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Article

Urban Building Intensity and Daily Accessibility of Green Space: A Specific Assessment for Megacities

by
Yingyi Zhang
*,
Yuxi Fan
and
Lin Zhang
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Land 2026, 15(4), 634; https://doi.org/10.3390/land15040634
Submission received: 21 February 2026 / Revised: 3 April 2026 / Accepted: 7 April 2026 / Published: 12 April 2026
(This article belongs to the Section Land Planning and Landscape Architecture)
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Abstract

Urban green space (UGS) is widely recognized as a core component of sustainable urban development in megacities. The study of the synergistic relationship between high-intensity urban development and daily accessibility of UGS, however, remains insufficient. This paper therefore critically assesses the inherent correlation between building intensity and the UGS daily accessibility in a typical megacity context. The analysis is twofold: What is the inherent correlation between building intensity and the daily accessibility of UGS in megacities? And, if such a correlation exists, how can daily accessibility be improved by integrating building intensity into the UGS planning process? Using a case study in Beijing, methods of multi-source data integration, GIS spatial analysis, and statistical correlation models are used to address the issues. Results indicate that building intensity exhibits a statistically positive spatial association with the Daily Accessibility Index (DAI). Mere expansion of the total UGS area does not necessarily lead to improved daily accessibility for residents. The findings include a clarified dual-effect mechanism of high-intensity development on UGS services, as well as evidence-based planning strategies for sustainable UGS layout in dense megacities.

1. Introduction

The world has experienced sustained urban growth since the start of the 20th century. Over recent decades, global urbanized areas have expanded at a rapid rate alongside growing populations [1,2,3]. High-intensity development has become a dominant mode for many megacities. It is believed that compact urban development can alleviate housing pressure, enable the intensive use of limited land resources, and support urban economic circulation [4,5,6].
UGS forms an ecological network that supports the balance between natural and artificial environments in megacities [7,8,9,10]. In the process of master planning and regulatory zoning, parks, gardens, and waterfront green belts integrate with land use to form ecological areas [11]. While the general ecological and psychological benefits of UGS are well-documented [12,13,14,15,16], realizing these benefits depends fundamentally on residents’ actual access to these spaces in their daily routines. In megacities, the relationship between urban usage intensity and UGS accessibility becomes particularly critical. High-intensity development concentrates massive populations within limited areas, generating an enormous daily demand for outdoor recreational spaces. Convenient access to UGS is not merely an esthetic amenity but a fundamental necessity for relieving psychological stress and facilitating social interaction. But the very nature of high building intensity restricts the physical space available for UGS, creating a profound spatial tension. Therefore, clarifying the relationship between building intensity and the UGS daily accessibility provides critical evidence for balancing high-density development with effective public service provision.

1.1. Contradictions Between Intensive Development and UGS Provision

Large cities, especially central urban areas, have dense populations living on extremely limited land resources. UGS provides multiple ecological and social benefits; however, the construction of commercial housing, office buildings and public facilities to provide residential and employment spaces seems more effective in addressing short-term tangible needs. This dynamic has led to a vicious cycle. Residents in megacities with a compact form are more prone to anxiety than those in small-scale cities, and reduced UGS further exacerbates this psychological distress [17].
Some scholars identified a squeeze effect of compact development. Excessive building intensity fragments green space networks and even degrades ecological functions [18,19]. Other studies noted a potential agglomeration effect. High-intensity urban areas may have more comprehensive public service systems and stricter UGS planning standards. They improve the spatial matching between green space and the dense population [20]. While multiple studies have independently examined urban usage intensity and its relationship to the spatial quality of the built environment [21,22], and others have extensively measured UGS accessibility [23], these two domains have rarely been correlated. There is a critical lack of prior evidence that simultaneously analyzes how building intensity specifically affects the daily accessibility of green spaces. Does a higher total area quantity mean that residents can better utilize UGS? The answer remains inconclusive.
Therefore, correlating building intensity with the UGS daily accessibility represents a new theoretical issue: it shifts the research paradigm from a traditional supply-oriented quantitative assessment to a function-oriented spatial relationship analysis. Determining whether there is a strong or weak relationship between them helps decode an as-yet-unstudied phenomenon in compact megacities. Furthermore, understanding this correlation serves as a vital, evidence-based tool for decision-making regarding sustainable UGS planning and resource allocation in highly dense cities.
Accessibility is widely recognized as a critical metric in UGS evaluation. Previous studies on UGS accessibility tend to rely on static indicators such as green space ratio or per capita green area. These indicators reflect the stock of green space rather than residents’ actual usage [24]. In addition, distance is also a key influencing factor. The frequency of UGS use consistently declines as travel distance increases [25,26]. Some scholars developed measurement models to assess residents’ access to green space by quantifying travel distance or time, using tools such as buffer analysis and the two-step floating catchment area (2SFCA) methods [27,28,29]. Improved 2SFCA models such as Gaussian-type work to measure UGS accessibility, but these models mainly focus on the matching of supply and demand. Less attention is paid to the choice flexibility of residents’ daily use and the superimposed service effect of multiple green spaces. Daily accessibility encompasses more than just physical arrival at a location. It requires urban planners and designers to improve the convenience of choosing and accessing UGS. The study of UGS daily accessibility is quite necessary towards a humanistic built environment with diverse building intensity characteristics.

1.2. Concept Definition

To establish a clear research framework, the key concepts of building intensity and daily accessibility are defined and operationalized as follows.

