Next Article in Journal
The Effect of Street Orientation on the Temporal Variation in Thermal Environment Within Streets in Different Climate Zones
Previous Article in Journal
Study on the Influence Factors of Surrounding Tunnel Longitudinal Deformation Caused by Pit Excavation Based on Nonlinear Pasternak Modeling
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Influence of High-Density Community Spaces on the Walking Activity of Older Adults: A Case Study of Macau Peninsula

1
Faculty of Innovation and Design, City University of Macau, Macau
2
Tanghua Architect & Associates, Shenzhen 518042, China
*
Authors to whom correspondence should be addressed.
Buildings 2025, 15(9), 1505; https://doi.org/10.3390/buildings15091505
Submission received: 17 March 2025 / Revised: 20 April 2025 / Accepted: 24 April 2025 / Published: 30 April 2025
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

Abstract

Macau’s aging communities face growing challenges in meeting the needs of older residents due to rising population density and extremely limited land resources. The concentration of outdated residential buildings—home to a substantial older adult population—exacerbates issues related to age-associated physical decline. For seniors who prefer familiar environments, the spatial constraints inherent in these densely built urban areas increasingly conflict with their specific gerontological needs, indicating the urgent need for urban renewal. This study employs a multi-methodological framework to examine aging populations in Macau’s high-density urban contexts. In Phase I, questionnaire surveys combined with SPSS 26.0-based cluster analysis are employed to (1) stratify older adults according to walking behavior patterns; (2) identify subgroup-specific needs and (3) establish key demographic correlates. Based on the socio-ecological framework, Phase II implements spatial analytics through ArcGIS demarcation of pedestrian catchment areas. This phase further integrates point-of-interest (POI) distribution analysis with space syntax-derived axial map evaluations to formulate typological mobility guidelines for different age cohorts. This study outlines the community walking space requirements of older adults in Macau and explores the influence of high-density community spaces on older adults. A practical evaluation method is proposed to assess age-friendly features of urban pathways, identifying the key environmental factors and their respective impacts. These preliminary findings may inform basic planning principles and adaptive design approaches for older adult-oriented pedestrian spaces.

1. Introduction

According to the World Health Organization (WHO), individuals aged 60 years and above are classified as older adults. Global demographic trends show a consistent increase in both the absolute numbers and proportion of aging populations. Projections reveal that by 2030, the global population aged 60+ will reach 1.4 billion, constituting one in six people worldwide—a significant rise from 1 billion in 2020. This aging trend is expected to accelerate. The number of older adults is projected to double to 2 billion by 2050, accounting for approximately 21% of the global population [1].
Since the mid-1990s, Macau’s older population (aged 60+) has exceeded 7% of its total population. In recent years, the proportion of older residents has steadily risen from 18.9% in 2021 to 21.6% in 2024. Projections indicate that this trend will continue, with the share reaching 25.8% by 2041 [2]. These figures demonstrate that Macau’s aging rate outpaces the global average, positioning it as a critical case study on social aging in the coming decades.
Cultural preferences for home-based eldercare prevail among Macau’s older adults after retirement, making neighborhood environments crucial to their well-being. These high-density urban districts serve as their primary living spaces, significantly influencing late-life quality and social engagement [3,4].
China’s national urban planning standards allocate 85.1–105 m2 of construction land per capita in newly developed areas, corresponding to the recommended population density of 9500–11,750 persons/km2 [5]. In contrast, the Macau Peninsula’s urban core demonstrates an exceptionally high density of 50,000 persons/km2, establishing it as a quintessential high-density urban model [6]. This spatial configuration presents three primary pedestrian challenges: overcrowded walkways, degraded streetscape quality, and insufficient age-appropriate amenities [7,8,9]. A literature review reveals an absence of dedicated activity spaces specifically for older adults in high-density community environments. Community walking space serves as the primary outdoor environment for older adults, as walking remains their preferred mode of physical activity. Therefore, pedestrian pathways should not only feature accessible and safe barrier-free facilities but also meet the needs of recreational and social interaction [10,11].
High-density neighborhoods have a different impact on older adults’ walking activities from average-density neighborhoods. In these compact urban environments, outdoor public spaces, green areas, and street widths are more limited, and pedestrian areas are narrower. However, the higher density of restaurants, supermarkets, and essential services enhances accessibility and life convenience for older adults [12,13,14].

2. Literature Review

Streets, critical “organs” of urban systems and primary public spaces, hold fundamental significance in sustaining daily urban life [15]. These linear urban spaces serve not only as essential infrastructure for pedestrian mobility but also as community-based platforms for daily activities. Their design and quality exhibit significant correlations with both the physical health and mental well-being of older adults [16,17].
Statistical analyses confirm significant positive correlations between pedestrian network density and key health indicators among older adults, including chronic disease prevalence, loneliness levels, and subjective well-being. Well-designed linear infrastructure configuration contributes to measurable improvements in both physiological health and psychological well-being within this demographic [18,19].
Urban planning interventions should prioritize age-responsive spatial design, systematically mitigating mobility constraints through enhanced accessibility. This requires the deliberate integration of gerontologically informed features that align with the psychosocial and physiological needs of older adults [20].
Based on existing literature, Corti established a socio-ecological framework that elucidates the multidimensional determinants of pedestrian mobility, encompassing social ecosystems, built environment characteristics, and individual predispositions [21]. Alfonzo advanced this framework with the Hierarchy of Walking Needs model, which outlines a five-tiered hierarchy spanning foundational prerequisites (feasibility and accessibility) to qualitative enhancements (safety, comfort, and pleasurability) [22]. Empirical studies have consistently demonstrated the predictive relevance of this hierarchical construct in urban walking behaviors [23].
Academic research has increasingly focused on environmental determinants of pedestrian engagement within community contexts. A seminal study on spatial behavior among older adults revealed significant correlations between pedestrian infrastructure quality (walkability indices, seating availability, fitness zones, and vegetative landscapes) and walking activity patterns in older adults [24,25].
Furthermore, methodologically rigorous studies employing activity log triangulation combined with logistic regression modeling have systematically quantified the impacts of activity site attributes. These analyses distinguish between primary infrastructure components (pedestrian networks, open spaces, greenbelts, and sports facilities) and supportive amenities (hydration stations, sanitation facilities, rest areas, and waste management systems) while evaluating spatial proximity effects. These studies conclusively identify pedestrian pathways and vegetated elements as the most statistically significant predictors of older adults’ mobility patterns [26,27].
Academic research has traditionally focused on the macroscopic impacts of the built environment on residential living patterns. However, recent methodological innovations have led to space syntax applications in gerontological urban studies, particularly in analyzing environmental influences on older adults [28]. However, existing scholarly efforts have yet to utilize an integrated approach that combines surveys, Points of Interest (POIs), and space syntax to study the walking activities of older adults in high-density urban areas. Space syntax, a theoretical framework, quantitatively analyzes the relationship between spatial structures and human behavior, with a focus on how spatial organization connects existing spaces with the social environment [29]. Originating from Hillier’s pioneering work in the 1970s, this analytical paradigm has evolved into a cohesive theoretical framework. Its capacity for quantifying spatial–organizational characteristics has gained substantive traction across interdisciplinary urban research domains, particularly in gerontologically sensitive environmental evaluations [30,31]. Quantitative analysis of public spaces based on space syntax enables the examination of connections between spatial configurations and inferred patterns of human behavior. In community spaces, connectivity stimulates gathering and interaction. For older adults, who often spend leisure time in community public spaces, prolonged outings and unique physiological and psychological needs are significantly influenced by the spatial environment [20,32,33].

