Evaluation of Quality of Life in Urban Life Circles from a Composite Perspective of Subjective Needs and the Supply of Public Amenities: A Case Study of Changbai Island, Shenyang

Abstract

Evaluating the quality of life in life circles is an important prerequisite for effective life circle governance. Most studies evaluate the quality of life in life circles from either the living environment or public amenity supply perspective, and few adopt a composite perspective of these. This study developed an index system for evaluating the quality of life in life circles from both these perspectives, based on data from Changbai Island, Shenyang. We measured the living environment, amenity service, and quality of life indexes of urban life circles and analysed the spatial relationship between the living environment and amenity services, as well as the spatial effect of public amenities on the quality of life in communities. The findings are summarised as follows: (1) Quality of life tends to decline radially from the east to surrounding areas; (2) Life circles in Changbai Island are arranged in the following descending order according to the quality of life: life circles IV and II (equal), life circle III, and life circle I; (3) Various types of public amenities produce differentiated effects on the quality of life in life circles; specifically, middle schools can significantly improve the quality of life in surrounding communities, whereas community hospitals, large supermarkets, and community shops can have adverse effects if they are extremely close to residential areas. Our findings provide empirical evidence for evaluating the quality of life in urban life circles, evaluating methodology, and referencing for life circle governance.

1. Introduction

1.1. Research Background

As China’s urbanisation enters the stage of stock optimisation, improving quality of life has become an important subject in urban governance studies. In accordance with China’s Code of Urban Residential Areas Planning and Design (GB50180-2018), which has been effective from 2018, life circles of different scales are regarded as basic units for studying residential area planning and public facility construction. The planning of life circles in residential areas has become a focus in the field of urban studies. Compared to the planning of traditional residential areas defined by population scale and territorial scope, the planning of life circles highlights residents’ needs and subjective evaluation and adopts a dynamic perspective of time and behaviours instead of a static spatial perspective and data statistics [1]. The supply of different public amenities can produce differentiated effects on residents’ needs and subjective evaluations. Accordingly, The findings in this study will provide an important reference for the renewal and governance of life circles in residential areas. From a composite perspective of residents’ subjective needs and public facility supply, this study aimed to explore a scientific life circle governance pattern that is beneficial to all residents, saves costs, and is efficient in public facility supply [2]. Our findings are of great significance to the sustainability of urban development and scientific urban planning and management.

1.2. Literature Review

The Economics of Welfare, written by A.C. Pigou in 1920, first proposed the concept of ‘quality of life’ to depict non-economic wellbeing [3]. The quality of urban life specifically refers to the wellbeing of individuals and local communities in cities and metropolitan areas at large [4,5,6]. The World Health Organization defines quality of life as an individual’s perception of their status in life in the context of the culture and value system in which they live and in relation to their goals, expectations, standards, and concerns [7]. Quality of life is a genuine and subjective experience, which presents challenges when attempting quantitative assessments [8,9]. It depends on the assortment of amenities that are valued by individuals and businesses. The differences in the amount and mix of those amenities affect the geographic ‘sorting out’ of households and businesses as well as welfare [10]. Numerous quantitative studies have been conducted on quality of life [11,12]. The quality of life index proposed by Roback and Rosen, has a wide-ranging influence and considers the effects of the spatial distribution of various amenities on residents’ use efficiency [13,14].

Domestic studies on quality of life in China primarily focus on life circles and discuss the rationality of the supply of public amenities and the ability to meet residents’ basic living needs [15,16]. In 1987, Chen and Xu introduced the concept of the ‘life circle’ to China and evaluated the quality of urban life on a life circle scale [17]. Expanding on this idea, Bian and Xi argued that the planning of life circles should initially focus on a high-quality supply of urban public resources and that subsequent related studies should be human-centred [18]. In addition, other related studies have discussed aging-friendly amenities [19,20], the convenience of public transport facilities [21], amenity accessibility [22], the optimised allocation of educational resources [23], and spatial accessibility [24]. Recently, quality of life has become an important subject in urban and rural planning studies and can be measured using diverse dimensions (e.g., family, work, health, service accessibility, urban society, and neighbourhood environment) [25]. Existing studies on quality of life generally include the evaluation of living environments [26,27] and service evaluations of public amenities [28].

