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Article

Age-Friendly Public-Space Retrofit in Peri-Urban Villages Using Space Syntax and Exploratory Factor Analysis

1
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2
School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
3
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(13), 2219; https://doi.org/10.3390/buildings15132219
Submission received: 22 May 2025 / Revised: 20 June 2025 / Accepted: 23 June 2025 / Published: 24 June 2025
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

Abstract

Population ageing is revealing acute mismatches between inherited village layouts and older residents’ everyday needs in China’s peri-urban fringe. This study combines space-syntax diagnostics with an exploratory factor analysis to create a building-oriented retrofit workflow. Using Liulin Village, Beijing, as a test bed, axial-line modelling pinpoints the low-integration alleys and mono-functional retail strips, while elder-user surveys distil four latent demand factors, led by personal convenience. Overlaying these two layers highlights the “high-demand/low-fit” segments for intervention. Prefabricated 3 m × 6 m health kiosks, sunrooms and rest pergolas—constructed from light-gauge steel frames and assembled with dry joints—are then inserted along a newly permeated corridor–core walking loop. The modules follow a 600 mm dimensional grid and can be installed or removed within a single working day, cutting the on-site labour by roughly one-third relative to that required for conventional masonry kiosks and enabling their future relocation or reuse. The workflow shows how small-scale, low-carbon building interventions can simultaneously improve accessibility, social interaction and functional diversity, providing a transferable template for ageing-responsive public-space retrofits in rapidly transforming village contexts.

1. Introduction

China’s rapid socio-economic growth is accompanied by an equally rapid—and spatially uneven—demographic shift toward old age. According to the Seventh National Population Census (2020), older adults (≥60 years) already account for 23.81% of the rural population and 15.82% of the urban population; the rural–urban gap widened from 1.2 percentage points in 2000 to 8.0 points in 2020 [1]. This divergence is expected to grow, deepening the disparities in resources, services and infrastructure and putting disproportionate pressure on the public-space provisions in peri-urban villages [2]. Many such villages now face hollowing-out, out-migration and functional imbalance, all of which erode the everyday living environment—particularly for older residents.
In the World Health Organization’s Age-Friendly Cities and Communities (AFCC) framework, accessible, safe and inclusive public space is a core condition for active ageing [3]. The international scholarship has accordingly moved from macro-level indicator systems [4] to micro-scale walkability audits [5], co-creation design protocols [6] and participatory “photo-voice” methods [7], calling for evidence-based, replicable design guidelines [8]. In the high-density East Asian setting, limited open space and rapidly shifting family structures subject older adults to “hidden exclusion” [9] and intensify the need to retrofit transit nodes and large public buildings [10].
Here, age-friendly public space is defined as a network of places that enable older people to participate in public life safely, decently and proactively through barrier-free access, convenient amenities, comfortable environments and socio-psychological support. The aim is not merely to remove physical barriers but also to satisfy social, recreational and empowerment needs.
The Chinese research initially concentrated on inclusive design in urban parks and squares [11]. More recently, the attention has shifted to urban fringe villages, hybrid settlement units located at the urban–rural interface and characterised by fluid land use, population mixes and functional layouts [12]. These villages absorb urban spill-over while retaining rural social networks, creating highly complex and uncertain demand–supply patterns in public space.
At the village scale, Su and Zhang proposed square retrofits based on behavioural observations [13]; Wang explored the age-friendliness of village lanes using a need–ability model [14]; and Liu and Wu designed a multi-tier open-space network for the “younger-old” [15]. Yet most studies have been single-case and qualitative, lacking systematic spatial diagnostics or cross-case comparisons.
Space syntax quantifies configurational properties such as integration and intelligibility to explain pedestrian movement [16]; its strong correlation with footfall has been confirmed through meta-analyses [17] and applied to rural layout optimisations [18], tourist village morphologies [19] and historic settlement connectivity [20]. Exploratory factor analysis (EFA) extracts the latent demand structures from questionnaire data and has been used for village landscape appraisals [21], tourism-oriented planning [22], green infrastructure evaluations [23] and public service audits [24]. However, very few studies have combined space syntax with an EFA to expose mismatches between older adults’ needs and spatial form—particularly in peri-urban contexts [25,26,27].
As a result, three research gaps have emerged: gap 1 is the limited empirical evidence on the ageability of villages on the urban fringe; gap 2 is the lack of an integrated Spatial Syntax + Education for All (EFA) methodology linking the needs of older people to spatial form; and gap 3 is the lack of a complete workflow from the quantitative diagnosis to the design strategy.
To address these gaps, we propose an integrated analytical framework that couples space-syntax metrics (global integration and choice) with an exploratory factor analysis (EFA) of residents’ perceptions. Space syntax provides the objective, network-scale measures that past single-case studies have lacked (gap 1), while the EFA converts subjective survey items into statistically validated latent constructs, overcoming the descriptive nature of earlier qualitative work (gap 2). By applying both tools to the same neighbourhood, we establish a data-driven link between the spatial configuration and perceived age-friendliness, thereby filling the empirical–conceptual disconnect identified in gap 3.