3. Methods

3.1. Research Area

The Macau Special Administrative Region Urban Master Plan (2020–2040) designates the northern Macau Peninsula as a primary residential zone, characterized by exceptionally high population density due to concentrated demographic distribution patterns [6]. Since the 1980s, housing development in the region has transitioned to high-rise construction, marked by substantial vertical density and rapid urban intensification [34,35]. This urban transformation has precipitated critical demographic challenges, including hyper-concentration effects and a reduction in community attachment—measurable through declining neighborhood belonging indices [36].
This study focuses on the Yau Han Community in Macau, which includes community spaces within primary residential areas of the northern Macau Peninsula. This study area covers 4.616 square kilometers, nearly 50% of the Macau Peninsula (Figure 1). Located near the Portas do Cerco border crossing, the Yau Han Community originated from land reclamation projects in the 1930s. Over time, it has developed into a major residential community in Macau, distinguished by both the highest population density and the largest proportion of older adults [6].

3.2. Data Sources and Methods

This study designed a survey questionnaire adapted from the Neighborhood Environment Walkability Survey Abbreviated (NEWS-A) [37]. Developed by Saelens et al. in 2003 [37], the NEWS-A questionnaire has been widely used, with its practicality well established [38,39]. The questionnaire was informed by survey designs from research on older adults’ walking activities in Shenzhen [40] and tailored to incorporate features of questionnaires for older adults in high-density cities like Shenzhen. The final questionnaire is presented in Appendix A.
A questionnaire-based sampling survey in the Iao Hon community collected data on outdoor activity locations of older adults. These data were used to calculate walking preferences across various travel intervals and evaluate the attractiveness of different POIs within the walking space. By determining typical travel distances for older adults across age groups and identifying the maximum acceptable walking distance, a general maximum walking range was established to create a pedestrian activity map. This map provides a foundation for assessing whether the POIs and existing walking environment conditions within the defined range meet the needs of older adults. Additionally, the questionnaire captured basic demographic information, travel patterns, primary travel activities, and key concerns regarding the walking space configuration among older adults.
Alongside questionnaire analysis results, SPSS (26.0) statistical methods were employed to examine age-stratified mobility patterns and spatial requirements [41]. Correlation analyses identified considerations for the older adult-focused community in community pedestrian spaces, which were then used to inform the parameter selection for space syntax modeling.
The study utilizes comprehensive geospatial datasets, including:
  • Macau-wide Point of Interest (POI) data (source: https://www.openstreetmap.org/, accessed on 6 November 2024).
  • City-scale AutoCAD (2024) maps.
  • Road network data extracted and processed from OpenStreetMap.
These integrated datasets provide a robust foundation for spatial analysis and enable the examination of pedestrian accessibility patterns.
Quantitative analysis of public spaces based on space syntax enables the examination of spatial connections and inferred patterns of human behavior. A syntax model is developed in this study to explore the degree of spatial connectivity in community spaces and its impact on older adults. The insights facilitate targeted analysis of frequently used walking spaces. This approach provides a theoretical foundation and practical recommendations for the design of age-friendly community public spaces [42,43]. The complete research procedure is systematically illustrated in Figure 2.
This study uses ArcGIS to select pedestrian paths, steps, residential roads, and other non-arterial routes, alongside reachable activity zones—vehicular roads excluded—to simulate walking accessibility for older adults [44,45]. With space syntax theory, this study systematically analyzes community walking spaces [46]. DepthMapX-0.7.0_win64 software is used for the quantitative assessment of public pedestrian areas, identifying streets with elevated choice values [47,48]. The resulting metrics facilitate a comprehensive evaluation of how community public spaces influence older adults’ mobility patterns and activity characteristics.

4. Results

4.1. Macau Peninsula Community Research and Analysis

In-depth field investigations and comprehensive statistical analyses reveal two key findings in the Yau Han community: (1) a disproportionately large older adult population demonstrating relatively strong mobility and (2) a 5.93 percentage point increase in the proportion of residents aged ≥ 60 years, rising from 7.13% in 2011 to 13.42% in 2021 in the Yau Han area [1]. As population aging accelerates, communities in the Macau Peninsula face growing pressure to serve local older adults.
The survey collected 143 questionnaires, with 130 valid responses. Respondents were aged 60 and above, distributed as follows: 60–69 years (50%), 70–79 years (37%), 80–84 years (8%), and 85+ years (5%). Given the rapid health decline observed after age 80, finer age stratification was applied to the 80+ cohort to improve analytical accuracy [49].

4.1.1. Analysis of Outing Frequency and Walking Distance Patterns

In Table 1, the analysis demonstrates an age-dependent decline in outing frequency, with the highest proportion of older adults making ≥5 weekly outings, observed in the 60–79 age cohort. This proportion decreases substantially in older age groups, indicating progressive mobility constraints associated with aging. However, a notable exception is observed among nonagenarians, where a subset continues to maintain regular outing patterns, revealing significant heterogeneity in late-life mobility behaviors.
Furthermore, an analysis of walking distance preferences reveals that most older adults prefer moderate walking ranges (2000–3000 m), likely balancing daily activity needs with energy conservation. However, among older age cohorts, a significant shift toward shorter walking distances (400–800 m) is observed, potentially reflecting age-related declines in physical capacity.
In addition, a small subset of older adults continues to engage in long-distance walking, showing high levels of stamina. This trend aligns with the general observation that while older adults tend to maintain a higher tolerance for walking distances, their ability to sustain long-distance mobility decreases with age.

4.1.2. Outing Activities and Preferences

An analysis of older adults’ mobility patterns identifies three primary community destinations: parks (418 visits), supermarkets (393 visits), and restaurants (332 visits), which serve as essential nodes in daily routines (Table 2). The chi-square test (χ2 = 122.091, p = 0.000 *) strongly rejects the null hypothesis of independence, indicating statistically significant differences in destination selection across activity types.
Activity-Specific Destination Preferences:
(1)
Recreational Walking: Parks were the most frequently visited destination (25.3%), revealing their critical support for older adults’ ambulatory routines. This spatial preference underscores the significance of urban green spaces in supporting geriatric well-being.
(2)
Shopping Behavior: Supermarkets dominated as primary destinations (25.3%), reflecting older adults’ prioritization of daily necessities. This spatial concentration aligns with routine provisioning behaviors characteristic of compact urban environments.
(3)
Work-Related Mobility: Workplaces constituted a primary destination (23.55%), with a notable overlap in park and supermarket visits, suggesting integration of occupational and personal activities.
(4)
Dining Preferences: Restaurants were the predominant dining destination (24.86%), demonstrating their functional specificity in meal-related mobility. Additionally, these spaces serve as social hubs for older adults, where communal dining fosters interpersonal connections and community bonds.
(5)
Child Transportation: Schools served as the primary destination (22.78%) for child pick-up and drop-off activities, demonstrating the active role of older adults in family education.
(6)
Dog Walking: Parks and supermarkets maintained comparable visitation rates, indicating flexible mobility patterns among older adult dog owners.
(7)
Social Activities: Destination choices were relatively scattered, though the notably high library visitation rates may indicate specific event-driven gathering or group-oriented preferences.
Age-Specific Activity Patterns:
Table 3 reveals clear trends in location and activity preferences across various age groups, with notable differences as age increases.
(1)
The 60–69 Age Group: This cohort shows diverse activity patterns, frequently visiting parks, supermarkets, workplaces, and restaurants. Their high engagement in walking, shopping, working, and dining activities demonstrates an active and socially engaged lifestyle.
(2)
The 70–79 Age Group: Older adults frequently visit supermarkets and restaurants, but workplace attendance decreases, in line with retirement trends.
(3)
The 81–84 and 85+ Age Groups: These groups show a significant reduction in activity levels, with an increased reliance on basic facilities such as parks and supermarkets. However, their visit frequency is lower compared to younger groups. Members of the 85+ group rarely engage in physically demanding activities such as work or school runs and have minimal participation in dining out or social events.