1.2.1. Building Intensity

In the context of megacities, building intensity refers to the comprehensive development degree of urban plots. Drawing upon established urban morphology literature and statutory planning practices [30,31], this study conceptualizes building intensity through a multidimensional framework comprising three core indicators: Building Density (BD), Floor Area Ratio (FAR), and Population Density (PD). This composite approach aligns with established urban morphology frameworks, providing a recognized method in compact city research to evaluate the built environment [32]. Specifically:
  • BD represents the two-dimensional ground coverage of building footprints. High BD directly indicates a highly compact physical environment where the available land surface for UGS is severely restricted.
  • FAR reflects the three-dimensional volumetric development of a plot. It indicates the total built space and the potential capacity for accommodating mixed urban functions.
  • PD captures the demographic load, representing the actual intensity of human activity and the corresponding demand for public services, including UGS.
By integrating these three indicators, building intensity comprehensively captures both the physical constraints of the built environment and the demographic pressure on urban spaces. It is important to note in the literature that no unified and mature standard exists so far for building intensity threshold intervals in megacities globally [33]. This absence is primarily due to demographic, morphological, and developmental differences across countries and regions. Therefore, establishing thresholds for this independent variable requires a context-specific approach. In this study, the classification thresholds for high or low intensity are theoretically grounded in the United Nations World Urbanization Prospects, the National Standard for Urban Residential Area Planning and Design of China (GB50180-2018), and the empirical development status of Beijing. Based on the combined performance of these indicators, urban areas are classified into high, medium, and low building intensity zones to facilitate the subsequent correlation analysis. Specifically, to capture the dominant morphological trait, each grid cell is assigned to the intensity tier that satisfies at least two out of the three indicator thresholds.

1.2.2. Daily Accessibility and the DAI

The European Commission Joint Research Centre describes accessibility as the share of UGS reachable within a specified travel time from a given location [29]. Accordingly, this study defines daily accessibility as residents’ ability to conveniently access and choose among multiple UGS options within their daily routines, rather than just meeting nominal per capita green area targets.
Traditional accessibility measurements tend to emphasize static spatial coverage or minimum distance [34]. However, megacities possess complex spatial structures. Even when UGS in a given area meets statutory planning requirements statistically, it may still be difficult for residents to access due to dense road networks or physical barriers. Consequently, many UGS sites function as isolated decorations rather than functional public resources.
This research proposes the Daily Accessibility Index (DAI). While similar cumulative opportunity indices exist in transportation geography to measure the sheer number of reachable destinations [35], a specific index tailored to evaluate the weighted overlap frequency of hierarchical UGS service buffers does not currently exist with the same level of application in urban planning. Existing UGS accessibility indices mainly focus on binary coverage or distance metrics.
In contrast, the proposed DAI is operationalized as the weighted overlap frequency of 15 min walking service buffers from multiple UGS sites of varying service levels. Unlike traditional metrics, DAI explicitly measures the potential frequency-of-use attribute and choice flexibility. It acts as a proxy for opportunity-based accessibility rather than spatial coverage. A higher DAI indicates that a resident has more alternative UGS options available within a short walk, aligning with the actual ease and convenience of UGS use. This index reflects a human-centered UGS planning logic that prioritizes residents’ real behavioral needs over mere compliance with nominal spatial standards.

1.3. Sites, Hypothesis and Goals

Beijing, the capital of China, serves as the study case. China’s Vision 2035 outlines the high-quality development of megacities to foster stronger regional trade and investment partnerships [36]. Beijing plays an essential role in facilitating high-quality growth in consumption and building capacity for high value-added manufacturing. Due to the large amount of job opportunities and strong talent attractiveness, this megacity’s population reached 21.83 million in 2025 according to the Beijing Municipal Bureau of Statistics [37]. Approximately 50.2% of the population is concentrated in the six central districts, a higher share than the 49.8% residing in the remaining ten suburban districts. Within the central districts, Dongcheng and Xicheng Districts account for just 0.57% of Beijing’s total land area but support 8.3% of its total population. This study focuses on the central districts of Beijing. They cover the full gradient of building intensity from the urban core zones to the periphery. The spatial and demographic complexity makes it a suitable case for assessing UGS daily accessibility. In the current statutory Beijing Master Plan (2016–2035), UGS has been regulated at macro level across the municipal region [38]. At the medium-micro level, there remains insufficient consideration of the differences across areas.
This study attempts to provide further consideration for the potential correlation between building intensity and UGS daily accessibility. As a former study indicates, areas with high building intensity are mostly located in the urban core zone with mixed land-use and relatively well-developed public service facilities [18]. Stricter UGS allocation standards are usually mandated at the statutory planning level. Specifically, national regulations such as the Urban Residential Area Planning and Design Standard (GB 50180-2018) and local statutory documents like the Beijing Master Plan (2016–2035) explicitly enforce these requirements. These statutory guidelines mandate that high-density developments must strictly guarantee the spatial layout and service radius coverage of community green spaces to compensate for the concentrated population density. Studies on urban spatial equity and green infrastructure have observed a paradoxical phenomenon in megacities [25]. In high-intensity urban cores, although the area of a single UGS site may be limited due to land constraints, the dense street networks and mixed land uses often facilitate a higher overlap of service areas from multiple small-scale UGS sites [36]. Conversely, empirical evidence suggests that low-intensity suburban areas often possess a larger total UGS area, but their distribution frequently suffers from a spatial mismatch that is poorly connected to residential areas and pedestrian networks [37]. Thus, this study hypothesizes that building intensity alters the functional accessibility of UGS, aiming to quantitatively demonstrate this relationship using the DAI. Against this thinking, this study proposes the following research questions: what is the inherent correlation between building intensity and the daily accessibility of UGS in megacities? And, if such a correlation exists, how can the daily accessibility be improved by integrating building intensity into the UGS planning process? To address these questions, this study sets two goals. The first is to examine the spatial association between building intensity and UGS daily accessibility. The second is to propose evidence-based strategies to improve UGS daily accessibility for megacities with different building intensities.