For evaluations of living environments, Aulia argued that quality of life is affected by diverse factors, including the architectural and natural environment, socioeconomic development, educational level, and cultural entertainment [29]. Johnston et al. argued that residents’ evaluation of the living environment is affected specific by factors, including natural environmental factors, social factors, and residential location [30]. Additionally, Asami stated that sustainability is an important factor in measuring living environments [31]. Tian et al. contended that evaluations of quality of life must consider economic sustainability, environment friendliness, resource-saving, convenience, social harmony, and progress [32]. With the development of measurement technology, mathematical models, and big data technology, many researchers are attempting to develop quality of life evaluation systems in diverse dimensions (e.g., social environment, physical environment, ecological environment, economic environment, and residents’ subjective evaluations) based on the findings of previous studies [33,34,35]. Based on the fuzzy mathematics principle, Gu et al. created a multi-level comprehensive evaluation model for the quality of life in communities, considering factors such as residential area planning and design, property management levels, building quality, and supporting facilities [36]. Using the analytic hierarchy process and Q-cluster analysis methods, Ren created a system to evaluate the residential space of Dalian in diverse aspects (e.g., living convenience, health, residential safety, environmental comfort, and travel convenience) [37]. Similarly, Xu et al. evaluated the residential space in Shanghai in terms of aspects such as location value, residential conditions, safety, transportation services, and community services [38]. In summary, there have been numerous fruitful studies evaluating the quality of life in residential areas with emphasis on the living environment. However, although these studies adopted diverse evaluation perspectives, they failed to develop a unified and comprehensive evaluation system and were somewhat deficient in addressing residents’ needs for life circle planning.

Regarding the service evaluation of public amenities, urban planning studies have examined the spatial proximity of urban public amenities to residential areas and their ability to provide convenience to residents [39]. The accessibility of amenities is directly affected by their spatial layout; further, residents’ daily lives and amenity diversity are also important concerns for the fair allocation of urban amenities [40]. Common evaluation methods of public amenities include the container method, shortest distance method, least travel cost method, cumulative opportunity method, kernel density method, two-step floating catchment area method, and gravity model methods [41,42,43]. To enhance the accuracy of predictions, many researchers have introduced mathematical models to improve the above-mentioned methods [44,45]. Zhong et al. contended that the measurement of the diversity of amenities should be more refined, humanised, and aligned with residents’ daily conduct codes [46]. Existing measurement indices for the diversity of amenities include the Berger–Parker Index, Shannon–Wiener Index, and Simpson Index; compared to other indices, the Shannon–Wiener Index is widely used by researchers in various fields because it can objectively evaluate the variation of each cell in the absence of preference hypotheses presented by decision-makers [47]. Frank and Pivo applied the Shannon–Wiener Index to urban planning in 1994 [48], which was subsequently used to measure the diversity of public amenities [40,49]. In summary, most studies on the supply of public amenities focus on the rationality and fairness of the allocation of public amenities, thus, reflecting an accurate status of the supply. However, these studies examine objective physical spaces but fail to contribute to the improvement of residents’ quality of life [50,51].

Compared to a single perspective, applying a composite perspective of residents’ subjective needs and public service supply can provide feedback on the overdevelopment, underdevelopment, or moderate development of urban life circles. Our findings will serve as a useful guide for scientific definitions, governance strategies, and the efficient allocation of internal social resources for urban life circles.

1.3. Research Purposes

Based on the data of Changbai Island, Shenyang, this study developed a dual-index evaluation system in which the living environment index was used to measure residents’ subjective needs for quality of life, and the amenity service index was used to measure public amenity supply. Using this evaluation index, we examined the quality of life in life circles from a composite perspective of subjective needs and public amenities and ascertained the intrinsic cause of spatial disparity in the quality of life between life circles. Our findings provide a reference for the planning, renewal, and governance of urban life circles and offer suggestions for urban renewal and the rational allocation of fiscal resources.

