2023_Denser habitats by audrc

Denser habitats: A longitudinal study of the impacts of residential density on objective and perceived neighbourhood amenity in Brisbane, Australia

Sarah Foster a, * , Billie Giles-Corti a , Julian Bolleter b , Gavin Turrell a

a Centre for Urban Research, School of Global Urban and Social Studies, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, Australia

b Australian Urban Design Research Centre (AUDRC), School of Design, The University of Western Australia, Level 2, 1002 Hay St Perth Western, Australia

ARTICLE INFO

Keywords:

Built environment

Neighbourhood perceptions

Urban consolidation

Compact cities

Walkability

Social cohesion

NIMBY

ABSTRACT

Higher residential densities are fundamental to creating sustainable, liveable and healthy neighbourhoods; however, community resistance to densification remains a barrier to infill development. We examined the relationship between residential density and the anticipated benefits and (feared) harms that trigger opposition using longitudinal data collected from mid-age adults (n = 3028) in Brisbane, Australia (2007–2016). Participants completed a questionnaire and objective measures were generated for each individual's 1 km buffer at each timepoint. Longitudinal fixed-effects models examined associations between change in density and change in: (1) objective measures of the built environment and crime; and (2) residents' neighbourhood perceptions controlling for time-varying and time-invariant factors, stratified by distance to the central business district (CBD). Dwellings/ha increased, on average, by 1.5 dwellings (p < 0.001), however density levels and the magnitude of change differed by distance to the CBD. Different relationships were apparent depending on distance to the CBD, however despite some exceptions, as densities increased participants' neighbourhoods typically changed in ways that made them objectively more walkable, and subjectively more socially connected, pleasing places to live. The study provides empirical evidence that will help governments and developers communicate the benefits of density and pre-empt or mitigate potential problems that infill developments impose on local communities.

1. Introduction

The United Nations refers to urbanization as ‘one of the twenty-first century's most transformative trends’ (United Nations, 2017a). By midcentury, around 70 % of the world's population will reside in urban areas (United Nations, 2019). Managing population growth and urbanisation, together with pressing economic, social, cultural, and environmental issues is a priority for cities (United Nations, 2017b). Indeed, to deliver sustainable, equitable and healthy cities, the New Urban Agenda reiterates the need to prioritize compact development and urban renewal, and prevent urban sprawl (United Nations, 2017a). While the COVID-19 pandemic initially appeared to undermine this ambition, there is growing evidence that over-crowding rather than density per se posed a greater risk to disease transmission (Frumkin, 2021), and the need to densify our rapidly growing cities remains a priority.

Numerous environmental, economic and health co-benefits stem from increasing residential densities and limiting low density suburban development (United Nations Habitat, 2014). Density is a core element

* Corresponding author.

that underpins a walkable neighbourhood, as the larger population base increases the viability of local shops, services, and public transport routes, including the frequency of service (Giles-Corti et al., 2016). There is now considerable evidence from multi-city (Gascon et al., 2019; Kerr et al., 2016) and longitudinal studies (Bentley et al., 2018; Chandrabose et al., 2021; Giles-Corti et al., 2013; Hirsch et al., 2014; Kamruzzaman et al., 2016; Knuiman et al., 2014) that higher residential densities, and the destinations required for daily living that density supports, increase walking for transport. Evidence reviews also highlight the importance of compact walkable neighbourhoods to increased public transport use (Giles-Corti et al., 2016; Nieuwenhuijsen, 2020), less car dependency and sedentary time (Koohsari et al., 2015), lower BMI (Leal & Chaix, 2011) and greater social interaction (Bird et al., 2018; Thompson & Kent, 2014). While the evidence is not universally supportive, with some studies linking increased densities and local destinations with negative community outcomes such as crime (Cozens, 2008; Foster et al., 2013; Foster et al., 2021), on balance, the consensus is that developing compact communities is an effective intervention to

E-mail addresses: sarah.foster@rmit.edu.au (S. Foster), Billie.Giles-Corti@rmit.edu.au (B. Giles-Corti), julian.bolleter@uwa.edu.au (J. Bolleter), gavin.turrell@ rmit.edu.au (G. Turrell).

https://doi.org/10.1016/j.cities.2023.104565

Received 12 April 2023; Received in revised form 8 September 2023; Accepted 16 September 2023

Availableonline28September2023

improve multiple health and sustainability outcomes. Indeed, for the past 20 years, health promotion agencies have been advocating for increases in residential density as a means to increase population levels of physical activity and improve health (Giles-Corti et al., 2012; Giles-Corti et al., 2014a; Udell et al., 2014).

The need to increase residential densities particularly resonates in Australia. Although Australia has a highly urbanised population, with 89 % of Australians residing in urban areas, most Australians live in lowdensity housing in the suburbs (ABS, 2017). Indeed, Australia's capital cities have some of the lowest densities globally (Arundel et al., 2017; Hurley et al., 2017), and consequently rank among the least walkable (Boeing et al., 2022). To contain urban sprawl, Australian state and territory planning departments have enacted planning policies to develop higher density activity centres and transit-oriented development (TOD) (Bolleter & Weller, 2013). Across the nation, state capital city policies propose that most new housing should be in established suburbs, with targets ranging from 85 % in Adelaide (Government of South Australia, 2017), 70 % in Melbourne and Sydney (Urban Taskforce Australia, 2017; Victoria State Government, 2017), 60 % in SouthEast Queensland (Department of Infrastructure, Local Government and Planning, 2017), to 47 % in Perth (Department of Planning Lands and Heritage, 2018). Nonetheless, low density greenfield housing estates continue to be developed on the peri-urban fringe, and while these have a role in the provision of more affordable entry-level housing, the resulting urban sprawl is unhealthy, costly, and unsustainable (GilesCorti et al., 2016).

One of the key reasons that Australian and low-density cities elsewhere fail to achieve their urban infill targets is because of a public antipathy and resistance to densification (Doberstein et al., 2016; Searle, 2007; Smith & Billig, 2012). Despite densification being key to the vibrant walkable neighbourhoods that Australians aspire to live in (National Heart Foundation of Australia, 2020), and a growing community recognition that density is necessary to achieve walkable neighbourhoods (Ruming, 2014; Smith & Billig, 2012), this acceptance can quickly dissipate when density increases are proposed in someone's own neighbourhood (Doberstein et al., 2016). As defined by Dear (1992), NIMBY (not-in-my-backyard) syndrome refers to ‘protectionist attitudes of and oppositional tactics adopted by community groups facing an unwelcome development in their neighbourhood’ [41 p,288], particularly mid and high-rise apartment developments (McNee & Pojani, 2022). There is often strong resistance from residents in local areas facing urban consolidation, where increased density is seen as a denigration of the quintessential Australian suburb. While NIMBYism is typically synonymous with self-interested motives, successful opposition is often framed around defending the interests of the wider community (e.g., protecting local wildlife) (Einstein et al., 2020). In some instances, ‘Save our Suburb’ pressure group campaigns and strong local opposition have resulted in density concessions or the shelving of major urban consolidation projects (Searle, 2007; Shepherd, 2016). Indeed, there appears to be a mismatch between how density increases are interpreted by planners and policymakers, and how they are perceived by the community (Churchman, 1999; Churchman, 2002). What a planner envisages as a vibrant sustainable community with diverse and affordable housing (Rowley et al., 2017), the community can interpret as a threat to the physical and social character of the local area (Nematollahi et al., 2015; Rowley et al., 2017), particularly when higher densities are planned.

