Rethinking Spatial Equity in Retail Facilities for Young Urbanites in Strip Cities: A Case of Lanzhou

Abstract

Whether young people can equitably access everyday living and shopping facilities has become a key indicator of fairness in youth-friendly cities. However, existing research has paid limited attention to young people’s daily activity spheres, particularly to how linear urban forms may intensify core–periphery disparities in accessibility. Using Lanzhou, a typical Chinese strip city, as a case study, this paper develops a multidimensional evaluation framework integrating coverage, richness, and sharing. Drawing on 1650 youth questionnaires and detailed geospatial data, it applies a preference-based behavioural accessibility model in conjunction with the Gini coefficient to examine the spatial equity of daily shopping facilities. Results indicate that Lanzhou’s facilities exhibit a pattern of central concentration and peripheral scarcity. Chengguan and Anning districts form highly accessible cores, supported by dense commercial areas and university resources. In contrast, Qilihe and Xigu suffer from pronounced facility deficits, with walking coverage rates of five to fifteen minutes below 70%. Accessibility patterns reveal coexisting contradictions of “high supply but low access” and “low supply with difficult access”. Equity metrics indicate a moderate overall level (Gini = 0.272), yet notable inter-district disparities persist, with peripheral areas imposing higher access costs on young residents. The study confirms a persistent spatial dilemma whereby the quantity of facilities does not guarantee equitable access. It argues that urban governance should shift from equal allocation towards demand-responsive and perceived-equity approaches, thereby extending daily shopping opportunities to peripheral zones. Enhancing neighbourhood-scale connectivity through pedestrian and cycling networks would improve both practical usability and spatial fairness for young urban populations.

1. Introduction

Against the backdrop of accelerating global urbanisation, achieving spatial equity and promoting inclusive development have become key objectives of modern urban governance. However, the spatial rights of young people—the very force driving societal vitality and future innovation—have long remained marginalised within planning discourse [1]. According to the Seventh National Population Census (2020), China’s youth population aged 14–35 stands at approximately 400 million, accounting for 28.3% of the total population [2]. As a vital segment of the urban population, young people exhibit distinct preferences regarding their spatial environments and public services in daily life. However, institutional frameworks often lack independent mechanisms to identify and respond to these specific needs [3]. Consequently, young people struggle to effectively articulate their requirements within public service systems and fail to receive adequate safeguards [4]. Thus, despite sufficient facility provision, equity in their actual access and usage remains deficient.

In recent years, China has accelerated its development. Following the release of the Medium- and Long-Term Youth Development Plan (2016–2025), numerous regions have launched pilot initiatives. These focus on transforming public service systems to be youth-friendly, addressing issues such as education, employment, housing, and the ‘15-min living circle’ [5,6]. Explorations in cities like Nanjing, Chengdu, and Guangzhou have institutionally strengthened the embedding of youth public service provision and participation mechanisms. However, from a spatial implementation perspective, community-level practices remain underdeveloped. Young people are often treated as appendages to families or schools, with their distinct consumption preferences, travel rhythms, and daily shopping needs inadequately reflected in facility provision. This creates a disconnect between policy frameworks and spatial governance. Concurrently, academic discourse has increasingly focused on the gap between the supply of public service facilities and their actual utilisation. Soja’s (2009) theory of ‘spatial justice’ emphasises that space is not merely physical distribution but a product of social relations [7]. Spatial justice must transcend superficial metrics of facility quantity and distribution, focusing instead on whether groups genuinely achieve resource accessibility and utilisation. Similarly, Talen (1998) notes that uniform provision does not equate to equitable access; fairness requires balancing demand variations and group capabilities [8]. Addressing this issue, numerous studies have introduced frameworks of horizontal equity [9] and vertical equity [10], emphasising that individuals should enjoy equal opportunities while advocating preferential allocation for disadvantaged groups [11,12]. Methodologically, indicators such as the Lorenz curve, Gini coefficient, and Theil index are widely employed to measure spatial inequality in facility allocation and service access [13,14,15].

Despite significant progress in accessibility and spatial equity research across various public service domains, such as education, healthcare, and green spaces [16,17], further expansion is necessary regarding population demand characteristics and spatial patterns. Existing studies predominantly focus on specialised groups, such as children and the elderly [18], while spatial justice issues affecting young people, a highly mobile group with diverse daily behaviours, remain underexplored. Although policy frameworks, such as the 2030 Agenda for Sustainable Development and China’s Youth-Friendly Cities Initiative, emphasise inclusivity and intergenerational fairness, young people still face substantial barriers in accessing affordable retail and leisure spaces [19,20]. In practice, they experience fewer opportunities to meet everyday consumption needs within a convenient distance. The ribbon-shaped urban form, typical of cities constrained by topography, creates strong contrasts in population activity and facility distribution. Accessibility patterns often show central aggregation and peripheral scarcity. Commercial and public service resources are concentrated along transport corridors, leaving outer areas relatively deprived. This spatial imbalance intensifies inequalities in young people’s access to daily shopping. Low-income, migrant, and tenant youths bear higher travel-time costs and have limited access to nearby facilities. Consequently, even when nominal accessibility appears adequate, actual convenience and equity remain lacking. Against this backdrop, the present study examines Lanzhou, a representative strip city in China’s Yellow River Basin, through the lens of youth perspectives. It examines the spatial equity of community shopping facilities and the mechanisms that shape these patterns. The research addresses two questions: whether youth-oriented shopping facilities exhibit spatial distribution inequities within topographically constrained urban forms, and how socio-economic and morphological factors jointly influence these inequities. To this end, the study proposes an analytical framework with three dimensions—coverage, diversity, and accessibility—to evaluate spatial equity across travel-time thresholds and offers optimisation strategies for marginalised areas.