2. Materials and Methods

2.1. Study Area

The study area is Changbai Island, Heping District, Shenyang City (Figure 1), covering an area of 87.8 km². It is multi-functional and mostly provides residential facilities apart from ecological, leisure, and recreational functions and is important for the planned southward expansion of Shenyang in the next few years. According to the planning of Shenyang’s life circles, Changbai Island is currently divided into four life circles (Figure 2). Built completely in 2008, it is adjacent to the Hunhe River on the east and north, a railroad on the west, and the Hunnan West Road (an urban ring road) on the south, forming a relatively independent urban area. Surrounded by a water system, it has a 10-km inland river and a pleasant ecological environment. Due to its superior educational resources, the residential population has increased sharply, and the intensity of land development continues to increase, resulting in a mismatch between public amenities and population. Hence, the study area is representative of Shenyang, which has a significant variation in its quality of life.

2.2. Data and Pre-Processing

Data Sources

The basic data in this study were sourced from Baidu’s Point of Interest (POI) data, building vector data, and Open Street Map data, in which the total 19,817 POIs mainly pertain to shopping, catering, life, companies, scientific, educational, cultural, sports and leisure, healthcare, financial and insurance, transport, and accommodation services, along with business housing, government agencies, and social organisations. The study area has 1333 residential buildings distributed across 74 residential communities.

For the POI data, public amenities within a 15 min walking distance were classified into five categories: public management and service (Category A), commercial service (Category B), convenience service (R11, R21, and R31), community service (R12, R22, and R32), and transport station amenities (Category S).

Table 1. Classification and weight of public amenities in life circles within a 15 min walking distance.

Category of Amenities	Weight of Index	Specific Amenities	Weight of Condition	Weight of Attribution
Public management and service amenities (Category A)	0.6	Middle schools	0.40	0.24
		Elementary schools	0.25	0.15
		Cultural activity centres	0.05	0.03
		Gymnasium and multi-function sports fields	0.15	0.09
		Community hospitals	0.15	0.09
Commercial service amenities (Category B)	0.10	Large supermarkets (including shopping malls, vegetable markets, and fresh food supermarkets)	1	0.100
Convenience service amenities (R11, R21, and R31)	0.03	Parking lots for residents’ private cars	1	0.030
Community service amenities (R12, R22, and R32)	0.15	Kindergartens	0.70	0.105
		Community shops (including supermarkets, drug stores, laundries, and hair salons)	0.30	0.045
Transport station amenities (Category S)	0.12	Subway stations	0.60	0.072
		Bus stations	0.40	0.048

lists 11 specific types of public amenities.

2.3. Study Methods

2.3.1. Technical Roadmap

Using the communities with a walking distance of 1 km as basic research units, this study comprehensively evaluated the quality of life in communities. Figure 3 shows the technical roadmap.

The following six points guided this research:

(1)

Create an evaluation system for the quality of life in life circles, involving the living environment and amenity service indices;

(2)

Calculate the living environment index of each community based on related open-source data;

(3)

Based on the POI data, calculate the amenity service index of each building within a walking distance of 1 km and determine the amenity service index of each community accordingly;

(4)

Calculate the global Moran index of each dimension. Calculate the index of each dimension by using the result as the weight of each index, thus determining the quality of life index of each community;

(5)

Analyse the local spatial relationship between living environment and amenity service capacity through a local spatial bivariate analysis;

(6)

Conduct a correlation analysis to explore the spatial relationship between the quality of life in a community and public amenities within a walking distance of 1 km.

2.3.2. Quality of Life Index System

In this study, we created a dual evaluation index system to measure the quality of life in life circles from two perspectives (living environment and amenity services). Specifically, the living environment index was used to measure residents’ subjective needs to maintain their quality of life in urban life circles, and the amenity service index was used to measure the supply of public amenities and reflect the use convenience of amenities.

Table 2. Quality of life index system.

Dimension	Sub-Index	Weight	Data Type	Basic Indicator or Indicator Description	Weight
Living environment index	Housing construction	0.153	Population statistics Spatial data	Building age	0.033
				Per-capita living area	0.053
				Living density	0.067
	Residential area environment	0.424	Spatial data	Static traffic condition	0.002
				Greening rate	0.171
				Landscape environment	0.251
	Real estate value	0.423	Statistical data	Real estate price	0.193
				Median housing price	0.230
Amenity service index	Amenity accessibility	0.210	Spatial data	Accessibility	——
	Amenity diversity	0.638	Statistical data	Amenity diversity	——
	Convenience of public transport facilities	0.152	Spatial data	Quantity of available transport facilities	——

describes the detailed dual evaluation index system.