Community concerns about infill developments vary depending on the type, height and scale of the development (Cook et al., 2012; Rowley et al., 2017). Residents worry that infill developments will destroy their neighbourhood's character (Maginn & Foley, 2017), reduce greenspace (Searle, 2004), jeopardise privacy, block sunlight and views (Arvola & Pennanen, 2014), and increase noise (Rowley et al., 2017), and traffic and parking issues (Holling & Haslam McKenzie, 2010). However, underlying community concerns about the physical changes that come with density are ‘deeper concerns’ that relate to the social changes that

density could bring (Nematollahi et al., 2015). It has been suggested that existing residents fear that density will introduce ‘undesirable’ people into traditionally family-orientated, homogenous suburbs, leading to increased social problems and declining property values (Burke, 1991). Indeed, a survey of three suburban precincts designated for future TOD sites in Perth found residents were most concerned about the prospect of more incidental social interactions with new residents, and in one of the study areas, this resistance stemmed from the assumption that new residents would increase crime and anti-social behaviour (McNee & Pojani, 2022; Nematollahi et al., 2015).

As reflected in some concerns raised in opposition to new housing developments, there are often socio-economic dynamics to community resistance (Einstein et al., 2020). A Melbourne study identified spatial disparities in third-party objections and appeals against planning applications for high density housing developments – with more objections lodged in advantaged neighbourhoods (Cook et al., 2012). In the USA, studies reiterate that wealthier and typically white neighbourhoods are more likely to resist local development (Dear, 1992; Einstein et al., 2020; Tighe, 2010), and that socio-economically advantaged individuals are more likely to engage in the planning and consultation process, even in more disadvantaged areas (Dear, 1992; Einstein, 2019). Indeed, as summarised by Dear (1992, p.293), ‘the single best predictor of opposition is income’ Paired with this, host community resistance tends to be heightened when the proposed housing development provides affordable or subsidised housing (Davison et al., 2013), and that oft-cited arguments against affordable housing developments (e.g., declining property values) serve to mask more unpalatable concerns about the social status or ethnicity of future residents (Davison et al., 2013; Tighe, 2010).

While larger scale developments are key to delivering the densities needed to meet infill targets and the population thresholds that deliver walkable neighbourhoods, much of the infill in Australian cities has been achieved through indiscriminate or ad hoc ‘background infill’ (Bolleter, 2016). This involves the opportunistic subdivision of individual suburban lots in established areas by ‘mom and dad’ investors (Cuff & Dahl, 2009) where a single house is typically replaced by less than five dwellings, or a second detached house is built at the rear of an existing house (i.e., battle-axe blocks) (Bolleter, 2016). These developments result in a net gain of only one or two dwellings, meaning density increases are still too low to upgrade the local transport services and amenities that underpin walkable neighbourhoods (Bolleter, 2016; Rowley et al., 2017). Moreover, this form of development reduces tree canopy and access to green infrastructure (Saunders et al., 2020)decreasing biodiversity, increasing heat island effects, and negatively affecting streetscapes (Bolleter, 2016). While background infill is less conspicuous than the larger-scale developments that provoke widespread community resistance, when poorly designed these small-scale projects also draw community opposition (Kelly et al., 2011), and perhaps more importantly, can produce poor long-term outcomes in neighbourhoods.

While community resistance to urban consolidation remains widespread, within the planning and public health disciplines it is widely accepted that as densities increase, so too will the walkability and amenity of the local community (Udell et al., 2014). Numerous international and Australian studies have explored public attitudes to compact development and the concerns that trigger residents' opposition (Doberstein et al., 2016; Lewis & Baldassare, 2010; McCrea & Walters, 2012; Nematollahi et al., 2015; Smith & Billig, 2012), yet to date, no known empirical studies have examined the causal relationship between residential density and the anticipated built and social environmental benefits or (feared) harms that it brings. Drawing on longitudinal data collected from mid-age adults (n = 3028) living in Brisbane, Australia, between 2007 and 2016, this study examined the impact that increasing residential densities had on local communities and residents. The study examines the relationship between residential density and:

(1) Objective built and social environmental characteristics thought to change in response to density increases, including proximity to public transport, parks and shops, land-use mix and neighbourhood crime; and

(2) Residents' perceptions of their neighbourhood, including traffic volume, neighbourhood surroundings (e.g., greenery and attractive buildings), social cohesion, safety from crime, and whether the suburb is a desirable place to live.

2. Methods

2.1.

Context

This study was set in Brisbane, the capital city of Queensland. Brisbane has a population of approximately 1.2 million residents (ABS, 2020), is the third most populated Australian city, and one of the fastest growing Australian cities, yet has some of the lowest densities (Gallagher et al., 2019). In response to Brisbane's sprawling form, planners, and policymakers have been striving for urban consolidation since the 1980's in the form of ‘inter-connected communities that were more selfcontained in terms of services and employment and as such, would generate less demand for travel in private vehicles' (Burton, 2017). Reflecting such aspirations, Brisbane's current planning stipulates an infill target of 60 % (Burton, 2017; Department of Infrastructure, Local Government and Planning, 2017). In recent years Brisbane has typically achieved 65 % infill development (with 35 % greenfield) (Department of Infrastructure, Local Government and Planning, 2017).

2.2.

Data

This study uses data from the HABITAT (How Areas in Brisbane Influence HealTh and AcTivity) study – a multilevel longitudinal study of mid-aged adults living in the Brisbane Local Government Area, Australia (Turrell et al., 2020). The primary aim of HABITAT is to examine patterns of change in health and well-being over the period 2007–2016 and to assess the relative contributions of environmental, social, psychological, and socio-demographic factors to these changes. In this paper, we use data from all waves of the study which were collected in May–July 2007, 2009, 2011, 2013, and 2016. The HABITAT study received ethical clearance from the Queensland University of Technology Human Research Ethics Committee (Ref. Nos. 3967H & 1300000161).

Details about study's sampling design have been published elsewhere (Burton et al., 2009; Turrell, 2020). Briefly, a multi-stage probability sampling design was used to select a stratified random sample (n = 200) of Census Collector's Districts (CCD), and from within each CCD, a random sample of people aged 40–65 years (on average 85 people per CCD). A structured self-administered questionnaire was developed, and copies sent to 17,000 potentially eligible participants in May 2007 using a mail survey method developed by Dillman (Dillman, 2000). After excluding 873 out-of-scope contacts (i.e., deceased, no longer at the address, unable to participate for health-related reasons) 11,035 usable surveys were returned yielding a baseline response rate of 68.3 %: the corresponding response rates from in-scope and contactable participants in 2009, 2011, 2013, and 2016 were 72.6 % (n = 7866), 67.3 % (n = 6900), 67.1 % (n = 6520), and 58.8 % (n = 5187), respectively. The baseline sample (2007) was broadly representative of the wider Brisbane population aged 40–65 years (Turrell et al., 2010).