2. Literature Review

Against the backdrop of global urban governance shifting from rapid expansion towards people-centred approaches, spatial equity in public service provision has emerged as a pivotal indicator for assessing urban sustainability. The United Nations’ 2030 Agenda for Sustainable Development emphasises prioritising access to services for vulnerable and marginalised groups within urban spaces to mitigate the risks of intergenerational transmission, such as inequitable resource distribution. Drawing upon spatial justice theory [7,8], achieving spatial equity requires attention not only to the quantity and spatial distribution of facilities but also to accessibility, information access, and user experience for different groups [21].

Recent research has therefore incorporated spatial costs, demographic composition, and behavioural trajectories into accessibility metrics, enabling more systematic evaluation of public service equity [22]. From a group perspective, the spatial needs of young people are gaining increasing attention. Studies show that the availability of community resources and the quality of social spaces significantly influence youth participation, mental health, and resilience [23]. However, because young people are often regarded as highly mobile and adaptable, their needs are easily overlooked in urban facility planning [24,25]. As a result, their genuine requirements seldom enter public decision-making, leaving them in a passive position within spatial resource allocation. Retail-oriented community facilities not only meet daily consumption needs but also strongly shape young people’s social interactions and sense of identity [26]. However, prevailing allocation models primarily rely on static indicators such as service radius and population density, which fail to reflect the diverse and dynamic nature of youth consumption behaviour [27,28]. Evidence suggests that young consumers exhibit a hybrid pattern, combining offline experiences with online payments, which calls for community shopping facilities with greater spatial responsiveness and functional versatility [29,30]. At the same time, social and technological changes in the digital era are reshaping youth socialisation and consumption. A survey in Guangzhou revealed that nearly half of young people living alone prefer online socialising, with around 30% spending comparable time in both virtual and physical environments. While online interaction brings convenience, it can also heighten loneliness and spatial alienation [31]. Consequently, facility allocation must not only consider physical spatial distribution but also respond to young people’s actual behavioural needs and experiential perceptions.

Strip cities, constrained by topography such as mountains and river valleys, exhibit highly concentrated spatial resources, including public services, along their principal axes. Supply shortages in peripheral areas are particularly pronounced [32]. Against this backdrop, for mobile youth, those in rented accommodation, and low-income youth, the combination of limited facility provision and high travel costs often creates a tangible reality of accessibility disruption [33]. It not only diminishes daily convenience and spatial belonging but also exacerbates social equity disparities. In recent years, the integrated application of GIS spatial analysis, location-based service (LBS) trajectories, and travel big data has significantly advanced research on the equity of public service facilities. Accessibility analysis frameworks based on LBS trajectories and deep learning can mine real-time travel patterns and identify high-frequency behavioural hotspots, overcoming the spatio-temporal limitations of static analysis [34,35]. The application of multi-source data (mobile phone signalling, social media check-ins) further reveals the cross-district shopping mobility of young people, whose behavioural boundaries have clearly transcended administrative boundaries [36]. Concurrently, He et al. (2024) combined spatial justice theory with fairness-aware machine learning algorithms to propose a predictive model that dynamically identifies potential areas of inequity, providing a forward-looking tool for policy intervention [37]. Regarding methodological expansion, scholars have applied two-step travel search methods [38], information entropy models combined with network accessibility analysis [39], and fairness-efficiency balance indices [40], providing robust tools for urban public service facility allocation and accessibility. In recent research employing reinforcement learning to optimise facility siting demonstrates the method’s potential for improving accessibility equity across diverse groups, highlighting the possibility of AI-driven spatial equity interventions [41]. However, these approaches also exhibit significant limitations, including high data requirements, limited model transparency, and difficulties in accurately reflecting real behavioural mechanisms. Their effectiveness within complex urban environments remains insufficiently validated.

International experience indicates that spatial equity for young people has become an increasingly significant issue in European and Global South cities. European cities such as Berlin, Amsterdam, and Copenhagen have integrated youth needs into facility planning through participatory assessments, mobile service provision, and pedestrian-friendly community design. In the Global South, cities including São Paulo, Johannesburg, and Delhi have highlighted structural challenges faced by young people, encompassing lengthy commutes, fragmented commercial networks, and pronounced social spatial inequalities [42,43]. Collectively, these studies demonstrate that youth accessibility is not solely determined by the distribution of retail and transport facilities, but also hinges on mobility constraints, income disparities, and governance capacity within distinct urban contexts. Whilst Chinese cities actively advance the ‘15-min neighbourhood’ framework, assessments of equity for youth and demand-responsive planning mechanisms remain inadequate. This deficiency is particularly pronounced in linear or ribbon cities, where resource polarisation frequently coexists with fragmented accessibility. Against this backdrop, this study takes Lanzhou as a representative linear city to construct an assessment framework for youth daily shopping facilities based on coverage, diversity, and shared accessibility. It reveals how urban form and group attributes jointly shape young people’s spatial opportunities for accessing daily retail services. As Chinese cities actively advance the ‘15-min living circle’ framework, assessments of equity for young people and demand-responsive planning mechanisms remain inadequate. This is particularly evident in ribbon cities, where resource polarisation and fragmented accessibility frequently coexist. Against this backdrop, this study takes Lanzhou as a representative linear city and constructs an evaluation framework for daily shopping facilities targeting young people based on three dimensions: coverage, diversity, and shared accessibility. It reveals how urban form and group attributes jointly shape spatial opportunities for young people to access daily retail services.