2. Materials and Methods

2.1. The Integrated Analytical Framework

2.1.1. The Space-Syntax Module

Space syntax models streets (or axial lines) as nodes in a network and employs indices such as the integration and intelligibility to expose the relationship between the spatial layout and pedestrian movement. This technique has been widely applied to traditional village conservation [28], land-use changes at the urban fringe [29], facility accessibility optimisation [30], narrative sequencing in classical gardens [31] and inter-generational co-living communities [32]. A recent meta-analysis confirmed the strong, positive link between integration and footfall [33]. Because peri-urban villages retain rural morphologies while absorbing urban functions, we built an axial-line model of Liulin in DepthmapX (Version: 0.8.0; Developer: UCL Bartlett School of Architecture; London, UK) and calculated the global integration (Rn), local integration (R3) and intelligibility to identify latent topological bottlenecks.
We carried out an Angular Segment Analysis in DepthmapX using the following settings: (i) an axial-line map cleaned with a 10 m minimum-length filter and a snapped tolerance of 5 m; (ii) a grid (analysis) resolution = 1 m; (iii) a two-directional (both) analysis; (iv) three radii: Rn (global), R3 (three-step topological) and R5 (five-step topological); (v) angular weighting set to the default (1/angle); and (vi) the outputs exported as the global and local integration and choice values for each segment.

2.1.2. The Exploratory Factor Analysis Module

Exploratory factor analysis (EFA) reduces a correlation matrix into latent constructs and is routinely used for public service evaluations [34], age-friendly city metrics [35] and railway station retrofitting. We administered a 26-item, five-point Likert questionnaire to residents aged ≥ 60, probing accessibility, amenity convenience, social support and environmental comfort. Sampling adequacy was confirmed (KMO = 0.802 > 0.7; Bartlett’s test p < 0.001). Principal component extraction (eigenvalue > 1) with varimax rotation produced n core factors; factor loadings ≥ 0.50 were retained. The factor weights indicate the relative priority of each elder need dimension.

2.1.3. Method Fusion and Technical Roadmap

This study combines a space-syntax analysis with an exploratory factor analysis to develop an integrated diagnostic approach to age-friendly optimisation of the public space in peri-urban villages. The technical roadmap (Figure 1) simultaneously quantifies the spatial relationships and extracts multidimensional influence factors, enabling us to evaluate how well existing spaces accommodate older adults’ activities and where they fall short. Specifically, we use space-syntax metrics to measure the public-space topology and identify structural deficiencies; we then apply the EFA to isolating age-relevant demand factors and ranking their relative weights; finally, we cross-reference the two sets of results to generate precise, evidence-based design strategies.

2.2. The Regional Context and Case Representativeness

2.2.1. Site Selection

This study adopts Liulin Village (116°13′–116°16′ E, 40°07′–40°09′ N) in Sujiatuo Town, Haidian District, Beijing, as its case site (Figure 2). Situated just northwest of the Sixth Ring Road, Liulin occupies a well-connected location and epitomises the urban–rural fringe, where rural morphology and emerging urban functions intersect. While ongoing urbanisation has helped the village attract migrants and spur local economic growth, rapid population ageing has created acute pressure for age-friendly upgrades to its public-space network [36]. Liulin is therefore both a representative and a policy-relevant test bed. Although recent investment has improved its basic infrastructure, significant gaps remain—particularly in universal design and functional programming aimed at older residents.

2.2.2. Spatial Typology

For a finer-grained assessment of Liulin’s age-friendliness, the village’s public realm was disaggregated into four categories (Table 1): (1) Street–Alley Space, (2) Plaza Space, (3) Green Space and (4) Service-Facility Space. The typology follows the path–node–domain hierarchy widely employed in space-syntax studies [37] and reflects the mixed living–production–leisure functions typical of peri-urban villages [38].

2.2.3. Community Co-Design

To extend the participation beyond data collection, this project adopts a three-step pathway that keeps older residents involved throughout the design process:
(1) The concept workshop (Feb 2025; ~20 residents, aged 60–78)
Participants rearranged the modular layouts on scale models and ranked their fixture priorities (shade, seating, chess tables, etc.).
(2) The resident review group (five members: four elders + the village deputy head)
The group met monthly to examine the design iterations, record their usability observations and recommend maintenance actions once the retrofit is in place.
This pathway ensured that older residents influenced the schematic design, prototype refinement and subsequent monitoring, embedding genuine community input into the entire retrofit cycle.