2.2.1. Objective built environment measures

The main area-level unit-of-analysis used in this study comprises a 1 km road network catchment surrounding each participant's residential address. Each of the objective built environment measures were calculated within each catchment as of June each data collection year. June broadly corresponded to the individual-level data collections, ensuring temporal correspondence between the built environment data and

survey data.

2.2.2. Primary exposure variable

Residential density was measured by calculating the number of dwellings per hectare of residential land within each network catchment. Data to measure residential density came from two sources: the Brisbane City Council's Cadastre and Land Use Activity Database, and MapInfo's StreetPro (Pitney Bowes Software).

2.2.3. Objectively measured outcome variables

2.2.3.1. Land use mix. This measure was derived from data that quantified the proportion of land area within each network catchment that was zoned residential, commercial, industrial, recreational, and other. Using an entropy equation described by Leslie et al. (2007), the five types of land use were combined to form a measure that ranged from 0 to 1, with 0 representing complete homogeneity of land use within the catchment, and 1 representing an even distribution of the five types of land use (Leslie et al., 2007).

2.2.3.2. Bus stop count. Within each network catchment, this was measured as the number of Brisbane City Council bus stops.

2.2.3.3. Distance to bus stop, park, and shop. These were measured as the walking distance (metres) to the closest amenity within each network catchment.

2.2.3.4. Crime. Data on reported crime counts in Brisbane were procured from the Queensland Police Service (QPS). We calculated the total number of reported crimes occurring within each network catchment. Three QPS categories of crime were used: crimes against the person (homicide, assault, sexual offenses, robbery, and other offenses against the person), unlawful entry (to a dwelling without violence, to a shop with intent, and other unlawful entry with intent), and social incivilities (drug offenses, prostitution offenses, trespassing and vagrancy, and good order offenses).

2.2.4. Subjectively measured neighbourhood perceptions

2.2.4.1. Overall rating of suburb. Participants were presented with a single statement that asked ‘Overall, how would you rate your suburb as a place to live?’ , with the five response options ranging from excellent to poor.

2.2.4.2. Social cohesion. This was measured by a five-item modified version of the Buckner Social Cohesion Scale (Buckner, 1988). Participants were presented with statements about common values, trust, and social relationships between themselves and residents of their neighbourhood, with response options ranging from strongly disagree to strongly agree. Higher scores indicate higher levels of social cohesion.

2.2.4.3. Perceived crime. For other neighbourhood perceptions, statements were adapted for the Australian population from the Neighbourhood Environment Walkability Scale (NEWS) questionnaire (Cerin et al., 2006), which has acceptable validity and reliability (Cerin et al., 2009; Turrell et al., 2011). Participants were presented with a series of statements and asked to respond on a 5-point Likert-type scale, ranging from strongly disagree to strongly agree. Principal component analysis (PCA) with varimax rotation was used to identify a series of factors. Participants were asked about the level of crime in their neighbourhood, and perceptions of personal safety in parks, on the streets, and using public transport in their area. Six items loaded on one factor that was interpreted as ‘perceptions of crime’ , with higher scores indicating greater concerns about crime and safety.

2.2.4.4. Traffic volume. Participants were presented with three statements about the amount of traffic on local streets, whether the respondent lived on a main road or busy throughway, and the extent of motor vehicle fumes in the suburb. All items loaded on one factor that was interpreted as ‘traffic volume’ , with higher scores indicating more traffic.

2.2.4.5. Neighbourhood surroundings. Participants were presented with five statements about the extent of greenery in the suburb, and whether there were interesting things to look at; the extent of tree coverage on footpaths, and the extent to which there were attractive buildings and homes, and pleasant natural features (e.g., nature reserves, beach, rivers, and creeks). The five items loaded on one factor that was interpreted as ‘neighbourhood surroundings’ , with higher scores indicating a more aesthetically pleasing neighbourhood.

2.2.5. Neighbourhood- and individual-level covariates

2.2.5.1. Neighbourhood disadvantage. Each of the 200 CCD comprising the HABITAT sample was assigned a socioeconomic score using the ABS' Index of Relative Socioeconomic Disadvantage (IRSD) (Australian Bureau of Statistics, 2008): the Index reflects each area's overall level of disadvantage based on 17 socioeconomic attributes, including education, occupation, income, unemployment, and household tenure. For analysis, the CCDs were grouped into quintiles based on their IRSD scores with Q1 denoting the 20 % most disadvantaged areas and Q5 the least disadvantaged 20 %.

2.2.5.2. Education. Highest educational qualification completed was coded as bachelor's degree or higher (including post graduate diploma, Masters, or doctorate); diploma (associate or undergraduate); vocational (trade or business certificate, or apprenticeship), or no post-school qualifications.

2.2.5.3. Occupation. Respondents reported their employment status at the time of the survey, and if employed, their job title and main tasks and duties performed. This information was coded in accordance with the ABS' Australian and New Zealand Standard Classification of Occupations (ANZSCO) (Australian Bureau of Statistics, 2013). For analysis, ANZSCO was re-coded into 3 categories: managers and professionals (managers and administrators, professionals, and associate professionals); white collar employees (clerical, sales and service); and blue-collar workers (trades, production workers, labourers). Four additional categories were also created; retired, home duties, other (unemployed, permanently unable to work, students), and not easily classified (insufficient information for their employment status and/or occupation to be reliably ascertained).

2.2.5.4. Household income. Respondents were asked to estimate the total pre-tax income for their household using a single question comprising 13 income categories. For analysis, these were re-coded into seven categories: AUS$130,000 pa or more; $129,999 – $72,800; $72,799–52,000; $51,999–26,000; $25,999–0; don't know or don't want to answer; and missing (i.e., participants who left the income question unanswered).

2.2.6. Primary stratification variable

Preliminary exploratory analysis showed that each of the objectively measured built environment and crime variables differed (crosssectionally and longitudinally) based on the proximity of each road network catchment to the Brisbane city central business district (CBD). Compared with more distal catchments, those closer to the CBD tended to be more residentially dense, have a greater diversity of land uses, exhibit shorter distances between origins and destinations (e.g., home to public transport), and experience higher rates of crime. To

accommodate this spatial complexity, we categorised network catchments based on their proximity to the CBD as follows: inner areas (0–5 km), inner-middle areas (>5–10 km), outer-middle areas (>10–15 km), and outer areas (>15 km).

2.3. Analysis

The baseline sample comprised 11,035 participants aged 40–65 living in 200 Brisbane neighbourhoods (CCD). For this study, we retained those who completed and returned a survey at all five waves (n = 4134). Of these, we excluded participants who moved residence between 2007 and 2016 (n = 1056) and persons who returned a survey in lieu of the sampled participant (n = 7). In addition, we excluded those with missing data at any wave for the subjective measures of neighbourhood perceptions or the individual-level covariates (n = 43). The final analytic sample comprised residentially stable participants who responded at every wave of the study, and who provided complete and usable data (n = 3028). Table 1 presents the sociodemographic

Table 1

Sociodemographic characteristics of the HABITAT baseline sample compared with the analytic samples in 2007 (Wave 1) and 2016 (Wave 5).

