Investigating the measurement properties of livability: a scoping review

Abstract

Connecting evidence-based public health recommendations to livability, a popular and relatable construct, can increase the policy relevance of research to improve community design. However, there are many different definitions and conceptualizations of livability and little consensus about its measurement. Improved measurement, including standardization, is needed to increase understanding of livability’s influence on health and to facilitate comparisons across contexts. This study sought to review existing livability measures, how they were created, and evidence regarding their reliability and validity. A scoping review of three databases (PubMed, Google Scholar, and Web of Science) identified 744 eligible studies. After screening, 24 studies, 15 from the original search and 9 through backward citation searches, were included in the review. Most studies were carried out in an urban context. There was minimal consensus across studies on the conceptualization of livability. However, measure domains and indicators overlapped significantly. While the process used to validate the measures varied, most studies reported high levels of reliability and found that livability was correlated with similar measures (e.g. place satisfaction, neighborhood safety, and sense of place) and self-reported health and wellbeing. Further research is needed to develop parsimonious, standardized measures of livability in order to create and sustain livable communities worldwide.

Introduction

Livability, an emerging concept increasingly incorporated in public health and the social sciences, is defined as the ability of a place and its systems to reliably meet basic human needs, provide opportunities for cultural or artistic expression, and foster a general sense of community, along with prioritizing social inclusivity, safety, sustainability, and opportunity (Badland et al. 2014). Livability is a critical construct for creating healthier communities, as investment in livability is an asset-based intervention that involves community connection, resource enhancement, and opportunity creation. Livability fundamentally is both a primary prevention and health promotion strategy, as quality of life is not just the absence of disease but optimal health and wellbeing throughout the life course. One of the most well-known conceptualizations of livability is Maslow’s hierarchy of needs (Ahmed et al. 2019), which has been criticized for colonizing this knowledge from the Blackfoot indigenous way of life without proper attribution (Feigenbaum and Smith 2020). Maslow misrepresented Blackfoot teachings with an individual focus, where instead, the actual goal was community actualization with no one left behind. This tension has been observed broadly as the reign of individualistic, independent approaches to public health problems and other social issues, versus the interdependent, community-minded solutions we need (The Care Collective et al. 2020). The pursuit of a livable life in societies shaped by colonization, such as the United States, has resulted in residential racial segregation and the inequitable distribution of power, investments, and resources across racial lines (Hudson et al. 2020). Conversely, geographies of opportunity, or areas defined with greater wealth, opportunity, and infrastructure, are remnants of spatial policies created to uphold white supremacy (Schnake-Mahl et al. 2020). As a result, livability is shaped by these forces and related to gentrification as the creation of ‘livable places’ is leveraged for economic gain by the real estate and land development sectors, resulting in the displacement of racial and ethnic minority populations, communities with people of lower incomes, and other traditionally marginalized demographics.

Livability is a popular concept, as evidenced by the multitude of global frameworks, such as the United Nations Sustainable Development Goals, the New Urban Agenda, and the WHO Healthy Cities Movement, representing interdisciplinary approaches to guide development that promotes livability. Livability has also been studied widely in various fields, including but not limited to urban development, economics, sustainability, and sociology. A comprehensive review of the theoretical influences on livability identifies life-course theory (Ruth and Franklin 2014, Lowe et al. 2020) as the most salient, which advocates for life-affirming structures throughout the human life cycle. Other theoretical influences of livability include ecosocial theory, which posits that the livability of a place, or the material and the social world a person resides in, are biologically embodied and observed epidemiologically as health disparities between different populations (Krieger 2014). These theoretical approaches point toward the need to have valid measures of livability, which more accurately represent the lived experience versus promotion of economic gain that can be tested in causal pathways of place and health.

Area-level constructs such as livability are garnering more attention in the public health realm as the popularity of ecosocial frameworks to measure and intervene upon determinants of health increases (Krieger 2014). Public health scholars have demonstrated success with connecting health research agendas to livability to increase the policy relevance of public health research findings and ultimately lead to better community design (Badland et al. 2014). Measuring livability is essential for translating evidence-based public health recommendations, as most of these recommendations necessitate cross-sectoral collaboration since the policies and programs that govern them are nested within other sectors (e.g. housing, transportation, and economic development), also known as a ‘health in all policies’ approach (Ollila 2011). Livability as a place-based measure thus holds much promise for allocating resources and efforts to improve public health.

Despite this, there is very little measurement science on livability. There is wide variation in the conceptualization of the construct of livability and how to measure it. While scholars have published about livability measurement and have discussed its construct validity, there is no consensus or best practice guidance on how to best measure this construct, leaving much doubt about how to operationalize this measure (Paul and Sen 2020). This leaves policymakers and urban planners to identify and/or develop indicators based on their personal lived experiences or interests, which may not be salient to the communities they serve. These indicators may or may not capture the dynamicity, multidimensionality, and multilevel nature of livability, resulting in a poor capture of the lived experiences of residents.

Two fundamental measurement properties are reliability and validity. Reliability is the consistency of a measure, or if an instrument can measure something in a reproducible fashion (Streiner et al. 2015). Validity is the accuracy of a measure or its ability to measure what was intended (Streiner et al. 2015). This includes construct validity, or whether the measurement reflects an underlying latent construct. While some scholars argue that standardized measures of livability are impossible to achieve due to the heavy context-dependency of this construct (Paul and Sen 2020), others state that there are enough shared elements of the human experience upon which we can establish consistent conceptualizations of livability that are generalizable to people regardless of where they reside (Kashef 2016). Contemporary livability measures have been criticized for depending too heavily on economic indicators, predisposing them to incentivize capitalist investments to improve places with high tourist traffic, such as downtown areas, or streets with high financial gain, such as economic corridors (Okulicz-Kozaryn and Valente 2019). Grassroot efforts to decolonize livability and highlight how dependent we are on one another are emerging (The Care Collective et al. 2020). Measurement of livability through a decolonized lens can contribute to the formalization, evaluation, and scaling up of these grassroot efforts, amplifying community voices, and prioritizing resident-led solutions.