2.3. The Exploratory Factor Analysis: Quantifying the Age-Friendly Demand

2.3.1. Data Collection

A mixed-methods dataset was compiled for Liulin Village’s older residents through a structured questionnaire and on-site walk-throughs. The survey focused on the satisfaction with, and the demand for, public-space attributes and used a five-point Likert scale to obtain interval-level scores, ensuring objectivity and statistical reliability [39,40]. Only permanent residents aged sixty or above were sampled to keep the analysis tightly aligned with the target group. Ninety questionnaires were distributed, and eighty were returned validly, yielding a response rate of 88.9%. To deepen the inquiry, twelve additional elders were interviewed informally, adding qualitative nuance to specific place-based concerns [41,42].
Table 2 below shows the six core items with the highest loading coefficients (>0.60) among the three factors identified through the exploratory factor analysis (EFA).
After data cleaning, the eighty valid responses were subjected to the exploratory factor analysis in SPSS (Version: 26; Manufacturer: IBM Corp.; Armonk, NY, USA). The procedure distilled the elders’ priorities into a coherent set of latent factors—neighbourly harmony, the diversity of social venues, feelings of safety and belonging, barrier-free circulation, transit convenience, protective infrastructure, solar exposure and shade, greenery, exercise facilities and cultural activity space (Figure 3 and Figure 4). These weighted factors provide a quantitative foundation for age-friendly design while foregrounding the distinctive social and affective needs of Liulin’s senior population.

2.3.2. The KMO Test and Principal Component Extraction

The exploratory factor analysis (EFA) condenses a large set of observed variables into a smaller number of common factors by exploiting their underlying correlation structure.
Before performing the EFA [43], we assessed the sampling adequacy using the Kaiser–Meyer–Olkin (KMO) index and verified the overall correlation with Bartlett’s test of sphericity. A KMO value closer to 1 indicates that the partial correlations are low and that a factor analysis is appropriate, whereas values below 0.60 suggest inadequacy. Bartlett’s χ2 statistic tests whether the correlation matrix deviates significantly from an identity matrix; p < 0.05 confirms sufficient inter-item correlation.
Factors were extracted using a principal component analysis (PCA), retaining only components whose eigenvalues exceeded 1. Varimax rotation produced a clearer factor-loading matrix, making it easier to interpret how each observed variable contributed to the latent constructs that shaped the older residents’ public-space needs.
The generic factor model is expressed as
X i = j = 1 m λ i j F j + ε i   i = ( 1 , 2 , , n )
X i is the i th observed variable in the survey—for example, a specific spatial attribute or design indicator; F j is the j th latent (common) factor extracted by the analysis, such as a “spatial-quality factor” or an “age-friendliness factor”; λ i j is the loading that expresses the contribution of the factor F j to the variable X i ; and ε i is the unique error term that captures the portion of X i not explained by the common factors.
The cumulative variance contribution of the retained factors is calculated as
CV = k = 1 m λ k i = 1 n λ i ×   100 %
where λ k is the eigenvalue of the k th factor, and i = 1 n λ i is the sum of all eigenvalues.

2.4. A Space-Syntax Evaluation of the Public-Space Configuration

2.4.1. The Integration Analysis

In space syntax, integration measures the degree to which a spatial unit is connected to the entire network; higher values usually signal better accessibility and stronger attraction. The global integration map for Liulin (Figure 5) identifies West Main Street and the riverfront promenade as the most integrated axes, corroborating field observations that both corridors serve as major conduits for daily movement and leisure. By contrast, the village committee square and the north zone supermarket forecourt exhibit low integration, which limits their potential to function as core activity hubs. Interviews revealed that gatherings in the committee square are scattered, and while the supermarket node draws foot traffic, poor street connectivity still hampers its overall accessibility. Similar patterns—of commercial vitality being positively correlated with integration—have been documented elsewhere [44], whereas low-integration nodes often require micro-circulation upgrades or façade interventions to boost their permeability [45,46].

2.4.2. The Intelligibility Analysis

Intelligibility—assessed by regressing global integration (Rn) against local integration (R3)—yields R2 = 0.633, indicating only modest coherence between local and global spatial cues (Figure 6). This suggests that Liulin’s street system remains car-oriented and that the pedestrian logic is weak. Many alleys are poorly connected, especially near the north gate and around the committee square, making it difficult for these segments to integrate into the village-wide network. The survey data confirm that older adults and other mobility-impaired groups rarely use these paths [47,48], while vehicular movement concentrates on the main spine and the public car park loop. By comparison, the regression between R5 and Rn delivers R2 = 0.853, implying that the existing grid is well suited to motor traffic [49] but lacks the continuity and legibility required for a walkable, age-friendly environment.
In terms of the street-level drivers of the modest intelligibility, line-by-line inspection of the axial graph reveals four configurational obstacles that decrease the R2 value. (1) Long blocks force pedestrians to detour and increase the topological depth. (2) Cul-de-sacs interrupt through-movement. (3) Abrupt width constrictions narrow sight-lines and discourage a continuous walking flow. (4) There are missing mid-block links: only two formal cut-throughs connect the east–west alleys to the main north–south spine. Collectively, these features decouple local visibility from global choice, explaining Liulin’s modest intelligibility (R2 = 0.633) and informing the retrofit priorities discussed in Section 4.