Baseline sample Analytic samplea

a Comprises survey respondents who participated in the study at all five waves, and who resided at the same address over the study period (2007–2016).

b Occupation information was coded and categorised in accordance with the Australian and New Zealand Classification of Occupations (ANZCO)(Australian Bureau of Statistics, 2013).

c Includes unemployed, permanently unable to work, and students

d Respondents who provided no information about their occupation, and those who provided insufficient information for their occupation to be reliably ascertained

e Respondents who left the income question unanswered

f Neighbourhoods were assigned a socioeconomic score using the Australian Bureau of Statistics Index of Relative Socioeconomic Disadvantage (Australian Bureau of Statistics, 2008)

characteristics of the analytic sample, arrayed beside the characteristics of the HABITAT baseline sample which was representative of the wider Brisbane population (Turrell et al., 2010). The profiles of the baseline and analytic samples were similar, suggesting that despite exclusions, drop-outs and missing data, the analytic sample remains reasonably representative of the Brisbane population.

Analyses for this study focuses on change between 2007 and 2016 and is conducted in four stages. First, we use descriptive statistics to examine how the objective built environment measures and subjective neighbourhood perceptions change. Second, using fixed effects (FE) linear regression with adjustment for within-CCD clustering, we investigate the extent and nature of change in residential density for Brisbane city overall, and stratified by Euclidean distance to the CBD. The primary unit of analysis for this approach is the 1 km road network buffer and we estimate within-buffer change in density whilst controlling for changes in measured time-varying factors (e.g., buffer size, neighbourhood disadvantage) and time-invariant characteristics (e.g., topography). Third, we again use FE regression to examine associations between change in residential density and change in the built environment and objectively measured crime within participants' 1 km road network buffer. We investigate these changes for all of Brisbane city and by distance to the CBD. Fourth, we examine change in residential density and change in residents' perceptions of their neighbourhood environments by proximity to the CBD using FE regression. For these analyses, we estimate within-person change in perceptions while controlling for change in measured sociodemographic factors (e.g., age, education, income) and all time-invariant factors (e.g., sex, ethnicity, childhood circumstances). The latter factor is especially appealing: by controlling for person-specific time invariant factors we able to eliminate omitted variable bias (Allison, 2009; Firebaugh et al., 2013) and hence provide support for a causal interpretation of the relationship between change in density and change in neighbourhood perceptions. Output from the FE models is reported as beta coefficients and their 95 % confidence intervals. All analyses were conducted in Stata SE Version 15.1 (Stata Corporation, 2016).

3. Results

The socio-demographic characteristics of the HABITAT baseline sample in 2007 (n = 11,035) and our analytic sample (n = 3028) in 2007 and 2016 are presented in Table 1. There were only minor differences

between the samples, with the analytic sample including more participants with a bachelor's degree or higher, more managers and professionals, and participants with slightly higher incomes.

Table 2 presents the means and inter-quartile ranges for the objective built environment measures and subjective neighbourhood perceptions. On average, residential density and count of bus stops within participants' 1 km road network buffer increased over time, land use mix remained stable, and the distance to the nearest bus stop, park and shop decreased. Crime against the person and unlawful entry both declined, albeit by different margins, whereas social disorder fell between 2007 and 2011, only to increase again and surpass 2007 levels by 2016. On average, participants' subjective neighbourhood perceptions also remained relatively stable between 2007 and 2016, with marginal declines in the overall rating of the suburb, perceived traffic volume, and perceived crime.

Table 3 presents mean residential densities at each timepoint, stratified by distance to the CBD, and change in density between timepoints. The values for residential density between 2007 and 2016 increased gradually over time. For the overall sample, the number of dwellings per hectare of residential land within the 1 km buffer increased, on average, by 1.5 dwellings (p < 0.001). However, the density levels and the magnitude of change differed by distance to the CBD. For participants in inner neighbourhoods (i.e., 0–5 km), densities were approximately 30 dwellings/ha in 2007, and increased 14 % to almost 35 dwellings/ha in 2016 (p < 0.001). For participants in innermiddle areas (i.e., 5–10 km), densities were almost 16 dwellings/ha in 2007 and increased to about 17 dwellings/ha in 2016 (p < 0.001). In contrast, densities in outer-middle (i.e., 10–15 km) and outer areas (i.e., 15+ km) increased only slightly between 2007 and 2016 from 13.7 to 14.6 and 13.9 to 14.5 dwellings/ha respectively. Despite the lower baseline density levels and smaller magnitude of change in outer-middle and outer areas, the increases in residential density between 2007 and 2016 were statistically significant (p < 0.001).

Relationships between change in residential density and change in objective built environment measures and crime within participants' 1 km buffers are presented in Table 4. For participants living in amenityrich inner neighbourhoods (i.e., 0–5 km), as residential density increased, there was little change to the built environment, however changes to the built environment were observed elsewhere. Those in inner-middle neighbourhoods (i.e., 5–10 km) experienced increases in land use mix (p = 0.034) and the count of bus stops (p = 0.001), those in

Table 2 Objective built environment measures and subjective neighbourhood perceptions, Brisbane, Australia, 2007 to 2016.

The study includes five waves of data (i.e., 2007, 2009, 2011, 2013 and 2016); however only data from the first (i.e., 2007), middle (i.e., 2011) and last timepoint (i.e., 2016) are presented for clarity

a Inter-quartile range: 25th percentile, median, 75th percentile

b Only three waves of data collected for this measure: 2007, 2009, and 2011

Table 3

a Fixed-effect regression coefficients and 95 % confidence intervals. Models adjust for change in road network buffer size and change in neighbourhood disadvantage

b Residential density was calculated as the number of dwellings per hectare of residential land within each 1 km residential network buffer

* ≤0.01.

** ≤0.001

outer-middle neighbourhoods (i.e., 10–15 km) experienced reductions in their distance to both a bus stop (p = 0.072) and park (p = 0.069), and those in outer areas (i.e., 15+ km) saw increases in the count of bus stops (p = 0.082) but a reduction in land use mix (p = 0.037). For the overall sample (n = 3028), increases in residential density were associated with an increase in the count of bus stops (p = 0.052).

Increased residential density had little impact on crime against the person. However, there were significant increases in social disorder in inner-middle (i.e., 5–10 km) and outer-middle (i.e., 10–15 km) neighbourhoods (p = 0.044 and p = 0.000, respectively), and unlawful entry reduced in all neighbourhoods except outer-middle areas, where the rate increased (p = 0.018). For the overall sample (n = 3028), increases in residential density were associated with a decrease in unlawful entry (p = 0.010).

Table 5 presents relationships between the change in residential density and change in residents' perceptions. For the overall sample, increases in residential density were associated with increased perceptions of social cohesion (p = 0.004) and rating the suburb as a desirable place to live (p = 0.046). This was consistent with the findings for participants living in inner neighbourhoods (i.e., 0–5 km), however other perceptions differed depending on where participants lived. Those in inner-middle neighbourhoods (i.e., 5–10 km) perceived a reduction in traffic volume (p = 0.075) and an increase in social cohesion (p = 0.000), and those in outer-middle neighbourhoods (i.e., 10–15 km) perceived an increase in crime (p = 0.004).