The development of robust measures that are precise and accurate will allow for their testing in multiple contexts more efficiently and effectively to further our understanding of the causal influence of this construct on health and wellbeing. Investigating the measurement science of livability can facilitate discussion and support agreement on measure development best practices and/or measure consensus. To our knowledge, we lack a comprehensive picture of the validity and reliability of existing tools used to assess livability. As this construct grows in popularity, it is important to better understand the processes with which others have created livability tools with strong measurement properties (e.g. highly reliable and valid) to advance our understanding of livability and its influences on health and well-being. Therefore, the aim of this study is to examine the ways in which livability has been measured and the properties of livability measures studied (namely, measure reliability and validity), by 1) taking inventory of various livability indicators and domains used in measures, 2) examining the processes of creating composite livability measures or identifying salient indicators and domains, and 3) reviewing methodological approaches to assessing measure reliability and validity.

Methods

A scoping review of empirical livability measures was conducted to achieve this study aim. Unique from a systematic review in its focus on topic breadth versus methodological detail, a scoping review is a method for collecting and summarizing literature and empirical studies in a specific area (Peters et al. 2020). The methodology was structured by the Joanna Briggs Institute’s (JBI’s) scoping review framework, and results are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) process guidelines (Tricco et al. 2018).

The structured search strategy was executed on the OVID interface of PubMed, Google Scholar, and Web of Science due to their lack of discipline specificity and ability to capture livability research across sectors. Our search strategy included a broad range of documents from both research and practitioner perspectives, including but not limited to empirical or review studies, white papers, grey papers, formal reports, and conference presentations. We searched for livability measurement at any geographic scale, with a special focus on documents reporting findings from the assessment of properties of livability measures. Searches were conducted between December 2021 and February 2022 and focused on publications in the last 10 years to account for changes in livability due to the digital age. Search terms included a combination of the following words with Boolean operators located anywhere in the publication: (‘livability’ OR ‘liveability’ OR ‘neighborhood quality of life’ OR ‘neighbourhood quality of life’ OR ‘community’) AND (‘measure’ OR ‘measurement’) AND (‘health outcome’ OR ‘health behavior’ OR ‘health behaviour’ OR ‘wellbeing’ OR ‘well-being’ OR ‘risk factor’) AND (‘subjective indicators’ OR ‘perception’ OR ‘survey’) AND (‘reliability’) AND (‘validity’).

Publications were included if they developed a measure of livability or a synonymous construct (e.g. - quality of urban life, neighborhood quality of life), regardless of whether the study conducted was informed by a theoretical framework or a purely empirical approach. All geographical areas and differing geographical scales (ranging from neighborhood scale to cross-country analysis) were included. Studies were included regardless of whether they focused on a certain population (e.g. older adults). Articles that were not in the English language were excluded. Articles were excluded if reliability or validity was not assessed or if the construct measured was related but conceptually different than livability (e.g. quality of life and neighborhood cohesion). Articles identified in the initial scoping search were then examined for a backwards citation search to identify any additional articles for inclusion.

Results

Our search yielded a total of 744 studies. Forty-nine duplicate records were removed, and 23 studies were deemed ineligible for inclusion. After title and abstract review, 113 studies were considered for full-text review. Fifteen articles were identified for inclusion, and 9 additional articles were identified through backwards citation searching, resulting in a total of 24 research articles as detailed by the PRISMA flowchart (Figure 1). While the goal was to have a broad array of documents, such as grey literature or professional reports, most of these types of publications were excluded due to the lack of assessment of reliability or validity. Two-thirds of the studies were published in the last 5 years, with over half of these published in 2018 and 2019 (Table 1). Studies were most often published in urban planning or engineering journals (n = 8) or broad social science journals that were not field-specific (n = 7) but there were several studies that were published in public health-focused journals (n = 4).

Figure 1.

PRISMA flow chart representing the search process to identify and select studies in this scoping review.

Table 1.

Characteristics of studies (n = 24).

				Geographical
Name	Location	Income	Measured Term	Scope	Scalea
Akbari, Moayedfar et al. (2018)	Isfahan, Iran	Lower middle	Livability	Urban	Neighborhood
Alderton et al. (2019)	Bangkok, Thailand	Upper middle	Urban liveability	Urban	Local Administrative Unit (city/town/etc.)
Badland et al. (2014)	Australia	High	Urban liveability	Urban	Local Administrative Unit (city/town/etc.)
Baig et al. (2019)	Hyderabad, Pakistan	Lower middle	Livability	Urban	Local Administrative Unit (city/town/etc.)
El-Morshedy et al. (2018)	Sheikh Zayed & 6th of October, Egypt	Lower middle	Neighborhood life quality	Satellite cities	Local Administrative Unit (city/town/etc.)
Higgs et al. (2019)	Melbourne, Australia	High	Urban liveability	Urban	Local Administrative Unit (city/town/etc.)
Iyanda et al. (2018)	Minna, Nigeria	Lower middle	Livability	Urban	Neighborhood
Jianxiao et al. (2020)	Hong Kong	High	Livability	Urban	Local Administrative Unit (city/town/etc.)
Khorasani and Zarghamfard (2018)	Villages near Tehran, Iran	Lower middle	Livability	Peri-urban villages	Local Administrative Unit (city/town/etc.)
Kim et al. (2022)	15 US metro areas	High	Age-friendliness of community	Urban	Local Administrative Unit (city/town/etc.)
Koenig (2010)	Chicago, IL	High	Quality-of-life	Urban	Neighborhood
Kovacs-Györi and Cabrera-Barona (2019)	8 global cities	Mix of levels	Livability	Urban	Local Administrative Unit (city/town/etc.)
Leby and Hashim (2010)	Subang Jaya, Malaysia	Upper middle	Liveability	Urban	Neighborhood
Lee (2021)	Seoul, Korea	High	Livability	Urban	Local Administrative Unit (city/town/etc.)
Leh et al. (2020)	Kuala Lampur, Malaysia	Upper middle	Liveability	Urban & suburban	Neighborhood
Linlin and Yilin (2009)	18 cities in Shandong Province, China	Upper middle	City livability	Urban	Local Administrative Unit (city/town/etc.)
Lowe et al. (2020)	4 major cities in Australia	High	Liveability	Urban	Local Administrative Unit (city/town/etc.)
Okulicz-Kozaryn (2013)	Global cities	N/A	Livability	Urban	Local Administrative Unit (city/town/etc.)
Okulicz-Kozaryn and Valente (2019)	European cities	Mix of levels	Livability	Urban	Local Administrative Unit (city/town/etc.)
Rezvani and Mansourian (2013)	Firouzabad and Saheb, Iran	Lower middle	Quality-of-life	Rural to urban	Local Administrative Unit (city/town/etc.)
Saitluanga (2014)	Aizawl City, India (Himalayan region)	Lower middle	Urban livability	Urban	Neighborhood
Salaripour et al. (2022)	Hamedan City, Iran	Lower middle	Urban liveability	Urban	Neighborhood
Surjono et al. (2021)	Malang City, Indonesia	Lower middle	Livability	Urban	Local Administrative Unit (city/town/etc.)
Yanmei (2012)	United States	High	Neighborhood livability	Urban, suburban, & rural	Country