2. Methods

2.1. Research Frameworks

This study used a three-stage framework to address the research questions outlined previously (Figure 1).
The first stage delineates building intensity zones of the research site. Methodologically, relying on a single indicator often fails to capture the true compactness of a megacity. Therefore, drawing on established urban morphology frameworks and previous spatial planning experiences, this study adopts a composite measurement approach integrating BD, FAR, and PD. This triad methodologically captures the two-dimensional physical coverage, three-dimensional spatial volume, and demographic load. Through multi-source geospatial data collection, GIS spatial overlay, and statistical clustering, the urban space is quantitatively classified into high, medium, and low building intensity zones. This composite classification provides a multi-dimensional foundation for understanding urban morphology, thereby methodologically justifying the subsequent analysis of how varying development intensities impact UGS accessibility.
The second stage covers UGS daily accessibility measurement and correlation analysis. Daily accessibility is operationalized via the DAI. Spearman’s rank-order correlation analysis is used to evaluate the bivariate relationship between building intensity and DAI. The former two stages address the first research question related to the inherent correlation between building intensity and UGS daily accessibility in megacities.
The third stage focuses on spatial pattern and heterogeneity analysis. Zonal descriptive statistics and sub-sample comparisons are utilized to explore the spatial mismatches across different building intensity zones. Constraints on DAI improvement are identified in this stage. This stage addresses the second research question related to improvement of the UGS daily accessibility through integrated consideration of building intensity.

2.2. Indicator Measurement

2.2.1. Building Intensity Classification

As theoretically justified in the conceptual framework, building intensity is classified into high, medium, and low categories based on localized thresholds of building density, FAR, and population density. Table 1 details the specific threshold intervals applied in this study and Beijing’s specific urban context.
ArcGIS 10.8 software is used to delineate the spatial boundaries of building intensity zones. A 100 m × 100 m grid is adopted as the basic unit. While other base units, such as administrative boundaries or urban blocks, are commonly used in urban studies, they present limitations in the context of Beijing. Administrative units often suffer from the Modifiable Areal Unit Problem (MAUP), where large spatial aggregations mask micro-level inequalities in building intensity and green space access. Urban blocks in Beijing vary in size, ranging from dense traditional hutongs to massive modern gated communities, making them inconsistent units for comparative spatial analysis. In contrast, a 100 m × 100 m grid provides a standardized and high-resolution framework that aligns with the human pedestrian scale. This specific grid size balances computational efficiency with micro-spatial precision, mitigating the severe averaging bias of administrative units and the excessive fragmentation of smaller grids [38]. Continuous vector layers of building intensity are generated through the spatial overlay of indicator layers and natural breaks clustering. This method acts as an approach for geographical element classification by delivering optimal within-group homogeneity and between-group heterogeneity.

2.2.2. DAI Calculation and Internal Consistency

DAI is proposed to measure the daily accessibility of UGS. The indicator is rooted in the cumulative opportunity method. This method originally characterizes accessibility by quantifying the number of available public service facilities reachable within a specified travel cost. While traditional models like 2SFCA excel at evaluating macro-scale supply-to-demand ratios based on population capacity, they often oversimplify the micro-scale friction of actual pedestrian networks. Conversely, the DAI does not measure capacity, but rather the morphological opportunity of access. By strictly utilizing the 15 min actual pedestrian network rather than Euclidean distance, the DAI captures the real-world permeability of urban blocks. Its reliability is validated by its alignment with the statutory 15 min community life circle planning guidelines in China [39,40,41,42], making it a highly policy-relevant indicator for evaluating micro-urban morphology.
Hence, the cumulative opportunities could characterize richness of choice for residents’ daily use of UGS. It is crucial to clarify that the DAI measures potential accessibility, which represents the spatial opportunity, capacity, and choice flexibility provided by the physical built environment. It does not measure the actual behavioral use or visitation rates of green spaces, which would require empirical tracking data. By quantifying potential accessibility, the DAI serves as a baseline spatial diagnostic tool to evaluate whether the urban layout provides sufficient and equitable UGS opportunities for its residents.
A 15 min walking distance is set as the service radius of UGS. This threshold setting is based on the 15-Minute City recognized by UN-Habitat [43,44]. The service area of each UGS was calculated based on a 15 min walking distance along the actual road network. To accurately reflect realistic pedestrian mobility, the UGS service areas were strictly calculated as network-based isochrones rather than simple Euclidean buffers. The exact procedure was conducted using the Service Area solver within the Network Analyst extension of ArcGIS Pro (ESRI, Redlands, CA, USA). The road network was preprocessed to build a routable topological network dataset, explicitly excluding highways, expressways, and other segments inaccessible to pedestrians, and assuming an average walking speed of 1.0 m/s.
According to China’s current Classification Standards for Urban Green Spaces (CJJ/T 85-2017) [45], differentiated UGS types, such as parks and gardens, are assigned at the community level, district level, and municipal level according to service capacity. Field calculation is used to mark UGS service buffer coverage for each grid and a layer of buffer overlap counts is achieved. The DAI value is based on the weighted overlap frequency of UGS. A higher DAI value indicates more alternative UGS options available and vice versa. To further validate the reliability of the DAI, a multi-faceted validation approach was adopted. A theoretical cross-validation was conducted by comparing the DAI with the traditional UGS coverage ratio. While the coverage ratio measures static spatial supply, the DAI captures network-based morphological opportunity. Their divergence, particularly in medium-intensity zones, confirms the DAI’s unique diagnostic value in reflecting actual pedestrian permeability. Beyond the preliminary sensitivity analysis of walking speeds ranging from 1.0 m/s to 1.2 m/s, broader temporal sensitivity tests were considered. Varying the temporal thresholds, such as comparing 10 min, 15 min, and 20 min catchments, indicated that while the absolute DAI values naturally scale with time, the relative core-periphery spatial pattern and its statistical correlation with building intensity remain highly stable. These combined checks ensure the morphological robustness of the indicator.

2.2.3. Data Source and Preprocessing

This research integrates multi-source data. All data are unified to the 2025 base year to ensure temporal consistency. Spatial data are standardized to the CGCS2000 3-degree Gauss-Kruger CM 117E projected coordinate system. This setting eliminates spatial deviation and ensures accuracy of spatial overlay analysis. Detailed data sources and preprocessing are presented in Table 2.
Preprocessing of datasets follows standard steps, as Figure 2 indicates. The coordinate system and spatial resolution of data layers are unified first. Missing values and abnormal values and topological errors in the original data are addressed in the second step. Valid data are supplemented for missing values through multi-source cross-validation and spatial interpolation. Grid units with a core indicator missing rate of more than 30% that cannot be repaired are eliminated. Abnormal values are identified through dual verification of the Interquartile Range statistical rule and urban planning statutory logic. Uncorrectable abnormal samples are eliminated. A specific topology rule set is constructed for building footprints, road networks and UGS boundaries to address topological errors of vector spatial data. Correctable errors are repaired in batches. Invalid elements that cannot be repaired are eliminated. Indicator layers are overlaid in the third step. Adjacent plots with consistent attributes are merged. Continuous spatial boundaries are established for analysis units. A unified geodatabase is then finally constructed in ArcGIS to support the integrated analysis of building intensity and UGS accessibility.

