighbourhood centre

Brisbane

Sydney

Suburb average

–

Dwellings per hectare

Perth

Policy target

Metro level % of all residential lots

% of suburbs that achieved the policy target

Dwellings per hectare

15 (suburban) 30 (urban)

0.60 (gross) 4.61 (net)

3.96 (gross) 7.89 (net)

1.32 (gross) 4.61 (net)

Dwellings per hectare

15

2.33 (gross) 12.30 (net)

11.13 (gross) 18.25 (net)

11.23 (gross) 18.53 (net)

–

12% greater than 15 (net) 2% greater than 30 (net) (n = 443) 37% (net) (n = 562)

28

740

Table 2: Policy implementation indicators for transit access

City

Policy implementation indicator % of residential lots within 400 m of a bus stop, 600 m of a tram stop or 800 m of a train station

Policy target

Metro-level % of all residential lots

Suburb average

LGA average

% of suburbs that achieved the policy target 14.1 (n = 403) 3.5 (n = 403)

95

69.40

67.00

71.99

% of residential lots within 400 m of a bus stop

95

62.93

59.23

63.32

% of residential lots within 600 m of a tram stop

–

12.51

14.31

17.74

–

% of residential lots within 800 m of a train station

–

11.66

13.06

14.19

–

Perth

% of residential lots within 400 m of a bus stop or 800 m of a train station

60

63.80

60.64

71.24

Brisbane

% of residential lots within 400 m of a transit stop

90

61.4

56.82

53.96

% of residential lots within 400 m of a bus stop serviced every 30 minutes, or 800 m of a train station serviced every 15 minutes

100

37.76

38.97

45.66

% of residential lots within 400 m of a bus stop serviced every 30 minutes

–

34.61

34.83

42.10

–

% of residential lots within 800 m of a train station serviced every 15 minutes

–

7.48

9.59

9.11

–

Melbourne

Sydney

54.1 (n = 307) 12.8 (n = 337) 1.6 (n = 562)

29

Table 3: Policy implementation indicators for public open space

741

Policy target (% of dwellings)

Metro-level % of all residential lots

Suburb average (%)

LGA average (%)

City

Policy implementation indicator

Melbourne

% of residential lots within 400 m of public open space

95

81.8

80.0

81.7

% of residential lots within 300 m of public open space

100

64.4

59.1

62.6

% of residential lots within 400 m of public open space 0.4–1.0 ha

50

39.7

37.2

38.4

% of residential lots within 800 m of public open space 1–5 ha

50

89.2

81.0

87.5

% of residential lots within 2 km of public open space 5–20 ha

50

76.2

67.2

70.9

% of residential lots within 400 m of public open space larger than 0.5 ha

90

–

a

63.3

65.0

% of residential lots within 2.5 km of public open space larger than 5 ha

90

–a

97.8

99.3

% of residential lots within 400 m of public open space larger than 0.5 ha

50

58.5

60.8

55.8

% of residential lots within 2 km of public open space larger than 2 ha

50

98.4

97.1

98.0

Perth

a

Brisbane a

Sydney

a

% of suburbs that achieved the policy target

12 (n = 403) less than 1 (n = 298) 32 (n = 298) 88 (n = 298) 70 (n = 298) 8 (n = 173) 55 (n = 173) 67 (n = 562) 98 (n = 562)

Only a single LGA, Brisbane City Council, was analysed.

742

30

Acknowledgements: Melanie Lowe, Paula Hooper, Julianna Rozek and Billie Giles-Corti were supported by the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Healthy Liveable Communities (#1061404). Melanie Lowe, Jonathan Arundel, Paula Hooper, Carl Higgs and Rebecca Roberts were supported by the Clean Air and Urban Landscapes Hub of the National Environmental Science Programme. Jonathan Arundel, Carl Higgs and Rebecca Roberts were supported by The Australian Prevention Partnership Centre (#9100001). Paula Hooper is supported by a Healthway Research Fellowship (#32892). Billie Giles-Corti is supported by a NHMRC Senior Principal Research Fellowship (#1107672).