2.3.3. Analysis of Living Environment Index

Using related studies [17,32,34,35,36,37,38] for reference, we created an index system comprising sub-themes such as housing construction, residential area environment, and real estate value and further derived eight basic indicators such as building age, per-capita living area, living density, static traffic condition, greening rate, and median housing price.

Table 3. Living environment index system.

Dimension	Sub-Index	Basic Indicator	Indicator Description or Calculation Method	Reference
Living environment index	Housing construction	Building age	It refers to the period from the date of house completion to the present date.	Xu, J.H. [38]
		Per-capita living area	It refers to the per-capita residential floor area among the residential population.	Chen, Q.H. [17]; Tian, M. [32]
		Living density	It refers to the population per unit area in residential areas.	Xie, R.Z. [34]
	Residential area environment	Static traffic condition	It is measured in terms of the number of parking spots per household.	Gu, X. [36]
		Greening rate	It refers to the ratio of greening area to total land area.	Ren, X.H. [37]
		Landscape environment	It is a subjective evaluation score determined by field surveys.	Chen, F. [35]; Ren, X.H. [37]
	Real estate value	Real estate price	It partly reflects the residential area environment and public security level.	Gu, X. [36]
		Median housing price	It reflects the quality of a residential area in a market-oriented environment.	Xu, J.H. [38]

lists the calculation and description for each indicator.

2.3.4. Analysis of Amenity Service Index

According to related studies [39,40,46], the amenity service index herein is based on the availability of amenities within a walking distance of 1 km and comprises dimensions such as amenity accessibility, diversity, and convenience of public transport facilities.

Amenity accessibility can be measured using the cumulative probability algorithm [49], namely, calculating the probability of residents’ access to various public amenities. Due to the difference in residents’ travel demands and willingness, the distance decay function (Equation (2)) and choice probability of the Huff model (Equation (3)) [42] were introduced to improve the measure of amenity accessibility:

$$A_i={{\displaystyle \sum }}_{j=1}^{l}Pro{b}_{i}f\left(i,j\right)R_j,$$

(1)

$$f\left(i,j\right)=\left\{\begin{array}{c}{e}^{-\frac{1}{2}{\left(\frac{d_{ij}}{\sigma }\right)}^2},0<d_{ij}\le r_{ij}\\ 0 , d_{ij}>r_{ij}\end{array}\right.,$$

(2)

$$Pro{b}_i=\frac{C_j{d}_{ij}{}^{-\beta }}{{{\displaystyle \sum }}_{s\in l}{C}_s{d}_{is}{}^{-\beta }}.$$

(3)

In Equation (1), $A_i$ denotes the amenity accessibility in the residential area $i$; $l$ denotes the number of amenity types available to residents; $Pro{b}_i$ denotes the probability of selecting the amenity point $j$ in the residential area $i$; $f\left(i,j\right)$ denotes the accessibility of the amenity point $j$ to the residential area $i$; and $R_j$ denotes the weight of amenities of Category j. In Equation (2), $d_{ij}$ denotes the walking distance from the residential area $i$ to the amenity point j and $\sigma$ denotes the resistance coefficient of the Gaussian decay curve. In Equation (3), $C_j$ denotes the attraction of the amenity point $j$ to residents (it is replaced with the amenity weight $R_j$ because POI data cannot reflect this capacity) while $\beta$ denotes the resistance coefficient of the Huff model (default: 2).

Figure 4 shows the distance decay weight of amenities. To ensure the discriminability of decay values within the distance of 0.5 to 1 km, $\sigma$= 1/√6 was used as the resistance coefficient of the distance decay function. The weight of each type of public amenity was determined using expert scoring and influence weighting methods (Table 1).

Amenity diversity was measured in terms of the Shannon–Wiener Diversity Index [47] and reflects the diversity of amenity types in areas accessible to residents. The relevant equation is as follows:

$$V_i=-{{\displaystyle \sum }}_{i=1}^m{p}_{i}ln{p}_i,$$

(4)

where, $V_i$ denotes the diversity of POIs in the residential area $i$; $m$ denotes the number of amenity types in the residential area $i$; and $p_i$ denotes the ratio of available amenities to all amenities in the residential area $i$.