4.1.3. Walking Space Pathway Factors and Activity Facilities That Influence Activity

From the response rates in Table 4, sun and rain shelter facilities (8.871%), road width (8.358%), public toilets (8.065%), and resting facilities such as seating (7.478%) were selected by a significant proportion of respondents. This indicates that these facilities play an important role in residents’ daily lives and are a key factor in improving the quality of life in the community.
In contrast, the lower response rates for overpasses, underpasses (1.466%), and event gatherings (3.592%) may reflect that residents’ demand for these facilities is relatively low, or that their basic needs in this regard have already been met.
The prevalence indicators further corroborate the above observations, with high availability of shade and shelter facilities (93.077%), road width (87.692%), public toilets (84.615%), and resting facilities such as seating (78.462%). This suggests that these facilities are not only widely available but also commonly used in the community.
The low prevalence of overpasses, underpasses (15.385%), and activity gathering spaces (37.692%) suggests that community planners should optimize the design of overpasses and underpasses by adding barrier-free facilities, such as elevators, to enhance accessibility.
It is worth noting that while the response rate for activity facilities such as fitness equipment, chess tables and chairs, and ball courts is not the highest, the chi-square test value (X2 = 11.577, p < 0.001) is highly significant. This indicates that residents’ demand for these types of facilities varies significantly, with a strong differentiation in preferences. In addition, although the response rate (4.912%) and prevalence rate (51.538%) for barrier-free designs (such as ramps, blind alleys, and stair handrails) are relatively low, they should not be overlooked as an important symbol of community inclusiveness. As society ages, optimizing barrier-free facilities will be crucial for upgrading community activity spaces.

4.2. Basic ArcGIS Database Construction and Analysis of the Status Quo Situation of Community Public Space

With the Yau Han community as the central focus, this study combined ArcGIS facility point crawling and road network data extraction to analyze the accessibility of community spaces. The ArcGIS network analysis tool was then used to map various walking range areas. These zones were defined by extracting spatial ranges of 400 m, 600 m, 1000 m, 1500 m, and 2000 m, using the midpoint of the round-trip distance based on the walking segments gathered in the questionnaire. See Figure 3.

4.2.1. Analysis of Integration, Choice, and Connectivity of Public Space

Using the DepthMap X-0.7.0_win64 software, the axial model of the Macau Yau Han community road network was created. In this model, the warmth or coldness of the line segments corresponds to changes in the variable values. Specifically, warmer colors represent higher degrees of spatial integration, choice, and connectivity, while cooler colors indicate lower degrees of these variables.
This study applies the theory of space syntax to examine the relationship between Macau’s facilities and the walking range of older adults. With a 2000 m walking range as an example, a comparative analysis is conducted between the degrees of integration, choice, and connectivity.
As shown in Figure 4, the central spatial axes within the 2000 m walking radius from the Yau Han community primarily exhibit warm colors, indicating a high degree of global integration. In contrast, the cooler-colored roads are sparse and mostly located outside the surrounding map area.
As shown in Figure 5, the central road and several roads within the community network are basically represented by warm colors, indicating higher spatial connectivity. However, the overall connectivity is dispersed and sparse, reflecting the extent to which the roads are frequented by pedestrians on a daily basis.
Figure 6 illustrates spatial choice, which measures the likelihood and frequency of a road connecting various spaces. Roads with higher choice values are more likely to attract older adults for recreation and social activities. As shown in Figure 6, one of the roads with the highest choice value serves as a critical center for these activities.
Based on the comparative analyses of integration, selection, and connectivity and an examination of the distribution of facility points, the influence of each predictor variable on the response variable is reflected through the unstandardized coefficients, standard error (S.E.), critical ratio (C.R.), and significance level (p), as shown in the regression coefficient table of the model.
The significant effect of connectivity on facility points, as shown in Table 5, is supported by a critical ratio (C.R.) of 4.817, which is much higher than the conventional level of significance. Additionally, the p-value of 0.000 ***, significant at the 1% significance level, strongly supports the hypothesis that connectivity has a significant positive impact on facility points.
The effect of selectivity is also significant, as indicated by its unstandardized coefficient of 0 and critical ratio (C.R.) value of 14.047 with a p-value of 0.000***. These results confirm its significant positive effect, establishing selectivity as an important predictor variable, even though its unstandardized coefficient is zero.
Integration has a significant negative effect on facility points, with an unstandardized coefficient of −0.403. This suggests that, when other variables are held constant, each unit increase in integration corresponds to an average decrease of 0.403 units in facility points.
The standardized coefficient of −0.392 indicates a strong negative predictive relationship with facility points at the standardized scale. The standard error (S.E.) of 0.029 demonstrates high precision in the coefficient estimation.
The critical ratio (C.R.) of −13.91, along with a p-value of 0.000 ***, strongly supports the hypothesis that spatial integration has a significant negative effect on facility points.
Based on the analytical findings, this study employs the connectivity axial map from space syntax as the foundational layer for subsequent analyses of facility distribution and pedestrian spaces across various spatial scales. The choice axial map serves as a reference framework to identify and examine streets with the highest choice values within different walking radii of the Yau Han community.