Figure captions Figure 1. Average net dwelling density (by suburb) in dwellings per hectare (top); suburbs achieving this level of policy implementation (bottom) Figure 2. Melbourne: percentage of residences (by suburb) within 1 km of an activity centre with a supermarket (left); suburbs that comply with the state policy (right) Figure 3. Perth: percentage of residences (by suburb) within 400 m of a bus stop, or 800 m of a railway station (left); suburbs that comply with the state policy (right) Figure 4. Melbourne: percentage of residences (by suburb) within 400 m of a bus stop, 600 m of a tram stop, or 800 m of a train station (left); suburbs that comply with the state policy (right) Figure 5. Brisbane: percentage of residences (by suburb) within 400 m of a transit stop (left); suburbs that comply with the state policy (right) Figure 6. Sydney: percentage of residences (by suburb) within 400 m of a bus stop with a service every 30 minutes or within 800 m of a train with a service every 15 minutes (left); suburbs that comply with the state policy (right) Figure 7. Melbourne: percentage of residences (by suburb) within 400 m of public open space (left); suburbs that comply with the state policy (right) Figure 8. Perth: percentage of residences (by suburb) within 400 m of public open space 0.4– 1.0 hectare in size (left); suburbs that comply with the state policy (right) Figure 9. Perth: percentage of residences (by suburb) within 800 m of public open space 1–5 hectare in size (left); suburbs that comply with the state policy (right) Figure 10. Sydney: percentage of residences (by suburb) within 400 m of public open space larger than 0.5 hectare (left); suburbs that comply with the state policy (right) Figure 11. Sydney: percentage of residences (by suburb) within 2 km of public open space larger than 2 hectare (left); suburbs that comply with the state policy (right) Figure 12. Brisbane City Council: percentage of residences (by suburb) within 400 m of public open space larger than 0.5 hectare (left); suburbs that comply with the state policy (right)

Melbourne

Brisbane

Perth 1

/

Sydney ' l·

.....\.,,..-·-"!.

�-:1/. '

.,Q,

'-·--r _,,_,_\

Net density ( dwel Iings per hectare), by suburb -

V

/

0-5 5-10 10-15 15-26 26-30 30-40 40-60 �60

Policy target

• Not met(< 15) - Met(� 15)

�1' .,Q,

Study region

D

0

50

100 km

0

50

100 km

0

50

100 km

0

50

100 km

1 Perth's recent policy target is 26 dwellings per hectare with little evidence of implementation; hence, it is evaluated against the 15 dwellings per hectare target to facilitate comparison with other cities.

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling d ata: Australian Bureau of Statistics (ABS), 2011 under CC BY 3.0 Map tiles: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

Statistical Division i 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD)

% of residences within 1km of a supermarket, by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 80%) - Met(� 80%)

Study region

D

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC BY 3.0 Supermarket data: In-house geocoding of web-scraped locations for major supermarket ch ains, 2018 Map tiles: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

50

100 km

Statistical Division L________J Urban Sections of State • Excluded (no dwellings or SAl IRSD)

% of residences within 400 m of a bus stop, or 800 m of a railway station, by suburb

-

0.0- 12.5 12.5- 25.0 25.0- 37.5 37.5- 50.0 50.0- 62.5 62.5- 75.0 75.0- 87.5 87.5- 100.0

Policy target

• Not met(<60%) - Met(� 60%)

Study region

D

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC BY 3.0 Transit data: Transperth , 2017, under CC BY 4.0 Map tiles: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

50

100 km

Statistical Division Urban Sections of State • Excluded (no dwellings or SAl IRSD)

!.......... 1

% of residences within 400 m of a bus stop, 600 m of a tram stop, or 800 m of a train station, by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 95%) - Met(� 95%)

Study region

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC BY 3.0 Transit data: Public Transport Victoria, 2017, under CC BY 4.0 Map tiles: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

50

100 km

D

Statistical Division L........ 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD)

A �.
% of residences within 400 m of a transit stop, by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 90%) - Met(� 90%)

Study region

CJ

Statistical Division Urban Sections of State :___________ • Excluded (no dwellings or SAl IRSD) !