3. Results

3.1. The Prioritisation Conflicts Revealed by the Factor Weights

The exploratory factor analysis further distilled the questionnaire data into the key behavioural demand factors for the older residents (Table 3) and verified the data’s adequacy: the KMO value was 0.877, and Bartlett’s test was highly significant (p = 0.000), indicating strong inter-item correlation and suitability for factor extraction (Table 4).
According to the total variance explanation (Table 5), four principal factors were retained, together accounting for 82.653% of the variance in the elders’ demand. Detailed examination shows that the first is the personal convenience factor—Barrier-Free Access, Transport Accessibility, and Safety Facilities—reflecting seniors’ heavy reliance on basic infrastructure; the second is the socialisation and belonging factor—Harmonious Neighbourhood Relations, Variety of Social Spaces, and Sense of Safety and Belonging—highlighting the role of public space in social interactions and psychological support; the third is the environmental and landscape factor—Green Spaces and Planting Landscape, as well as Sunlight Requirements and Shading Design—indicating the strong pull of a pleasant natural setting; and the fourth is the activity function factor—Sports and Fitness Facilities and Cultural Activity Space—showing the elders’ emphasis on a diverse array of activity facilities.
Overall (Table 6), these four factors exhibit a clear hierarchy of importance. The personal convenience factor carries the greatest weight, indicating that older residents are most concerned with accessibility and safety. Next comes the socialisation and belonging factor, confirming that opportunities for interaction and psychological support are also critical. The environmental and landscape factor and the activity function factor rank lower, though they still contribute to the overall satisfaction. Accordingly, age-friendly retrofits should first address convenience and social space needs and then progressively enhance the environmental quality and activity programming.

3.2. Space–Behaviour Mismatches

The space-syntax metrics combined with the field observations reveal pronounced spatial–behaviour mismatches in Liulin Village. To convey these misalignments succinctly, Table 7 lists the three most pronounced cases where the spatial integration values diverge from the on-site elder activity or feedback. Although some highly integrated areas offer strong accessibility and connectivity, their functions have become overly singular. The supermarket belt along the main spine, for example, occupies space that could serve for rest and social interaction, leaving elders’ everyday needs unmet despite the favourable topology.
Conversely, areas with low connectivity pose serious accessibility problems and force older residents to curtail their activities. Several side alleys lack effective links to other zones and are visually enclosed, making elders feel unsafe and discouraging their prolonged use or socialising. This exacerbates spatial isolation and psychological alienation.
In short, Liulin’s present layout fails to accommodate the elders’ needs, particularly in terms of its connectivity and functional diversity. The observed mismatches underscore structural shortcomings that must be addressed by reconfiguring links and enriching functions to create a genuinely age-friendly public-space network.

4. Discussion

4.1. Topological Restructuring Strategies

4.1.1. Multilevel Network Permeation

The multilevel permeation strategy raises the integration values of low-connectivity areas through micro-scale interventions, thereby improving their overall accessibility. In Liulin’s side alleys, poor connectivity restricts elders’ activity. Introducing short axial links to break visual and physical dead ends could significantly enhance reachability. These measures include inserting small connector paths or pedestrian walkways between alleys, shortening the travel distances between elderly activity nodes and ensuring that new links are fully barrier-free.
The post-plan analysis shows an across-the-board rise in integration (Figure 7), with the main street and the secondary lanes improving most. The committee square now anchors a larger catchment, while the redesigned southern main street and the northern gateway exhibit higher centrality. The integration around the public car park has also increased, confirming the effectiveness of tying this node more tightly into the street grid (Figure 8).
To enable a direct comparison between the pre- and post-intervention conditions, we applied the same spatial-syntax metrics (global integration and selectivity) to both time points: the current spatial integration analysis (Figure 5) and the planned spatial integration analysis (Figure 8). Concretely, the plan inserts four new pedestrian connectors with a total length of ≈180 m—about 7% of Liulin’s 2.5 km local street network—and adds roughly 420 m2 of paved surface (≈3% of the existing public-space area). The output results are presented to allow readers to see how the intervention enhances the overall integration values.
Strengthening the internal pedestrian links—especially between the main spine and the side lanes—significantly boosts the intelligibility (Figure 9): the R2 value for the global versus local integration (R3) climbs to 0.701, more than a 10% gain, and the R5–Rn fit reaches 0.815. Although still lower than the vehicle-oriented baseline, the walk-network logic is markedly clearer, signalling a shift toward pedestrian priority and improved age-friendliness.
In sum, Liulin’s pre-retrofit layout offered low intelligibility and fell short of older residents’ requirements for convenience, legibility and perceived security. The optimised scheme, by contrast, delivers a coherent, multi-scalar pedestrian hierarchy that privileges slow movement, shortens the trip chains and opens latent social micro-spaces along the way. These physical upgrades constitute the spatial bedrock upon which subsequent functional and programmatic layers of an age-friendly, sustainability-oriented public realm can be confidently assembled.