4.2.2. Analysis of Facility Sites and Activity Facilities Within Different Walking Distances

1. Analysis of 400 m walking range facility points and activity facilities:
The 400 m walking radius primarily serves octogenarians (80+ years), a group that exhibits increased dependence on essential community facilities, such as parks and supermarkets. This demographic tends to have constrained mobility patterns and a pronounced preference for low-intensity daily routines in close proximity to residential areas.
As shown in Figure 7, within the 400 m walking radius of the Yau Han community, a variety of restaurants and supermarkets are strategically distributed, reflecting Macau’s unique urban morphology. Field observations confirm that these commercial establishments are predominantly located at ground level and seamlessly integrated with residential structures, effectively catering to the mobility needs of octogenarians (80+ years).
For this older adult demographic, primary mobility needs to focus on recreational walking and park accessibility. The study area covers four public parks within the 400 m pedestrian catchment zone, strategically located to facilitate age-friendly mobility patterns. Yau Han Park and San Mei On Park demonstrate superior vegetation coverage and landscape quality, whereas Macau Ru Yi Plaza and Yong Ning Plaza exhibit relatively limited green space.
Field investigations reveal that Yau Han Park and San Mei On Park feature wider pedestrian pathways, along with essential amenities such as public restrooms, exercise equipment, and seating areas. Notably, Yau Han Park includes additional sunshade installations. These infrastructure provisions effectively address older adults’ needs for age-appropriate facilities.
In comparison, Yong Ning Plaza primarily functions as an open square, equipped with comprehensive fitness equipment and seating areas but lacking critical amenities such as public restrooms and sunshade installations, which are crucial for older adult mobility. On the other hand, Nina Tower Plaza predominantly caters to children’s recreational activities, presenting limited suitability for the spatial needs of older adults.
2. Analysis of 600 m walking radius facility points and routes:
The 600 m walking radius predominantly serves octogenarians (80–84 years), who show marked preferences for weather protection structures, accessible design features (e.g., ramps and tactile paving), and landscape elements, including floral arrangements and vegetation. This demographic also exhibits heightened needs for safety, comfort, and environmental aesthetics.
Within the 600 m walking radius of the Yau Han community, residential clusters are interspersed with a variety of restaurants and supermarkets, along with an increased availability of public restrooms. As shown in Figure 8, compared to the 400 m radius, two additional green spaces are accessible: Macau Triangle Garden and Border Gate Plaza. While the latter primarily serves cross-border pedestrian flow, Macau Triangle Garden demonstrates vegetation coverage and amenities similar to Yau Han Park, effectively meeting the needs of octogenarians. However, San Mei On Park’s limited scale and absence of weather protection infrastructure indicate the need for targeted improvements to enhance its age-friendly functionality.
3. Analysis of 1000 m walking radius facility points and routes:
The 1000 m walking radius primarily serves septuagenarians (70–79 years), octogenarians (80–84 years), and nonagenarians (85+ years). These groups share common needs for weather protection structures, with distinct preferences: younger groups emphasize nighttime illumination and accessible design features (e.g., ramps and tactile paving), while older groups prioritize landscape elements, including floral arrangements and vegetation. Both subgroups exhibit elevated demands for safety, comfort, and environmental aesthetics. As shown in Figure 9, library accessibility is a key destination preference for individuals aged 70–79, with six library facilities identified within this spatial radius.
Based on the 600 m radius park analysis, two additional green spaces are identified. However, their limited spatial capacity, basic provisions such as only exercise equipment and seating areas, and the absence of proximate public sanitation facilities may significantly reduce their appeal to older adults.
Field investigations confirm that all seven parks are equipped with nighttime illumination systems. Within the 1000 m walking radius, six public libraries offer comprehensive services, including multimedia lending, document reproduction, access to the Macau SAR Government Gazette, periodical reading areas, broadband internet, and electronic databases. These libraries maintain superior environmental quality with well-organized seating arrangements, effectively supporting older adults in their daily reading and borrowing activities.
4. Analysis of 1500–2000 m walking radius facility points and routes:
The 1500–2000 m walking radius mainly serves sexagenarians and septuagenarians (60–79 years), whose mobility is mainly focused on weather protection structures and nighttime illumination systems. The spatial analysis, shown in Figure 10 and Figure 11, reveals a consistent distribution pattern, with restaurants, educational institutions, and supermarkets exhibiting a strong spatial correlation along high-connectivity street networks.
A comparative analysis of sub-1000 m walking radii reveals distinct spatial clustering patterns: parks and libraries predominantly concentrate within the 1000 m threshold, with a more dispersed distribution beyond this range. Notably, several green spaces (e.g., Reservoir Park, San Kio Garden, and Loi Yeung Garden) lack proximate public sanitation facilities, which may reduce their appeal to older adults.
Public sanitation facilities demonstrate a relatively uniform spatial distribution across street networks, in contrast to other amenities, effectively addressing the accessibility concerns of older adults during outdoor mobility. Field investigations confirm that all mapped restroom locations feature accessible toilet provisions, ensuring the availability of age-friendly infrastructure.

5. Discussion

The questionnaire survey findings reveal that adults aged 60–69 show increased involvement in school runs, dog walking, and event participation, indicating a balanced commitment to family responsibilities and social–leisure activities. Among those aged 70–79, participation in school runs significantly declines, while walking, shopping, and dining activities remain consistent, indicating a sustained focus on lifestyle quality and health. Social activity engagement decreases, though some participation in community events remains. Those aged 80+ experience physical limitations that restrict their activity range and intensity. Therefore, this group prefers low-intensity, home-based activities.
This study systematically examines the pedestrian environment surrounding the Yau Han community through two analytical perspectives: (1) the spatial configuration of older adult-oriented walking radii and (2) space syntax metrics, including connectivity and choice values. The spatial distribution of restaurants and retail establishments demonstrates pronounced clustering patterns, which show a proportional correlation with street connectivity metrics. This spatial configuration enables older adults, particularly those with mobility constraints, to access essential amenities within minimal walking distances, thereby significantly reducing the need for extended pedestrian journeys. In addition to their commercial functions, restaurants and supermarkets serve as vital social hubs. The concentrated spatial distribution of dining establishments facilitates frequent social interactions among older adults, fostering neighborhood connections and reinforcing community networks. This spatial pattern effectively mitigates social isolation and enhances a sense of place attachment [50,51]. The proximity of dining and retail facilities enables older adult residents to independently perform essential daily activities, thereby reducing their reliance on family support or external care services. This spatial accessibility helps maintain and enhance their self-sufficiency and autonomy [52,53].
Response rates for overpasses, underpasses, and activity gathering spaces are relatively low, indicating either limited direct demand for these facilities among residents or that existing facilities adequately meet basic needs. Parks, as primary recreational spaces, exhibit a relatively well-distributed spatial pattern across the Macau Peninsula. While some green spaces feature comprehensive amenities, including exercise equipment, restrooms, weather protection structures, seating areas, and substantial vegetation coverage, most lack critical infrastructure elements—particularly public sanitation facilities and sunshade installations—which significantly influence older adults’ mobility patterns. Parks serve as central hubs for older adult recreational engagement, significantly supporting their physical and psychological well-being. These green spaces provide essential opportunities for nature immersion and social interaction, effectively enhancing community involvement, socialization frequency, and meaningful interpersonal connections among older adults [54,55,56].
Therefore, based on an analysis of the correlation between the connectivity of restaurants, supermarkets, and pedestrian spaces, greater connectivity correlates with higher population and business density. This, in turn, increases the likelihood of older adults’ visitation and improves the demand for age-friendly community pedestrian spaces.

6. Conclusions

Rapid population aging highlights limitations in older adult care and services. Consequently, to renew the aging neighborhoods in Macau, it is crucial to optimize public spaces based on the needs of both the community and older adults. With increasing social attention on aging issues, the construction of aging-friendly communities is gradually being integrated into urban planning requirements across cities [57].
This study examines the mobility characteristics of older adults in Macau’s high-density Youhan community, with a focus on trip frequency, walking distances, activity preferences, and pedestrian infrastructure conditions. Through ArcGIS-based facility distribution modeling across varying walking thresholds and space syntax analysis of spatial connectivity, this study identified optimal pedestrian corridors. By integrating these findings with age-related mobility constraints and facility accessibility patterns, this study systematically documented limitations in community walking environments, establishing an empirical foundation for enhancing older adults’ quality of life and well-being.
First, older adult mobility patterns demonstrate age-dependent variations: travel frequency progressively declines, and walking distances significantly shorten with aging. Sexagenarians and septuagenarians (60–79 years) predominantly prefer moderate walking ranges (1200–3000 m), striking an optimal balance between daily activity needs and energy conservation within Macau’s urban context. However, older adults aged 80 and above generally prefer shorter walking distances (800 m or less). Walking, shopping, and dining represent the three primary purposes for outings, with parks, supermarkets, and restaurants as the corresponding destinations.
Second, weather protection structures, pathway dimensions, public sanitation facilities, and seating provisions constitute the primary determinants of older adult mobility preferences, while recreational infrastructure—such as exercise equipment, game tables, and sports courts—represents a secondary consideration. Communities should give due consideration to the diverse needs of different ages, genders, and interest groups in their planning. Activity facilities should be rationally laid out to promote community vitality and resident interaction. In addition, barrier-free designs (e.g., ramps, tactile paving, and stair handrails) serve as important indicators of community inclusiveness and must not be overlooked. Amid societal aging, it becomes crucial to optimize overpasses and underpasses and to expand spaces for activity and gathering based on potential needs. The enhancement of barrier-free facilities will serve as a key determinant of overall community quality.
Third, Macau’s road network exhibits a high degree of integration, with connectivity closely aligned with choice values. Based on the unique physiological and psychological needs of older adults, facilities such as supermarkets and restaurants meet their requirements. In contrast, other facilities that fall short of these standards—primarily due to inadequate supporting amenities—include restrooms, as well as libraries that are located farther away and more dispersed. This study recommends the incorporation of missing public restrooms, recreational parks, and libraries, along with more seating, sunshades, and barrier-free facilities. Community spaces that effectively accommodate older adults’ needs significantly enhance walking frequency and the likelihood of social interaction, thereby optimizing spatial utilization efficiency and vitality.
Within older adults’ walking ranges, space syntax identifies paths with higher choice values in community pedestrian spaces. Future planning could integrate older adults’ activity needs to develop multifunctional spaces in small public areas of these walking zones. Proposed enhancements include green planters, streetlamps, seating, and rain/sun shelters. These diverse functions meet the specific daily needs of older adults, foster broader social environments, promote neighborhood harmony, increase public space vitality, and create older adult-friendly community walking spaces. This study developed a methodological framework that integrates questionnaire surveys, space syntax theory, and ArcGIS analysis in high-density communities. It assessed age-specific accessibility within walking distances, systematically examined community pedestrian spaces, and determined optimal walking environments for different older adult age groups.
The study’s innovation lies in the development of a quantifiable framework to investigate older adults’ spatial requirements and environmental perceptions of pedestrian spaces in high-density communities. This empirical evidence supports age-friendly spatial interventions in Macau communities, facilitating improvements in older adults’ living environments.
With the progression of research on age-friendly walking spaces in dense urban areas, future studies could explore the diverse needs of different older adult groups. For instance, in public spaces, it is crucial to improve walking spaces by integrating rest and entertainment functions, along with convenient amenities such as rain-sheltering landscapes and shaded corridors. This can enhance usability, tailored to the design and climatic conditions in each city. Such investigations may yield improved ways to improve community spaces. These findings could inform the development of practical evaluation tools and neighborhood planning methods, making high-density areas more comfortable and supportive for older adults.