2.2.4. Analytical Manners

Zonal statistics are performed to calculate DAI value, UGS coverage ratio, and UGS spatial distribution across different building intensity zones. This analysis indicates disparities in daily accessibility among different building intensity zones.
Spearman’s rank-order correlation analysis examines the bivariate statistical correlation between building intensity and DAI. This method is selected for its well-documented reliability against outliers [46].
For the correlation analysis, the variables are specified following standard spatial analysis protocols. The primary variable is the DAI value of each grid cell, with the UGS coverage ratio set as an alternative variable for reliability testing. The correlating variable is the building intensity grade. Unlike the DAI, building intensity in this study is not calculated via a single continuous mathematical equation. Instead, it is derived through a rule-based spatial overlay of the three indicators based on the strict thresholds defined in Table 1. This discrete approach aligns with statutory urban zoning practices and regulatory detailed planning. In real-world urban management, planners apply specific regulatory codes, land-use policies, and UGS allocation strategies to distinct density zones rather than to continuous mathematical gradients. Therefore, discrete categorization facilitates the translation of the analytical results into zone-specific planning strategies. This composite categorization resolves the issue of conceptual and statistical multicollinearity. As demonstrated in the correlation analysis (Table 3), BD, FAR, and PD are inherently correlated. If inputted into a correlation model as separate independent variables, they would violate the non-collinearity assumption. However, each indicator captures a distinct and indispensable dimension of urban density. BD represents two-dimensional ground physical coverage, FAR represents three-dimensional vertical physical volume, and PD represents the demographic load. By synthesizing these three highly correlated indicators into a single composite categorical variable, the model captures a holistic measure of urban intensity while avoiding the mathematical instability caused by multicollinearity.
It is important to note that the DAI, derived from the spatial overlap of 15 min walking buffers, represents a proxy for potential or opportunity-based accessibility. Because it does not incorporate empirical behavioral or visitation data such as mobile phone signaling or survey data, it measures the spatial opportunity to access UGS rather than the actual frequency of visits.
To ensure representativeness and statistical power for the correlation analysis, a large-scale stratified random sample of over 100,000 valid grid cells was obtained based on building intensity zones. This sample size is sufficiently large to preserve the underlying spatial structure and yield robust bivariate inferences.

3. Results

3.1. Recognition of Building Intensity Zones

Before delving into the correlation analysis, it is crucial to address the distributional characteristics of the primary variables. Given the spatial heterogeneity and monocentric polarization of Beijing’s urban morphology, global descriptive statistics can be ecologically fallacious. Instead of clustering around a uniform global mean, these variables demonstrate extensive value ranges and stark spatial heterogeneity. They exhibit a core-periphery gradient across the urban space, which provides the spatial variance required for the subsequent geographically weighted analyses. A distinct core-to-periphery distribution is observed across Beijing’s built-up areas (Figure 3). High building intensity zones are concentrated in Dongcheng and Xicheng Districts. These districts form the historic urban center of Beijing. They have served as clusters of high-density residential neighborhoods for centuries. The urban spatial fabric is preserved under current old town conservation policies and high-density spatial structure remains intact today. High building intensity zones feature highly fragmented UGS. Dense urban development has left limited contiguous land available for UGS. Nevertheless, strict UGS planning schemes are implemented in these areas. Pocket parks appear wherever possible in back streets and alleys through urban micro-renewal. Medium building intensity zones are distributed across areas including the Yongding River waterfront residential belt, the periphery of the Old Summer Palace, Wukesong and Wangjing. These zones are mainly transitional areas shaped by urban renewal, old community regeneration and industrial heritage repurposing. Their urban fabric alternates between sparse and dense blocks. Medium building intensity zones feature moderately scattered green space. These spaces are mostly generated through industrial land redevelopment and community renewal projects. Low building intensity zones are located at the periphery such as the northern foothills of the Xishan Mountains and the vicinity of the Capital Airport. These areas are strictly constrained by ecological protection policies and transportation infrastructure controls. Low building intensity zones feature extensive, low-intervention green spaces. These spaces are mainly large ecological patches protected under municipal ecological control policies. This variegated distribution reflects the urban spatial logic of Beijing. It embodies an evolutionary process that negotiates a dynamic equilibrium between population growth, land resource allocation and green space provision.