The convenience of public transport facilities (hereinafter referred to as amenity convenience) provides an important support for the amenity service index. According to a study by Zhong et al. [46], public transport facilities include bus stations, subway stations, and parking lots. Their $R_j$ values are 1.8, 1.5, and 1.2, respectively. The distance decay coefficient (σ) was set to 0.2, and distance decay was not considered beyond the distance of 500 m. The calculation equation is as follows:

$$T_i=max\left(f\left(i,j\right)R_j\right).$$

(5)

Amenity accessibility, diversity, and convenience were calculated on a building scale. Their calculation results were aggregated on a community scale to determine the average values of each community.

2.3.5. Evaluation Method for Quality of Life

Evaluating quality of life is a complex process that involves the joint participation of diverse urban elements. To better reflect the spatial characteristics of objective data, we introduced the global Moran index, which can reflect the spatial clustering characteristics of data, to determine the index weights. The calculation equation is as follows:

$$I=\frac{n{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}{{\displaystyle \sum }}_{i=1}^n{\left(x_i-\overline{x}\right)}^2},$$

(6)

where $I$ denotes the global Moran index (the value range is [−1, 1]; $I$ > 0 denotes spatial positive correlation; the larger the value, the more significant the spatial correlation; $I$< 0 denotes spatial negative correlation; the smaller the value, the more significant spatial variation); $x_i$ and $x_j$, respectively, denote the values of a certain indicator in residential areas $i$ and $j$; and $\overline{x}$ denotes the average value of a certain indicator in all residential areas.

The living environment, amenity service, and quality of life index needed to be calculated by weighting the basic indicators. The calculation process was as follows:

(1) The calculation dimensionality and unit vary among the basic indicators, and thus they were standardised first.

(2) We calculated the global Moran index values of the type-$u$ sub-indices and type-$j$ basic indicators (i.e., $I_u$ and $I_{j,u}$, respectively) and determined the weight of a sub-index ($w_u$) and the weight of a basic indicator ($w_{j,u}$).

$$\left\{\begin{array}{c}{w}_u=\frac{I_u}{{{\displaystyle \sum }}_u{I}_u}\\ w_{j,u}=\frac{I_{j,u}}{{{\displaystyle \sum }}_j^n{I}_{j,u}}\end{array}\right..$$

(7)

2.3.6. Local Spatial Bivariate Analysis

To further reveal the local spatial relationship between the living environment and amenity service capacity, a bivariate local Moran index [52] was introduced to measure their spatial clustering. The objective was to identify their basic spatial characteristics and reveal the matching between the living environment and amenity services in Changbai Island in terms of the spatial matching pattern and spatial matching degree. The calculation equation is as follows:

$$I^{\prime }=\frac{x_i-\overline{x}}{S_k^2}{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\frac{x_j-\overline{x}}{S_I^2}.$$

(8)

where $I$ denotes the global Moran index; $x_i$ and $x_j$, respectively, denote the values of a certain indicator in the residential areas $i$ and $j$; $\overline{x}$ denotes the average variable value of all residential areas; and $S_k^2$ and $S_I^2$ denote the variance of observed variable values.

2.3.7. Pearson Correlation Analysis

To reveal the spatial correlation between public amenities and quality of life in communities, a Pearson correlation analysis [53] was conducted, with the amenity–community distance as a dependent variable and quality of life index as an independent variable. The results of the correlation analysis were used to determine the effect of various public amenities on the quality of life in surrounding communities.

3. Results

3.1. Analysis Results of the Quality of Life in Changbai Island

Table 2 and

Table 4. Spatial autocorrelation results of indices of different dimensions, sub-indices, and quality of life index.

	Dimension I				Dimension II				Quality of Life Index
	Housing Construction	Residential Area Environment	Real Estate Value	Living Environment Index	Amenity Accessibility	Amenity Diversity	Traffic Convenience	Amenity Service Index
Moran index	0.127	0.337	0.341	0.389	0.242	0.735	0.175	0.655	0.617
Z	1.9468	4.6548	4.8620	5.2703	3.5143	10.0576	2.5317	9.1709	8.3853
P	0.028	0.001	0.001	0.001	0.001	0.001	0.012	0.001	1.001

list the global Moran index of each basic indicator and weights of basic indicators and sub-indices determined by using the above method, respectively. As described in Table 4, indices of different dimensions, sub-indices, and the quality of life index show significant spatial positive correlation and a high spatial clustering degree.