4.1.2. Corridor + Core Reinforcement

The “corridor-and-core” strategy centres on creating an elder-priority slow-movement network that stitches together scattered public-activity nodes into a safe, efficient pathway system. Because extended walking can be taxing for older adults, these gentle-pace corridors improve the mobility while offering ample rest areas and opportunities for social interaction (Figure 10). In Liulin, key nodes—the village committee, the central plaza and the senior-activity centre—are linked by such slow-walk corridors, which are lined with benches, shade structures and other comfort features so that elders can move in a pleasant micro-climate. All routes are designed with low gradients and full barrier-free specifications to meet seniors’ mobility needs.
Scaling the corridor system faces three issues: (i) the mixed land tenure—memoranda with frontage tenants secure the rest node sites; (ii) upkeep—with 3% of kiosks requiring funds for cleaning and lamp replacements; and (iii) governance—a resident committee receives a one-page inspection checklist and a small annual grant to oversee maintenance.
To turn the “Corridor + Core” concept into an actionable design, four repeatable modules are proposed (as summarised in Table 8):
(1) Rest nodes (every 80 m): L-shaped bench clusters with armrests, 1.5 m wheelchair clearance, a low drinking fountain and solar-powered USB outlets;
(2) Social pockets (≈60 m2 in a widened verge): Chess tables, a community notice board and a raised planter that residents can adopt for herbs/flowers;
(3) Micro-gym bays (≈40 m2): Senior-friendly steppers, two tai-chi wheels and a stretching rail, surfaced with permeable rubber;
(4) Pop-up kiosk pads (≈25 m2, modular power points): These host seasonal activities—tea stalls in summer, calligraphy booths during festivals and vaccination vans in winter.
Spacing these modules along the 420 m east–west corridor and at the two “cores” supplies rhythm, rest and programmed sociability.

4.2. Functional Adaptation Strategies

4.2.1. Modular Spatial Design

The modular space concept is guided by the principle of “care and enjoyment”. New health monitoring kiosks and rehabilitation rooms offer convenient medical management and therapy for older residents. Sunrooms are introduced as key functional spaces, supplying abundant daylight and a comfortable setting for everyday relaxation. Staggered building volumes allow different programme areas to be interlocked spatially, preserving functional independence while adding depth and usability to the plan. A preliminary bill-of-quantities shows that 60% off-site prefabrication cuts the on-site labour by about one-third compared with that for a masonry kiosk of an equal floor area.
Functional expansion enhances the versatility further by diversifying building forms, broadening public service offerings and activating courtyard spaces. Dedicated zones for social interaction, cultural events and elder health services ensure that the environment meets basic daily needs while also providing rich social and cultural experiences. By maximising variety and flexibility, the modular strategy raises the age-friendliness of public space, safeguards quality of life and supports social participation. Purpose-built subareas increase the operational efficiency and accommodate seniors’ health, social and leisure requirements better, thereby strengthening their reliance on—and engagement with—the public realm (Figure 11). Each 3 m × 6 m module uses light-gauge steel frames and dry joints, allowing for on-site assembly or removal within one working day.
Such modularity amplifies every arm of sustainability. Socially, it safeguards dignity by letting seniors choose between quiet retreat and lively engagement; economically, it minimises sunk costs because modules can be added, re-skinned or relocated as needs evolve; and environmentally, it favours lightweight timber or recycled steel frames, demountable joints and photovoltaic shading canopies, reducing embodied carbon and facilitating end-of-life reuse. Purpose-built sub-areas—clearly zoned yet flexibly furnished—thus raise the overall age-friendliness of the public realm, boost the operational efficiency and strengthen older residents’ daily reliance on, and emotional attachment to, their neighbourhood open spaces. Each module is a bolt-on steel frame with plug-in panels that can be re-skinned or repurposed (e.g., tea kiosk → reading nook) in under two hours.
The modular space design solution is a conceptual top-level prototype based on spatial syntactic diagnostics and has not yet been validated on site with elderly residents. In the next phase, the programme will be evaluated and iterated through two co-creation workshops with ≥20 residents over 60 years old and immersive VR roaming.
We conclude by noting that the modular space’s design remains untested by its end users. Future work will conduct the planned workshops and VR evaluations to assess its usability and perceived benefits and improve the programme accordingly.
Scaling these modular units to other peri-urban villages will face at least three hurdles: (1) the heterogeneity of land tenure—the plot boundaries and informal add-ons differ widely, so the modules must be flexible between 2 m and 4 m widths and sit on both collective and privately leased land; (2) the local governance capacity—village committees vary in their technical skills and budget, calling for a simplified “tool-kit” manual and a phased co-funding model; (3) life-cycle operation and maintenance—cleaning, lighting and minor repairs require an agreed caretaker scheme, which could be covered by user committees or micro-contracts with retiree residents.
Three lines of enquiry are planned: (1) a multi-site pilot deploying three prototype modules in at least two additional peri-urban villages and comparing the pre/post scores; (2) adaptability studies, documenting the seasonal re-purposing of modules (e.g., vegetable swaps in spring, calligraphy booths at festival time) and noting design tweaks that aid or hinder such changes; and (3) a cost–benefit analysis, weighing the capital and maintenance outlays against the measured social gains to build an investment case for wider rollout.