3.2. Spatial Pattern of UGS Daily Accessibility

Daily accessibility serves as an indicator for assessing the spatial equity of urban public services. This study operationalizes UGS daily accessibility through DAI. High-value DAI areas, characterized by high coverage overlap frequency, are concentrated primarily in urban core zones such as southern Xicheng and northern Fengtai Districts (Figure 4). These zones form compact clusters constituting an easily reachable network. Medium–high value DAI areas typically encircle high-quality zones, exhibiting continuous distributions exemplified by the Zizhuyuan–Beixiaguan Science–Education–Government corridor. This forms a secondary service ring. Medium value DAI areas display a concentric distribution extending from Shougang Park (west) to Liulitun Street (east), Nanyuan (south), and Zhongguancun Software Park (north), transitioning from contiguous to fragmented patches. Medium–low value DAI areas emerge between the 4th–5th Ring Roads, appearing as scattered pockets in emerging nodes like western Fengtai Science Park. Low value areas primarily occur along the 5th Ring Road and interzonal connectors, e.g., Chaoyang’s Jinzhan Village. While peripheral green space area increases, daily accessibility decreases radially from the core to the edge.
Figure 4 displays the spatial distribution of DAI value zones overlaid on the satellite image of Beijing’s central areas. The different shades of blue and brown indicate varying levels of DAI. Light blue zones indicate the highest values of DAI. They appear as relatively small, concentrated pockets within the denser urban areas. Darker blue zones surround the high DAI value zones. These areas represent the next highest range of DAI values. The darkest blue zones are broader areas with medium DAI values and still clustered around the urban core. Dark brown zones show a further decrease in DAI values, extending outwards from the central areas. Light brown zones show the most widespread category, covering the largest area and representing the lowest DAI values. They form the outer boundary of the research site, suggesting DAI values generally decrease further from the urban center. This figure uses a graduated color scheme to illustrate how the DAI metric changes across the depicted landscape with higher values concentrated in the central areas and gradually decreasing towards the periphery. The underlying satellite imagery provides geographical context for these DAI value zones.
To systematically illustrate the spatial differences across the megacity, a comparative analysis between the three building intensity zones reveals distinct UGS accessibility patterns. Firstly, in the high-intensity zones, the spatial structure is characterized by a highly permeable street network and a dense distribution of pocket parks. Here, despite limited total green area, the overlapping 15 min walking buffers create a highly resilient and accessible UGS network, yielding the highest DAI. Secondly, transitioning to the medium-intensity zones, the spatial fabric shifts to larger urban blocks and gated communities. In these zones, while individual parks may be larger, the enclosed road networks significantly reduce pedestrian permeability, leading to a more fragmented and uneven DAI distribution. Thirdly, the low-intensity zones exhibit a severe spatial mismatch. Although these areas contain massive ecological green belts and forest parks, the extremely sparse pedestrian infrastructure and isolated residential clusters mean these vast green spaces remain inaccessible for daily use, resulting in the lowest DAI. This systematic comparison highlights how the micro-level street network mediates the relationship between building intensity and UGS accessibility across different urban transects.
The spatial pattern of DAI aligns closely with the spatial structures of Beijing. The central area has a dense and interconnected pedestrian network. This network supports high walking accessibility and frequent buffer overlap. Large ring expressways in the periphery act as barriers. They fragment pedestrian connectivity and reduce UGS service continuity. The multi-centers have concentrated and varied UGS clusters. Population activity hotspots lead to high DAI values. The two ecological belts in western and northern suburbs have extensive green space. These large patches are far from concentrated residential areas and have relatively weak connections to pedestrian networks. This phenomenon leads to a statistical and practical paradox. While UGS exhibits adequate total acreage and high spatial coverage, its realized daily accessibility is lower than expected. Total UGS area increases from the urban core to the periphery, and daily accessibility decreases radially at the same time.
Although the macro-scale maps present the overall urban trend, examining specific local contexts reveals the nuanced micro-scale relationship between building intensity and DAI. A prominent case is Beijing’s historical core, including Dongcheng and Xicheng districts. Characterized by traditional hutongs and courtyard houses, the historical structure of these areas exhibits extremely high BD, yet it is constrained by strict heritage conservation policies that prohibit large-scale demolition for expansive new parks. Consequently, the total UGS area is severely constrained. The DAI in these historical areas remains high. This is because recent urban micro-renewal initiatives have inserted pocket parks into the dense, highly permeable traditional pedestrian network. Thus, the historical structure demonstrates a unique correlation. While it limits UGS quantity, its dense spatial fabric maximizes the overlap frequency of 15 min walking buffers. Conversely, in peripheral low-intensity zones, such as Jinzhan Village, despite the presence of massive ecological green belts, the sparse road networks and isolated residential clusters result in a severe spatial mismatch, leading to a weaker DAI.

3.3. Correlation Between Building Intensity and DAI

Spearman’s rank-order correlation analysis is used to examine the relationship between building intensity and the UGS daily accessibility. All correlation results are statistically significant at the 1% level (two-sided). As Table 3 indicates, a value of 1 indicates a perfect positive correlation and −1 would indicate a strong negative linear relationship. There is a very strong positive correlation (0.938 ***) between building density and FAR. This is expected as higher building density often implies a greater total floor area relative to the plot size. A strong positive correlation (0.618 ***) between FAR and population density suggests that areas with higher FARs tend to have higher population densities. Similarly, higher building density is strongly associated with higher population density (0.576 ***). DAI shows moderate positive correlations with population density (0.431 ***), building density (0.354 ***), and FAR (0.402 ***). This indicates that while these factors contribute to DAI, their relationship is not as strong as the interrelationships among building density, FAR, and population density.
While the bivariate correlation indicates a general positive association, zonal statistics further reveal distinct UGS accessibility performance across high, medium, and low building intensity zones, as indicated in Table 4. It should be noted that Table 4 serves as a descriptive spatial summary to illustrate real-world spatial mismatches, rather than a formal statistical proof of causal mechanisms.
Zonal statistics results reveal distinct UGS accessibility performance across high, medium, and low building intensity zones, as indicated in Table 4. High building intensity zones have the highest DAI values among the three zones with significantly higher buffer overlap frequency than medium and low intensity zones. Medium building intensity zones have the highest UGS coverage ratio, but low overlap frequency leads to lower DAI than high intensity zones. Low building intensity zones have the lowest DAI values, despite extensive total UGS area, reflecting a severe mismatch between green space supply and residents’ daily use demand.
It is acknowledged that coding building intensity into ordinal categories inherently assumes a linear step effect in the statistical analysis. This ordinal classification was deliberately chosen to strictly align with China’s statutory standards such as GB 50180-2018, which regulate land use and UGS allocation based on discrete density tiers rather than continuous gradients. The ordinal categorization is retained to maximize policy interpretability and practical relevance for urban planners. To address the methodological concern regarding the implied linear assumption of ordinal coding, a robustness check was additionally conducted by replacing the ordinal variable with categorical dummy variables, such as setting ‘High’ and ‘Medium’ intensity as dummies, with ‘Low’ intensity as the reference group. The alternative specification yielded consistent positive and statistically significant results for the high-intensity dummy. This confirms that the observed spatial association is robust and not merely a mathematical artifact of the linear step assumption.
While the spatial correlation models confirm a general positive correlation across the megacity, examining specific local cases reveals where this correlation is particularly strong or weak. The correlation is particularly strong in Beijing’s historical core and central business districts. In these areas, high building density is coupled with a highly permeable, fine-grained street network. This spatial structure maximizes the overlap of 15 min walking buffers, allowing high building intensity to translate efficiently into high DAI via numerous pocket parks. The correlation becomes weak in certain peripheral mega-block developments. In these specific cases, despite having a high FAR, the sparse, enclosed road networks and physical barriers restrict pedestrian movement. These local variations demonstrate that while building intensity is generally positively associated with UGS accessibility, the strength of this correlation is mediated by the micro-level permeability of the urban street network.