4. Discussion

This study investigated the influence of increasing residential densities on real and perceived neighbourhood amenity – as captured by objective measures of the neighbourhood built environment and crime, and residents' perceptions of their local neighbourhood. The relationship between increasing densification and changes in specific aspects of neighbourhood amenity appear to be context-specific, depending on distance to the CBD (i.e., inner, inner-middle, outer-middle, or outer neighbourhoods). However, overall, increases in residential density around participant homes were associated with positive changes to the objective built environment (e.g., increased number of bus stops, decreased distance to a bus stop or park), while the relationship between density and crime was mixed. Increased density was also generally associated with improvements in residents' perceptions of their local area (i.e., in social cohesion, suburb ratings, and traffic volume). Thus, while there were some caveats, on balance, it appeared that as residential densities increased, neighbourhoods changed for the better and became more walkable, socially connected, and pleasing environments to live in.

The magnitude of the density increases were small and indicative of

the type of development unfolding across much of Brisbane, with ad hoc redevelopment producing apartments, detached housing and some middle density in inner suburbs (Gallagher et al., 2019), and fragmented infill housing in middle and outer suburban zones (Newton & Glackin, 2014). This is consistent with the notion that Brisbane's outer, lowdensity suburbs – which are in most need of increased public transport and amenity – may be unappealing to major developers because of the lower land values and poorer transport infrastructure (Dodson, 2010). Indeed, the current density targets for Brisbane are flawed if these outer areas are to attract the type of development necessary to underpin healthy walkable neighbourhoods. Planning guidelines for Brisbane outline net density targets of at least 30 dwellings/ha in urban neighbourhoods and 15 dwellings/ha in suburban neighbourhoods (Economic Development Queensland, 2015). In this study, participants living in neighbourhoods closest to the CBD had the highest densities, increasing to almost 35 dwellings/ha (i.e., achieving the urban density target). Increases were smaller for participants in other zones, with inner-middle neighbourhoods (i.e., 5–10 km) increasing to approximately 17 dwellings/ha (i.e., meeting the suburban target but well below the urban target), and outer-middle and outer neighbourhoods increasing to about 14.5 dwellings/ha (i.e., slightly below the suburban target).

It should be acknowledged that the densities reported in this study are net density averages for participants living in the different zones, meaning many participants live in neighbourhoods that fall short of the targets. Indeed, previous research found that just 2 % of Brisbane suburbs reached the 30 dwellings/ha target and 13 % met the suburban 15 dwellings/ha target (Arundel et al., 2017). While on average the residential densities in participants' 1 km buffers met (or were close to) the Brisbane density targets, questions remain as to whether these policy settings are sufficient to impact behaviour. In a review commissioned by the National Heart Foundation, Giles-Corti et al. (2014) concluded that minimum net residential densities of 20 dwellings/ha were required to encourage walking, whereas 36 dwellings/ha were the minimum necessary to support public transport (Giles-Corti et al., 2014b). More recently, an urban liveability checklist recommended that minimum gross density targets of around 25 dwellings/ha were needed to increase walking, cycling and public transport use, and decrease driving (Badland et al., 2019; Boulange et al., 2017). Thus, while the density increases in this study were sufficient to impact some built environment attributes and neighbourhood perceptions, for most participants, and particularly those in outer suburban areas, they fall well short of the density thresholds needed to change behaviour (Boulange et al., 2017) and create healthy liveable neighbourhoods (Badland et al., 2019).

Nonetheless, the increases in residential density observed correlated with changes to the built environment that increased their walkability. The planning and public health literature consistently underscores the need to increase densities to create environments that support utilitarian

S. Foster et al.

4 Change in residential density and change in the built environment and crime between 2007 and 2016, within participants 1 km road network buffer, by distance to the Brisbane City central business district

Inner areas are 0 –5 km from the Brisbane City Central Business District (CBD); Inner-Middle area > 5 –10 km from the CBD; Outer-middle areas are > 10 –15 km from the CBD; Outer areas are > 15 km from the CBD a Fixed-effect regression coefficients and 95 % confidence intervals. Models adjust for change in road network buffer size and change in neighbourhood disadvantage

Table 5 Change in residential density and change in residents' neighbourhood perceptions between 2007 and 2016, by distance to the Brisbane City central business district a . N = 3028

–Middle ( > 10 –15

–Middle ( > 5 –10 km) ( n = 1171)

(0 –5 km) ( n = 382)

a Residential density was calculated as the number of dwellings per hectare of residential land within each 1 km residential network buffer

b Fixed-effect regression coefficients and 95 % confidence intervals. Models adjust for observed time-varying factors (road network buffer size, neighbourhood disadvantage, age, education, occupation, and household income) and observed and unobserved time-invariant factors (sex, ethnicity, childhood circumstances, topography, among others).

walking and public transport use (Boulange et al., 2017; Giles-Corti et al., 2016). Increased density is also required to deliver the 15-minute city currently capturing the attention of policymakers (Bloomberg City Lab, 2020), academics (Moreno et al., 2021) and the community (C40 Cities and Arup, 2021) alike. In this study, even modest density increases were associated with small increases in the count of bus stops (innermiddle and outer-middle areas), land use mix (inner-middle areas), and proximity to a bus stop and park (outer-middle areas), culminating in neighbourhoods that, compared with baseline, were more supportive of walking and other activities of daily living. However, improved access to these local destinations may not be sufficient to impact residents' behaviour unless they also deliver a level of quality. For example, the addition of bus stops does not indicate whether the service frequency is sufficient to drive behaviour change (Higgs et al., 2019; Higgs et al., 2022), with a previous study identifying that just 12 % of Brisbane households had a proximate bus stop with a weekday service every 30minutes (Gunn et al., 2018). It should also be noted that densities increased most in Brisbane's inner neighbourhoods but there were no corresponding changes in the objective built environment measures. These inner neighbourhoods already deliver superior walkability, access to public transport and service frequency (Arundel et al., 2017) compared to outer Brisbane areas, but multi-city evidence suggests there is considerable scope for further density increases with subsequent gains for population health and sustainability (Cerin et al., 2022). Importantly, density increases had some impact on built environments in the middle and outer neighbourhoods where spatial and health inequalities are greatest (Arundel et al., 2017) and residents could benefit most from more supportive built environments.

As noted, the density increases in the study were reminiscent of background infill rather than large-scale apartment developments. Community resistance to infill development covers a range of issues, including the loss of local character and amenity (Maginn & Foley, 2017), greenspace (Searle, 2004), privacy, sunlight and views (Arvola & Pennanen, 2014), and increased noise (Rowley et al., 2017), traffic and parking issues (Holling & Haslam McKenzie, 2010), and social problems (Burke, 1991; Nematollahi et al., 2015), many of which were captured in this study. However, density increases were associated with enhanced perceptions of the suburb as a place to live (in inner areas) and social cohesion (in inner and inner-middle areas), and a decrease in perceived traffic volume (in inner middle areas). All these positive changes occurred for residents living in Brisbane's inner and inner-middle areas, where the density increases were larger than the incremental changes occurring in the outer suburbs. Further, many of the common concerns about density were captured in the subjective ‘neighbourhood surroundings’ measure, with items addressing greenery, interesting scenery, tree coverage on footpaths, attractive buildings, and pleasant natural features. It was anticipated that, if the density increases were unpalatable to residents, their perceptions of neighbourhood surroundings would decline, however this was not the case. Taken together, the perceptions results indicate that neighbourhoods became more socially cohesive and appealing as they densified, without any perceived loss of amenity.