^aGeographic scale options range from, in the order of smallest to largest, 1) home, 2) neighborhood, 3) local administrative unit (city/town/etc.), 4) country, and 5) world

Location

Studies varied in their geographic origin, with the majority located in Asia (n = 9), followed by the Middle East (n = 4), North America (n = 3), Australia (n = 3), and Africa (n = 2) (Table 1). Three studies were comparisons of cities in a certain country (Linlin and Yilin 2009, Lowe et al. 2020, Kim et al. 2022), and an additional two had a global focus and conducted cross-country analyses of metropolitan areas (Okulicz-Kozaryn 2013, Kovacs-Györi and Cabrera-Barona 2019). Several studies leveraged livability in an evaluative capacity for urban planning interventions (Koenig 2010, Akbari et al. 2018). Most studies were conducted in countries that are lower middle income (n = 9) or high income (n = 8), followed by countries that are upper middle income (n = 4). Studies were evenly split by Global North and Global South and represented much global diversity in approaches and resulting conceptualizations of livability (Table 1).

Geographical scope and scale

Most studies included in this review examined livability in only an urban context (n = 19), but all studies discussed urbanicity in some way (e.g. proximity of study area to metropolitan area, study sample moving from rural to urban settings). A few studied livability in suburban and rural contexts (n = 3), in addition to unique urban typologies such as peri-urban villages (Khorasani and Zarghamfard 2018) and satellite cities (El-Morshedy et al. 2018). Most studies included in this review conceptualized livability at the town-level (n = 16), followed by the neighborhood level (n = 7). One study conceptualized livability at the country level with one survey item embedded in a nationwide survey (Yanmei 2012).

Domains of livability

As almost every paper included in the review acknowledged that livability is difficult to define and conceptualize due to its expansive and context-specific nature. Studies varied in what exact term they used when discussing livability (e.g. urban livability, city livability, and quality of life). Only two studies operationalized livability as unidimensional (i.e. with one domain), and most scholars conceptualized livability with 2+ domains, ranging up to 11 domains (Table 2). However, studies varied in the number of dimensions used for livability measurement, with most studies using five domains (n=6). Identified domains of livability can collapse into many types of categories, ranging from physical versus nonphysical to numerous sectors in urban governance. Examples of most commonly used domains are social (intangible or perceived characteristics of livability such as social capital or collective efficacy, n = 10), environmental (characteristics related to natural geography or ecology such as proximity to green or blue spaces, n = 9), economic (characteristics related to goods and services and market-driven resources, n = 6), and physical (material or built characteristics of livability such as sidewalks or bike trails, n = 5). The number of domains were not correlated with the quality of the measure.

Table 2.

Conceptualization of livability across studies: domains and indicators.

Number of Domains	Study	Data Type	Domain Names (Number of Items/Indicators)
One	Yanmei (2012)	Survey (self-report)	livability (1)
	Lee (2021)	Survey (self-report)	livability (2)
Two	Leh et al. (2020)	Survey (self-report)	physical (7), social (6)
Three	Khorasani and Zarghamfard (2018)	Survey (self-report) and spatial (observational)	physical (unk), social (unk), environmental (unk)a
	El-Morshedy et al. (2018)	Survey (self-report)	physical (75), psychological (6), social (6)
	Linlin and Yilin (2009)	Spatial (observational)	economic (5), social (7), environmental (2)b
	Akbari, Moayedfar et al. (2018)	Survey (self-report)	economic (4), social (5), environmental (3)c
Four	Surjono et al. (2021)	Survey (self-report) and spatial (observational)	infrastructure (6), economy (2), urban environment (4), social life (4)
	Leby and Hashim (2010)	Survey (self-report)	functional (5), physical (7), social (8), safety (6)
	Salaripour et al. (2022)	Survey (self-report)	economical (7), social-cultural (15), health (7), physical-environmental (10)
	Iyanda et al. (2018)	Survey (self-report)	housing unit characteristics (9), neighborhood facilities (6), safety environment (3), economic vitality (5)
Five	Kovacs-Györi and Cabrera-Barona (2019)	Survey (self-report)	sense of community (4), urban form (3), urban function (3), mobility (6), and housing (5)
	Jianxiao et al. (2020)	Social media (observational) and spatial (observational)	peace and harmony (1), complete services (9), comfortability (2), well-equipped facilities (24), residents’ satisfaction (6)
	Saitluanga (2014)	Survey (self-report)	economic (11), social (9), household (5), accessibility (5), satisfaction from socioeconomic environment (9)
	Koenig (2010)	Survey (self-report)	human (12), social (12), physical (12), financial (12), personal (12)
	Baig et al. (2019)	Survey (self-report)	cultural (3), environmental (3), social (3), infrastructure (3), economic (3)
	Kim et al. (2022)	Survey (self-report)	outdoor space and building (15), transportation (6), housing (7), community and community services (11), social participation (23)
Seven	Higgs et al. (2019)	Spatial (observational)	transport (3), walkability (7), housing (1), social infrastructure (16), employment (1), green infrastructure (2), ambient environment (1)
	Lowe et al. (2020)	Spatial (observational)	walkability (3), transit access (3), public open space (5), housing affordability (0), employment (0), food environments (0), alcohol environments (0)d
Ten	Okulicz-Kozaryn (2013) Okulicz-Kozaryn and Valente (2019)	Survey (self-report)	political and social environment (5), economic environment (2), socio-cultural environment (2), medical and health considerations (8), schools and education (1), public services and transportation (7), recreation (5), consumer goods (5), housing (3), natural environment (3)e
	Rezvani and Mansourian (2013)	Survey (self-report)	quality of environment (5), housing (3), education (2), health (3), personal wellbeing (5), participation (3), recreation and leisure (3), information and communication (6), employment (3), income and wealth (6)
	Alderton et al. (2019)	Spatial (observational)	amenities, employment, environmental management, food, health and wellbeing, housing, public open space, social connectedness, social infrastructure, transportf
Eleven	Badland et al. (2014)	Spatial (observational)	natural environment (7), crime and safety (5), education (15), employment and income (5), health and social services (6), housing (6), leisure and culture (0), local food and other goods (2), public open space (0), transport (10), social cohesion & local democracy (5)g

^aKhorasani and Zarghamfard (2018) used 77 items, but did not report how many items were in each domain.