(1)

As shown in Figure 5a, the living environment index shows a concave distribution pattern (i.e., high in the middle and low on both sides), with significant spatial variation. Figure 6 shows the basic indicators of different sub-indices of the living environment index. For housing construction, the communities in Changbai Island were built from 2001 to 2022, with a relatively young age and a per-capita living area above the national average. For the residential area environment, most of the communities are densely populated and have few parking spots per household, but the overall evaluation of green landscape and environmental quality is high. For real estate value, most of the communities have housing prices above the average level of Shenyang, and old and new communities are significantly different in terms of housing price.

(2)

As shown in Figure 5b, Changbai Island has three clustering areas with a significantly high amenity service index in its east, middle, and southwestern corner, respectively. Figure 7 shows the basic indicators of different sub-indices of the amenity service index. For amenity accessibility, the road network density in the central part is relatively high, and public amenities are well-supplied. Amenity diversity in eastern communities is higher than that in other communities of the study area. Traffic convenience on the west side of South Nanjing Street is superior to that on its east side.

(3)

As shown in Figure 5c, the measured values of the quality of life index tend to decline radially from the east to surrounding areas. In the eastern communities of Changbai Island, there is an obvious clustering area with a high quality of life index, indicating a high level of community construction and sufficiency of public amenities. The values of the quality of life index are universally low among communities on South Nanjing Road and near the railway, indicating the underdevelopment of public amenities and public traffic.

3.2. Spatial Correlation between Living Environment and Public Amenities

Figure 8 and Figure 9 show the analysis results of the bivariate local Moran index for the living environment and amenity service indices. Their local Moran index (0.155) passes the significance test at the level of p < 0.5%, indicating a spatial positive correlation. Most communities in life circle IV show a high–high clustering pattern. In northern life circle I, most communities show a high–low clustering pattern, and few communities show a low–low clustering pattern. In the southwest of the land lot, there is one community with a high–high clustering pattern and one community with a low–high clustering pattern. Overall, the supply and demand of public amenities are balanced in Changbai Island, except for a supply–demand imbalance in one dimension (Figure 9).

To maintain a supply–demand imbalance in public amenities, it is more advisable to adjust the supply of public amenities. Hence, local spatial autocorrelation was continuously conducted using the sub-indices of the living environment index and amenity service index.

Figure 10 shows the analysis results of the bivariate local Moran index for the living environment index and amenity accessibility. The local Moran index is 0.076 (approximate to 0), indicating no spatial correlation. Some communities in life circle IV and along the Hunhe River show a high–high clustering pattern. Some communities in the core area of life circle III show a high–low clustering pattern and low–low clustering pattern. There are two low–high clustering areas along the Hunhe River and in the central area. Overall, the living environment index and amenity accessibility show no obvious spatial clustering pattern in the communities of Changbai Island.

Figure 11 shows the analysis results of the bivariate global Moran index for the living environment index and amenity diversity. The global Moran index is 0.174 (passing the significance test at the level of p < 0.5%), indicating a strong positive spatial correlation. The Zhonghai International community in life circle IV shows a high–high clustering pattern. The communities on both sides of Changbai North Road in life circle I show a high–low clustering pattern, and some communities on the edge show a low–high clustering pattern. In life circle III, one community has a low–low clustering pattern. Due to the influence of railways and urban trunk roads, the spatial correlation between the living environment and amenity diversity varies significantly from eastern communities to western communities in Changbai Island. These transport facilities affect the quality of life in communities and spatial layout of public amenities.

Figure 12 shows the analysis results of the bivariate global Moran index for the living environment index and amenity convenience. The global Moran index is −0.167 (passing the significance test at the level of p < 0.5%), indicating a negative spatial correlation. Some communities in life circle I show a high–high clustering pattern. Communities in the central area of Changbai Island show a low–high clustering pattern and a high–low clustering pattern. There is no low–low clustering pattern in the communities of Changbai Island. Overall, the relationship between residents’ subjective needs for quality of life and the objective supply of public transport facilities shows a significant spatial variation in Changbai Island.