However, the findings were not universally positive, with mixed results for the incidence of objective crime and perceived crime. For most participants, there was either a decrease in crime or no significant change, but those living in outer-middle areas experienced increases in social disorder and unlawful entry, and perceived crime increased. One of the main reasons communities are resistant to densification is the fear that it will increase social problems, including crime (Burke, 1991; Nematollahi et al., 2015), and our findings in outer-middle neighbourhoods appear to substantiate these concerns. Yet density is often pilloried for its impact on crime (Newman & Hogan, 1981) when it is the other attributes of a walkable neighbourhood (e.g., increased access to shops and services, street connectivity) that occur concurrently with density that increase vulnerability to crime (Cozens, 2008; Foster et al., 2013). Indeed, both unlawful entry and social disorder could be

considered by-products of living in more vibrant, walkable neighbourhoods (Foster et al., 2014; Foster et al., 2021). For example, property offenses tend to be opportunistic, committed as people carry out their routine activities or enroute to/from these activities (Brantingham & Brantingham, 1993) and consequently they cluster near shopping centres, recreation facilities and public transport hubs (Beavon et al., 1994; Brantingham & Brantingham, 1993; Brown, 1982). This highlights the need for crime prevention through environment design (CPTED) measures to be implemented as areas densify (e.g., territoriality, surveillance, access control, image/maintenance, activity programme support and target hardening) (Cozens et al., 2005). For instance, the design of density could impact the observed relationship between increases in density and crime. The background infill (e.g., battle-axe blocks or villa developments) likely unfolding in the outer-middle areas adds to the density, but most developments of this type do not encourage natural surveillance as they have no connection to the street. Indeed, previous Australian research set in low density greenfield suburbs found that house attributes that increased the potential for natural surveillance (e. g., balconies or verandas, visible windows) correlated with fewer physical incivilities in the street (Foster et al., 2011).

The strengths of this study relate to the large sample of participants who remained at the same address as their neighbourhoods evolved around them, the longitudinal study design with five waves of data including objective and subjective measures collected at each timepoint, the modelling approach that focused on within-area and within-person change meaning results are not subject to bias from any (measured or unmeasured) time-constant selection factors or confounders, and the holistic exploration of how density changes influence a range of both positive and negative outcomes. However, there are also limitations. The study comprised mid-aged and older adults living in Brisbane (i.e., a relatively low-density midsized city) and consequently results may not be generalisable to other populations and settings. Nonetheless this is an important population to examine as people become increasingly reliant on their local neighbourhood as they age (Vine et al., 2012). Analyses were also limited to the neighbourhoods where participants lived, so the changes to the built environment and crime may not reflect changes occurring in wider Brisbane. While the focus on participants who remained in situ and had complete data was key to the study design, it may have introduced some bias. For instance, it's possible that participants who were resistant to neighbourhood change and had negative experiences may have moved out of the neighbourhood, impacting the results. To test this, we ran a sensitivity analysis. There were some differences between the sociodemographic characteristics and residential densities of our analytic sample compared to: (1) participants who were excluded because they moved house (supplementary Table S1); and (2) participants who were excluded because they didn't respond to all waves of the survey (supplementary Table S2). However, in both analyses, the observed differences were small in magnitude suggesting any bias resulting from their exclusion was likely to be minimal. Furthermore, the study examined the impact of increasing residential density on a range of outcomes, yet on average there were relatively small increases in density across our participants neighbourhoods, suggesting sporadic background infill, rather than the comprehensive development needed to meet urban consolidation policy targets (or provoke community resistance). Finally, the density exposure measure was relatively coarse, and there is evidence to suggest that more nuanced measures that capture the design and form of density could have different associations with residents' experiences (Foster et al., 2016).

5. Conclusion

Increasing residential density is vital to meet Australia's urban consolation targets and deliver more sustainable, liveable, and healthier neighbourhoods; however, community resistance to densification remains a barrier to infill development in established areas (Searle, 2007). In this study, the overall magnitude of the density increases were small

and generally indicative of sporadic single lot development, rather than the large-scale apartment projects that draw community ire – but are necessary to achieve Brisbane's urban density targets. Despite the incremental increases in residential density, the findings provide longitudinal evidence to support many of the anticipated benefits of density. As residential densities increased, participants' local areas changed in ways that, for the most part, made them objectively more walkable, and subjectively more socially connected and pleasing places to live. The study provides empirical evidence that could help governments and developers communicate the benefits of density and pre-empt or mitigate any potential problems that infill development could bring local communities.

CRediT authorship contribution statement

Sarah Foster: Conceptualisation, Methodology, Writing – Original Draft. Gavin Turrell: Methodology, Formal analysis; Writing - Review & Editing. Julian Bolleter: Writing - Review & Editing. Billie Giles-Corti: Writing - Review & Editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgements

The HABITAT study is funded by the Australian National Health and Medical Research Council (NHMRC; Nos 497236, 339718, 1047453). SF is supported by an ARC Future Fellowship (FT210100899).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.cities.2023.104565.

References

ABS. (2017). 3222.0 - Population projections, Australia, 2017 (base) -2066 Canberra: Australian Bureau of Statistics

ABS. (2020). 1410.0 - Data by region, 2014–19. In Data by region Canberra: Australian Bureau of Statistics

Allison, P. D. (2009). Fixed effects regression models California: Sage Publications Ltd. Arundel, J., et al. (2017). Creating liveable cities in Australia: Mapping urban policy implementation and evidence-based national liveability indicators. In Centre for urban research Melbourne, Australia: RMIT University

Arvola, A., & Pennanen, K. (2014). Understanding residents’ attitudes towards infill development at Finnish urban suburbs. In Unknown editor (Ed.), World SB14 Barcelona (pp. 1–10). Barcelona: World SB14 Barcelona Australian Bureau of Statistics. (2008). In A.B.o. Statistics (Ed.), Information paper: An introduction to Socioeconomic Indexes for Areas (SEIFA), 2006. Cat. No. 2039.0 Canberra, Australia: Commonwealth of Australia

Australian Bureau of Statistics. (2013). In A.B.o. Statistics (Ed.), Australian and New Zealand standard classification of occupations, version 1.2. Cat. No. 1220.0 Canberra, Australia

Badland, H., Higgs, C., & Giles-Corti, B. (2019). In RMIT University (Ed.), The healthy liveable communities urban liveability checklist Melbourne

Beavon, D. J. K., Brantingham, P. L., & Brantingham, P. J. (1994). The influence of street networks on the patterning of property offences. In R. V. Clarke (Ed.), Crime Preventions Studies New York: Criminal Justice Press

Bentley, R., et al. (2018). A longitudinal study examining changes in street connectivity, land use, and density of dwellings and walking for transport in Brisbane, Australia. Environmental Health Perspectives, 126(5)

Bird, E. L., et al. (2018). Built and natural environment planning principles for promoting health: An umbrella review. BMC Public Health, 18(1), 930 Bloomberg City Lab. (2020). Paris Mayor: It's time for a '15-minute city'. 2020 8 November Available from: https://www.bloomberg.com/news/articles/2020-02-18/paris -mayor-pledges-a-greener-15-minute-city

Boeing, G., et al. (2022). Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities. The Lancet Global Health, 10(6), e907–e918

Bolleter, J. (2016). Background noise: A review of the effects of background infill on urban liveability in Perth. Australian Planner, 10, 1–14

Bolleter, J., & Weller, R. (2013). Made in Australia: The future of Australian cities Perth: University of Western Australia Publishing

Boulange, C., et al. (2017). Examining associations between urban design attributes and transport mode choices for walking, cycling, public transport and private motor vehicle trips. Journal of Transport and Health, 6

Brantingham, P. L., & Brantingham, P. J. (1993). Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13(1), 3–28

Brown, M. (1982). Modelling the spatial distribution of suburban crime. Economic Geography, 58(3), 247–261.