^bdomains correspond to Linlin and Yilin (2009).

^cdomains correspond to Akbari, Moayedfar, & Mirzaie Khondabi (2018).

^dLowe et al. (2020) did not identify spatial indicators for certain dimensions, reported in this table as (0).

^eOnly one of domains and indicators reported, as both studies used the Mercer Quality of Life index.

^fDomains were conceptualized independently prior to indicator selection; chosen indicators were sorted by priority.

^gBadland et al. (2014) stated that identified indicators for domains reported as (0) need further development.

Conceptualization of livability

While most studies typically involved some discussion of the theoretical influences of livability, some studies relied on a purely empirical approach, such as choosing indicators based on data availability or ease of measurement (Linlin and Yilin 2009, Rezvani and Mansourian 2013, Alderton et al. 2019). No studies used a purely theoretical approach or developing a measure of livability by selecting relevant indicators from the viewpoint of a particular theory (Streiner et al. 2015); however, one study did use the empirical data selected to inform a post hoc conceptual model (Alderton et al. 2019). However, most studies discussed theoretical approaches as they apply to livability (Lowe et al. 2020, Surjono et al. 2021), such as the person-environment fit model from Lawton, Nahemow’s ecological theory of aging (Lawton and Nahemow 1973, Kim et al. 2022), Galster and Hesser’s theory of neighborhood satisfaction (Galster and Hesser 1981, Yanmei 2012), and the role of livability in enhancing people’s capabilities as related to Nussbaum and Sen’s capability approach (Nussbaum and Sen 1993, Kovacs-Györi and Cabrera-Barona 2019). Some studies utilized existing livability frameworks, unique from theory in that they are developed for a more applied purpose (Glanz et al. 2015). Examples of frameworks include the United Nations’ Sustainable Livelihoods Framework (Koenig 2010) and the World Bank’s City Development Strategy Framework, which includes livability as a principle (Akbari et al. 2018).

Livability measures were designed differently based on if they were developed for economic gain or competition versus scientific advancement or representation of the lived experiences of residential populations, both in terms of the processes used to select indicators (e.g. if they engaged community residents) and in the content of the indicators themselves (e.g. selection and weighting of economic versus social indicators). Measures funded by for-profit institutions and used predominantly for economic purposes (e.g. the Mercer quality-of-life rankings) examined in two studies in this review (Okulicz-Kozaryn 2013, Okulicz-Kozaryn and Valente 2019), conceptualized livability as seeking a ‘universal ideal neighborhood’. Other social scientists advocated for livability measurement as a shift toward better understanding the intricacies and interdependencies of livability to meet the different needs of community residents across the life course (Stephens et al. 2018) and relied on primary data collection tools such as surveys to better capture livability perspectives as understood and experienced by residents.

The number of indicators within domains of livability ranged from 1 to 16, and the number of indicators within overall measures varied across studies and ranged from 1 item (Yanmei 2012) to 87 items (ElMorshedy et al. 2018). Multiple studies used data reduction techniques to remove extraneous items (Akbari et al. 2018) or other statistical methods such as factor analysis to determine what domains the final measure should consist of (Linlin and Yilin 2009, Saitluanga 2014). The variability of the number of indicators selected largely depended on how they were utilized (e.g. empirical analysis, indicator development for benchmarking, and evaluative capacity) (Table 3).

Table 3.

Reliability and validity of empirical livability measures.