Author Contributions

Conceptualization, X.C.; Methodology, X.C. and H.T.; Software, N.W.; Validation, N.W.; Investigation, X.C. and N.W.; Data curation, X.C.; Writing—original draft, X.C.; Writing—review & editing, X.C.; Supervision, H.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data used in this paper mainly came from Open Street Map (https://www.openstreetmap.org/, 4 November 2024), POI data (https://www.google.com/maps/, 6 November 2024). Other data are contained within the article.

Conflicts of Interest

Author Hua Tang was employed by the company Tanghua Architect & Associates. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A

Survey Questionnaire

In order to understand how older adults use the walking space in the Macau Peninsula, we would like to send you a questionnaire. Please fill in the questionnaire according to your actual situation. Please tick the options that match you. Your information is used for practical research purposes only and does not involve private information. Thank you for your cooperation! Here’s to good health!
  • 1. Your age: [Single-Choice Question] *
  • ○60–69 years
  • ○70–79 years
  • ○80–84 years
  • ○85+ years
  • 2. Number of trips per week: 1–7 times [Fill in the blank] *
  • _________________________________
  • 3. You are about ______ [Single-choice question] *
  • ○Less than 400 m (about 5 min)
  • ○400–800 m (5–10 min)
  • ○800–1200 m (10–15 min)
  • ○1200–2000 m (15–25 min)
  • ○2000–3000 m (25–40 min)
  • ○3000–4000 m (40–50 min)
  • ○Above 4000 m (50+ min)
  • 4. An acceptable walking distance (one that is comfortable and not tiring) is ______ [Single-choice question] *
  • ○Less than 400 m (about 5 min)
  • ○400–800 m (5–10 min)
  • ○800–1200 m (10–15 min)
  • ○1200–2000 m (15–25 min)
  • ○2000–3000 m (25–40 min)
  • ○3000–4000 m (40–50 min)
  • ○Above 4000 m (50+ min)
  • 5. Tolerated walking distance (maximum acceptable walking distance) is ______ [Single-choice question] *
  • ○Less than 400 m (about 5 min)
  • ○400–800 m (5–10 min)
  • ○800–1200 m (10–15 min)
  • ○1200–2000 m (15–25 min)
  • ○2000–3000 m (25–40 min)
  • ○3000–4000 m (40–50 min)
  • ○Above 4000 m (50+ min)
  • 6. Activities you do when you go out [Multiple-choice 1uestion] *
  • □Recreational Walking
  • □Dog Walking
  • □Child Transportation
  • □Shopping
  • □Dining
  • □Participation in Events/Meetings
  • □Work-Related Mobility
  • □Other_________________
  • 7. Where do you usually go when you go out? [Multiple-choice question] *
  • □Park
  • □Restaurant
  • □Supermarket
  • □School
  • □Library
  • □Workplace
  • □Other _________________
  • 8. Factors affecting the condition of the walking trail for the event [Multiple-choice question] *
  • □Road width
  • □Separate footpaths
  • □Proportion of hard-surfaced roads
  • □Roadside landscaping
  • □Obstacles, such as parking, littering, and other encroachment factors on the trail
  • □Overpasses and underpasses
  • □Pedestrian flow
  • □Other_________________
  • 9. Please choose your favorite activity facility [Multiple-choice question] *
  • □Fitness equipment, chess tables and chairs, courts, and other activity facilities
  • □Shade and shelter from the sun and rain
  • □Nighttime lighting facilities
  • □Barrier-free design, e.g., ramps, blind corridors, stair railings, etc.
  • □Trashcans
  • □Rest facilities such as chairs
  • □Other permanent landscaping such as flower beds, greenery, sculptures, etc.
  • □Public toilets
  • □Inspirational meeting
  • □Other_________________