Buckner, J. (1988). The development of an instrument to measure neighborhood cohesion. American Journal of Community Psychology, 16, 771–791

Burke, T. (1991). Medium density housing in the United States: Implications for Australia. Urban Policy and Research, 9(3), 157–169

Burton, N. W., et al. (2009). HABITAT: A longitudinal multilevel study of physical activity change in mid-aged adults. BMC Public Health, 9(1), 76

Burton, P. (2017). South East Queensland: Change and continuity in planning. In Planning metropolitan Australia (pp. 156–177). Routledge

C40 Cities and Arup. (2021). Green and thriving neighbourhoods: a pathway to net zero, featuring the '15-minute city' C40 Cities and Arup

Cerin, E., et al. (2006). Neighborhood environment walkability scale: Validity and development of a short form. Medicine and Science in Sports and Exercise, 38(9), 1682

1691

Cerin, E., et al. (2009). Cross-validation of the factorial structure of the Neighborhood Environment Walkability Scale (NEWS) and its abbreviated form (NEWS-A). International Journal of Behavioral Nutrition and Physical Activity, 6(1), 32

Cerin, E., et al. (2022). Determining thresholds for spatial urban design and transport features that support walking to create healthy and sustainable cities: Findings from the IPEN Adult study. The Lancet Global Health, 10(6), e895–e906

Chandrabose, M., et al. (2021). Urban densification and physical activity change: A 12year longitudinal study of Australian adults. American Journal of Epidemiology, 190 (10), 2116–2123

Churchman, A. (1999). Disentangling the concept of density. Journal of Planning Literature, 13(4), 389–411.

Churchman, A. (2002). Environmental psychology and urban planning: where can the twain meet? In R. Bechtel, & A. Churchman (Eds.), Handbook of environmental psychology (pp. 191–200). New York: John Wiley & Sons, Inc

Cook, N., et al. (2012). Resident third party objections and appeals against planning applications: Implications for higher density and social housing. In AHURI final report no. 197 Melbourne: Australian Housing and Urban Research Institute Limited Cozens, P. (2008). New urbanism, crime and the suburbs: A review of the evidence. Urban Policy and Research, 26(4), 429–444

Cozens, P., Saville, G., & Hillier, D. (2005). Crime prevention through environmental design (CPTED): A review and modern bibliography. Journal of Property Management, 23(5), 328–356

Cuff, D., & Dahl, P.-J. (2009). Rx for the R1: Sustaining the neighbourhood. In J. Bolchover, & J. Solomon (Eds.), Sustain and develop (pp. 24–33). New York: 306090, Inc

Davison, G., et al. (2013). Understanding and addressing community opposition to affordable housing development. In AHURI final report Melbourne: Australian Housing and Urban Research Institute

Dear, M. (1992). Understanding and overcoming the NIMBY syndrome. Journal of the American Planning Association, 58(3), 288–300

Department of Infrastructure, Local Government and Planning. (2017). Shaping SEQ: South East Queensland regional plan 2017 Brisbane: Queensland Government Department of Planning Lands and Heritage. (2018). Perth and peel @ 3.5 million Perth, Western Australia.

Dillman, D. A. (2000). Mail and internet surveys: The tailored design method. New York: John Wiley & Sons

Doberstein, C., Hickey, R., & Li, E. (2016). Nudging NIMBY: Do positive messages regarding the benefits of increased housing density influence resident stated housing development preferences? Land Use Policy, 54, 276–289

Dodson, J. (2010). In the wrong place at the wrong time? Assessing some planning, transport and housing market limits to urban consolidation policies. Urban Policy and Research, 28(4), 487–504

Economic Development Queensland. (2015). In Department of Infrastructure Local Government and Planning (Ed.), Neighbourhood planning and design PDA guideline no. 05 Brisbane: State of Queensland

Einstein, K. L. (2019). The privileged few: How exclusionary zoning amplifies the advantaged and blocks new housing And what we can do about it. Urban Affairs Review, 57(1), 252–268

Einstein, K. L., Glick, D. M., & Palmer, M. (2020). Neighborhood defenders: Participatory politics and America’s housing crisis. Political Science Quarterly, 135(2), 281–312

Firebaugh, G., Warner, C., & Massoglia, M. (2013). Fixed effects, random effects, and hybrid models for causal analysis. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 113–131). Dordrecht NL: Springer Science

Foster, S., Giles-Corti, B., & Knuiman, M. (2011). Creating safe walkable streetscapes: Does house design and upkeep discourage incivilities in suburban neighbourhoods? Journal of Environmental Psychology, 31, 79–88

Foster, S., et al. (2013). Planning safer suburbs: Do changes in the built environment influence residents’ perceptions of crime? Social Science & Medicine, 97, 87–94.

S. Foster

Foster, S., et al. (2014). Does walkable neighbourhood design influence the association between objective crime and walking? International Journal of Behavioral Nutrition and Physical Activity, 11(1), 100

Foster, S., et al. (2016). Are liveable neighbourhoods safer neighbourhoods? Testing the rhetoric on new urbanism and safety from crime in Perth, Western Australia. Social Science & Medicine, 164, 150–157

Foster, S., et al. (2021). Safe habitats: Does the association between neighborhood crime and walking differ by neighborhood disadvantage? Environment and Behavior, 53(1), 3–39

Frumkin, H. (2021). COVID-19, the built environment, and health. Environmental Health Perspectives, 129(7), Article 075001

Gallagher, R., Sigler, T., & Liu, Y. (2019). Targeted urban consolidation or ad hoc redevelopment? The influence of cadastral structure and change on the urban form of Brisbane, Australia. Urban Geography, 41(2), 1–22.

Gascon, M., et al. (2019). Correlates of walking for travel in seven European cities: The PASTA Project. Environmental Health Perspectives, 127(9)

Giles-Corti, B., Ryan, K., & Foster, S. (2012). Increasing density in Australia: Maximising the health benefits and minimising harm Melbourne: National Heart Foundation of Australia

Giles-Corti, B., et al. (2013). The influence of urban design on neighbourhood walking following residential relocation: Longitudinal results from the RESIDE study. Social Science & Medicine, 77(0), 20–30

Giles-Corti, B., et al. (2014a). Action area 1 built environments: Create built environments that support active living, in blueprint for an active Australia 2nd edn Melbourne: National Heart Foundation of Australia

Giles-Corti, B., et al. (2014b). Low density development: Impacts on physical activity and associated health outcomes Sydney: National Heart Foundation of Australia

Giles-Corti, B., et al. (2016). City planning and population health: A global challenge. The Lancet, 388(10062), 2912–2924

Government of South Australia. (2017). In Department of Planning Transport and Infrasructure (Ed.), The 30-year plan for greater Adelaide Adelaide: Government of South Australia

Gunn, L., et al. (2018). Creating liveable cities in Australia: A scorecard and priority recommendations for Brisbane Melbourne: RMIT University, Centre for Urban Research

Higgs, C., et al. (2019). The Urban Liveability Index: Developing a policy-relevant urban liveability composite measure and evaluating associations with transport mode choice. International Journal of Health Geographics, 18(1), 14.