4.2.2. Dynamic Mixed Use

Dynamic mixed use is vital to Liulin’s public-space scheme. By shifting their functions over the day, spaces can meet elders’ emotional and social needs better while boosting their utilisation and cultural identity. The programming begins with the real, everyday requirements and aims to foster a sense of belonging. During daylight hours, plazas can operate as marketplaces or service hubs, offering fresh produce, household goods and pop-up information or health-check stations—basic supports that also reinforce mutual aid and security.
The village committee should act not only as spatial manager but also as social catalyst, organising periodic events that strengthen cohesion and cultural recognition, thereby deepening residents’ attachment to the community.
After dark, the same areas can be converted into dance plazas. Group dancing is both exercise and a key social outlet; a designated area lets elders keep fit while nurturing ties and inter-generational exchange. Additional nighttime zones can host dancing, singing or small gatherings, enriching the social options and encouraging participation.
Such time-based flexibility heightens spatial efficiency and more fully addresses emotional needs. By dynamically adjusting its functions, older residents experience stronger cultural affiliation and greater social engagement, ultimately enhancing their quality of life and delivering comprehensive, age-responsive care (Figure 12).

4.3. Transferability of the Findings

Liulin Village is representative in three respects: (1) “morphological type”, a grid-cum-alley layout inherited from pre-urban farmland consolidation; (2) the “socio-economic mix”, ageing long-term residents plus an influx of migrant renters; and (3) the “policy environment”, where the village is subject to the same “incremental improvement” funding scheme applied across Beijing’s urban fringe. For these reasons, the diagnostic workflow (space syntax + EFA) and the “corridor + core”/modular interventions are likely to translate to many peri-urban settings in North China.
Two factors could limit their direct transfer: the “local governance capacity”, where weaker village committees might struggle with maintenance-heavy features, and the “demographic mix”, since a younger or tourism-oriented settlement would prioritise different activity programmes. Future applications should therefore (i) adapt the governance toolkit to the local institutional strengths and (ii) re-run the EFA using site-specific survey items before finalising the design modules. Comparative studies in other Chinese provinces, as well as in rapidly peri-urbanising regions of Southeast Asia and Latin America, would be invaluable for testing the framework’s broader applicability.
In practical terms, the transferability depends on the following:
Factors that help adoption: The similar grid-plus-alley morphology common in North China fringe villages; nationwide “incremental improvement” funding that supports small plug-in modules; and the rapid population ageing (≥20% aged 60+) in many peri-urban areas;
Factors that may constrain adoption: Weaker local governance, where maintenance budgets and resident committees are harder to sustain, and differing land-tenure patterns (e.g., scattered private plots) that limit corridor continuity.
Future applications should therefore adapt the governance toolkit to the institutional capacity and secure tenure agreements before the selection of the final layout.

5. Conclusions

Using space syntax and an exploratory factor analysis, this study identified the principal shortcomings in the age-friendliness of Liulin Village’s public realm. The space-syntax results show a clear mismatch between the spatial layout and elders’ needs: high-integration zones are functionally mono-used—the supermarket frontage displaces seating and rest areas—while low-connectivity alleys suffer from visual enclosure and safety deficits that suppress activity. The factor analysis further reveals four coupled dimensions—personal convenience; socialisation and belonging; environmental and landscape; and activity function—with personal convenience exerting the greatest influence on elders’ behaviour. Together, these findings indicate that age-friendly retrofitting at the urban fringe must advance along two tracks: spatial repair, aimed at improving accessibility and connectivity, and functional optimisation, aimed at diversifying its uses and raising its age-responsive quality.
Building on these insights, this research also highlights broader sustainability dividends. Spatial repair through additional micro-links and shaded “corridor–core” loops shortens walking distances and encourages active mobility, thereby lowering transport-related emissions and reinforcing climate-adaptation goals. Functional diversification—particularly time-shared programming of plazas for morning markets, midday health services and evening leisure—maximises the land use efficiency and catalyses community micro-economies without new land uptake. By converting under-utilised edges into socially animated fronts, the proposed interventions strengthen weak-tie social capital and enhance perceived safety, advancing the social sustainability pillar alongside environmental and economic gains.
This study has several limitations. First, it does not differentiate between the needs of the “older-old” and “younger-old”, who vary markedly in their health, mobility and social demands. Future work should disaggregate these cohorts and integrate physiological data—such as gait speed or heat stress tolerance—to refine the design guidance. Second, the analysis centres on the spatial layout and function, giving limited attention to external factors such as social policy, land-tenure arrangements and cultural practices. Subsequent research should therefore incorporate institutional and ethnographic perspectives, as well as longitudinal monitoring, to achieve more holistic and durable upgrading of age-friendly spaces across diverse peri-urban contexts.