Name	R	V	Reliability Results	Validity Results
Akbari et al. (2018)	X	X	Cronbach’s alpha	Bivariate analyses of differences between geographical areas, predictive validity
			Livability (α = 0.89)	Distressed area of city has lower livability scores.
Alderton et al. (2019)		X		Content validity
				Leaders from the Bangkok Metropolitan Administration reviewed livability conceptual frameworks and indicators
Badland et al. (2014)		X		Content validity
				Multidisciplinary research team used stringent criteria for indicator inclusion, one of which was relevance for policymaking
Baig et al. (2019)		X		Correlation analyses, convergent and divergent validity
				Lower income neighborhoods reported lower perceived livability. Higher livability was also associated with higher availability of restaurants, garbage collection service, utilities, employment, proximity to schools, maintenance of parks and lighting, security, health facilities, reliability on amenities, housing affordability, and commercial accessibility
El-Morshedy et al. (2018)	X	X	Cronbach’s alpha	Regression analyses, predictive validity
			Subscales: psychological (α = 0.837) and social (α = 0.761); physical not calculated	Physical aspects highly predictive of nonphysical (psychological and social) components of neighborhoods
Higgs et al. (2019)		X		Correlation analyses, convergent and divergent validity
				Higher livability scores in a certain area associated with higher levels of walking, cycling, public transit, and lower levels of private vehicle use
Iyanda et al. (2018)	X	X	Cronbach’s alpha	Structural equation modeling, statistical validity
			Subscales: housing (α = 0.934), safety (α = 0.919), economic vitality (α = 0.862), and neighborhood facilities (α = 0.829)	Average Variance Extracted (AVE) and Maximum Shared Variance (MSV) scores lower than Cronbach’s, indicating no major validity problem
Jianxiao et al. (2020)		X		Correlation and sentiment analyses, predictive validity
				Higher levels of urbanization are associated with higher livability scores. However, Instagram posts of individuals in suburban areas usually express more positive content and happier emotion than posts in central urban areas.
Khorasani and Zarghamfard (2018)	X	X	Cronbach’s alpha	Correlation analyses, predictive validity
			Livability measure (α = 0.995)	Higher population in peri-urban villages and in adjacent cities, rate of population growth, distance from adjacent cities or main road, dorm quota, migrator origin, and non-agricultural land use quota; and lower distance to Tehran and immigrants relative to the total population is associated with higher livability
Kim et al. (2022)	X	X	Cronbach’s alpha	Correlation analyses, convergent and predictive validity
			Subscales: outdoor spaces and buildings (α = 0.91), transportation (α = 0.96), housing (α = 0.82), health and community services (α = 0.95), and social participation (α = 0.97)	Older adults living in communities with higher AFC scores reported better self-rated health. Communities with higher AFC scores were associated with higher accessibility of healthcare facilities
Koenig (2010)	X	X	Survey item pre-testing	Bivariate analyses of differences between pre-test to post-test, predictive validity
			Pre-testing reported but no additional information about the process or results	Quality-of-life scores increased among participants after moving to affordable housing
Kovacs-Györi and Cabrera-Barona (2019)	X	X	Cronbach’s alpha	GIS analyses, predictive validity
			Livability measure (α = 0.7366)	Presence of street furniture and trees predicted livability with 80% accuracy
Leby and Hashim (2010)	X	X	Cronbach’s alpha & survey pilot testing	Content validity
			Subscales: social (α = 0.851), physical (α = 0.742), functional (α = 0.754), safety (α = 0.654). Survey was pilot tested with 10 residents to test practicality and communicability of questions.	Panel of research experts in housing studies reviewed questionnaire
Lee (2021)	X	X	Cronbach’s alpha	Correlation analyses, convergent validity
			Livability measure (α = 0.800)	Higher livability is associated with higher perceptions of accessibility, pleasantness, safety, and neighborhood relations.
Leh et al. (2020)		X		Correlation analyses, convergent validity
				Higher access to public transport, satisfaction with the transit system, cleanliness, environmental quality, volunteerism, safety, community interaction, sense of community, and happiness were associated with higher livability.
Linlin and Yilin (2009)		X		Content validity
				Scholars posit that coastal cities have higher livability than inland cities, potentially due to the excellent environment, pleasant climate, prosperous economy, perfect public facilities, and more comfortable life.
Lowe et al. (2020)		X		GIS analyses, predictive validity
				Suburban areas, which also had poorer access to amenities, had lower livability indicator scores than urban core areas.
Okulicz-Kozaryn (2013)		X		Correlation analysis, convergent validity
				Higher livability was associated with higher reported quality of life and trust.
Okulicz-Kozaryn and Valente (2019)		X		Correlation analyses, convergent validity
				Higher livability is associated with higher subjective wellbeing.
Rezvani and Mansourian (2013)	X	X	Cronbach’s alpha	Regression analyses, predictive validity
			Subscales: quality of environment (α = 0.683), housing (α = 0.607), education (α = 0.707), health (α = 0.869), personal wellbeing (α = 0.765), participation (α = 0.644), rec and leisure (α = 0.686), info and communications (α = 0.712), employment/income (α = 0.816) and wealth (α = 0.762)	Promotion of village to town of both Firouzabad and Saheb increased perceived quality of life among residents.
Saitluanga (2014)		X		Bivariate analyses of differences between geographical areas, convergent validity
				Centrally located neighborhoods had higher livability than neighborhoods that were more peripheral.
Salaripour et al. (2022)	X	X	Cronbach’s alpha	Correlation analysis, convergent validity
			Livability (α = 0.863)	Higher perceptions of physical-environmental dimension of livability associated with higher comfort and convenience. Higher perceptions of socio-cultural dimension of livability associated with higher sense of place. Higher perceptions of economic dimension of livability associated with higher access to facilities and services. Higher perceptions of health dimension of livability associated with higher participation.
Surjono et al. (2021)	X	X	Cronbach’s alpha & composite reliability	Structural equation modeling, construct validity
			City livability (a > 0.5), composite reliability >0.7	Factors that are most influential in determining urban livability are social life, economy, and urban environment.
Yanmei (2012)		X		Regression analyses, convergent validity
				Satisfaction with police protection and neighborhood shopping and favorable neighborhood amenities (proximity to open spaces or bodies of water) was positively associated with livability; Satisfaction with public transportation and less favorable neighborhood amenities (parking lots or factories within a half block) was negatively associated with lower livability levels.

Most studies leveraged subjective indicators in some capacity (n = 20), usually operationalized by survey measures. Sample sizes of these studies varied, with some studies engaged in primary data collection relying on smaller samples of 100–300 people (Koenig 2010, Leby and Hashim 2010, Rezvani and Mansourian 2013) ranging up to studies conducting secondary data analyses of large datasets, such as the American Housing Survey, which has a sample size of 94,557 people (Yanmei 2012). Primary data collection of subjective indicators typically involved random sampling to achieve representation of population demographics living in the area (Rezvani and Mansourian 2013, Saitluanga 2014), targeted sampling methods such as cluster sampling of certain neighborhoods or housing complexes (Leby and Hashim 2010, Akbari et al. 2018, Baig et al. 2019, Leh et al. 2020), or a more focused approach on a certain demographic, such as older adults (Kim et al. 2022). Most studies used survey methods to collect primary data (n=15), followed by spatial or administrative data, or secondary data combined to create a measure (n = 6). One study also leveraged qualitative methods through social media sentiment analysis to supplement to validate quantitative indicators (Jianxiao et al. 2020).

The purpose of livability measurement varied across studies, but most scholars leveraged livability measurement for descriptive purposes. Some studies used it to benchmark urban development progress, leveraging the power of livability measurement to unite across sectors. Others used livability metrics to compare cities to better understand urban development ‘lessons learned’ across different environmental contexts. Less frequently, livability was used to measure the ‘unlivability’ of certain geographical areas to target urban planning interventions in those areas (Akbari et al. 2018). One scholar also used livability measurement in an evaluative capacity to assess the impact of residents moving to affordable housing (Koenig 2010).

Assessment of reliability of livability measures

Reliability or the consistency of a measure, can be assessed either by examining the stability of the measure through administration to different people (inter-observer reliability), on different occasions (inter-observer reliability), or to the same person over a certain time (test–retest reliability) (Streiner et al. 2015). Reliability is also assessed through the internal consistency of a measure based on a single administration, usually through calculation of the correlations among all the items in a measure with statistics, such as Cronbach’s alpha or Kuder-Richardson. In this review, only half of the studies examined assessed the reliability of livability measures. Many of these studies calculated the internal consistency of measures, expressed through the Cronbach’s alpha. Cronbach’s alpha reliability scores range from 0 to 1, with higher scores indicating better reliability and 1 indicating perfect reliability of a measure. Most studies reported a Cronbach’s alpha of overall measures or measure subscales above 0.7, a statistically accepted threshold for high reliability (Rezvani and Mansourian 2013, Akbari et al. 2018, ElMorshedy et al. 2018, Iyanda et al. 2018, Khorasani and Zarghamfard 2018, Kovacs-Györi and Cabrera-Barona 2019, Lee 2021, Surjono et al. 2021, Kim et al. 2022, Salaripour et al. 2022). A few studies (Koenig 2010, Leby and Hashim 2010) also conducted survey pre-testing or pilot testing, but no studies reported test–retest reliability or other forms of reliability assessment.