Higgs, C., et al. (2022). Policy-relevant spatial indicators of urban liveability and sustainability: Scaling from local to global. Urban Policy and Research, 1–14

Hirsch, J., et al. (2014). Changes in the built environment and changes in the amount of walking over time: Longitudinal results from the multi-ethnic study of atherosclerosis. American Journal of Epidemiology, 180(8), 799–809

Holling, C., & Haslam McKenzie, F. (2010). Integrated transit orientated development: Is it appropriate for Perth? In I. Alexander, S. Greive, & D. Hedgcock (Eds.), Planning perspectives from Western Australia: A Reader in theory and practice (pp. 274–288). Perth: Fremantle Press

Hurley, J., Taylor, E., & Dodson, J. (2017). Why has urban consolidation been so difficult. In N. Sipe, & K. Vella (Eds.), The Routledge handbook of Australian urban and regional planning (pp. 123–135). New York: Routledge Kamruzzaman, M., et al. (2016). Built environment impacts on walking for transport in Brisbane, Australia. Transportation, 43(3), 53–77

Kelly, J.-F., Breadon, P., & Reichl, J. (2011). Getting the housing we want Melbourne: Grattan Institute

Kerr, J., et al. (2016). Perceived neighborhood environmental attributes associated with walking and cycling for transport among adult residents of 17 cities in 12 countries: The IPEN study. Environmental Health Perspectives, 124(3), 290–298 Knuiman, M., et al. (2014). A longitudinal analysis of the influence of the neighborhood built environment on transport walking: The RESIDE study. American Journal of Epidemiology, 180(5), 453–461 Koohsari, M. J., et al. (2015). Neighborhood environmental attributes and adults’ sedentary behaviors: Review and research agenda. Preventive Medicine, 77, 141–149. Leal, C., & Chaix, B. (2011). The influence of geographic life environments on cardiometabolic risk factors: A systematic review, a methodological assessment and a research agenda. Obesity Reviews, 12(3), 217–230 Leslie, E., et al. (2007). Walkability of local communities: Using geographic information systems to objectively assess relevant environmental attributes. Health & Place, 13 (1), 111–122

Lewis, P. G., & Baldassare, M. (2010). The complexity of public attitudes toward compact development. Journal of the American Planning Association, 76(2), 219–237

Maginn, P., & Foley, N. (2017). Perth: From 'large provincial city' to 'globalizing city'. In S. Hamnett, & R. Freestone (Eds.), Planning Metropolitan Australia (pp. 124–147). Routledge

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(2), 190–206

McNee, G., & Pojani, D. (2022). NIMBYism as a barrier to housing and social mix in San Francisco. Journal of Housing and the Built Environment, 37(1), 553–573 Moreno, C., et al. (2021). Introducing the “15-minute city”: Sustainability, resilience and place identity in future post-pandemic cities. Smart Cities, 4(1), 93–111

National Heart Foundation of Australia. (2020). What Australia Wants–Living locally in walkable neighbourhoods National Heart Foundation of Australia

Nematollahi, S., Tiwari, R., & Hedgecock, D. (2015). Desirable dense neighbourhoods: An environmental psychological approach for understanding community resistance to densification. Urban Policy and Research, 34(2), 132–151

Newman, P., & Hogan, T. (1981). A review of urban density models: Toward a resolution of the conflict between populace and planner. Human Ecology, 9(3), 269–303.

Newton, P., & Glackin, S. (2014). Understanding infill: Towards new policy and practice for urban regeneration in the established suburbs of Australia’s cities. Urban Policy and Research, 32(2), 121–143 Nieuwenhuijsen, M. J. (2020). Urban and transport planning pathways to carbon neutral, liveable and healthy cities; A review of the current evidence. Environment International, 140, 105661

Rowley, S., Ong, R., & James, A. (2017). Perth’s infill housing future: Delivering innovative and sustainable housing Perth, Western Australia: Curtin University

Ruming, K. J. (2014). Urban consolidation, strategic planning and community opposition in Sydney, Australia: Unpacking policy knowledge and public perceptions. Land Use Policy, 39, 254–265

Saunders, A., et al. (2020). Leaf my neighbourhood alone! Predicting the influence of densification on residential tree canopy cover in Perth. Landscape and Urban Planning, 199, 103804

Searle, G. (2004). The limits to urban consolidation. Australian Planner, 41(1), 6 Searle, G. (2007). Sydney's urban consolidation experience: Power, politics and community Brisbane: Griffith University, Urban Research Program

Shepherd, B. (2016). Plans for high-density housing in Perth's western suburbs canned by Planning Minister: WA's Planning Minister rejects previously approved local government plans to increase housing density in Perth's leafy western suburbs. In ABC premium news Sydney

Smith, C. A., & Billig, N. S. (2012). Public perceptions of compact suburbia in progressive, Burgeoning communities. Journal of Urban Design, 17(3), 313–335 Stata Corporation. (2016). Stata statistical software release 14.1. College Station TX: Stata Corporation.

Thompson, S., & Kent, J. (2014). Connecting and strengthening communities in places for health and well-being. Australian Planner, 51(3), 260–271

Tighe, J. R. (2010). Public opinion and affordable housing: A review of the literature. Journal of Planning Literature, 25(1), 3–17

Turrell, G., et al. (2010). Neighborhood disadvantage and physical activity: Baseline results from the HABITAT multilevel longitudinal study. Annals of Epidemiology, 20 (3), 171–181

Turrell, G., et al. (2011). Test-retest reliability of perceptions of the neighborhood environment for physical activity by socioeconomic status. Journal of Physical Activity & Health, 8(6), 829–840

Turrell, G., et al. (2020). Cohort Profile: HABITAT A longitudinal multilevel study of physical activity, sedentary behaviour and health and functioning in mid-to-late adulthood. International Journal of Epidemiology, 50(3), 730–731h

Udell, T., et al. (2014). Does density matter? The role of density in creating walkable neighbourhoods - Discussion paper Melbourne: National Heart Foundation of Australia

United Nations. (2017a). New urban agenda. In Habitat III Quito, Ecuador: United Nations United Nations. (2017b). The sustainable development goals report 2017 New York: United Nations

United Nations. (2019). The strategic plan 2020-2023 (p. 45). United Nations

United Nations Habitat. (2014). A new strategy of sustainable neighbourhood planning: Five principles. Nairobi, Kenya: United Nations Human Settlements Programme. Urban Taskforce Australia. (2017). Urban ideas: Planning for Sydney's future apartments [cited 2023 3 March]; Available from: https://www.urbantaskforce.com.au/urban -ideas-2/

Victoria State Government. (2017). In Department of Environment Land Water and Planning (Ed.), Plan Melbourne 2017–2050 Melbourne: Victoria State Government

Vine, D., Buys, L., & Aird, R. (2012). The use of amenities in high density neighbourhoods by older urban Australian residents. Landscape and Urban Planning, 107(2), 159–171