Author Contributions

Conceptualization, Q.L. and W.L.; Methodology, Q.L., Z.Y., J.L. and Y.L.; Investigation, J.C. and X.W.; Data curation, J.C., X.W., J.L. and Y.L.; Writing—original draft, Q.L., Z.Y., J.C. and X.W.; Writing—review & editing, Q.L., Z.Y. and W.L.; Visualization, Z.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Beijing Social Science Foundation Project (24JCC077), the Subject of Beijing Association of Higher Education (MS2022276), the Research Project of Beijing University of Civil Engineering and Architecture (ZF16047), the Graduate Education and Teaching Quality Improvement Project of BUCEA (J2024004) and the Graduate Innovation Project of BUCEA (PG2025018).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Technical roadmap. Source: the author’s own drawing.
Figure 1. Technical roadmap. Source: the author’s own drawing.
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Figure 2. Satellite image of the Liulin Village study area. Source: Google Satellite Maps.
Figure 2. Satellite image of the Liulin Village study area. Source: Google Satellite Maps.
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Figure 3. Correlation analysis among variables. Source: the author’s own drawing.
Figure 3. Correlation analysis among variables. Source: the author’s own drawing.
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Figure 4. Distribution of the scores across dimensions. Source: the author’s own drawing.
Figure 4. Distribution of the scores across dimensions. Source: the author’s own drawing.
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Figure 5. Current spatial integration analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
Figure 5. Current spatial integration analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
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Figure 6. Current intelligibility analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
Figure 6. Current intelligibility analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
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Figure 7. The global integration before and after the update. Source: the author’s own drawing.
Figure 7. The global integration before and after the update. Source: the author’s own drawing.
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Figure 8. Planned spatial integration analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
Figure 8. Planned spatial integration analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
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Figure 9. Planned intelligibility analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
Figure 9. Planned intelligibility analysis. Source: the author’s own drawing. (Color intensity correlates with value magnitude: deeper shades (e.g., warm red) indicate higher values; lighter shades (e.g., cool blue) indicate lower values).
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Figure 10. The “Corridor–Core” structural strategy. Source: the author’s own drawing.
Figure 10. The “Corridor–Core” structural strategy. Source: the author’s own drawing.
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Figure 11. Spatial modularization design strategy. Source: the author’s own drawing.
Figure 11. Spatial modularization design strategy. Source: the author’s own drawing.
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Figure 12. Dynamic mixed-use strategy. Source: the author’s own drawing. (Arrows indicate vertical movement direction: upward (↑) and downward (↓)).
Figure 12. Dynamic mixed-use strategy. Source: the author’s own drawing. (Arrows indicate vertical movement direction: upward (↑) and downward (↓)).
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Table 1. Classification of spatial types. Source: the author’s own drawing.
Table 1. Classification of spatial types. Source: the author’s own drawing.
Space TypePrimary FunctionsIllustration
1Street–Alley SpaceDaily mobility; social interactionBuildings 15 02219 i001
2Plaza SpaceLeisure; socialising; event venuesBuildings 15 02219 i002
3Green SpaceWalking; relaxation; exerciseBuildings 15 02219 i003
4Service-Facility SpaceRetail; cultural activities; senior gatheringsBuildings 15 02219 i004
Table 2. Key questionnaire items. Source: the author’s own drawing.
Table 2. Key questionnaire items. Source: the author’s own drawing.
Latent FactorItem CodeSurvey StatementFactor LoadingCronbach’s α
Harmonious Neighbourhood Relations (HNRs)Q-HNR-1I exchange daily greetings with my neighbours.0.780.84
Q-HNR-2I can easily ask neighbours for help when needed.0.74
Sense of Safety and Belonging (SSB)Q-SSB-1I feel safe walking alone here after dusk.0.810.79
Q-SSB-2Public spaces make me feel part of this community.0.66
Cultural Activity Space (CAS)Q-CAS-1Venues for opera, chess, or calligraphy are within a 10-min walk.0.690.80
Q-CAS-2Local culture events are held regularly.0.71
Table 3. Descriptive statistics. Source: the author’s own drawing.
Table 3. Descriptive statistics. Source: the author’s own drawing.
Primary FactorCore Needs of the Older ResidentsMeanStandard DeviationSample Size
Socialisation and Belonging FactorHarmonious Neighbourhood Relations4.10.75680
Variety of Social Spaces4.350.67780
Sense of Safety and Belonging4.360.69880
Personal ConvenienceBarrier-Free Access4.180.68980
Transport Accessibility4.210.6580
Safety Facilities4.260.68980
Environmental and Landscape FactorSunlight Requirements and Shading Design4.30.70180
Green Spaces and Planting Landscape4.20.71980
Activity Function FactorSports and Fitness Facilities4.190.74880
Cultural Activity Space4.390.70380
Table 4. The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity. Source: the author’s own drawing.
Table 4. The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity. Source: the author’s own drawing.
KMO Measure of Sampling AdequacyBartlett’s Test
Approx. Chi-SquareDegrees of FreedomSignificance
0.877572.007450.000
Table 5. Total variance explained. Source: the author’s own drawing.
Table 5. Total variance explained. Source: the author’s own drawing.
Initial EigenvaluesSum of Squared Loadings (Extraction)Sum of Squared Loadings (Rotation)
ComponentTotalPercent of Variance (%)Cumulative (%)TotalPercent of Variance (%)Cumulative (%)TotalPercent of Variance (%)Cumulative (%)
16.31463.13963.1396.31463.13963.1392.8728.70528.705
20.7167.16370.3020.7167.16370.3022.05320.52649.231
30.6826.82377.1250.6826.82377.1251.76317.63166.862
40.5535.52882.6530.5535.52882.6531.57915.79182.653
50.5025.01587.668
60.4324.31791.986
70.3093.08895.073
80.1981.98297.055
90.1691.69398.748
100.1251.252100
Table 6. The component score coefficient matrix. Source: the author’s own drawing.
Table 6. The component score coefficient matrix. Source: the author’s own drawing.
Core Needs of the Older ResidentsCompone-nt1Compone-nt2Compone-nt3Compone-nt4Component Score CoefficientWeight (%)
Harmonious Neighbourhood Relations0.1870.08−0.3440.3040.0888.8
Barrier-Free Access−0.3220.752−0.2390.1290.3333.0
Sense of Safety and Belonging0.2220.005−0.1550.1430.11511.5
Varieties of Social Spaces0.511−0.202−0.153−0.0890.1213.0
Transport Accessibility−0.264−0.2650.8740.0690.2121.0
Green Spaces and Planting Landscape−0.1590.2610.479−0.2730.0969.6
Sunlight Requirements and Shading Design0.621−0.141−0.048−0.4020.13513.5
Safety Facilities−0.0080.5290.029−0.3550.12512.5
Sports and Fitness Facilities−0.31−0.1890.0240.9580.2323.0
Cultural Activity Space0.188−0.1290.1280.0760.077.0
Table 7. Key space–activity mismatches. Source: the author’s own drawing.
Table 7. Key space–activity mismatches. Source: the author’s own drawing.
LocationIntegration Rank (Rn)Observed Elder Use (12 h Tally)Interview Feedback (n ≥ 5)Mismatch
West market frontage1st (highest)7% of total observations“Too noisy/no benches”High spatial value, low presence of elders
North gate cul-de-sacs18th–24th (low)19% of observations“Quiet, shady, but dead-end”Low spatial value, high presence of elders
Central spine super-block8th (medium)0% of observations“Can’t cross safely”Moderate spatial value, no presence of elders
Table 8. “Corridor + Core” design modules. Source: the author’s own drawing.
Table 8. “Corridor + Core” design modules. Source: the author’s own drawing.
ModuleFootprint/SpecKey FacilitiesElders’ Needs Addressed
Rest node15 m2; permeable pavers; shade trellisL-bench ×2, drinking fountain, USB outletRest and hydration
Social pocket60 m2; widened sidewalkTwo chess tables, a notice board, a raised planterSocial interaction
Micro-gym bay40 m2; rubber surfaceStepper, tai-chi wheels, stretching railLow-impact exercise
Pop-up kiosk pad25 m2; level concrete; power socketSeasonal tea stall/health boothFlexible programming
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MDPI and ACS Style