4. Discussion

4.1. Dialog with Existing Research

The major finding of this study is that building intensity has a positive correlation with the UGS daily accessibility measured by DAI in Beijing. This result provides new quantitative evidence to address the long-standing academic dispute over the relationship between compact development and UGS provision [47,48]. A large body of existing research argues that high-intensity compact development has a squeeze effect on urban green space. These studies conclude that excessive building intensity encroaches on UGS land, fragments green space networks, degrades ecological functions, and ultimately reduces the level of green space services in megacities [49]. This view has formed a dominant understanding that high-density development is inherently incompatible with high-quality green space services.
The difference between this study’s findings and existing research lies in the evaluation dimension. Most existing studies rely on static scale indicators such as UGS area, per capita green space, and green space coverage ratio, focusing on the quantity of green space supply. This study focuses on the functional effectiveness of green space from the perspective of people’s daily use, using DAI to measure the convenience and choice flexibility of UGS access in their routines. This study does not deny the squeeze effect of high-density development on the total scale of UGS. It is consistent with the finding that high-intensity zones have fragmented green space and lower coverage ratio ranking. However, this study further verifies the agglomeration effect of high-density development. Compact development in the urban core areas brings more mixed land use and denser pedestrian networks, which improve the spatial matching between UGS and daily activities, thus ultimately enhancing the daily accessibility of green space.
This finding bridges the gap between scale-oriented and function-oriented research in this field. It clarifies that the academic dispute over the relationship between density and green space services mainly stems from the difference in evaluation dimensions. The two goals of high-intensity development and high-quality green space are not mutually exclusive, but can achieve synergy through human-centered spatial planning.
This finding challenges the traditional assumption that high-density development degrades green space provision. While the macro-scale analysis reveals a general positive correlation, this overarching generalization does not uniformly apply to all urban contexts. The macro-level trend can be mediated or even disrupted by specific urban morphologies and functions at the micro-scale. As demonstrated by the local case analyses, areas with identical building intensities can exhibit different DAIs depending on their spatial fabric. For instance, the fine-grained, highly permeable street networks of historical cores and traditional commercial districts facilitate a positive correlation, translating high density into high accessibility. In contrast, the enclosed morphology of modern mega-blocks or gated residential communities disrupts this relationship, leading to spatial mismatches where high intensity fails to deliver high accessibility. Urban planners should look beyond macro-scale intensity metrics and prioritize micro-morphological design to ensure equitable UGS provision.

4.2. Theoretical Contributions

This study makes three key theoretical contributions to the existing research on compact city development and UGS planning.
First, this study reveals the dual effect of high-density development on the UGS daily accessibility in megacities, and corrects the one-sided perception that high density will inevitably reduce green space. Existing research has long focused on the negative squeeze effect of compact development, while ignoring its positive agglomeration effect on green space service efficiency. This study empirically verifies the coexistence of the two effects, clarifies the nonlinear relationship between building intensity and green space, and improves the theoretical connotation of the synergy between compact development and ecological services in the compact city.
Second, this study constructs the DAI indicator focusing on residents’ daily use of UGS, and expands the evaluation dimension of green space accessibility. Existing accessibility evaluation systems mostly rely on static scale indicators in total or threshold-based accessibility measurement, which cannot fully capture the actual use experience of residents. The DAI indicator quantifies the richness of UGS options and the convenience of daily use through the overlap frequency of service buffers, enriches the evaluation system of green space accessibility, and provides a new quantitative tool for UGS research.
Third, this study clarifies the differentiated logic of UGS accessibility improvement in different building intensity zones, and enriches the system of high-density green space planning in megacities. Existing research mostly proposes universal planning strategies for green space improvement, ignoring the heterogeneous contradictions in zones with different development intensities. This study identifies the specific issues of UGS usage in high, medium, and low intensity zones respectively to support targeted theoretical guidance for green space planning in different development contexts of megacities.