The supply–demand relationship in life circle I needs to be improved, and the evaluation of housing construction and amenity diversity is low (Figure 9b and Figure 11), indicating that the population density is high and supply of public amenities cannot meet residents’ current needs. In life circle III, the evaluation of amenity accessibility is low. Amenity accessibility and living environment are mismatched (Figure 9), therefore it is necessary to improve the road condition and increase the supply of public amenities. In the communities of life circles II and IV, the housing construction level is high, and the living environment quality matches the amenity service level well. According to development level, life circles are arranged in the following descending order: life circles IV and II (equal), life circle III, and life circle I.

3.3. Spatial Correlation between Quality of Life and Public Amenities

Table 5. Coefficient of correlation between public amenities and quality of life.

	Independent Variable	Dependent Variable: Quality of Life Index
		p Value	Sig Value	R2	Number of Cases	p Value	Sig Value	Number of Cases
Public management and service amenities (Category A)	Middle school	−0.3908	0.0035	0.153	54	−0.1420	0.2276	74
	Elementary school	−0.1910	0.1133		70
	Cultural activity centre	0.3281	0.0339	0.108	42
	Gymnasium and multi-function sports field	−0.2255	0.0979		55
	Community hospital	0.3331	0.0106	0.111	58
Commercial service amenities (Category B)	Large supermarket	0.2371	0.0419	0.056	74
Community service amenities (R12, R22, and R32)	Kindergarten	−0.1915	0.1022		74	−0.0673	0.5686	74
	Community shop	0.2648	0.0226	0.070	74
Transport station amenities (Category S)	Subway station	0.0171	0.9557		13	0.1268	0.2818	74
	Bus station	0.1090	0.3554		74
Convenience service amenities (R11, R21, and R31)	Parking lots for residents’ private cars	−0.0363	0.7586		74

lists the test results of correlation between quality of life and public amenities in the communities of Changbai Island. At the level of major categories, the quality of life in communities is not correlated with Categories A; B; R12; R22, and R32; S; or R11, R21, and R31. At the level of specific amenities (Figure 13), the quality of life in communities is significantly correlated with middle schools, cultural activity centres, community hospitals, and community shops (significance level: 0.05). Universally, the presence of middle schools has positive effects on the quality of life in communities. Due to the not-in-my-back-yard effect (e.g., noise), community hospitals, large supermarkets, and community shops produce negative effects on the quality of life in communities.

4. Discussion

As a subjective experience, quality of life is usually difficult to describe quantitatively. To address this problem, we proposed a subjective–objective approach to measuring quality of life. Compared to a single perspective of subjective needs for the living environment [26,27] or objective supply of public amenities [14,15,16,22,23], the subjective–objective approach combines the advantages of both subjective and objective perspectives and provides suggestions for optimising quality of life from different perspectives.

Community-based research, large-scale analysis, and residents’ perspectives on the supply level of public amenities and services in the living circles is another highlight of this study. Existing studies have been conducted on a small scale due to workload and time cost [36], which is not only time-consuming and laborious but also small in scope, or too macroscopic, without considering the details of urban human urban settlements [37,38]. The analysis units used in existing research are city districts [28,39,41], blocks [40], or grids [25], and it is difficult to ascertain the objective situation of urban living environments and amenity service supply at the micro-scale. The new method proposed in this study can guide the community from a wide range of standpoints. It considers details, reflects the differences in community living environments, objectively presents public amenity supply from residents’ perspective, and provides suggestions to optimise living quality from different perspectives by combining subjective and objective advantages.

5. Conclusions and Suggestions

5.1. Conclusions

To identify key factors affecting the quality of life in urban life circles, we developed a dual-index evaluation system comprising the living environment index and amenity service index and accordingly measured the quality of life in the communities of Changbai Island, Shenyang. Our findings are summarised as follows.

First, quality of life is influenced by factors such as spatial location and economic benefits and tends to decline radially from the east to surrounding areas. Overall, the values of the living environment index show a concave distribution pattern (i.e., high in the middle and low on both sides) and are universally high in communities along the Hunhe River. In Changbai Island, there are three obvious clustering areas with high values of the amenity service index (east, middle, and southwestern corner).