Li, Q.; Yang, Z.; Cui, J.; Wu, X.; Liu, J.; Li, W.; Liu, Y. Age-Friendly Public-Space Retrofit in Peri-Urban Villages Using Space Syntax and Exploratory Factor Analysis. Buildings 2025, 15, 2219. https://doi.org/10.3390/buildings15132219

AMA Style

Li Q, Yang Z, Cui J, Wu X, Liu J, Li W, Liu Y. Age-Friendly Public-Space Retrofit in Peri-Urban Villages Using Space Syntax and Exploratory Factor Analysis. Buildings. 2025; 15(13):2219. https://doi.org/10.3390/buildings15132219

Chicago/Turabian Style

Li, Qin, Zhenze Yang, Jingya Cui, Xingping Wu, Jiao Liu, Wenlong Li, and Yijun Liu. 2025. "Age-Friendly Public-Space Retrofit in Peri-Urban Villages Using Space Syntax and Exploratory Factor Analysis" Buildings 15, no. 13: 2219. https://doi.org/10.3390/buildings15132219

APA Style

Li, Q., Yang, Z., Cui, J., Wu, X., Liu, J., Li, W., & Liu, Y. (2025). Age-Friendly Public-Space Retrofit in Peri-Urban Villages Using Space Syntax and Exploratory Factor Analysis. Buildings, 15(13), 2219. https://doi.org/10.3390/buildings15132219

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