References

  1. Harper, S. Economic and social implications of aging societies. Science 2014, 346, 587–591. [Google Scholar] [CrossRef]
  2. Government of Macau Special Administrative Region. Macao Population Forecast (2022–2041); Documentation and Information Dissemination Centre of DSEC: Macau, China, 2023. Available online: https://www.dsec.gov.mo/getAttachment/54879f3c-21b7-4759-a35a-ba177765f28b/C_PPRM_PUB_2021_Y.aspx (accessed on 20 September 2024).
  3. Zhang, Y.; Jiang, X. The relationship between walking ability, self-rated health, and depressive symptoms in middle-aged and elderly people after controlling demographic, health status, and lifestyle variables. Medicine 2023, 102, e34403. [Google Scholar] [CrossRef] [PubMed]
  4. Wang, X.; Shi, R.; Niu, F. Optimization of furniture configuration for residential living room spaces in quality elderly care communities in Macao. Front. Archit. Res. 2022, 11, 357–373. [Google Scholar] [CrossRef]
  5. Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Code for Classification of Urban Land Use and Planning Standards of Development Land; China Architecture & Building Press: Beijing, China, 2011; Available online: https://www.planning.org.cn/law/uploads/2013/1383993139.pdf (accessed on 5 October 2024).
  6. Government of Macau Special Administrative Region. Draft Consultation Text of the Master Plan of the Macau Special Administrative Region (2020–2040); Land, Public Works and Transport Bureau: Macau, China, 2020; Available online: https://www.cnbayarea.org.cn/policy/policy%20release/policies/content/post_696729.html (accessed on 8 October 2024).
  7. Wan, Y.K.P. The social, economic and environmental impacts of casino gaming in Macao: The community leader perspective. J. Sustain. Tour. 2012, 20, 737–755. [Google Scholar] [CrossRef]
  8. Wu, S.T.; Chen, Y.S. The social, economic, and environmental impacts of casino gambling on the residents of Macau and Singapore. Tour. Manag. 2015, 48, 285–298. [Google Scholar] [CrossRef]
  9. Zhou, J.; Wang, P.; Xie, L.; Wang, Y. Research on key factors of public green space environment in Macau community based on sustainable development. J. Exp. Nanosci. 2023, 18, 2170359. [Google Scholar] [CrossRef]
  10. Barnett, D.W.; Barnett, A.; Nathan, A. Built environmental correlates of older adults’ total physical activity and walking: A systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 103. [Google Scholar] [CrossRef] [PubMed]
  11. Bautmans, I.; Lambert, M.; Mets, T. The six-minute walk test in community dwelling elderly: Influence of health status. BMC Geriatr. 2004, 4, 6. [Google Scholar] [CrossRef]
  12. He, H.; Li, T.; Yu, Y. Associations between built environment characteristics and walking in older adults in a high-density city: A study from a Chinese megacity. Front. Public Health 2020, 8, 577140. [Google Scholar] [CrossRef]
  13. Chen, J.; Tao, Z.; Wu, W. Influence of urban park pathway features on the density and intensity of walking and running activities: A case study of Shanghai city. Land 2024, 13, 156. [Google Scholar] [CrossRef]
  14. Du Cros, H.; Kong, W.H. Congestion, popular world heritage tourist attractions and tourism stakeholder responses in Macao. Int. J. Tour. Cities 2020, 6, 929–951. [Google Scholar] [CrossRef]
  15. Wang, L.; Han, X.; He, J. Measuring residents’ perceptions of city streets to inform better street planning through deep learning and space syntax. ISPRS J. Photogramm. Remote Sens. 2022, 190, 215–230. [Google Scholar] [CrossRef]
  16. Bull, F.C.; Al-Ansari, S.S.; Biddle, S. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br. J. Sports Med. 2020, 54, 1451–1462. [Google Scholar] [CrossRef] [PubMed]
  17. Yamamoto, N.; Maruyama, K.; Saito, I. Latent profile analysis approach to the relationship between daily ambulatory activity patterns and metabolic syndrome in middle-aged and elderly Japanese individuals: The Toon Health Study. Environ. Health Prev. Med. 2023, 28, 57. [Google Scholar] [CrossRef]
  18. Gaglione, F.; Gargiulo, C.; Zucaro, F. Where can the elderly walk? A spatial multi-criteria method to increase urban pedestrian accessibility. Cities 2022, 127, 103724. [Google Scholar] [CrossRef]
  19. Bai, X.; Soh, K.G.; Omar Dev, R.D. Effect of brisk walking on health-related physical fitness balance and life satisfaction among the elderly: A systematic review. Front. Public Health 2022, 9, 829367. [Google Scholar] [CrossRef]
  20. Yung, E.H.K.; Conejos, S.; Chan, E.H.W. Social needs of the elderly and active aging in public open spaces in urban renewal. Cities 2016, 52, 114–122. [Google Scholar] [CrossRef]
  21. Giles-Corti, B.; Donovan, R.J. Relative influences of individual, social environmental, and physical environmental correlates of walking. Am. J. Public Health 2003, 93, 1583–1589. [Google Scholar] [CrossRef]
  22. Alfonzo, M.A. To walk or not to walk? The hierarchy of walking needs. Environ. Behav. 2005, 37, 808–836. [Google Scholar] [CrossRef]
  23. Banger, A.; Grigolon, A.; Brussel, M. Identifying the interrelations between subjective walkability factors and walking behaviour: A case study in Jeddah, Saudi Arabia. Transp. Res. Interdiscip. Perspect. 2024, 24, 101025. [Google Scholar] [CrossRef]
  24. Yuan, K.S.; Wu, T.J. Environmental stressors and well-being on middle-aged and elderly people: The mediating role of outdoor leisure behaviour and place attachment. Environ. Sci. Pollut. Res. 2021, 28, 1–10. [Google Scholar] [CrossRef]
  25. Zang, P.; Qiu, H.; Zhang, H. The built environment’s nonlinear effects on the elderly’s propensity to walk. Front. Ecol. Evol. 2023, 11, 1103140. [Google Scholar] [CrossRef]
  26. Chen, Q.; Zhang, Z.; Mao, Y. Investigating the influence of age-friendly community infrastructure facilities on the health of the elderly in China. Buildings 2023, 13, 341. [Google Scholar] [CrossRef]
  27. Ma, Y.; Zou, G.; Shin, J. Locating community-based comprehensive service facilities for older adults using the GIS-NEMA method in Harbin, China. J. Urban Plan. Dev. 2021, 147, 05021010. [Google Scholar] [CrossRef]
  28. Zhang, Y.; Wu, Z.; Wu, Z. Using space syntax in close interaction analysis between the elderly: Towards a healthier urban environment. Buildings 2023, 13, 1456. [Google Scholar] [CrossRef]
  29. Wu, Y.; Liu, Q.; Hang, T. Integrating restorative perception into urban street planning: A framework using street view images, deep learning, and space syntax. Cities 2024, 147, 104791. [Google Scholar] [CrossRef]
  30. Ye, Y.; Richards, D.; Lu, Y. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landsc. Urban Plan. 2019, 191, 103434. [Google Scholar] [CrossRef]
  31. Luo, Y.; Lin, Z. Spatial Accessibility Analysis and Optimization Simulation of Urban Riverfront Space Based on Space Syntax and POIs: A Case Study of Songxi County, China. Sustainability 2023, 15, 14929. [Google Scholar] [CrossRef]
  32. Hirosaki, M.; Ohira, T.; Kajiura, M. Effects of a laughter and exercise program on physiological and psychological health among community-dwelling elderly in Japan: Randomized controlled trial. Geriatr. Gerontol. Int. 2013, 13, 152–160. [Google Scholar] [CrossRef]