Second, the matching between residents’ subjective needs for quality of life and the supply of public amenities in Changbai Island is generally reasonable, except for a mismatch in a few aspects. For the living environment and amenity diversity, the evaluation results are significantly different from eastern to western areas. The clustering results of living environment and amenity convenience show that the relationship between the living environment and public transport facilities need to be adjusted. According to the evaluation scores, life circles are arranged in the following descending order: life circles IV and II (equal), life circle III, and life circle I.

Third, the various types of public amenities produce differentiated effects on the quality of life in urban life circles. The major categories of public amenities are not significantly correlated with the quality of life in communities. Among the specific public amenities, middle schools significantly improve the quality of life in surrounding communities, whereas community hospitals, large supermarkets, and community shops produce adverse effects on the quality of life if they are extremely close to residential areas.

5.2. Suggestions

Our empirical findings can provide a decision-making reference for the optimisation of urban governance and delineation of urban life circles. Additionally, they provide strategic support for the optimisation of life circles and the intensification of public amenity allocation in Changbai Island. The strategies of life circle optimisation are as follows:

Life circle I should be given priority in urban renewal, dominated by the renewal of amenity services and supplemented by community renewal. Regarding amenity services, it is necessary to increase local amenity diversity by increasing the quantity and types of public amenities. Given the high population density, amenity diversity should give priority to public infrastructure (e.g., convenience shops, medical and health amenities, and basic education) to meet residents’ daily life needs.

Life circle III needs to be further improved and optimised. Specifically, its overall evaluation is good, but it is poor in amenity accessibility and living environment coordination. Hence, equal attention must be given to community renewal and amenity service improvement. As shown in Figure 6, community renewal should focus on greening area expansion and the development of public amenities for green space (e.g., parking lots). Additionally, appropriate measures (e.g., improving road conditions and developing more amenities) must be taken to improve the walking accessibility of public amenities.

Life circles II and IV need to be partially improved. Their overall quality is high (e.g., sufficient supply of public amenities, and good match between public amenities and residents’ subjective needs). In life circles II and IV, there are communities with low evaluation, which should be renewed as required according to the above strategies for life circles I and III. Overall, it is necessary to maintain the environmental atmosphere of existing residential communities and improve the quality of existing amenity services.

The development of public amenities in life circles should be aligned with the principle of ‘sharing and joint development’. Different life circles can share public life amenities synergistically to improve their efficiency and promote the intensive use of land. Moreover, public amenities in life circles should be developed on a balanced and as-needed basis.

To improve the layout of public amenities, various categories of public amenities should be well-planned according to residents’ needs. Based on the planning principles for neighbouring organisations and residential areas, elementary schools should be located in the centre of life circles. The planning of middle schools should consider factors such as community population, local population growth trend, enrolment system, and household registration system. The planning of cultural activity centres and community hospitals should consider the using habits of the target audience.

5.3. Limitations and Future Steps

This study has a few limitations. First, it evaluated the quality of life in urban life circles from the composite perspective of residents’ subjective needs and public service supply and explored the intrinsic factors affecting the quality of life. Due to the constraints on data acquisition and study scope, as well as the incomprehensiveness of study data, the current findings are only applicable to built-up urban areas. Hence, quality of life needs to be investigated based on the actual status of urban development.

Second, in the selection of living environment indicators, this study only considered quantifiable indicators and indicators from an objective perspective. However, when assessing the quality of life, individual perception should be the primary criterion. Additionally, certain unquantifiable indicators (e.g., indoor environmental comfort and air quality) which require improvement were not included in the evaluation framework.

Third, regarding public amenities, the constructed walk model requires improvement. Residents’ actual range of travel is affected by individual characteristics and travel habits. Therefore, the accessibility model based on road network data cannot accurately reflect residents’ actual travel demand but can roughly estimate their travel range based on existing data of residential areas, community entrances and exits, and boundaries.

Future research should focus on further improving the evaluation system of quality of life in living circles. In addition to the above shortcomings, it is necessary to consider the potential impact of urban policies, residents’ living choices, and residents’ living habits on the quality of life.