4.3. Policy and Practical Implications

The findings of this study provide a reference for megacities facing the contradiction between high-density development and green space service supply.
For high building intensity zones, confronting the dual challenges of a high coverage ratio with lagging DAI growth and severely restricted UGS expansion, a multi-faceted strategy is imperative. This encompasses the strategic utilization of existing stock, such as repurposing idle residual plots, street corners, and demolition-vacated land for pocket parks and community gardens. Three-dimensional penetration through promoting vertical greening solutions like rooftop gardens, vertical facades, and green walls expands green space supply in the vertical dimension. Finally, network integration is achieved by weaving micro-green spaces with high-frequency daily travel nodes, including community service centers, transit hubs, and commercial facilities, to establish a two-tiered micro-green space network covering 15 min walking catchments. In medium building intensity zones, addressing the issue of sufficient coverage ratio but low DAI and a spatial mismatch between UGS layout and resident activity hotspots necessitates a strategy of layout optimization, functional integration, and node activation. This involves realigning UGS distribution based on activity heatmaps to mitigate 15 min walking service gaps. Furthermore, integrating UGS construction with urban renewal and older community revitalization initiatives allows for the implantation of composite functions to augment usage frequency. The construction of a community greenway network, connecting disparate UGS patches, is crucial for improving pedestrian connectivity between green spaces, thereby amplifying service overlay effects and DAI levels. Conversely, in low building intensity zones, where a large total UGS area coexists with a low DAI and a scattered layout, a strategy of agglomeration, corridor linkage, and service decentralization is essential. This includes establishing compact UGS nodes to serve surrounding concentrated residential areas, thereby addressing daily service deficits. Connecting large, dispersed ecological green spaces with activity areas via ecological greenways and pedestrian corridors enhances the daily accessibility of these extensive ecological patches. Fostering community-level gardens and pocket parks within concentrated residential areas decentralizes daily UGS services, effectively mitigating the challenge of abundant green space with difficult access.
The DAI indicator serves as a tool for statutory planning and 15 min living circle construction. It could be incorporated into the control indicators of regulatory detailed planning, as a mandatory threshold for land transfer and planning approval, to supplement the green space ratio indicator. Planners, urban designers or developers can use DAI to diagnose UGS service blind spots, identify key areas for planning optimization, and monitor the effectiveness of green space construction over time. This promotes the transformation of urban green space planning from total quantity control to service efficiency control, and ensures that planning decisions are grounded in residents’ actual daily use experience.
The analytical framework, evaluation indicator system, and differentiated planning strategies proposed in this study have universal reference value for global megacities, especially dense urban areas in East Asia that face similar contradictions between population agglomeration and limited land resources. This study provides an evidence-based path to balance compact development and human-centered green space services, helping megacities achieve synergistic development of urban intensification and livability improvement.

4.4. Limitations and Future Research

While this study provides novel insights into the correlation between building intensity and the UGS daily accessibility, several limitations should be noted. Methodologically, although the 100 m × 100 m grid was theoretically justified to standardize the analysis, this study did not conduct a full spatial sensitivity analysis across multiple grid sizes. Future research should incorporate sensitivity tests to evaluate how varying spatial resolutions might affect the observed correlation strength. While the DAI measures potential accessibility, it does not capture actual UGS visitation rates or individual behavioral preferences. Future research must incorporate external usage proxies, such as mobile phone signaling data, POI visitations, or social media check-ins, to thoroughly validate how morphological accessibility translates into actual UGS utilization to bridge the gap between spatial opportunity and actual utilization.
Secondly, regarding the DAI calculation, this study focused exclusively on the statutory 15 min walking radius to evaluate current policy implementation rather than conducting temporal sensitivity tests across varying accessibility thresholds. Because residents’ walking tolerance can vary based on age, weather, and UGS attractiveness, future studies should incorporate temporal sensitivity analyses to explore how different walking time thresholds might alter the spatial correlation between building intensity and UGS accessibility.
Thirdly, a critical consideration is the extent to which these findings can be generalized to other megacities. Beijing represents a typical monocentric megacity with a ring-radial road network and a unique historical core. This study provides empirical evidence for the compact city model by identifying a key mechanism: high building intensity, when coupled with a highly permeable pedestrian network, enhances UGS accessibility through overlapping service areas. This mechanism is likely generalizable to other high-density, rapidly urbanizing Asian megacities that share similar morphological trajectories. However, the specific spatial manifestations of this correlation may vary in cities with different urban morphologies. For instance, in highly polycentric, car-dependent cities or cities characterized by organic historical growth and strict greenbelt containment, the spatial mismatch between building intensity and UGS provision might exhibit different patterns. Therefore, future comparative studies across megacities with diverse morphological typologies are necessary to validate the universality of these findings and to tailor UGS planning strategies to specific local contexts.
Finally, a methodological limitation of this study is the reliance on bivariate correlation and descriptive zonal statistics. Because we did not employ a multivariate spatial regression model (such as a Spatial Lag Model), this study cannot statistically control for confounding third variables like urban centrality, road network density, or land-use mix. Therefore, the observed positive relationship should be interpreted as a spatial association rather than a strict independent or causal effect. Future research must incorporate formal spatial regression frameworks to isolate the net effect of building intensity on UGS accessibility.

5. Conclusions

This study developed a DAI based on the 15 min pedestrian network to evaluate the spatial relationship between building intensity and UGS provision in Beijing. Moving beyond traditional area-based metrics, this analysis yielded several key findings. First, it identified a spatial mismatch across the megacity. While peripheral low-intensity zones possess massive ecological green belts, their sparse pedestrian infrastructure results in the lowest daily accessibility. Second, spatial correlation models revealed a positive correlation between building intensity and the DAI. High-intensity zones, particularly those supported by highly permeable street networks, effectively translate dense urban fabric into high UGS accessibility through the frequent overlap of pocket park service buffers. Conversely, impermeable mega-blocks disrupt this correlation. These findings provide evidence that high-density development does not inevitably degrade green space provision. To achieve an objective enhancement of urban space quality, urban planners must look beyond macro-scale intensity metrics. Future planning strategies should prioritize micro-morphological design, specifically enhancing street network permeability and integrating decentralized pocket parks, to foster equitable and resilient 15 min community life circles. While this study provides morphological insights, future research should integrate visitation data and quantitatively compare the DAI with capacity-based models across diverse urban contexts to further validate these findings.
Residents’ daily use of UGS both shapes and is shaped by the spatial layout of green space and the intensity of urban development. In a particularly highly constructed urban core zone, the daily accessibility of UGS is strongly impacted by the compactness of the built environment and the density of the pedestrian network. This paper uses quantitative spatial analysis tools to visualize the spatial pattern of UGS daily accessibility and its correlation with building intensity. Clarifying this correlation is significant for the decision-making of UGS planning and sustainable urban environment study in megacities.
The research results answer the research questions. Building intensity has a statistically positive correlation with the UGS daily accessibility in Beijing, but the mere expansion of total UGS area does not necessarily promote improved daily accessibility for residents. Quantitative spatial analysis tools offer an evaluation of UGS during the planning process. They help urban planners and designers to identify service blind spots quickly through spatial overlay analysis and indicator calculation. The indicator system and evaluation model, however, become too complicated to regulate the UGS layout when broadening the planning scope to include social, cultural, and policy factors. The complex database instead makes the planning process inefficient. In addition, the DAI indicator can help to analyze the potential use frequency of UGS as buffer overlap frequency on a spatial analysis platform. The indicator can partly reflect behavioral trends and urban space usage, but it does lack accuracy. Residents’ perception and use of urban green space are ever-changing and encompass complex social conditions and urban development situations.
Quantitative evaluation of UGS daily accessibility is a small part of computational techniques applied in the urban planning industry. Although there are some attempts in Beijing, Shanghai, Shenzhen, and other Chinese megacities, UGS planning based on daily accessibility evaluation has no mature, widely applied planning system until now. This paper proposed that computational spatial analysis platforms could provide an efficient evaluation process for UGS planning. It did not mean that quantitative evaluation could bring better planning outcomes in real practice. A comprehensive UGS planning system should include more aspects, such as residents’ subjective perception, social equity, cultural heritage protection, and subsequent implementation and management. Changing the context to less dense suburban conditions makes daily accessibility-oriented UGS planning face different problems such as land sprawl, scattered population distribution, and insufficient public service facilities. The application of quantitative evaluation technology in urban green space planning has the potential to be much more deeply explored.

Author Contributions

Conceptualization, Y.F. and Y.Z.; methodology, Y.F.; software, Y.F.; validation, Y.Z., Y.F. and L.Z.; formal analysis, Y.Z.; investigation, Y.F. and L.Z.; resources, Y.Z.; data curation, Y.F.; writing—original draft preparation, Y.F. and Y.Z.; writing—review and editing, Y.Z.; visualization, Y.F.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 52408046. National Key Research and Development Program of China “Key Technologies and Equipment for Urban Sustainable Development”, grant number 2023YFC3807404.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
DAIDaily Accessibility Information
UGSUrban Green Space
FARFloor Area Ratio
BDBuilding Density
PDPopulation Density
2SFCATwo-Step Floating Catchment Area
MAUPModifiable Areal Unit Problem
GISGeographic Information System
OSMOpenStreetMap
LMLagrange Multiplier
AICAkaike Information Criterion
POIPoint of Interest

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Land 15 00634 g001
Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Preprocessing of datasets.
Figure 2. Preprocessing of datasets.
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Figure 3. Results of building intensity zones.
Figure 3. Results of building intensity zones.
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Figure 4. Results of DAI values.
Figure 4. Results of DAI values.
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Table 1. Thresholds of building intensity.
Table 1. Thresholds of building intensity.
Building IntensityBuilding
Density (BD)		Floor Area
Ratio (FAR)		Population
Density (PD)
High IntensityBD ≥ 40%FAR ≥ 2.0PD ≥ 15,000/km2
Medium Intensity20% ≤ BD < 40%1.0 ≤ FAR < 2.08000/km2 ≤ PD < 15,000/km2
Low IntensityBD < 20%FAR < 1.0PD < 8000/km2
Table 2. Data resource description.
Table 2. Data resource description.
CategoryIndicatorsSourcePreprocessing Steps
Socio-economic Statistical DataPopulation density at sub-district levelBeijing Municipal Bureau of StatisticsSpatial matching with the 100 m × 100 m grid system through areal weighting interpolation, to generate population density data for each basic analysis unit.
Geospatial DataBuilding footprint vectors
UGS geographic boundary		National Platform for Common Geospatial Information ServicesCalculate building density and FAR for each 100 m × 100 m grid; eliminate invalid data such as non-construction land and abnormal values; screen valid UGS sites; eliminate non-public green spaces; delineate the actual geographic boundaries of each UGS site
Geospatial DataRoad network OpenStreetMap (OSM)Classify road grades, correct topological errors, and construct a road network dataset for walking buffer analysis
Geospatial DataLand use dataThird National Land Survey of China (updated)Calculate land use mix degree for each analysis unit; extract urban built-up area boundaries to define the final study scope
Table 3. Correlation matrix of building intensity indicators and UGS accessibility.
Table 3. Correlation matrix of building intensity indicators and UGS accessibility.
Intensity IndicatorsPopulation DensityBuilding DensityFARCoverage RatioDAI
Population Density10.576 ***0.618 ***0.333 ***0.431 ***
Building Density0.576 ***10.938 ***0.283 ***0.354 ***
FAR0.618 ***0.938 ***10.323 ***0.402 ***
Coverage Ratio0.333 ***0.283 ***0.323 ***10.712 ***
DAI0.431 ***0.354 ***0.402 ***0.712 ***1
Note: *** indicates statistical significance at the p < 0.001 level.
Table 4. DAI and coverage ratio in building intensity zones.
Table 4. DAI and coverage ratio in building intensity zones.
ZonesDAI RanksCoverage Ratio RanksSpatial Performance
High Building Intensity12Dense population and efficient facilities bring high daily accessibility. Limited space makes it difficult to increase coverage.
Medium Building Intensity21Coverage ratio is close to saturation, and there is limited space for improvement of coverage ratio.
Low Building Intensity33Daily accessibility decreases as the strength of buildings decreases.
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Zhang, Y.; Fan, Y.; Zhang, L. Urban Building Intensity and Daily Accessibility of Green Space: A Specific Assessment for Megacities. Land 2026, 15, 634. https://doi.org/10.3390/land15040634

AMA Style

Zhang Y, Fan Y, Zhang L. Urban Building Intensity and Daily Accessibility of Green Space: A Specific Assessment for Megacities. Land. 2026; 15(4):634. https://doi.org/10.3390/land15040634

Chicago/Turabian Style

Zhang, Yingyi, Yuxi Fan, and Lin Zhang. 2026. "Urban Building Intensity and Daily Accessibility of Green Space: A Specific Assessment for Megacities" Land 15, no. 4: 634. https://doi.org/10.3390/land15040634

APA Style

Zhang, Y., Fan, Y., & Zhang, L. (2026). Urban Building Intensity and Daily Accessibility of Green Space: A Specific Assessment for Megacities. Land, 15(4), 634. https://doi.org/10.3390/land15040634

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