0

50

100 km

0

Healthy Li veable Ci ti es group, RMIT Uni versi ty 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australi an Bureau of Stati sti cs (ABS), 2011 under CC by 3.0 Transit data: Transli nk, 2017, under CC BY 4.0 Map ti les: CartoDB, under CC BY 3.0, featuri ng data by OpenStreetMap, under ODbL.

50

100 km

% of residences within 400 m of a bus stop with a service every 30 minutes or within 800 m of a train with a service every 15 minutes, by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met ( < 100%) - Met (100%)

Study region 0

50

100 km

0

Healthy Liveable Cities g roup, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Transit data: Transport NSW, 2017 CC BY 4.0 Map tiles: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

50

100 km

CJ

Statistical Division 1 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD)

% of residences within 400 m of public open space, by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 95%) - Met(� 95%)

Study region

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Public open space data: Victorian Environmental Assessment Council, 2017 Map tiles: CartoDB, under CC BY 3.0, f eaturing data by OpenStreetMap, under ODbL.

50

100 km

D Statistical Division l.________ 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD)

A �.q)_�ct?,J

% of residences within 400 m of public open space (0.4-1.0 ha), by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 50%) - Me t(� 50%)

Study region

0

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwe lling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Public open space data: Centre for the Built Environment and He alth, University of Western Australia, 2013 Map tile s: CartoDB, under CC BY 3.0, featuring data by Ope nStre e tMap, under ODbL.

50

100 km

Statistical Division i.......... 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD)

% of residences within 800 m of public open space (1-5 Ha), by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 50%) - Me t(� 50%)

Study region

0

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwe lling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Public open space data: Centre for the Built Environment and He alth, University of Western Australia, 2013 Map tile s: CartoDB, under CC BY 3.0, featuring data by Ope nStre e tMap, under ODbL.

50

100 km

Statistical Division i.......... 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD

% of residences within 400 m of public open space (> 0.5 Ha), by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 50%) - Met(50%)

Study region

CJ

Statistical Division L......... Urban Sections of State • Excluded (no dwellings or SAl IRSD) 1

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Public open space data: Office of the Government Architect, NSW, 2017 Map tiles: CartoDB, under CC BY 3.0, f eaturing data by OpenStreetMap, under ODbL.

50

100 km

% of residences within 2km of public open space (> 2 Ha), by suburb

(;.,.�,.r

-

,.,!';/......, .........?·--:,

�,./t____.:_...:. ----� .__..,\

··---�

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 50%) - Met(50%)

Study region

0

50

100 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Public open space data: Office of the Government Architect, NSW, 2017 Map tiles: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

50

100 km

CJ Statistical Division i.......... 1 Urban Sections of State • Excluded (no dwellings or SAl IRSD)

% of residences within 400m of public open space (> 0.5 Ha), by suburb

-

0.0 - 12.5 12.5 - 25.0 25.0 - 37.5 37.5 - 50.0 50.0 - 62.5 62.5 - 75.0 75.0 - 87.5 87.5 - 100.0

Policy target

• Not met(< 90%) - Met(� 90%)

Study region

0

10

20

30

40 km

0

Healthy Liveable Cities group, RMIT University 2019 CC BY-NC-ND 4.0 Boundary and dwelling data: Australian Bureau of Statistics (ABS), 2011 under CC by 3.0 Public open space data: Brisbane Ci ty Counci l, 2017 Map ti les: CartoDB, under CC BY 3.0, featuring data by OpenStreetMap, under ODbL.

10

20

30

40 km

L_________i Brisbane city data coverage • Excluded (no dwellings or SAl IRSD)

Highlights • • • •

Liveability indicators assessed implementation of Australian urban policy Policies were often inconsistent with evidence about how to achieve healthy cities Indicators showed policy implementation gaps and spatial inequities within cities Outer suburbs had poorer access to amenities than inner-city areas