Assessment of validity of livability measures

All studies in this review assessed the validity, or accuracy, of their measures but used different approaches. Every study in the review mentioned face validity, or the ability of a measure to appear reasonable and deemed appropriate to measure what it is supposed to (Streiner et al. 2015). While most studies commented on face validity, a few employed expert validity or measure review by respected professionals in the field to formally assess face validity (Leby and Hashim 2010, Badland et al. 2014, Alderton et al. 2019). A few scholars relied solely upon face validity (Linlin and Yilin 2009, Leby and Hashim 2010, Alderton et al. 2019), using statistical methods for construct validation, or the process of testing a measure to see how well it measures what it is supposed to measure, typically involving a series of experiments demonstrating differing types of validity (Streiner et al. 2015). Some scholars assessed convergent validity, or the ability of a measure to correlate with similar but conceptually distinct constructs. For example, higher livability was associated with higher reported quality of life and trust (Okulicz-Kozaryn 2013), higher availability of services and amenities (Baig et al. 2019), higher perceived neighborhood safety and pleasantness (Lee 2021), and higher sense of place (Salaripour et al. 2022). Predictive validity is the ability of a measure to predict a certain outcome that it is theoretically linked with. For example, those living in communities with higher livability scores reported better self-rated health (Kim et al. 2022), higher subjective wellbeing (Okulicz-Kozaryn and Valente 2019), higher presence of street furniture and trees (Kovacs-Györi and Cabrera-Barona 2019), higher population growth (Khorasani and Zarghamfard 2018), greater satisfaction with police protection (Yanmei 2012), greater satisfaction with or usage of transit (Higgs et al. 2019, Leh et al. 2020), and higher accessibility of services or commerce (Baig et al. 2019, Kim et al. 2022, Salaripour et al. 2022) (see Table 3 for more information about identified associations). Scholars also assessed the predictive validity of livability by comparing geographical areas or differing urban contexts, such as distressed areas compared to other areas of the city (Akbari et al. 2018), or proximity to urban cores (Saitluanga 2014, Lowe et al. 2020). One step further, scholars reported improvement of livability or quality of life measures when participants transitioned to affordable housing (Koenig 2010), or promotion of their residential area from rural to urban to result in greater resources (Rezvani and Mansourian 2013). Divergent validity, also known as discriminant validity, is the ability of a measure to demonstrate that it does not measure something what it is not supposed to (Streiner et al. 2015). Scholars discussed the discriminant validity of objective and subjective measures of livability: One scholar found that subjective and objective indicators are weakly but significantly correlated (Okulicz-Kozaryn 2013). On the other hand, another study did not identify a statistically significant relationship between objective and subjective indicators (Saitluanga 2014). Additional types of validity, such as concurrent validity (the ability of two different measures of the same construct to be administered and highly correlated) and criterion validity (the ability of a measure to hold up to a gold-standard measure of the same construct) were not assessed by any study in this review (Streiner et al. 2015).

Discussion

This scoping review is one of the first to investigate existing livability measures and assessment of their measurement reliability and validity properties. Studies represented a wide geographic range and varying geographic scales, indicating that livability is important for scholars and practitioners throughout the world, in high-income or low- to middle-income countries, and at multiple geographic levels of analysis and intervention. The highest concentration of livability research was in 2018 and 2019, followed by a pause potentially attributed to the COVID-19 pandemic, which substantially changed our way of life from 2020 onward. While several studies were published in journals targeted at a public health audience, there is much room for expansion of the study of livability in public health sciences with the goal of entrenching the livability or quality-of-life mindset in public health systems to advance primary prevention versus reactive approaches.

Studies ranged in their conceptualization of livability, with very little consensus on domains of livability. Nonetheless, there was significant overlap in domains and indicators, which speak to the relatability of livability across contexts, and the commonality of the person–environment interaction experienced by people in diverse residential contexts. Achieving consensus on the conceptualization of livability (i.e. clearly defining what livability is and what it is not) would support integration of future research and practice efforts to improve our ability to measure and influence livability. This is related to standardizing the measurement itself for livability, as it can improve the construct validity of future measurements. Achieving this standardization can lead to a discussion and clarification of the values underpinning livability and can foster collaboration across different fields that conceptualize livability differently. In addition, from a research perspective, harmonized or standardized livability measures are needed to identify robust tools that can be tested in causal pathways. Theoretical approaches also varied across studies, with empirical analysis and integration of frameworks being a more common approach due to the broad, applied nature of the construct. Scholars acknowledged the limitations of existing or routinely collected data for livability measure creation. Existing indicators are static and independent and do not fully capture the complexity of residents’ lived experience. In addition, no studies examined interdependences of livability domains, which points to the opportunity for data analytic methods to better understand intersections between domains. The approach of selecting indicators based on data availability is a pragmatic approach but limits scholarly ability to generate and test theories based on livability.

Livability measures are created and used for different purposes, reflected in the variation of how livability is conceptualized, and the weighting used for different domains. Measures are widely different if they were conceptualized by or co-created with community residents, versus if they were created for the purpose of economic gain or remuneration. Examination of these varied motivations from an ecosocial lens demonstrate how market-driven institutions are responsible for shaping the lives (and subsequently, epidemiological profiles) of different populations (Krieger 2014). Perceptions of livability are significantly lower among low-income and/or minority populations (Kovacs-Györi and Cabrera-Barona 2019). However, widely used livability indicators do not paint the same picture due to their bias towards attracting new residents or tourists to maximize economic potential (Ricciardi et al. 2013, Hudson et al. 2020). Decolonizing livability by reframing the term to center everyday lived experiences versus economic potential would improve the construct validity of measures and can facilitate resident engagement in solutions.

The immensely wide range of livability indicators used across studies suggests factor analytic methods as a strategy to identify parsimonious approaches to livability measurement. Indeed, a few scholars have conducted factor analyses of livability measures to identify domains and create robust measures (Linlin and Yilin 2009, Iyanda et al. 2018, Surjono et al. 2021); however, application of factor analytic approaches in different contexts can better inform how similarities and differences in livability norms across different settings and populations. Utilizing many indicators may be a preferred approach for indicator tracking and benchmarking; dashboards with many items can be easily created and monitored. However, this approach is not efficient for survey questionnaire or formal measure development, in both research or applied contexts; parsimonious approaches can support development of standard measures to facilitate data collection of livability in many contexts and to make comparisons over time. One challenge of livability measurement is its susceptibility to the modifiable areal unit problem (MAUP), a spatial analysis bias in which data aggregated at different spatial scales or according to different zonal systems provide inconsistent results (Buzzelli 2020). As a result, it is essential to specify the geographic scale at which livability is measured and to acknowledge the validity of the measure only at that scale. For example, livability measurement at the urban- or town-level, despite resonating more strongly with decision-makers in that area, may miss nuances in livability variation at the neighborhood-level, which may capture elements that affect the daily life of residents more specifically and can be missed by the birds’ eye view taken by decision makers. Thus, measurement of livability at different geographic scales is essential to providing complementary sets of information to strengthen validity.

Scholars utilized a variety of data collection and analytic methods to create livability measures. While most scholars have used the terms ‘subjective’ and ‘objective’ to characterize livability indicators, these terms are arbitrarily applied and render confusion among professionals who are not aware of the measurement nuances. For example, scholars indicate that the Mercer rankings are objective measures of livability, referring to the types of indicators (e.g. household income and commute time); however, these questions are asked using survey questions, and are subjective data (Okulicz-Kozaryn 2013). Spatial objective data (e.g. tree canopy coverage) or administrative data (e.g. population density) often serve as indicators out of convenience and cost. Perception-based measures of livability, such as surveys, are more suited for community engagement or advocacy purposes. Despite these different approaches, scholars agree that using both spatial and perception-based measures in a complementary fashion is the most accurate way to describe livability in a certain area.

Very few studies integrated subjective and objective measures into comprehensive livability measures and rather used them to complement or validate one another (Badland et al. 2014, Jianxiao et al. 2020). Findings from empirical studies examining associations between subjective and objective indicators are mixed (Okulicz-Kozaryn 2013, Saitluanga 2014), but discriminant validation of subjective indicators shows that objective indicators are not sufficient to capture perceptions of livability, warranting the development of robust subjective measurement tools for livability. While proposing methods using both types of indicators may be costly and potentially duplicating efforts, collecting both types may capture the nuances of livability to provide for better understanding of how this construct operates and the relative weight of different kinds of indicators on the overall level of livability of an area for its residents as well as the relative influence on health and wellbeing. A few studies discussed the economic bias of livability measurements, pointing out the prevalent use of objective indicators used to attract commercial activity, new residents, and tourists. However, studies that relied solely on subjective indicators also identified the importance of economic drivers of livability, but clarified that indicators should center resident perspectives versus for the purposes of economic gain.

The importance of operationalizing and studying livability as distinct from other constructs is paramount due to its relatability with residents and potential for creating policy and community change. Livability as a concept has the potential to connect people across cultures and differences by emphasizing our shared humanity, and the similarities in our ways of life. Livability and community are closely intertwined across the world. For example, the South African principle Ubuntu, ‘I am because we are’, emphasizes the importance of caring for others’ quality of life (Metz 2014). The livability-focused organization also highlights the Japanese principle of ikigai, or one’s sense of purpose or reason for living, and how our places should be designed in a way that cultivates that sense (Hansen 2022). Studies establishing the validity of measures of livability found that the construct is associated with other related but conceptually different measures. Some studies leveraged place satisfaction or city satisfaction as a substitute for livability. However, one scholar found that the correlation between livability index and overall satisfaction with city is low (0.36), and with the proportion of people in the city who are satisfied even lower (0.14) (Okulicz-Kozaryn 2013). This finding suggests that livability may be one of the many factors contributing to place satisfaction and should be treated and measured in the conceptually unique way it operates. Reliability was discussed often among scholars, but some studies that used an indicator versus measure development approach did not calculate reliability (Alderton et al. 2019, Lowe et al. 2020). However, additional methods for establishing the reliability of livability questions, such as inter-rater reliability or test–retest reliability, are sorely needed to strengthen survey measurement of this construct but may not be warranted if standard measures are not established. Comparison of measure performance in assessment of validity is challenging, as each scholar used a unique approach in demonstrating the validity of their measure of livability. Creation of standardized livability measures would facilitate the comparison of certain types of measures in different contexts (e.g. urban vs. rural) and among different populations (e.g. older adults and populations living with a disability).

One of the most significant challenges of livability measurement is the tradeoff between context-specific measurement and transferability or replicability of livability measures. Given the uniqueness of cities and their resulting environmental contexts, it is understandable why there is less agreement with livability measurement approaches across the world. However, standardization of this measure would allow for comparison across different environmental contexts, especially as the urban health field moves towards sharing lessons learned across cities. Initiatives such as the 1000 Cities Challenge aim to provide data comparability for over a thousand global cities, with the goal of enhancing the measurement of cities’ progress towards healthy and sustainable urban design. Livability of rural, low-resource communities or contexts who are home to migrant or other disadvantaged populations has yet to be studied to our knowledge. Livability data in urban settings in low- and middle-income countries (LMICs), especially disaggregated data, is often unavailable or exists in a very limited capacity (Prasad et al. 2016). The need for stronger livability data in these contexts is paramount, as LMICs are expected to account for more than 90% of urban population growth in the coming decade, and priority action areas for LMICs are different than high-income countries (HICs) (e.g. differences in infectious disease rates, pollution levels, etc.) (Prasad et al. 2016). One of the largest variations in livability discussed by scholars was across urban typology (e.g. urban, suburban, rural), and the undeniable abundance of resources brought about as urbanization of a space increases. For example, accessibility to health, social, and/or commercial services was mentioned by scholars but in different contexts. Research conducted among older adults aged 50 and above paints a picture of accessibility to health-care facilities as one of the most influential factors influencing their quality of life perceptions (Stephens et al. 2018), while another discusses accessibility of a variety of factors (e.g. restaurants and cultural venues) as one of the most influential factors affecting livability among adults from 20 to 68 years old (Lee 2021). Individual domains of livability and the influence they play in determining overall livability (especially for different populations and in different urban typologies) merits further research.

One important consideration of content validity, especially expert validity, is the individuals making the determination, and if they accurately represent the views of residents. Content validation through expert judgement is defined as ‘informed [opinions] from individuals with a track record in the field who are regarded by others as qualified experts and who can provide information, evidence, judgements, and assessments’ (Fernández-Gómez et al. 2020). Studies that leveraged expert validity followed a Western conceptualization of ‘expert’, such as highly educated professionals. No studies in this review classified long-time community residents as experts in the area they live in. While this approach is rarely used in measurement research, it is a method to decolonize our understanding of this construct and to center the lived experiences of underserved populations. Future research efforts aimed at supporting the decolonization of livability should prioritize community residents as experts for measure validation (Weger et al. 2018).

Much work is needed to establish a livability research agenda within the public health sciences. Leading scholars have discussed the overlap between livability and social determinants of health frameworks and have laid much of the groundwork for advancing livability measurement within public health (Badland et al. 2014). Scholarly methods are advancing and innovating to create participatory indicators for livability. Big data, social media analysis, and ecological momentary analysis are a few examples of innovative methods to better understand livability. One example is included in this review, with scholars using sentiment analysis of social media content to validate livability measures, assessing if areas with higher livability scores demonstrated happier emotions or more positive content (Jianxiao et al. 2020). Significant inequalities in livability within cities or at smaller geographic scales (e.g. block-by-block differences) also indicate the necessity for novel, low-cost methods (Lowe et al. 2020), especially in LMIC or HIC urban-underserved contexts where this data is unavailable (Prasad et al. 2016). People-based indicators co-created in a participatory process hold much potential for integrating equity considerations in public decision-making and resource allocation. Fields such as community science, rebranded from the previously named field of citizen science to be more inclusive of undocumented populations, and methods such as community-based participatory research, can capture community voices essential to creating livability measures that center equity and establish the construct validity of livability specific to minority or other underserved populations (Zwald et al. 2016, Minkler 2011). Scholars have also discussed the utility of ecological momentary assessments or ‘livability diaries’ to better understand the dynamicity of livability, a step up from using static measures to better understand this construct (Kanning et al. 2013). As quality of life during the COVID-19 pandemic and post-pandemic way of living are topics that are urgent yet widely debated, there is much potential for formal scientific research to unpack livability and contribute to strategies to prevent disease, promote environmental sustainability, and improve quality of life. It is important to note that measurement of livability itself does not foster cross-sector collaboration or movement towards a decolonized understanding of livability; however, sound and resident-informed measurement embedded within actionable long-term strategies holds much promise to guiding decisions, evaluating interventions, and eliminating inequities.

Limitations

This scoping review has several limitations. Due to the focus on measurement properties of livability, the included studies were predominantly academic papers, and formal reports and grey literature were not included, limiting our understanding of livability operationalization in professional practice (e.g. tools such as the livability indicators in the Australian Urban Observatory, UK City LIFE₁, the Arts & Livability Indicators developed National Endowment for the Arts, etc.). Despite this, several studies included in our review are longstanding, formal partnerships between academic and public sectors, and document the potential of livability measurement to advance cross-sector collaboration.

Search results were limited to studies published in English, potentially biasing results towards Eurocentric measurement of livability. However, identified studies were global in nature, in part due to the sociopolitical dominance of the English language in global scholarly publishing (Liu 2017). Future research can explore differences in livability conceptualization across studies published in other languages.

Out of the 113 full-text records examined, 22 studies were excluded if they measured livability, but did not discuss the measurement properties or calculate the reliability or validity of the measures. A limitation of this approach is that the breadth of livability conceptualization described in this review is perhaps narrower than the field; however, the strength of this approach is a focus on measures that have been tested scientifically, and statistically robust. The language of ‘validity’ was rarely used in the validation process of livability measures, potentially reflecting that attempts to validate livability measures are just beginning to emerge. Some articles that did not use this language may have been missed in this review, but the citation search was very thorough and led to inclusion of articles that would have been otherwise missed in the systemic review search process. While validity and reliability are essential concepts, they are not the only aspects of measurement important to assess in developed tools. Other issues essential to the measurement science of livability (e.g. measurement error, bias) were outside the scope of this review and could be addressed in the future work.

Livability also has many related terms that have significant conceptual overlap with the construct, such as ‘quality of urban life’ and ‘sustainability’. A limitation of this study is the exclusion of related terms, even if the conceptualization and domains studied were similar (Petrovič and Murgaš 2021). A more comprehensive assessment including these criteria and establishing differences between these concepts is necessary to improve our ability to reach conclusions from studies conducted using different terms and tools

Conclusion

Livability is increasingly being studied in public health and other fields, and there is much opportunity to apply existing and innovative research methods to better understand how livable places affect health, and how livability interventions can be implemented to improve population health and eliminate health disparities. Evidence-based public health recommendations, such as mixed-use design, access to green and blue spaces, and traffic calming, have been framed in terms of livability to increase the policy relevance of their findings to non-health sectors. There are numerous scientific approaches to advancing livability to improve health and eliminate health disparities, including but not limited to implementation science, experimental study designs, and community-based participatory research methods.

Further measurement research into the construct of livability can support the creation of valid and reliable measures, guidelines for livability indicator identification versus livability measure development, and the advancement of the understanding of this construct in causal pathways between place and health. This research is also useful for practitioners as they engage in examination of place-based factors in causal pathways and additional data-driven approaches to urban planning processes or projects to improve livability. Efforts to research livability and how it has shifted since COVID-19 can also provide opportunities for scholars and practitioners to come together to collaborate on this important topic. Innovative data collection and analysis methods, such as social media sentiment analysis or ecological momentary assessments are needed to create stronger measures to better understand this dynamic, nuanced, multidimensional construct.

Urban planners and decision-makers are increasingly convening around the shared goal of improving livability in an effort to collaborate more efficiently and effectively across sectors. Advancing measurement of livability in academic settings can fill a critical need for policy-makers who strive for data-driven decisions in urban planning processes and can facilitate collaboration between researchers and practitioners. Livability measurement in the public health sciences can promote action to addressing societal inequities by encouraging cross-sector collaboration and movement towards understanding and intervening on the structural factors that shape health instead of further investing in failed individualistic strategies. The ultimate goal of advancing the measurement science of livability is to decolonize and reframe the concept to center the everyday lives of existing residents versus leveraging livability for economic or political gain. Stronger livability measures that are resident-informed can amplify community priorities for future urban policies. Livability measurement holds significant utility for community engagement, participatory sciences, and urgent advancement of action to addressing ‘unlivable’ communities across the world.