  33. Chen, M.; Liu, Y.; Liu, F.; Chadha, T.; Park, K. Measuring pedestrian-level street greenery visibility through space syntax and crowdsourced imagery: A case study in London, UK. Urban For. Urban Green. 2025, 105, 128725. [Google Scholar] [CrossRef]
  34. Sheng, L. Explaining urban economic governance: The City of Macao. Cities 2017, 61, 96–108. [Google Scholar] [CrossRef]
  35. Ye, C.; Hu, L.; Li, M. Urban green space accessibility changes in a high-density city: A case study of Macau from 2010 to 2015. J. Transp. Geogr. 2018, 66, 106–115. [Google Scholar] [CrossRef]
  36. Sun, X.; Liu, Z. Public green space injustice in high-density post-colonial areas: A case study of the Macau Peninsula, China. Sustainability 2024, 16, 3774. [Google Scholar] [CrossRef]
  37. Saelens, B.E.; Sallis, J.F.; Black, J.B. Neighborhood-based differences in physical activity: An environment scale evaluation. Am. J. Public Health 2003, 93, 1552–1558. [Google Scholar] [CrossRef] [PubMed]
  38. Amaya, V.; Chardon, M.; Klein, H. What do we know about the use of the walk-along method to identify the perceived neighborhood environment correlates of walking activity in healthy older adults: Methodological considerations related to data collection—A systematic review. Sustainability 2022, 14, 11792. [Google Scholar] [CrossRef]
  39. Almeida, D.P.; Alberto, K.C.; Mendes, L.L. Neighborhood environment walkability scale: A scoping review. J. Transp. Health 2021, 23, 101261. [Google Scholar] [CrossRef]
  40. Sun, Y. Research on Evaluation and Planning Application of Public Outdoor Activity Fields in Communities on Aging Adaptability. Ph.D. Dissertation, Harbin Institute of Technology, Shenzhen, China, 2018. [Google Scholar]
  41. Wang, J.; Wang, Y.; Cai, H. Analysis of the status quo of the Elderly’s demands of medical and elderly care combination in the underdeveloped regions of Western China and its influencing factors: A case study of Lanzhou. BMC Geriatr. 2020, 20, 338. [Google Scholar] [CrossRef]
  42. Hillier, B.; Hanson, J. The Social Logic of Space; Cambridge University Press: Cambridge, UK, 1984. [Google Scholar] [CrossRef]
  43. Hillier, B. Space is the Machine: A Configurational Theory of Architecture; Cambridge University Press: Cambridge, UK, 2007. [Google Scholar]
  44. Ma, H.D.; Tong, Y. Spatial differentiation of traditional villages using ArcGIS and GeoDa: A case study of Southwest China. Ecol. Inform. 2022, 68, 101416. [Google Scholar] [CrossRef]
  45. Wang, J.; Zhang, X.; Chai, Y. A context-based approach for neighbourhood life circle delineation and internal spatial utilization analysis based on GIS and GPS tracking data. Appl. Spat. Anal. Policy 2023, 16, 1493–1515. [Google Scholar] [CrossRef]
  46. Liu, S.; Ge, J.; Bai, M. Uncovering the factors influencing the vitality of traditional villages using POI (point of interest) data: A study of 148 villages in Lishui, China. Herit. Sci. 2023, 11, 123. [Google Scholar] [CrossRef]
  47. Turner, A. Depthmap 4: A researcher’s handbook. Bartlett School of Graduate Studies. University College London, UK. 2004. Available online: https://discovery.ucl.ac.uk/id/eprint/2651/1/2651.pdf (accessed on 26 October 2024).
  48. Koohsari, M.J.; Sugiyama, T.; Mavoa, S. Street network measures and adults’ walking for transport: Application of space syntax. Health Place 2016, 38, 89–95. [Google Scholar] [CrossRef]
  49. Milanović, Z.; Pantelić, S.; Trajković, N. Age-related decrease in physical activity and functional fitness among elderly men and women. Clin. Interv. Aging 2013, 8, 549. [Google Scholar] [CrossRef]
  50. Domènech-Abella, J.; Lara, E.; Rubio-Valera, M. Loneliness and depression in the elderly: The role of social networ. Soc. Psychiatry Psychiatr. Epidemiol. 2017, 52, 381–390. [Google Scholar] [CrossRef]
  51. Turan, İ.A.; True, E.M. The perception of public space of the elderly after social isolation and its effect on health. Ain Shams Eng. J. 2023, 14, 101884. [Google Scholar] [CrossRef]
  52. Yang, S.; Li, C.; Mu, W. Locating Senior-Friendly Restaurants in a Community: A Bi-Objective Optimization Approach for Enhanced Equality and Convenience. ISPRS Int. J. Geo-Inf. 2024, 13, 23. [Google Scholar] [CrossRef]
  53. Chen, M.; Fu, Y.; Chang, Q. Life satisfaction among older adults in urban China: Does gender interact with pensions, social support and self-care ability? Ageing Soc. 2022, 42, 2026–2045. [Google Scholar] [CrossRef]
  54. Guo, S.; Song, C.; Pei, T. Accessibility to urban parks for elderly residents: Perspectives from mobile phone data. Landsc. Urban Plan. 2019, 191, 103642. [Google Scholar] [CrossRef]
  55. Enssle, F.; Kabisch, N. Urban green spaces for the social interaction, health and well-being of older people—An integrated view of urban ecosystem services and socio-environmental justice. Environ. Sci. Policy 2020, 109, 36–44. [Google Scholar] [CrossRef]
  56. Yuan, F.; Chen, M. A systematic review of measurement tools and senior engagement in urban nature: Health benefits and behavioral patterns analysis. Health Place 2025, 91, 103410. [Google Scholar] [CrossRef]
  57. Torku, A.; Chan, A.P.C.; Yung, E.H.K. Age-friendly cities and communities: A review and future directions. Ageing Soc. 2021, 41, 2242–2279. [Google Scholar] [CrossRef]
Figure 1. Study area of the Macau Peninsula.
Figure 1. Study area of the Macau Peninsula.
Buildings 15 01505 g001
Figure 2. Methodological framework.
Figure 2. Methodological framework.
Buildings 15 01505 g002
Figure 3. Base travel distance database for older adults.
Figure 3. Base travel distance database for older adults.
Buildings 15 01505 g003
Figure 4. Spatial integration and destination distribution within a 2000 m walking radius (Source: Author).
Figure 4. Spatial integration and destination distribution within a 2000 m walking radius (Source: Author).
Buildings 15 01505 g004
Figure 5. Spatial connectivity and destination distribution within a 2000 m walking radius (Source: Author).
Figure 5. Spatial connectivity and destination distribution within a 2000 m walking radius (Source: Author).
Buildings 15 01505 g005
Figure 6. Spatial choice value and destination distribution within a 2000 m walking radius (Source: Author).
Figure 6. Spatial choice value and destination distribution within a 2000 m walking radius (Source: Author).
Buildings 15 01505 g006
Figure 7. Location and name of parks within a 400 m walking distance.
Figure 7. Location and name of parks within a 400 m walking distance.
Buildings 15 01505 g007
Figure 8. Location and name of parks within a 600 m walking radius.
Figure 8. Location and name of parks within a 600 m walking radius.
Buildings 15 01505 g008
Figure 9. Parks, libraries, and public toilets within a 1000 m walking radius.
Figure 9. Parks, libraries, and public toilets within a 1000 m walking radius.
Buildings 15 01505 g009
Figure 10. Restaurants, supermarkets, parks, libraries, and public toilets within a 1500 m walking radius.
Figure 10. Restaurants, supermarkets, parks, libraries, and public toilets within a 1500 m walking radius.
Buildings 15 01505 g010