3. Materials and Methods

This paper constructs an analytical framework for assessing the equity of supply and behavioural accessibility of daily shopping facilities based on the travel characteristics of young people. As illustrated in Figure 1, the framework is designed to reveal the spatial mechanisms underlying inequalities in young people’s access to community shopping facilities in ribbon cities. First, the supply of daily shopping facilities is identified and processed using geospatial multi-source data. Meanwhile, questionnaire surveys capture heterogeneous shopping preferences and travel behaviours across different youth groups. Building on these inputs, the coverage, diversity, and shared accessibility of shopping facilities within 5–30 min daily living circles are evaluated to measure service provision levels. The spatial distribution patterns of different facility types are then examined using the standard deviation ellipse model, which helps reveal concentration tendencies and verify the structural effects associated with strip city morphology. Finally, Lorenz curves and Gini coefficients are employed to analyze disparities in accessibility across districts and population groups, thereby identifying potential mechanisms of structural deprivation.

Figure 1. Research framework and methodological workflow.

3.1. Research Area

This study selected the central urban area of Lanzhou, a typical strip city in China, as its research region. As shown in Figure 2, the study area encompasses four core districts: Chengguan, Qilihe, Anning, and Xigu. Situated in the upper Yellow River valley, Lanzhou is flanked by mountains to the north and south. The city extends linearly along the river axis, forming a characteristic linear spatial distribution pattern. Infrastructure and public service facilities exhibit pronounced axial concentration. According to the 2024 Statistical Bulletin on National Economic and Social Development of Lanzhou (https://tjj.gansu.gov.cn, accessed on 23 January 2025), the city’s permanent resident population stands at 4.4365 million, with an urbanisation rate of 84.83%. Among this, the youth population aged 14–35 numbers 1.49 million, accounting for 33.98%. In 2024, the city’s gross domestic product (GDP) reached 374.23 billion yuan, with a per capita GDP of 84,460 yuan, representing a year-on-year increase of 4.7%. The tertiary sector accounted for 64.9% of the economy, with consumption and services playing a dominant role in the urban economy.

Figure 2. Research area and time accessibility: (a) Location of Lanzhou within Gansu Province; (b) Location of the study area within Lanzhou; (c) Walking accessibility zones ranging from 5 to 30 min.

3.2. Dataset

(1)

POI Data

Point of Interest (POI) data for public service facilities in daily shopping categories from AutoNavi Maps and Bigemap platforms, with a total of 17,327 entries collected. These encompass convenience stores and supermarkets, butchers’ and greengrocers, community convenience outlets, and catering establishments. The first two categories of essential livelihood facilities account for 86.7% of the total (Table 1). The POI dataset includes attributes such as facility name, category, and latitude/longitude coordinates.

Table 1. Statistics on the Number of POIs for daily shopping facilities.

Major Category	Minor Category	Quantity	Source and Collection Date
Daily shopping facilities	Convenience stores and supermarkets	9114	https://lbs.amap.com/ (7 May 2023) http://www.bigemap.com/ (20 June 2025)
	Butcher’s and greengrocer’s (market)	1491
	Community service outlets	828
	Restaurant	5894

(2)

Road Data

Road network data was acquired from OpenStreetMap (http://www.openstreetmap.org), encompassing arterial roads, secondary roads, and minor roads within the main urban area. Within the ArcGIS platform (ArcGIS 10.8), the data underwent cleaning and preprocessing to remove fully enclosed motorways and urban expressways, retaining only walkable roads and crossing facilities. Intersections were segmented to construct a road network database suitable for walkability simulation.

(3)

Land Use and Community Data

The base geographic map for land use and community data utilises the Lanzhou vector map (AOI data), sourced from the Gansu Provincial Standard Map Service System (https://gansu.tianditu.gov.cn/bzdt/portal). This data has undergone spatial verification against OpenStreetMap (OSM) data to ensure spatial alignment of facilities.

(4)

Questionnaire Survey Data for the Youth Cohort

We employed a combined approach utilising an online questionnaire tool (https://www.wenjuan.com/) and random access to survey the four central urban districts of Lanzhou between June and July 2023. In accordance with the China Medium- and Long-Term Youth Development Plan (2016–2025) [44], individuals aged 14–35 were defined as ‘youth’ for this study. School-based students aged 14–18, subject to restrictions on mobile phone usage, were primarily surveyed through in-person visits. All respondents were informed of the voluntary and anonymous nature of the survey before completing the questionnaire. A total of 1650 valid samples were obtained, with respondents proportionally distributed across Chengguan District (37.2%), Qilihe District (24.8%), Anning District (27.9%), and Xigu District (10.1%), reflecting the demographic characteristics of the youth population in Lanzhou’s main urban areas. The questionnaire’s content covered shopping frequency, travel patterns, facility preferences, satisfaction ratings, and personal attributes (including gender, housing type, and income).

To further reflect the consumption and residential patterns of the youth demographic, this study first scraped shopping and dining review data from the Dianping platform (https://www.dianping.com/, collection date: 1 May 2023). This dataset includes geographical coordinates and review volume information, which serve to identify youth consumption hotspots and provide supplementary validation for questionnaire survey results. Secondly, by integrating youth apartment distribution data published on platforms such as 58.com (https://lz.zu.anjuke.com/, collected on 20 May 2023), we extracted the spatial locations of centralised apartments within Lanzhou’s central urban districts. This serves as an indicator of youth residential clustering, facilitating the examination of the alignment between facility layouts and youth living spaces.

(5)

Parameter Standards

Referencing the Technical Guidelines for Community Living Circle Planning (TD/T 1062-2021) and relevant literature [45,46], an adopted walking speed of 1.2 m/s was to reflect typical travel characteristics of the youth demographic. Regarding accessibility thresholds, a 5–30 min walking time based on the road network was employed as the analysis scope, integrating policy directives with the daily activity patterns of young people. Based on the modified road network dataset, cumulative network cost analysis in ArcGIS generates isochrones at 5 min intervals, fully accounting for path connectivity and real-world movement constraints. Compared to fixed-radius circular buffers, this method more accurately characterises walking accessibility within the built environment, serving to measure service accessibility levels across different scales.

Drawing upon travel survey data from Lanzhou’s youth cohort and comparative studies of medium-sized Chinese cities [47,48], the distance decay function’s impedance coefficient β best approximates actual walking behaviour for daily shopping scenarios within the range of 1.3–1.6. Balancing representativeness and behavioural explanatory power, this study adopts β = 1.5 to reflect a moderate attenuation effect of increasing walking distance on facility utilisation probability [49,50].

3.3. Methodology

(1)

Facility Coverage Model

Facility coverage measures the extent to which daily living and shopping facilities reach residents within urban communities, serving as a fundamental indicator of spatial distribution rationality and supply levels. Based on walking accessibility, four reachability thresholds are established at 5, 10, 15, and 30 min, respectively. ArcGIS network analysis simulates actual travel paths to generate service areas, which are then overlaid with residential land use to determine coverage levels across different zones.

$$F\left(t\right)={\displaystyle \frac{M_{ab}\left(t\right)}{R_a}}$$

(1)

$$M(t)=\mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a}\left(\bigcup _{j\in b} \left(S_{jb}\left(t\right)\cap R_a\right)\right)$$

(2)

where $F_{ab}\left(t\right)$ denotes the coverage of facility type $b$ within area $a$ at threshold $t$; $M_{ab}\left(t\right)$ represents the service coverage area of facility type $b$ within area $a$; $R_a$ signifies the total residential land area within area $a$; and $S_{jb}\left(t\right)$ indicates the walking service area of facility $j$ at threshold $t$.

(2)

Facility Richness Information Entropy Model

Facility richness serves as a crucial metric for assessing the diversity and spatial equilibrium of community retail amenities, directly reflecting their capacity to accommodate the multifaceted lifestyle demands of young residents. To characterise the complexity and distributional balance of facility structures across different areas, this study employs Shannon’s entropy model to quantitatively analyse the spatial distribution of various facility types within spatial units [51]. The study employs a 500 m × 500 m grid as the fundamental analytical unit to measure the typological proportion and spatial equilibrium of shopping facilities.

$$H_a=-\sum _{b=1}^n{p}_i{\mathit{ln}p}_i$$

(3)

$$H_a^{*}={\displaystyle \frac{H_a}{\mathit{ln}n}}\in \left[\mathrm{0,1}\right]$$

(4)

where $H\left(X\right)$ denotes the facility information entropy value for study area; $p_i={\displaystyle \raisebox{1ex}{$n_i$}\!\left/ \!\raisebox{-1ex}{$\sum n$}\right.}$ represents the proportion of the $i$-th facility category within that unit relative to the total number of facilities; $n$ denotes the total number of facility categories; and $H_a^{*}$ denotes the standardised entropy value.

(3)

Facility Sharedness Model

When assessing the equity of daily shopping facilities, focusing solely on facility quantity or coverage often fails to reveal actual usage disparities. This paper introduces the concept of facility sharedness to characterise the degree of mutual dependence among young people in different residential units on the same facility, thereby identifying genuine equity in facility utilisation. Based on individual travel behaviour and accessibility, the model first calculates individual access opportunities using a weighted gravity model, then evaluates facility sharedness based on these results.

Behavioural accessibility:

$$A_i=\sum _{j=1}^m{\displaystyle \frac{S_j\cdot W_j}{T_{ij}^{\beta }}}$$

(5)

where $S_j$ denotes the service capacity of facility $j$, $W_j$ represents the subjective preference weight for facility $j$, $T_{ij}$ signifies the shortest network distance from residential unit (youth hostel) $i$ to facility j, and $\beta$ is the distance decay coefficient ($\beta =1.5$) [52].

Based on this, the behavioural accessibility $A_{ij}$ of residential unit $i$ to facility $j$ is calculated and expressed as:

$$A_{ij}=W_j\cdot S_j\cdot c_{ij}^{-\beta }$$

(6)

Facility sharing rate:

Based on the reachability results, the sharing degree $K_j$ of facility $j$ is defined as:

$$K_j={\displaystyle \frac{\sum _{i=1}^n P_i\cdot A_{ij}}{\underset{i}{max} (P_i\cdot A_{ij})}}$$

(7)

where $P_i$ denotes the youth population size of unit $i$.

(4)

Facility Distribution Patterns Based on Standard Deviation Ellipses

The spatial distribution of retail facilities within urban areas directly influences their service coverage capacity and spatial equity performance. Standard deviation ellipses (SDEs) are employed to characterise the central tendency, directionality, and dispersion of facility point distributions, thereby revealing their overall spatial patterns within linear urban spaces. Centring is applied to each facility coordinate $(x_i,y_i)$ relative to the geometric centre $(\overline{x},\overline{y})$ of the facility point set:

$${\sigma }_x=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n(x_i-\overline{x}{)}^2}{n}}},\hspace{1em}{\sigma }_y=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n(y_i-\overline{y}{)}^2}{n}}}$$

(8)

$$\theta ={\displaystyle \frac{1}{2}}\mathit{arctan}\left({\displaystyle \frac{2\sum x_i^{\prime }{y}_i^{\prime }}{\sum (x_i^{\prime }{)}^2-\sum (y_i^{\prime }{)}^2}}\right)$$

(9)

$$a=\sqrt{2}\sqrt{{\displaystyle \frac{\sum (x_i^{\prime }\mathit{cos}\theta -y_i^{\prime }\mathit{sin}\theta {)}^2}{n}}},\hspace{1em}b=\sqrt{2}\sqrt{{\displaystyle \frac{\sum (x_i^{\prime }\mathit{sin}\theta +y_i^{\prime }\mathit{cos}\theta {)}^2}{n}}}$$

(10)

where $a$ and $b$ denote the major and minor semi-axes of the standard deviation ellipse, respectively, $\theta$ represents the azimuth angle, and $n$ denotes the total number of facilities.

(5)

Gini Coefficient

Let there be n social groups, with the average accessibility values to shopping facilities for each group denoted as $x_i$, and the overall average value as $\overline{x}$. The Gini coefficient $G$ can be expressed as [14]:

$$G={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^n|x_i-x_j|}{2n^2\overline{x}}}$$

(11)

where $x_i$ denotes the average accessibility of the $i$-th group, $\overline{x}$ represents the overall mean across all groups, and $n$ is the number of groups.

Furthermore, to further elucidate the extreme disparity in accessibility distribution, we employ the quantile ratio (Q-ratio) metric $Q=X_p/X_q$ to measure the comparative relationship between the highest and lowest accessibility groups, thereby revealing the extreme variation in accessibility distribution [53].

4. Results

4.1. Analysis of Heterogeneity in Shopping Preferences Among Young People

As the most dynamic and diverse segment within urban consumption structures, variations in the shopping behaviours of young people provide a direct reflection of community facilities’ performance in terms of fairness and suitability. Validity and reliability tests were conducted on the 1650 valid survey responses (Cronbach’s $\alpha =0.705$, $KMO=0.752$), revealing good consistency. Upon applying a preference-perception weighted gravity model, as illustrated in Figure 3, behavioural accessibility differences among youth groups for daily shopping facilities revealed the most pronounced variation by educational attainment. Doctoral students (0.533) and master’s students (0.519) exhibited significantly higher accessibility indices than undergraduates (0.460) and those with vocational qualifications or below (0.421), demonstrating the proximity characteristics of education-dominated spatial patterns. The 18–24 cohort demonstrates the highest behavioural accessibility index (0.522), significantly exceeding both the 14–17 group (0.423) and the 25–35 group (0.496), The 18–24 age group predominantly comprises university students and young professionals newly entering the workforce, exhibiting the strongest reliance on local community shopping facilities. Their activities are highly concentrated within campuses, rented accommodation, and areas with high concentrations of youth employment, making short-distance, convenience-driven consumption patterns particularly pronounced. In contrast, the 14–17 age group exhibits the lowest accessibility score (0.423), reflecting their limited independent mobility, greater reliance on family accompaniment for shopping, and restricted autonomy in consumption decisions. The accessibility level for the 25–35 age group (0.496) is slightly lower than that of the 18–24 age group. This aligns with their greater diversity of travel modes, broader activity spaces, increased cross-regional retail choices, and greater use of online delivery services. Concurrently, young professionals in administrative and public institutions demonstrated higher shopping consumption needs than freelancers (0.467), suggesting occupations with stable incomes exhibit a greater propensity for shopping. Notably, the income dimension exhibits an inverted U-shaped trend: accessibility peaks among those earning ¥5000–7000 (0.512), while lower-income (0.433) and higher-income groups (0.503) show comparatively lower scores. This finding indicates that young people in the middle-income bracket rely most heavily on community facilities for their daily shopping needs. In contrast, those at either end of the income spectrum may find their purchasing demands not fully met within the local neighbourhood due to insufficient purchasing power or consumption spillover effects.

Figure 3. Differential characteristics of facility preference among youth groups.

As shown in Table 2, we conducted further Spearman correlation analysis to examine the relationship between socio-economic characteristics of the youth cohort and their preferences for shopping facilities. Results indicate that gender (ρ = 0.021, p = 0.216) and educational attainment (ρ = 0.013, p = 0.033) exhibit weak correlations with shopping preferences, failing to reach statistical significance. However, females and those with higher educational levels demonstrate greater attention to community shopping facilities, whereas age (ρ = –0.005), occupation (ρ = –0.035), and income (ρ = –0.121) all exhibit negative correlations. In contrast, income demonstrated a moderately strong and statistically significant negative correlation with facility preferences (p = 0.005). According to Cohen’s (1988) effect size criteria [54], economic capacity emerges as a key determinant of young people’s reliance on shopping facilities. As income levels rise, young adults increasingly favour cross-district shopping or procuring goods via e-commerce and instant delivery platforms, thereby reducing their frequency of using physical commercial spaces within their local neighbourhoods. Concurrently, lower-income groups may also opt for online low-cost channels due to price sensitivity, further diminishing their attachment to local commercial facilities.

Table 2. Correlation Analysis Between Heterogeneous Characteristics of the Youth Population and Preferences for Lifestyle and Shopping Facilities.

Spearman	Gender	Age	Education	Occupation	Income
ρ	0.021	−0.005	0.013	−0.035	−0.121
p	0.216	0.133	0.033	0.006	0.005

Note: $\left|\rho \right|$ = 0.10–0.29 denotes a small effect, 0.30–0.49 denotes a moderate effect, and ≥0.50 denotes a large effect.

4.2. Spatial Configuration Analysis of Daily Shopping Facilities

4.2.1. Facility Coverage Analysis

As shown in Table 3, the spatial coverage of daily shopping facilities in Lanzhou’s main urban area displays a typical pattern of a dense core and a sparse periphery. Chengguan District, the city’s traditional commercial and administrative centre, shows the highest coverage levels. Within a five-minute walk, 71.2% of facilities are accessible, while the 30 min coverage rises to 92.6%, underscoring the pivotal role of this system in the city’s living circle. Anning District, benefitting from its concentration of educational and research institutions, has developed a relatively comprehensive network of living services. Accessibility within a fifteen-minute walking radius reaches 78.9%, increasing to 92.3% within thirty minutes, offering substantial convenience for young residents. In contrast, Qilihe District’s facilities cluster around central commercial and transportation nodes, such as Xizhan Shizi and Lanzhou Old Street, achieving 74.2% coverage within a fifteen-minute radius. Xigu District shows the most significant shortfall, with only 79.3% coverage within thirty minutes. Across the five- to fifteen-minute living range, only 55.6–66.8% of areas have access to basic shopping services, indicating a clear deficit zone. Notably, the twenty-minute accessibility threshold represents a transitional stage between the fifteen- and thirty-minute patterns. Overall, this spatial distribution closely reflects Lanzhou’s ribbon-like urban morphology shaped by river-valley topography. Commercial and everyday service facilities are densely concentrated along the central corridor, resulting in pronounced functional stacking. In contrast, peripheral areas beyond the core business districts exhibit evident trends of hollowing out. For young residents, these disparities in facility coverage directly influence daily living costs and opportunities for social participation, potentially intensifying inter-district mobility and spatial inequality.

Table 3. Coverage rate of daily shopping facilities.

Walking Distance	5 min	10 min	15 min	20 min	30 min
Cheng Guan	71.2%	75.7%	80.8%	85.6%	92.6%
Qi Li He	63.2%	69.6%	74.2%	78.6%	81.6%
An Ning	67.8%	73.3%	78.9%	84.6%	92.3%
Xi Gu	55.6%	63.3%	66.8%	72.3%	79.3%

4.2.2. Facility Richness Analysis

As illustrated in Figure 4, the distribution of daily shopping facilities within Lanzhou’s main urban area exhibits a typical Strip-like pattern characterised by high central density and low peripheral density. As a traditional old urban district, Chengguan District has formed multiple high-entropy cores (0.8–1.0) around Zhangye Road, Xiguan Crossroads, Linxia Road, Jiayuguan Road, and Gongxingdun. These areas feature a balanced distribution of various facilities, effectively supporting the diverse and multi-tiered needs of the youth demographic. Anning District, leveraging its concentration of higher education institutions, has developed a Strip-shaped zone of moderate-to-high values (0.6–0.8) around Anning East Road and Peili Square. Primarily featuring educational and training facilities, supplemented by shopping and dining options, it has established a composite’ education + lifestyle’ service circle that effectively aligns with the learning and living requirements of the youth demographic.

Figure 4. Abundance of Daily Shopping Facilities: (a) Kernel density of convenience stores and supermarkets (unit: facilities/km²); (b) Kernel density of fresh markets (unit: facilities/km²); (c) Kernel density of community service outlets and restaurants (unit: facilities/km²); (d) Composite richness index of lifestyle and shopping facilities (dimensionless).

In contrast, Qilihe District, despite forming localised high-entropy zones (>0.85) around the core commercial districts of Dunhuang Road and Jianlan Road, exhibits an overall level generally below 0.5. This indicates that outside these core commercial areas, facility types are limited, with excessive reliance on shopping and leisure facilities. Educational and medical resources are notably insufficient, reflecting an overly commercialised structural characteristic. Xigu District, significantly impacted by its high proportion of industrial land and large factory zones, exhibits entropy values below 0.3 across most areas. While shopping facilities account for over 35% of the area, cultural, educational, and healthcare functions are markedly deficient. These functions are only concentrated in scattered clusters within a few commercial districts such as Xigucheng, Fuli Road, and Sijiqing Street, presenting a pattern of point-like concentration.

From the spatial distribution of different facilities, convenience stores and supermarkets form high-density clusters in core commercial districts (Figure 4a), primarily concentrated along Zhangye Road, Linxia Road, and Gongxingdun in Chengguan District, as well as Dunhuang Road, Jianlan Road, and the Xigu commercial district in Qilihe. These areas constitute Lanzhou’s core commercial hubs. Meat and vegetable markets exhibit a more balanced distribution (Figure 4b). As government-led essential public facilities, they prioritise coverage over concentration to ensure universal access for residents. Community service outlets and catering facilities rely on support from universities and residential areas (Figure 4c). They form high-frequency consumption clusters centred on catering and convenience stores near universities, while adopting an embedded layout within residential zones, sustained by population density and daily living needs. As illustrated in Figure 4d, Lanzhou’s daily shopping facilities exhibit a spatially contradictory pattern of central overconcentration and peripheral insufficiency. While the city centre boasts a diverse range of facility types, it suffers from functional redundancy, whereas peripheral areas display service gaps and structural discontinuities. This spatial differentiation not only exacerbates consumption inequality among young people but also diminishes their sense of community belonging.

4.2.3. Facility Sharing Analysis

Table 4 reveals significant spatial disparities in the accessibility of daily shopping facilities within Lanzhou’s central urban districts, directly impacting the convenience of daily life for young residents. Firstly, Anning District and Chengguan District exhibit the highest levels of accessibility, with 31.6% and 27.3% of their areas, respectively, achieving high accessibility ratings. The Anning District, which is home to a concentration of higher education institutions, has a high youth population density. Daily consumption routes overlap significantly, with facilities experiencing high usage frequency. This demonstrates the youth demographic’s firm reliance on high-frequency essential facilities such as educational institutions, dining, and shopping. Chengguan District, relying on mature old urban areas and commercial districts such as Zhangye Road, Xiguan Crossroads, and the Yantan area, has formed a spatial pattern where commercial facilities are closely coupled with residential areas. This supports the diverse daily needs of the youth population. However, it is worth noting that Chengguan District also exhibits a relatively high proportion of low-sharing areas (27.4%). This reflects a contradiction where, despite the abundance of facilities in core commercial districts, complex consumption tiers and dispersed usage by non-local groups result in insufficient actual loyalty among the local youth population.

Table 4. Coverage rate of daily shopping facilities.

Sharing Degree	Highest	High	Normal	Lower	Lowest
Cheng Guan	27.3%	9.6%	12.8%	22.9%	27.4%
Qi Li He	19.8%	16.6%	10.9%	8.8%	43.9%
An Ning	31.6%	10.9%	18.9%	10.2%	28.4%
Xi Gu	21.6%	21.8%	7.7%	7.2%	41.7%

In contrast, Qilihe District and Xigu District show markedly inadequate support for the youth demographic. Qilihe District features only 19.8% high-accessibility zones, while low-accessibility zones account for 43.9%. This indicates excessive commercial concentration in a few core commercial districts, leaving peripheral youth populations unable to meet daily needs locally and necessitating cross-district consumption. Xigu District presents an even more pronounced situation, with high-accessibility zones comprising just 21.6% and low-accessibility zones reaching 41.7%. This correlates with Xigu District’s extensive industrial zones and factory areas, where dispersed youth residences and restricted living radii concentrate shopping facilities primarily in limited districts, such as Sijiqing and Fuli Road. Inadequate accessibility to educational, healthcare, and cultural facilities further intensifies their cross-district dependency and alienation from local communities.

4.3. Spatial Differentiation Characteristics of Public Service Facilities

As illustrated in Figure 5, the five categories of public service facilities within Lanzhou’s main urban area are predominantly distributed along the Strip axis of the Yellow River valley, exhibiting a typical ribbon-like urban pattern. Retail and daily shopping facilities display the smallest elliptical area (81.87 km²) yet the highest eccentricity (9414.2 m), demonstrating a high degree of dependence on the Strip axis of the Yellow River valley. It reflects a concentrated distribution with limited spatial extension. The elliptical centres of all five facility types exhibit significant spatial overlap, indicating pronounced functional coupling in their spatial layout. This is particularly evident at the tri-district junction of Chengguan, Qilihe, and Anning, where resources spanning education and training, daily shopping, cultural entertainment, leisure and wellness, and healthcare converge. This area has emerged as a ‘facility gravitational core’ for youth activities. While this concentration fulfils the diverse consumption and service demands of young people in the central urban area, it simultaneously leaves peripheral zones chronically underserved. Consequently, young people experience marked disparities in the convenience of consumption, learning, and daily living. Youth in the core areas of Chengguan and Anning enjoy readily accessible services. In contrast, those in Qilihe and Xigu must travel across districts, significantly increasing the spatio-temporal costs of their daily consumption.

Figure 5. Standard Deviation Elliptical Distribution Characteristics of Public Service Facilities in Lanzhou’s Main Urban Area.

Further comparison of different facility types reveals distinct spatial variations. Educational and training facilities exhibit the largest elliptical coverage area (118.59 km²), demonstrating the broadest reach and moderate skewness. It confers strong cross-district spillover effects, benefiting a broader youth demographic. Leisure and wellness facilities (94.9 km²) and healthcare facilities (89.93 km²) also display considerable spatial extension. Though concentrated along the central axis, they partially alleviate service shortages in peripheral areas. Cultural and entertainment facilities (86.63 km²) approach shopping facilities in scale but exhibit greater dispersion, with some peripheral nodes still providing supply. In contrast, daily shopping facilities are the most concentrated and least extensive across all five categories, revealing the most pronounced spatial shortcomings. Their highly centralised clustering and severe peripheral absence further amplify spatio-temporal barriers for young people in daily consumption, highlighting the inadequate resource diffusion capacity inherent in a ribbon-like urban structure. Consequently, the spatial shortcomings of daily shopping facilities underscore the inadequate resource diffusion capacity within a ribbon-like urban structure. Unlike the expansive layout of educational and recreational facilities, shopping facilities appear more compressed and embedded, struggling to meet the immediate needs of peripheral youth populations. Future optimisation of urban public services should adopt an extensional layout and multi-node distribution mechanism, driving a shift from concentrated quantity to equitable structural provision of facilities. This approach will enhance the daily convenience experienced by young people.

4.4. Assessment of Spatial Equity in Daily Shopping Facilities

The Lorenz curve calculated based on accessibility to daily shopping facilities (Figure 6) indicates that the main urban area of Lanzhou exhibits an average Gini coefficient of 0.272 and a composite Q-ratio of 1.31, suggesting that young people generally experience slight disparity in accessing daily shopping facilities and enjoy a high level of fairness [55]. However, significant differences persist between different districts. Further analysis by district reveals that Chengguan District exhibits a Gini coefficient of 0.2 and a Q-ratio of 2.85, indicating a relatively balanced spatial distribution of shopping facilities, enabling young people to access diverse services conveniently. The Anning District (Gini = 0.241) demonstrates the second-highest overall equity level, characterised by intense concentration and sharing of resources due to its proximity to universities and youth communities. Nevertheless, service accessibility remains inadequate in peripheral areas. In contrast, Qilihe and Xigu Districts exhibit markedly lower equity levels, with Gini coefficients of 0.316 and 0.332, respectively, and Q-ratios as high as 4.99 and 5.79. This indicates a pronounced point-like concentration of shopping facilities within these districts, resulting in a high dependency among young people on core commercial areas. Consequently, youth in peripheral areas bear higher time and economic costs.

Figure 6. Lorenz Curve of Equity in Public Service Facilities within Lanzhou’s Main Urban Area.

5. Discussion

This paper examines the spatial equity of access to daily shopping facilities for young people in Lanzhou, a typical strip city in China. Focusing on coverage, diversity, and sharing as three dimensions, it reveals the spatial equity characteristics of young people’s access to daily shopping facilities. Findings indicate that spatial structural patterns and group attribute differences jointly shape disparities in young people’s opportunities to access everyday consumption spaces. Lanzhou exhibits a prototypical ‘core-cluster-periphery-sparsity’ pattern in its daily shopping facilities, closely mirroring the strip city form constrained by its river valley topography. Chengguan and Anning districts form highly accessible cores due to the convergence of commercial functions, educational resources, and public services. Conversely, Qilihe and Xigu districts persistently suffer from excessive concentration of facility supply coupled with inadequate service provision, creating distinct ‘living-circle depressions’. This pattern confirms the spatial mechanism whereby strip cities concentrate resources along transport corridors while structurally neglecting peripheral areas [56,57]. For young people, this translates into markedly unequal access to daily shopping facilities across districts. Youth in peripheral zones face higher time costs and lower service quality, thereby diminishing their opportunities for social participation within local living spheres.

Beyond the aforementioned experiential patterns, the underlying mechanisms of spatial inequality within strip-city structures warrant further scrutiny. Constrained by mountainous river valleys, Lanzhou’s linear urban form concentrates commercial, educational and transport resources along its principal axis, while lateral connectivity remains generally inadequate. This diminishes the substitutability of neighbourhood shopping facilities and exacerbates distance friction effects. This morphological constraint further exacerbates disparities between central and peripheral public facilities, translating into asymmetric accessibility disadvantages for young people accessing everyday shopping amenities. This is particularly pronounced for time-sensitive youth groups with limited cross-district mobility. Beyond objective spatial patterns, facility accessibility is also closely linked to young people’s psychological and social perceptions. Perceived equity, subjective convenience, and community satisfaction collectively shape young people’s perceptions of their capacity to participate in urban daily life. While this study does not incorporate subjective perceptions into equity metrics, future research integrating perceived equity and well-being indicators with objective accessibility measures could contribute to developing a more comprehensive and explanatory framework for assessing youth urban spatial justice.

Concurrently, young people’s preferences for shopping facilities are influenced not only by travel distance but also exhibit distinct social attributes. Young adults with moderate incomes and stable occupations rely more heavily on neighbourhood shopping facilities for daily consumption, whereas high-income youth demonstrate a stronger propensity for online consumption. This finding aligns with Song et al. (2024) findings on Nanjing [58] and further corroborates Soja’s (2009) perspective that spatial justice is jointly influenced by institutional arrangements and group capability disparities [8]. Notably, the Gini coefficient measured in this study (0.272) indicates that young people’s flexible travel modes, diverse travel behaviours, and the increasing prevalence of e-commerce channels have mitigated spatial inequalities arising from topography and facility concentration to some extent. However, this buffering effect cannot replace structural supply improvements, particularly in underserved areas like Xigu and Qilihe, where spatial inequity risks remain pronounced.

Therefore, establishing youth-friendly living circles within a strip city context requires synergistic advancement in both spatial supply optimisation and behavioural demand alignment. Optimising commercial function layouts should promote decentralised distribution of commercial and service facilities, particularly in relatively underserved areas like Xigu and Qilihe. Community-level mixed-use renewal can establish grassroots service nodes that support daily consumption, thereby shortening the living radius of young people. Secondly, incorporating youth shopping sensitivity indicators into the 15 min neighbourhood assessment framework is essential. This approach should extend beyond mere facility counts to evaluate affordability, accessibility, and social inclusivity, thereby shifting from ‘equity in allocation’ to ‘equity in usage’ centred on perceived experience. Concurrently, establishing continuous pedestrian corridors, greenway networks, and micro-circulation public transport feeder systems will enhance local mobility efficiency and reduce daily travel costs for young people. Future efforts should establish a monitoring system for dynamic accessibility within living circles. By integrating Point of Interest (POI) data, travel trajectory data, and community consumption feedback, this system would continuously track changes in the equity of consumption spaces, enabling timely adjustments to policy and planning interventions.

Although this study has characterised the spatial equity of shopping facility accessibility for young people from multiple dimensions, certain limitations remain. Firstly, research data primarily derives from static POI information and questionnaire surveys. While these reflect the spatial distribution patterns of daily shopping facilities and young people’s preferences for such amenities, they struggle to capture the dynamic mobility characteristics and behavioural trends of young people in their daily lives. Secondly, this study does not account for the dynamic evolution of supply-demand relationships, which are influenced by factors such as weekday versus weekend patterns, seasonal variations, cross-district travel, and online consumption. It may underestimate temporal variations in the accessibility of young people. Future research should integrate mobile phone signalling data, location-based service (LBS) trajectories, and social media check-in data with GIS-based spatio-temporal accessibility models. This approach would enable more precise capture of young people’s facility usage behaviours and accessibility fluctuations across varying contexts and temporal scales. Furthermore, comparative studies examining distinct urban spatial characteristics help validate the transferability and applicability of the analytical framework proposed in this study.

6. Conclusions

This paper examines the spatial equity of young people’s access to daily shopping facilities in Lanzhou, a typical strip city, by constructing an evaluation framework based on three dimensions: coverage, diversity, and accessibility. Building upon the mechanisms discussed earlier, the conclusion further synthesises how Lanzhou’s strip-city morphology and youth-specific behavioural constraints jointly shape the observed accessibility disparities. Results indicate that shopping facilities in Lanzhou exhibit a typical spatial pattern of central concentration and peripheral scarcity. Chengguan District and Anning District form highly accessible cores through the multifunctional integration of commercial districts and university resources. Conversely, Qilihe District and Xigu District suffer from inadequate facilities and spatial fragmentation, particularly weakening consumption convenience and community belonging among peripheral youth groups. Equity measurements using Lorenz curves and Gini coefficients indicate that Lanzhou’s overall public facility equity is relatively high (Gini = 0.272), yet significant inter-district disparities persist. It further confirms the spatial dilemma of asymmetry between facility provision and actual access. Consequently, future urban governance should move beyond merely equitable distribution to a demand-responsive equity model centred on user experience and group perception. In planning practice, continuously extend daily shopping facilities to peripheral neighbourhood clusters. Cultivate grassroots service nodes through community-level mixed-use regeneration to reduce the daily travel costs of young people. Simultaneously, incorporate youth sensitivity indicators into the 15 min neighbourhood assessment framework. Enhance internal accessibility and connectivity within these neighbourhoods through pedestrian corridors and micro-circulation public transport systems, thereby fostering youth-friendly living environments within linear urban structures. Overall, the findings not only address local disparities in Lanzhou but also offer broader governance insights for other cities with linear morphologies or fragmented accessibility structures, highlighting the need for governance models that integrate spatial supply optimisation and user-centred experiential equity.