Figure 11. Restaurants, supermarkets, parks, libraries, and public toilets within a 2000 m walking radius.
Figure 11. Restaurants, supermarkets, parks, libraries, and public toilets within a 2000 m walking radius.
Buildings 15 01505 g011
Table 1. Age-stratified cross-tabulation analysis of outdoor mobility patterns: frequency and distance in older adults.
Table 1. Age-stratified cross-tabulation analysis of outdoor mobility patterns: frequency and distance in older adults.
CategoryFrequency/DistanceAgeTotal
60–69 Years 70–79 Years 80–84 Years 85+ Years
Number of outings per week1 time0 (0%)0 (0%)1 (50%)1 (50%)2
2 times(14.286%)2 (28.571%)2 (28.571%)2 (28.571%)7
3 times3 (17.647%)9 (52.941%)3 (17.647%)2 (11.765%)17
4 times7 (38.889%)8 (44.444%)2 (11.111%)1 (5.556%)18
5 times25 (64.103%)11 (28.205%)3 (7.692%)0 (0%)39
6 times23 (63.889%)13 (36.111%)0 (0%)0 (0%)36
7 times6 (54.545%)5 (45.455%)0 (0%)0 (0%)11
Total6548116130
Walking distance per outing1200–2000 m (15–25 min)9 (47.368%)6 (31.579%)4 (21.053%)0 (0%)19
2000–3000 m (25–40 min)37 (52.857%)31 (44.286%)2 (2.857%)0 (0%)70
3000–4000 m (40–50 min)15 (60%)9 (36%)1 (4%)0 (0%)25
400–800 m (5–10 min)0 (0%)0 (0%)1 (25%)3 (75%)4
Above 4000 m (50+ min)3 (75%)1 (25%)0 (0%)0 (0%)4
800–1200 m (10–15 min)1 (14.286%)1 (14.286%)3 (42.857%)2 (28.571%)7
Less than 400 m (about 5 min)0 (0%)0 (0%)0 (0%)1 (100%)1
Total6548116130
Acceptable walking distance1200–2000 m (15–25 min)7 (46.667%)4 (26.667%)4 (26.667%)0 (0%)15
2000–3000 m (25–40 min)22 (43.137%)27 (52.941%)2 (3.922%)0 (0%)51
3000–4000 m (40–50 min)29 (63.043%)16 (34.783%)1 (2.174%)0 (0%)46
400–800 m (5–10 min)0 (0%)0 (0%)1 (25%)3 (75%)4
Above 4000 m (50+ min)6 (85.714%)1 (14.286%)0 (0%)0 (0%)7
800–1200 m (10–15 min)1 (16.667%)0 (0%)3 (50%)2 (33.333%)6
Less than 400 m (about 5 min)0 (0%)0 (0%)0 (0%)1 (100%)1
Total6548116130
Tolerance of walking distance1200–2000 m (15–25 min)2 (28.571%)1 (14.286%)3 (42.857%)1 (14.286%)7
2000–3000 m (25–40 min)12 (33.333%)19 (52.778%)5 (13.889%)0 (0%)36
3000–4000 m (40–50 min)35 (58.333%)23 (38.333%)2 (3.333%)0 (0%)60
400–800 m (5–10 min)0 (0%) min0 (0%)0 (0%)1 (100%)1
Above 4000 m (50+ min)16 (76.19%)5 (23.81%)0 (0%)0 (0%)21
800–1200 m (10–15 min)0 (0%)0 (0%)1 (25%)3 (75%)4
Less than 400 m (about 5 min)0 (0%)0 (0%)0 (0%)1 (100%)1
Total6548116130
Table 2. Cross-tabulation analysis of outdoor activity patterns and destination preferences among older adults.
Table 2. Cross-tabulation analysis of outdoor activity patterns and destination preferences among older adults.
Activity TypeParkSupermarketsWorkplaceRestaurantSchoolLibraryOtherTotalsX2p
Recreational Walking100 (25.253%)87 (21.97%)47 (11.869%)74 (18.687%)31 (7.828%)29 (7.323%)28 (7.071%)396122.0910.000 *
Shopping100 (23.641%)107 (25.296%)50 (11.82%)76 (17.967%)35 (8.274%)30 (7.092%)25 (5.91%)423
Work-Related Mobility57 (22.008%)51 (19.691%)61 (23.552%)37 (14.286%)24 (9.266%)14 (5.405%)15 (5.792%)259
Dining82 (23.699%)73 (21.098%)36 (10.405%)86 (24.855%)28 (8.092%)22 (6.358%)19 (5.491%)346
Child Transportation38 (21.111%)34 (18.889%)23 (12.778%)28 (15.556%)41 (22.778%)7 (3.889%)9 (5%)180
Dog Walking19 (22.093%)22 (25.581%)13 (15.116%)14 (16.279%)6 (6.977%)6 (6.977%)6 (6.977%)86
Participation in events/meetings22 (22%)19 (19%)12 (12%)17 (17%)5 (5%)23 (23%)2 (2%)100
Total4183932423321701311041790
Note: * indicates statistical significance at the 1% level.
Table 3. Age-stratified cross-tabulation analysis of outdoor activity patterns and destination preferences.
Table 3. Age-stratified cross-tabulation analysis of outdoor activity patterns and destination preferences.
Category ParkSupermarketsWorkplaceRestaurantSchoolLibraryOthersRecreational WalkingShoppingWork-Related MobilityDiningChild TransportationDog WalkingParticipation in Events/MeetingsTotalX2p
Age60–69 years 58 (11.813%)52 (10.591%)47 (9.572%)38 (7.739%)26 (5.295%)12 (2.444%)15 (3.055%)53 (10.794%)52 (10.591%)47 (9.572%)38 (7.739%)26 (5.295%)15 (3.055%)12 (2.444%)49169.7110.002 *
70–79 years 47 (13.352%)45 (12.784%)13 (3.693%)42 (11.932%)14 (3.977%)18 (5.114%)12 (3.409%)37 (10.511%)45 (12.784%)14 (3.977%)39 (11.08%)13 (3.693%)6 (1.705%)7 (1.989%)352
80–84 years 11 (16.923%)8 (12.308%)1 (1.538%)6 (9.231%)2 (3.077%)3 (4.615%)2 (3.077%)11 (16.923%)8 (12.308%)1 (1.538%)6 (9.231%)2 (3.077%)1 (1.538%)3 (4.615%)65
85+ years4 (15.385%)2 (7.692%)0 (0%)3 (11.538%)0 (0%)1 (3.846%)4 (15.385%)6 (23.077%)2 (7.692%)0 (0%)3 (11.538%)0 (0%)0 (0%)1 (3.846%)26
Total12010761894234331071076286412223934
Note: * indicates statistical significance at the 1% level.
Table 4. Correlation matrix of pathway conditions and activity-supporting facilities.
Table 4. Correlation matrix of pathway conditions and activity-supporting facilities.
Multiple-Choice QuestionN (counts)Response Rate (%)Penetration Rate (%)X2p
Fitness equipment, chess tables and chairs, courts, and other activity facilities795.79260.769115.9770.000 ***
Shade and shelter from the sun and rain1218.87193.077
Night-time lighting facilities866.30566.154
Barrier-free design, e.g., ramps, blind corridors, stair railings, etc.674.91251.538
Road width1148.35887.692
Obstacles, such as parking, littering, and other encroachment factors on the trail997.25876.154
Separate footpaths936.81871.538
Proportion of hard-surfaced roads1017.40577.692
Roadside landscaping805.86561.538
Overpasses and underpasses201.46615.385
Pedestrian flow956.96573.077
Trashcans684.98552.308
Rest facilities such as chairs1027.47878.462
Other permanent landscaping such as flower beds, greenery, sculptures, etc.805.86561.538
Public toilet1108.06584.615
Inspirational meeting493.59237.692
Total13641001049.231
Note: *** indicates a significance level of 1%.
Table 5. Regression coefficients of space syntax models: integration, choice, and connectivity within a 2000 m walking radius.
Table 5. Regression coefficients of space syntax models: integration, choice, and connectivity within a 2000 m walking radius.
XYUnstandardized CoefficientStandardized CoefficientS.E.C.R.p
ConnectivitySites31.0340.1236.4424.8170.000 ***
ChoiceSites00.383014.0470.000 ***
IntegrationSites−0.403−0.3920.029−13.910.000 ***
Note: *** p < 0.01 indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, X.; Wang, N.; Tang, H. Influence of High-Density Community Spaces on the Walking Activity of Older Adults: A Case Study of Macau Peninsula. Buildings 2025, 15, 1505. https://doi.org/10.3390/buildings15091505

AMA Style

Chen X, Wang N, Tang H. Influence of High-Density Community Spaces on the Walking Activity of Older Adults: A Case Study of Macau Peninsula. Buildings. 2025; 15(9):1505. https://doi.org/10.3390/buildings15091505

Chicago/Turabian Style

Chen, Xiangyu, Ning Wang, and Hua Tang. 2025. "Influence of High-Density Community Spaces on the Walking Activity of Older Adults: A Case Study of Macau Peninsula" Buildings 15, no. 9: 1505. https://doi.org/10.3390/buildings15091505

APA Style

Chen, X., Wang, N., & Tang, H. (2025). Influence of High-Density Community Spaces on the Walking Activity of Older Adults: A Case Study of Macau Peninsula. Buildings, 15(9), 1505. https://doi.org/10.3390/buildings15091505

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop