Decoding Spatial Vitality in Historic Districts: A Grey Relational Analysis of Multidimensional Built Environment Factors in Shanghai’s Zhangyuan

Abstract

Enhancing the vitality of historic districts is a key challenge in China’s urban regeneration. This study takes Shanghai’s Zhangyuan Historic District as a case, constructing a framework with six spatial indicators—width-to-height ratio (W/H), interface transparency, connectivity, integration, Universal Thermal Climate Index (UTCI), and mean radiant temperature (MRT)—across spatial morphology, path accessibility, and thermal comfort. Using Grey Relational Analysis, the study quantitatively examines how these factors affect spatial vitality and pedestrian behavior. Findings indicate that, overall, W/H and connectivity are the primary drivers of vitality in plazas and alleys, while thermal comfort (MRT, UTCI) strongly affects stationary behaviors. By typology, plazas exhibit the strongest association with interface transparency (grey relational grade = 0.870), demonstrating that open sightlines and permeable interfaces promote pedestrian flow and staying. North–south alleys show pronounced associations with thermal comfort (MRT = 0.918; UTCI = 0.874), suggesting microclimate-friendly environments can substantially enhance vitality in linear walking spaces. East–west alleys are dominated by connectivity (0.831) and W/H (0.849), whereas integration shows a low grade (0.512), revealing weaker configurational coherence for this spatial type. At the micro-scale, connectivity outperforms integration in predicting pedestrian route choices, reflecting actual movement preferences. The study highlights the combined effects of multidimensional built environment factors and provides a scientific basis for targeted spatial optimization, sustainable renewal, and vitality-oriented design in historic urban areas.

1. Introduction

Historic districts, as spatial carriers highly concentrated with cultural, social, and economic activities, have attracted growing attention. Defined by UNESCO as one of the most diverse manifestations of human cultural heritage, historic districts serve as critical areas that authentically reflect traditional urban patterns and historical character [1,2]. In 1987, the International Council on Monuments and Sites (ICOMOS) explicitly stated that the preservation of historic districts should encompass not only the spatial morphology of plots and streets but also their functions and roles within the urban context [3].

In China, the State Council formally instituted the “historic district” concept in 1986, mandating retention of historic urban characteristics in spatial layouts and architectural styles [4]. In 2020, the government elevated urban renewal to a national strategic priority [5]. Currently, the renewal and conservation of historic districts are shifting from traditional “physical restoration” to an integrated approach that equally emphasizes functional revitalization and the enhancement of social vitality [6,7]. In Shanghai, municipal authorities have delineated 44 historic cultural preservation zones (12 in the central city and 32 in suburban districts), designating “heritage conservation and district revitalization” as core policy objectives. According to the Regulations on the Protection of Historic Cultural Areas and Outstanding Historic Buildings in Shanghai, these zones cover approximately 41 square kilometers. Within these zones, the government has designated 144 protected roads (over 100 km in total length), along with 163 municipal-level cultural relic protection units and 632 outstanding historic buildings. Despite these efforts, the historic districts generally face challenges of functional decline, population loss, and spatial renewal difficulties [8]. This facilitates multidimensional value realization through urban functional restructuring, cultural continuity, and public space regeneration [9]. Given the complexity of vitality dynamics and multivariate determinants, context-specific diagnostics of local conditions remain imperative to develop tailored spatial configurations [10].

Urban spatial vitality, reflecting a space’s capacity to attract and support human activities, social interactions, and economic functions, serves as a critical metric for evaluating spatial quality and functional efficiency [11]. In recent years, research on urban spatial vitality has made significant progress, expanding from traditional focuses on urban design and land use to encompass dimensions such as emotional perception, social interaction, and historic district conservation [12,13,14,15]. The underlying mechanisms of spatial vitality have gradually shifted from “single-factor dominance” to integrated frameworks of “multivariate coupling.” As the built environment serves as the spatial carrier for human activities, a scientific understanding of its mechanisms is fundamental to comprehending spatial vitality. While existing studies have examined the relationships between accessibility, thermal environment, and spatial vitality in historic districts [12], the influence mechanisms of multidimensional factors remain underexplored [16]. Meanwhile, conventional methods such as space syntax, though widely used in urban spatial structure analysis, have limited explanatory power for micro-scale behavioral responses in morphologically complex historic districts [17,18]. Therefore, there is an urgent need to introduce integrated analytical methods capable of handling small-sample, multivariate relationships to enhance the identification of coupling effects among spatial elements.

To this end, this study takes Zhangyuan, a representative historic district in Shanghai, as a case study. By constructing a comprehensive evaluation framework encompassing spatial morphology, accessibility, and thermal comfort, the research focuses on the capacity of these factors to support basic human activities related to spatial vitality. Grey Relational Analysis (GRA) is employed to quantitatively analyze the mechanisms by which multidimensional physical environmental factors influence spatial vitality in historic districts. Building on this framework, this study aims to (1) clarify how spatial morphology, accessibility, and thermal comfort interact to influence spatial vitality in historic districts; (2) establish an evaluation framework that quantitatively captures these multidimensional relationships; and (3) identify priority areas and optimization strategies for historic district renewal. These objectives are briefly outlined here, while Section 2.4 elaborates them further, in light of the literature review and gap analysis.

2. Literature Review

The intricate relationship between intangible block vitality and built environment characteristics remains a persistent academic challenge. The essence of spatial vitality lies in a space’s capacity to attract and support human activities. Jacobs pioneered the conceptualization of urban vitality, defining it as “street life over a 24 h period” [19]. Gehl subsequently emphasized that vitality transcends mere population density, fundamentally concerning “how people utilize street spaces” [20]. Montgomery further conceptualized vitality as “flows and behavioral responses within neighborhood spaces across temporal phases” [21]. These definitions collectively highlight a core understanding: spatial vitality is the integrated outcome of interactions between the physical environment and human behavior. Although, in theory, the generation of spatial vitality in historic districts involves multiple factors—including the built environment and socio-cultural context—the latter is often rooted in the unique spatial fabric and hierarchical structure of historic districts [20]. In recent years, research has systematically examined the mechanisms influencing urban vitality from the perspectives of spatial morphology, accessibility, and thermal comfort.

2.1. Factors Influencing Spatial Vitality

2.1.1. Spatial Morphology and Spatial Vitality

Spatial morphology is a key determinant of spatial vitality. The building–street interface, functioning as a mediator between interior and exterior environments, significantly enhances individuals’ spatial perception and environmental engagement [22]. From a user-oriented perspective, Gehl identifies interface transparency, storefront density, and architectural detailing quality as critical factors influencing pedestrian activity [15]. Similarly, Ewing argues that the proportion of windows and active frontages—such as shopfronts and entrances—serves as a key indicator for evaluating street vitality [23].

Recent empirical studies at both street and block scales have shown that spatial openness, street aspect ratio (width-to-height, W/H), and the proportion of historic building frontages are all significantly positively correlated with spatial vitality, collectively contributing to a vibrant public realm [24,25].

2.1.2. Accessibility and Spatial Vitality

Accessibility is defined as the relative distance or proximity between two spaces [26], or the ease with which a destination can be reached [27]. In the context of urban-vitality research, it is commonly interpreted as the convenience of human–space interaction [28]. Space syntax theory offers a fundamental framework for quantifying urban accessibility. Its most frequently employed indicators are connectivity and integration. Connectivity counts the number of nodes directly linked to a given node within a system. Higher connectivity implies greater spatial permeability and stronger adjacency relations, and is generally associated with the capacity to attract and channel pedestrian flows [29]. Integration measures the degree to which a spatial unit is aggregated with or segregated from all other units in the system. Spaces with high integration are more easily discovered and entered, and consequently exhibit higher usage frequencies [29].

Baran demonstrated that street networks with high integration facilitate recreational walking [30]. Alabi noted that urban core areas, due to their high integration and connectivity, stimulate walking activities more effectively [31]. Similarly, Can and Heath showed that neighborhoods with higher levels of social interaction often possess greater integration and connectivity, which supports the formation of “third places” such as cafes and community shops [32]. Such relationships have also been verified in studies of historic districts. Research on historic areas in Xi’an, Shenzhen, and Beijing indicates that street integration is positively correlated with both pedestrian density and commercial vibrancy [18,33,34]. Moreover, research at the intra-block scale highlights that secondary lanes—although modest in size—play a critical role in enhancing overall accessibility and the potential for social encounters [35].

2.1.3. Thermal Comfort and Spatial Vitality

Outdoor thermal comfort directly modulates pedestrians’ physiological and psychological perceptions, thereby influencing both the duration and type of activities undertaken in public space [36]. The extant literature consistently identifies thermal acceptability as a decisive factor in whether individuals choose to linger, engage in social interaction, or participate in recreational pursuits [37,38]. An environment perceived as thermally pleasant can prolong outdoor exposure, which in turn amplifies spatial vitality [39]. Although most investigations have concentrated on summer and winter extremes, recent evidence suggests that during transitional seasons people are inclined to spend more time outdoors and exhibit heightened sensitivity to minor temperature fluctuations [40].

Thermal comfort in outdoor environments is determined by a combination of variables that collectively influence the human energy balance. The primary factors include microclimatic parameters—such as air temperature, wind speed, relative humidity, and mean radiant temperature (MRT)—as well as personal variables like clothing insulation and metabolic rate [41]. Among these, mean radiant temperature is considered the core element, as it directly reflects the radiative heat from the sun and surrounding surfaces [41]. Given the interactive effects among these variables, a single meteorological parameter cannot fully capture the thermal environment as perceived by humans. Therefore, integrated thermal comfort indices, such as the Physiological Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI), are commonly used for assessment [42,43]. The PET is derived from the Munich Energy Balance Model for Individuals (MEMI) and is defined as the air temperature at which, in a typical indoor setting, the heat balance of the human body is maintained with the same core and skin temperatures as under the actual complex outdoor conditions. It is expressed in degrees Celsius, which facilitates direct interpretation and comparison with commonly known thermal conditions. In contrast, the UTCI is based on an advanced multi-node thermophysiological model coupled with a clothing model, and it reflects the equivalent temperature that would elicit the same thermophysiological strain as the reference conditions. UTCI is particularly sensitive to dynamic environmental fluctuations, such as wind speed and solar radiation, and has been demonstrated to provide more robust predictions of human thermal stress across diverse climatic regions [42,43]. Empirical analyses further demonstrate that UTCI exhibits a stronger association with urban space activity levels than any individual climatic variable, indicating its superior applicability for evaluating outdoor thermal comfort in complex urban contexts [44].

2.2. Synergy Between Influencing Factors

2.2.1. Spatial Morphology and Thermal Comfort

Urban geometry, surface cover, and urban texture (including buildings and vegetation) shape the microclimate by altering radiation fluxes, airflow patterns, and evapotranspiration rates [45]. Empirical evidence demonstrates that morphological parameters—including building density, W/H, and street orientation—are strongly correlated with thermal comfort indices [46,47,48,49]. In deep street canyons characterized by low W/H values (i.e., high, narrow streets), significant differences in the UTCI have been observed [50]. Muniz-Gäal reported that decreasing the W/H ratio during summer improves outdoor thermal comfort, as narrow street canyons enhance wind velocity and augment building-induced shading [51]. Likewise, Sun et al. examined three neighborhood typologies—low-rise, medium-rise, and high-rise districts—representing different building densities and canyon geometries [52]. Their results showed that shallow and medium-depth canyons (0.6 < W/H < 2.0) oriented along the north–south axis generally provided the highest thermal comfort, while low-rise areas favored winter comfort due to greater solar access, and high-rise areas tended to reduce comfort because of excessive shading and poor ventilation.

Beyond geometry, the permeability of spatial interfaces plays a pivotal role in modulating thermal comfort. Leng and Ma demonstrated that reducing interface density and increasing the proportion of street-level openings enhance air circulation efficiency, thereby alleviating thermal discomfort [53].

2.2.2. Synergy Between Accessibility and Thermal Comfort

Spatial morphology influences not only thermal comfort but also spatial accessibility. Existing research has explored how morphological transformations affect human behavioral patterns through these dual dimensions.

Wang Y. investigated how morphological elements—such as the hierarchy of primary and secondary streets, road density, and nodal geometry—affect the correlation between thermal comfort and accessibility during transitional seasons in hot-summer/cold-winter regions. The study demonstrated that optimizing specific morphological parameters, for example, by adjusting road density to balance ventilation and accessibility, refining the hierarchy between main and secondary streets to facilitate both airflow and pedestrian connectivity, and reshaping nodal geometries to mitigate local heat accumulation, can significantly improve the outdoor environmental performance of commercial districts [54]. Tao integrated accessibility and thermal comfort to identify high-quality public areas, and subsequently proposed vitality optimization strategies, such as enhancing underutilized but climatically favorable spaces with social or recreational functions, improving microclimate conditions in low-quality areas through shading, vegetation, or windbreaks, and reallocating spatial functions based on accessibility levels to match the needs of different user groups [55].

Research on thermal environments and spatial vitality in historic districts remains nascent, necessitating multidimensional systematic methodologies to decipher spatial mechanisms and optimization pathways [56].

2.3. Gaps in Existing Research

Although research on urban spatial vitality has deepened in recent years, studies specifically focusing on the vitality of historic districts remain limited [12]. First, the generation of spatial vitality is a complex process influenced by multiple interrelated factors. However, most existing studies tend to focus on a single spatial element such as spatial morphology, accessibility, or thermal environment [56], while relatively few have explored the interactive and synergistic effects of multidimensional factors [25]. Second, prior research has predominantly concentrated on urban arterials, commercial zones, and residential neighborhoods, with comparatively little attention given to historic districts [16]. Even when historic contexts are considered, the focus is often on individual buildings, making it difficult to reveal the mechanisms underlying spatial vitality at the district scale [57]. Third, current analytical frameworks for spatial vitality rely either on traditional approaches—such as surveys, field observation, and interviews [58]—or on data-driven approaches [59]. Data-driven methods usually draw from three main categories: Location-Based Service (LBS) data, which are useful for detecting activity hotspots but may not capture informal everyday uses; Global Positioning System (GPS) data, which provide high spatial and temporal precision for mobility tracking but lack social and perceptual dimensions; and social media data, which add semantic richness by revealing user perceptions and event dynamics but are often affected by demographic biases and uneven spatial coverage. These data sources have broadened the empirical basis of vitality studies, yet their explanatory power for micro-scale behaviors and socio-cultural interactions remains limited [60]. To highlight their core characteristics,

Table 1. Comparison of common data sources for spatial vitality studies.

Data Source Category	Core Features	Advantages	Limitations	References
Location-Based Service (LBS) Data	App-based location events and aggregated heatmaps	Large samples; timely hotspot detection; captures temporal rhythms	Demographic bias; limited semantic context; privacy constraints; weak indoor coverage	[61]
Global Positioning System (GPS) Data	High-frequency trajectories with high accuracy	Fine-grained path and mobility analysis; detects micro-movements	Small and biased samples; privacy issues; limited social meaning; poor capture of static uses	[62]
Social Media Big Data	Geotagged posts, check-ins, and reviews	Rich semantic content; reveals perceptions and event dynamics	Demographic bias; uneven spatial coverage; sparse outside events	[63]

provides a concise comparison. In contrast, field observation and behavioral mapping remain indispensable for understanding spatial dynamics in historic districts, particularly in uncovering how people use and interact with specific spaces. Moreover, while spatial analysis tools such as space syntax have been extensively employed to examine spatial structures, their effectiveness is limited in historic districts due to the intricate urban fabric and finer spatial scale. Conventional metrics such as integration and connectivity often lack explanatory power in micro-scale behavioral analysis. Therefore, there is a need to introduce integrative methods capable of addressing small sample sizes, multivariate relationships, and fuzzy associations—such as Grey Relational Analysis—to improve the identification of coupling effects among spatial variables [17,18].

2.4. Research Objectives

Accordingly, this study addresses the following core questions. First, what are the distribution patterns of human activities in historic districts, and how is spatial vitality manifested across different spatial typologies? Second, how do spatial morphology, accessibility, and thermal comfort collectively influence the generation of spatial vitality? Third, what are the differences in spatial vitality among various space types within historic districts, and what are the underlying mechanisms driving these differences?

This study takes the Zhangyuan Historic District in Shanghai as a case study to construct a three-dimensional indicator system encompassing spatial morphology–accessibility–thermal comfort. Through a combination of field investigations, simulation-based modeling, and Grey Relational Analysis (GRA), this research systematically investigates the impacts of multidimensional spatial factors on spatial vitality and proposes targeted spatial optimization strategies.

The specific research objectives are as follows:

(1)

To identify the key factors influencing spatial vitality in historic districts from the three dimensions of spatial morphology, path accessibility, and thermal comfort, and to clarify the correlations between spatial attributes and patterns of human activity;

(2)

To quantitatively examine the relationships between selected spatial indicators and behavioral patterns using field data and Grey Relational Analysis, revealing the effects of multidimensional spatial elements on vitality levels;

(3)

To summarize the morphological characteristics and vitality performance of typical spatial units, thereby providing practical design guidance for the refined renewal and targeted optimization of historic district spaces.

3. Framework and Methodology

3.1. Study Area

3.1.1. Study Area Overview

Zhangyuan Historic District (hereafter referred to as Zhangyuan) is located in the core area of the Nanjing West Road Heritage Protection Zone in Jing’an District, Shanghai (Figure 1). Covering a total area of approximately 43,800 square meters, it represents the largest and best-preserved Shikumen building complex in the city, comprising 13 municipally designated historic buildings and 24 district-level heritage conservation sites. Zhangyuan has undergone a renewal process that combines protective acquisition with adaptive redevelopment. This approach has preserved the original neighborhood fabric while enhancing urban functions and spatial quality, achieving an organic integration of heritage conservation and spatial vitality regeneration. This study selects the western section of Zhangyuan as the research site. The area retains the typical historical alley morphology while accommodating a diversity of spatial types and exhibiting high levels of pedestrian activity. These characteristics make it both representative and analytically valuable for exploring the mechanisms by which multidimensional built environment factors influence spatial vitality.

Zhangyuan Historic District (hereafter “Zhangyuan”) is located in the core of the Nanjing West Road Heritage Protection Zone in Jing’an District, Shanghai. Covering approximately 43,800 square meters, it is one of the larger and better-preserved contiguous Shikumen compounds in the city, comprising 13 municipally designated historic buildings and 24 district-level conservation sites. In recent years, Zhangyuan has pursued a renewal pathway of protective acquisition combined with adaptive redevelopment, retaining the original lilong fabric, while upgrading functions and spatial quality, and thus serving as a demonstrative case of integrating heritage conservation with vitality-oriented regeneration.

The selection of Zhangyuan as the case study is due to its high degree of typicality and extrapolative value. First, its Shikumen alleyways represent a characteristic historic urban fabric, and its “preservation-led revitalization” model aligns with mainstream renewal strategies for historic districts in China, lending the findings significant referential value for similar contexts.

3.1.2. Classification of Spatial Types and Selection of Sample Points

Based on an in-depth investigation of spatial morphology, road network structure, microclimatic conditions, and pedestrian flow patterns in the western area of Zhangyuan, eight representative spatial units were selected as research samples. These samples encompass three typical spatial typologies: plaza spaces, east–west alley spaces, and north–south alley spaces (Figure 2).

Type A refers to the central plaza space within the district, which serves multiple functions such as leisure, social interaction, pedestrian convergence, and commercial activities.

Type B includes east–west alley spaces, which connect the main urban axis with the central plaza and guide pedestrian flows from the urban periphery into the interior of the district.

Type C encompasses north–south alley spaces, which interlink various architectural clusters. These alleys are often flanked by blind walls of traditional lane houses and exhibit diverse spatial forms and scales.

3.2. Research Design

During the process of sample selection and indicator construction, the research team sought to control and exclude the interference of socio-economic factors such as commercial attributes and management/operation, focusing on the influence mechanisms of spatial morphology, accessibility, and thermal comfort—three physical spatial dimensions—on the vitality of the district.

In addition, the construction materials within the Zhangyuan Historic District are relatively homogeneous, consisting mainly of traditional brick-masonry and brick–wood structures. This uniformity effectively minimized material-induced variability in thermal performance, allowing the analysis to concentrate on differences arising from spatial form rather than material heterogeneity.

Through data collection, quantitative evaluation, and correlation analysis, the study explores the spatial distribution of vitality in the historic district and its relationship with the aforementioned built environment factors (Figure 3).

First, a multidimensional evaluation framework was established, consisting of vitality indicators and six spatial evaluation metrics. It is crucial to recognize that these dimensions do not operate in isolation; they interact synergistically to influence pedestrian behavior and spatial vitality. For instance, spatial morphology indirectly influences both thermal comfort and accessibility. The geometry of a street canyon (a morphological attribute) dictates solar access and airflow, thereby shaping the microclimate, while its network configuration defines connectivity and ease of movement. Conversely, the perceived accessibility of a route is not solely a function of distance but is also modulated by its thermal comfort; a well-connected but thermally stressful path may be underutilized. Our research framework is therefore designed to capture these cross-effects, examining how the interplay of morphology, accessibility, and thermal comfort collectively determines the vitality of different spatial units.

By integrating field surveys with open-source data, the study collected information on human activity patterns, spatial morphological characteristics, and microclimatic conditions in the Zhangyuan Historic District. All collected data were standardized for further analysis.

Subsequently, the Grey Relational Analysis (GRA) method was adopted to systematically assess the correlations between spatial vitality and each influencing factor. This approach identifies the key drivers of vitality across different spatial typologies, providing a scientific basis for determining priority areas for vitality enhancement and formulating targeted spatial optimization strategies.

3.2.1. Indicator System Development

In recent years, research on street vitality has evolved from qualitative assessments to more quantitative approaches. Mainstream theories generally divide vitality into two dimensions: external manifestations and internal components, focusing on the factors that influence vitality [64].

In this study, external manifestations of vitality are treated as dependent variables, quantified based on observable indicators such as pedestrian flow intensity, duration of stay, and frequency of spatial utilization [65,66,67]. Correspondingly, the internal components of the street environment are regarded as independent variables, referring to the underlying key spatial factors that influence vitality.

From the three dimensions of spatial morphology, accessibility, and thermal comfort, six indicators were selected (Figure 4): width-to-height ratio (W/H ratio), interface transparency, connectivity, integration, Universal Thermal Climate Index (UTCI), and mean radiant temperature (MRT).

3.2.2. Dependent Variable: Quantification of Block Vitality Indicators

To quantify the vitality level of street spaces, this study defines the Street Vitality Index as the sum of the number of passersby and the number of stationary individuals per minute. This indicator captures both the mobility and congregation aspects of human activity, reflecting the multidimensional nature of spatial vitality. The Street Vitality Index (Vᵢ) at observation point i is defined as follows:

$$\mathrm{V}\mathrm{ᵢ}=\mathrm{P}\mathrm{ᵢ}+\mathrm{S}\mathrm{ᵢ}$$

(1)

where

Vᵢ: Street Vitality Index at observation point i (persons/minute);

Pᵢ: Number of passersby per minute at observation point i;

Sᵢ: Number of stationary individuals per minute at observation point i.

The number of passersby represents mobility-driven vitality, indicating the extent to which the space functions as a transit corridor or node. Conversely, the number of stationary individuals reflects congregation-based vitality, highlighting the attractiveness and comfort of the space as a setting for social interaction or leisure activities. Compared with big data approaches—such as location-based services or mobile signaling—which may suffer from low spatial resolution and limited ability to capture fine-scale behavioral and perceptual variations within compact urban environments [60], the proposed vitality index offers higher precision and reliability. This is particularly advantageous for applications in small-scale, high-density historic blocks such as Zhangyuan.

3.2.3. Independent Variables: Quantification of Influencing Factors

Based on a comprehensive review of the existing literature and the specific spatial characteristics of urban alleys, this study establishes a three-dimensional framework of influencing factors, comprising spatial morphology, spatial accessibility, and thermal comfort. Representative indicators were selected under each dimension for quantitative analysis (

Table 2. Quantitative indicators and data sources for built environment factors influencing spatial vitality.

Indicator		Description	Data Source
Spatial morphology	Width-to-Height Ratio	Count the average width and building height of roadway/square, and calculate the ratio between them	Field research OpenStreetMap
	Interface Transparency	The ratio of the length of transparent materials (such as window glass) on the facade to the length of the facade on the ground floor
Accessibility	Connectivity	Indicates the number of connections between a space unit and its immediate adjacent space	DepthmapX
	Integration	Indicates the degree of aggregation or dispersion between a space unit and other spaces.
Thermal comfort	UTCI	Measurement of thermal comfort in open space based on multi-node Fiala thermoregulation model	Ladybug Tools
	MRT	MRT reflects the comprehensive radiant heat received by human body exposed to outdoor space

).

3.3. Data Sources and Analysis

3.3.1. Data Sources

The data employed in this study comprises three primary categories: spatial morphology, meteorological variables, and pedestrian activity. Data acquisition integrated on-site field surveys with open-source data extraction protocols.

Spatial morphology encompasses road network topology, building footprints, land use allocation and three-dimensional urban form. These data were initially retrieved from the open-source platform OpenStreetMap (OSM) and subsequently refined through supplementary in situ measurements obtained with a laser rangefinder; the resulting point cloud was imported to generate an accurate CAD base map.

Meteorological variables were downloaded from the EPWmap repository in EnergyPlus Weather (EPW) format, containing hourly air temperature, relative humidity, wind speed, and global solar radiation for an entire reference year. To ensure our field data reflected typical usage patterns under neutral weather conditions, we strategically selected a specific date and time for the survey. The on-site survey was conducted on 21 March 2023, a weekday during the spring transitional season. This choice was deliberate to minimize the influence of extreme weather (e.g., summer heat or winter cold) and anomalous activity patterns (e.g., weekend tourist surges), thereby allowing for a clearer analysis of the built environment’s impact. To supplement the macro-scale EPW data and capture the actual microclimatic conditions experienced by pedestrians, on-site measurements were taken concurrently with pedestrian observations. We utilized a Kestrel NK5500 handheld weather station (Nielsen-Kellerman Co., Boothwyn, PA, USA) at each observation point to record localized air temperature, humidity, and wind speed. These in situ data confirmed that the day featured stable and representative atmospheric conditions suitable for the study.

To capture pedestrian activity, a systematic sampling protocol was implemented. The observation window of 14:00 to 16:00 was selected to target the peak of afternoon leisure and social activities, a period that occurs after the midday lunch rush and before the evening commute. Within this two-hour window, pedestrian counts were conducted at three distinct intervals starting at 14:00, 15:00, and 16:00, with each count lasting for ten minutes. To minimize confounding effects from intersections, all observation stations were located at the mid-block sections of selected streets. Data recording combined direct visual observation with high-resolution panoramic videography, which enhanced objectivity and replicability. During the recording process, researchers systematically documented pedestrian position, instantaneous density, and dwell duration. The data from the three ten-minute intervals were then averaged for each location to represent its afternoon vitality level.

3.3.2. Data Processing

(1)

Accessibility

DepthmapX is an open-source analytical tool based on space syntax theory, widely used in the quantitative analysis of urban spatial structures. It employs methods such as Visibility Graph Analysis (VGA) and Axial Analysis to assess spatial connectivity, accessibility, and integration, thereby providing a scientific basis for urban design and spatial optimization [68].

First, the research team used the acquired spatial-morphology data to generate a base map of the study area at the required resolution. To mitigate boundary effects on analytical accuracy, a 150 m buffer was established beyond the district limits. The base map was exported in DXF format and imported into DepthmapX, where it was converted into a recognizable and operable spatial model.

Subsequently, Visibility Graph Analysis (VGA) implemented in DepthmapX was applied to the spatial model. Integration and connectivity were selected as the primary syntactic indices to quantify spatial accessibility and to provide visual representation of pedestrian potential within the historic block.

(2)

Thermal Comfort Simulation

Thermal comfort constitutes the pivotal metric for assessing the thermal quality of outdoor environments. In this study, simulations were conducted within the Grasshopper environment of Rhino, leveraging the Ladybug Tools suite. Ladybug Tools has been extensively validated for its accuracy in modelling thermal comfort in street canyons, courtyards, and other urban open spaces through a series of empirical studies [69]. All simulations were performed in Grasshopper using Ladybug Tools. MRT was computed with the Ladybug “Outdoor Solar MRT” method using an all-weather sky derived from EPW data, and UTCI was calculated with the Ladybug “UTCI” component. We report software, input data, geometric setup, and optical parameters for transparency.

Initially, a three-dimensional geometric model of the Zhangyuan district was constructed in Grasshopper, informed by preceding field surveys and topographic measurements. To reduce edge effects in view-factor and shading calculations, the modelling domain was expanded with an outer buffer. A small number of trees were present on site; therefore, vegetation was modelled as sparse, isolated trees. Subsequently, meteorological data were retrieved from the EPWmap repository and imported as boundary conditions for the thermal comfort simulation. Ladybug Tools were then employed to execute the simulation, yielding spatially explicit thermal comfort maps together with the associated descriptive statistics. The analysis grid was positioned at 1.5 m above ground—representing the typical height of human thermal sensation—with a horizontal grid spacing of 1.0 m. Surface optical parameters followed Ladybug default values.

For thermal comfort evaluation, the UTCI was adopted as the primary indicator. UTCI integrates air temperature, wind speed, relative humidity, and solar radiation, and has demonstrated robust applicability and scientific rigor for outdoor thermal assessments [70]. In springtime conditions, wind speed and MRT emerge as the dominant determinants of perceived comfort, whereby higher wind speeds or elevated MRT generally enhance thermal sensation [71]. Given that MRT exhibits pronounced spatial and temporal variability at fine scales and serves as a critical input for both UTCI and PET [72,73], MRT was additionally retained as a supplementary metric to refine simulation accuracy.

3.3.3. Data Analysis

Grey Relational Analysis (GRA), a cornerstone of grey systems theory, is a quantitative tool for elucidating inter-factor relationships within complex systems [74]. Originally proposed by Professor Deng Julong in the 1980s, the method assesses the strength of association between influencing factors and a target variable by comparing the geometric similarity of data sequences [75]. The procedural workflow is as follows.

First, a reference sequence and a set of comparative sequences are defined. In this study, the spatial vitality index serves as the reference sequence, while six quantitative indicators—nested within three overarching categories (spatial morphology, accessibility, and thermal comfort)—constitute the comparative sequences. Second, all indicator data are dimensionless-normalized to eliminate scale and unit effects, thereby ensuring scientific rigor and comparability. Third, following the computational framework of grey systems theory, the correlation coefficient between each element of the comparative sequences and the corresponding element of the reference sequence is calculated.

Finally, the correlation coefficients of each comparative sequence are weighted-averaged to yield a composite grey relational grade. The magnitude of this grade quantifies the contribution of each influencing factor to pedestrian vitality; a higher value indicates a more pronounced impact on block-level vitality.

4. Results and Analysis

4.1. Spatial Vitality Characteristics

The study standardized and analyzed the survey data across three time periods (

Table 3. Normalized vitality values across different spatial units and times.

Research Points	Time			Mean Vitality (Normalized)
	14:00	15:00	16:00
A1	0.333	1.000	0.667	1.000
A2	0.222	0.317	0.317	0.368
B1	0.127	0.048	0.016	0.000
B2	0.190	0.190	0.064	0.141
B3	0.333	0.222	0.253	0.342
C1	0.175	0.024	0.000	0.005
C2	0.159	0.222	0.190	0.211
C3	0.032	0.079	0.111	0.018

). Overlaying these values onto the site plan reveals a clear south east high, north west low vitality gradient across the Zhangyuan West district (Figure 5). Among the three spatial typologies, Category A plazas exhibit significantly higher vitality indices than the adjacent alley segments, with pedestrian activity levels approximately three to four times those observed in the lanes. Within this category, Plaza A1 stands out as the primary hotspot, demonstrating a pronounced concentration of pedestrian flow. In contrast, Plaza A2 maintains a relatively stable state, with a mean vitality index of 0.368 and minimal temporal fluctuation.

North–south oriented alleys consistently underperform their east–west counterparts, yet the two corridors immediately adjacent to Plaza A1, namely B3 and C2, achieve elevated indices of 0.342 and 0.211, respectively. This spatial pattern indicates a measurable radiative effect emanating from the high vitality core.

Temporally, the overall district vitality peaks at 15:00, exceeding the levels at 14:00 and 16:00, likely due to post-lunch leisure and shopping peaks. During this period, Plaza A1 reaches its maximum intensity, with an average of 39 passersby per minute and 26 stationary occupants per minute, representing the highest density of human activity in the district. In comparison, Plaza A2 and the Alleys B3 and C2 show relatively stable vitality throughout the observation period, while node C3 exhibits an upward trend, suggesting potential growth. Conversely, most other nodes display declining trends, with C1 experiencing a significant drop between 14:00 and 15:00.

4.2. Accessibility Characteristics

Figure 6 illustrates the simulated results of connectivity and integration across the Zhangyuan West district. The north–south arterial road exhibits the highest connectivity and integration values, with both indices declining gradually toward the interior, reflecting a hierarchical accessibility structure within the system. Plaza A1, connected to multiple alley paths, demonstrates elevated connectivity and superior integration, indicating high accessibility. Plaza A2, although geographically close to the arterial road, has fewer connecting paths, resulting in significantly lower connectivity than A1.

The east–west oriented Alley Zone B shows moderate overall accessibility based on these indices. Within this zone, Alley B3, which links the north–south arterial road and Plaza A1, achieves the highest connectivity and integration values. Alleys B1 and B2 are located on the northern edge of the block; however, B2 records the lowest integration, primarily due to a centrally recessed space that enhances enclosure and reduces permeability. Half of Alley B2 has a W/H ratio of approximately 0.3, similar to B1. However, a square recessed space in its central location increases the local W/H ratio to over 0.8, creating a path deviation and thereby lowering its overall integration in the space syntax analysis.

Alley Zone C, aligned north–south and located farther from the arterial spine, is characterized by narrow passages and limited extension. Consequently, its connectivity and integration fall below those of Zones A and B. Such spaces exhibit weak accessibility and are less effective in guiding or concentrating pedestrian flows.

4.3. Thermal Comfort Characteristics

The Ladybug Tools model was first validated against typical March meteorological data for Shanghai. Overall trends of simulated and observed values were consistent, confirming that UTCI and MRT can reliably represent the spring thermal environment of the study area. Figure 7 presents the corresponding UTCI and MRT outputs.

Figure 7a illustrates the UTCI spatial distribution for the Zhangyuan West district between 14:00 and 16:00. UTCI values range from 17.43 °C to 18.38 °C, all falling within the thermally comfortable band. This indicates that the afternoon thermal environment in spring is generally acceptable. Nevertheless, spatial heterogeneity is evident. The north–south arterial road registers higher UTCI values, whereas plazas exhibit values between 17.81 °C and 18.19 °C, and alleys cluster from 17.43 °C to 17.69 °C. The disparity arises primarily from differences in street aspect ratio. Consistent with previous findings [48], smaller W/H provided by taller buildings and narrower streets enhance shading and consequently reduce UTCI. Street orientation also exerts a minor influence, with north–south alleys presenting slightly lower UTCI than east–west ones.

Figure 7b shows the MRT distribution across the district. MRT ranges from 17.89 °C to 20.91 °C, closely mirroring the UTCI pattern. Plaza spaces (A1 W/H ≈ 1.6; A2 ≈ 1.1) record the highest MRT values (18.97–20.33 °C), reflecting elevated solar exposure and a stronger thermal response. Alley spaces display lower MRT values (17.80–18.97 °C), yielding marginally cooler but still comfortable conditions. North–south alleys, benefiting from pronounced shading due to their low W/H, exhibit consistently lower MRT. For example, the typical north–south alleys C1 and C3 have a W/H ratio of only 0.2, the lowest in the entire district, which keeps them in building shadows for most of the time. Specifically, point C2—an expanded terminus created by building setbacks—records higher MRT than the adjacent, more enclosed points C1 and C3. A similar pattern is observed in the east–west alleys: point B2 displays higher MRT than B1 and B3, this is precisely because the measurement point B2 is located within the aforementioned recessed space with a W/H ratio greater than 0.8; its open morphology allows it to receive more solar radiation, while B3 is situated in typical alley sections with W/H ratios below 0.6, offering better shading, underscoring the positive role of spatial morphology in enhancing pedestrian thermal comfort.

4.4. Correlation Analysis

Grey Relational Analysis was employed to quantify the influence of six independent variables on block-level vitality, with the independent variables treated as comparative sequences and the vitality index as the reference sequence. Due to the substantial differences in magnitude among variables, all data were normalized prior to analysis to eliminate dimensional effects (Figure 8).

After normalization, grey relational coefficients were calculated at each time interval and then averaged to obtain the relational grades. The relational grade, ranging from 0 to 1, measures the degree of similarity between each independent variable and the reference sequence; values closer to 1 indicate a stronger correlation and greater explanatory power for spatial vitality.

Table 4. Relational grade and ranking of block vitality index and spatial factors.

Indicator	Relational Grade	Rank
Connectivity	0.872	1
W/H	0.838	2
MRT	0.771	3
UTCI	0.744	4
Integration	0.674	5
Interface Transparency	0.621	6

summarizes the relational grades between the vitality index and each independent variable, while

Table 5. Grey correlation coefficients and rankings between spatial–environmental indicators and pedestrian behavior types (stationary vs. passing).

Indicator	Stationary Behavior	Rank	Passing Behavior	Rank
Connectivity	0.838	1	0.875	1
W/H	0.806	2	0.795	2
MRT	0.801	3	0.689	3
UTCI	0.779	4	0.671	4
Integration	0.676	5	0.667	5
Interface Transparency	0.660	6	0.637	6

presents the corresponding strengths of association for both stationary and passing pedestrian counts.

4.4.1. Overall Association Trend

At the district scale (Table 4), W/H (0.872), followed by connectivity (0.838), respectively, indicate that these two morphological variables are the primary drivers of spatial vitality. MRT (0.771) and UTCI (0.744) occupy intermediate positions, demonstrating that thermal comfort exerts a moderate regulatory influence on pedestrian activity. In contrast, integration and interface transparency exhibit comparatively low relational grades, suggesting that, within the study area, spatial integration and visual permeability exert a limited direct impact on vitality.

4.4.2. Behavior-Specific Association Patterns

As shown in Table 5, for stationary behavior, thermal comfort indicators (MRT and UTCI) exhibit higher relational grades than for passing behavior, indicating that favorable microclimatic conditions are more effective in attracting people to stay. For passing behavior, connectivity is the most critical influencing factor, reflecting that dynamic activities are more dependent on efficient spatial connectivity. These results are consistent with Jan Gehl’s perspective in Life Between Buildings: spontaneous staying activities are at the core of urban vitality, and appropriate spatial design can significantly enhance the occurrence of such activities.

4.4.3. Typology-Specific Association Structures

To further reveal how spatial typology moderates vitality mechanisms, relational grades were averaged for three categories: plazas (A1–A2), east–west alleys (B1–B3), and north–south alleys (C1–C3) (

Table 6. Average relational grade on spatial typologies.

Indicator	Plaza (A1 + A2)	East–West Alley (B1–B3)	North–South Alley (C1–C3)
Connectivity	0.773	0.831	0.889
Integration	0.560	0.512	0.912
MRT	0.604	0.736	0.918
UTCI	0.702	0.643	0.874
Interface Transparency	0.870	0.580	0.497
W/H	0.778	0.848	0.958

). The results show that width-to-height ratio (mean grade 0.872) and connectivity (0.838) consistently rank highest across all types, indicating that appropriate spatial scale and strong pedestrian accessibility are the primary conditions for stimulating human activity, whether in open plazas or alleyways.

By typology, plazas (A1, A2) show the strongest association with interface transparency (0.870), indicating that open sightlines and permeable interfaces play a decisive role in enhancing pedestrian flow and staying in plazas. North–south alleys (C1–C3) exhibit pronounced associations with thermal comfort indicators, with MRT and UTCI reaching grades of 0.918 and 0.874, respectively, showing that microclimate-friendly environments can significantly strengthen vitality in linear walking spaces. East–west alleys (B1–B3) are dominated by connectivity (0.831) and width-to-height ratio (0.849), but integration has a low grade of 0.512, revealing a weakness in configurational coherence for this spatial type.

Overall, the width-to-height ratio and connectivity significantly influence urban spatial vitality across different typologies, while alley vitality is further moderated by synergistic effects of site attributes, microclimate, and other factors.

5. Discussion

5.1. Critical Role of Spatial Morphology

Grey Relational Analysis indicates that W/H is the dominant predictor of overall spatial vitality within the Zhangyuan Historic District, demonstrating that pedestrian activity is highly sensitive to perceived scale. This finding aligns with Gehl’s assertion that human-scale dimensions are central to the attractiveness of urban space [15] and corroborates recent discussions on the importance of scale in walkable environments [76]. Both excessively high and low aspect ratios are associated with diminished spatial efficiency, whereas an optimal degree of enclosure fosters visual focus and encourages lingering behavior.

Interface transparency exerts a significant influence on plaza vitality. Plazas characterized by greater openness and unobstructed sightlines register higher pedestrian volumes and longer dwell times. In contrast, alleys lined with continuous solid facades create a strong sense of enclosure, restrict sightlines, and exhibit lower vitality. These outcomes are consistent with Kim’s argument that visual connectivity promotes spatial use [77], further substantiating the positive impact of interface design and permeability on pedestrian behavior and experience [78].

5.2. Modulatory Effect of the Thermal Environment

Thermal comfort variables exert a moderate influence on spatial vitality, with relational grades of 0.771 for MRT and 0.744 for UTCI. Plaza areas, benefiting from ample daylight combined with selective shading, achieve higher thermal comfort and consequently function as focal points for pedestrian accumulation. Within these spaces, the proportion of stationary users is significantly elevated. In contrast, alleys characterized by enclosure and restricted solar access exhibit lower MRT values and attract primarily transient passersby. This pattern confirms Kántor’s proposition that microclimate mediates the spatio-temporal distribution of behavior [79] and demonstrates that thermal comfort not only alters the likelihood of activity but also shapes its typology—whether lingering, conversing, or sitting [80]. As Leng and Ma observed, increasing the porosity of street façades can enhance air circulation and thereby improve perceived thermal comfort [53]. Moreover, the interaction between thermal comfort and spatial morphology is evident, as W/H and orientation dictate solar exposure and shading, which in turn shape patterns of human activity. This is demonstrated by the primary contrast between open plazas and enclosed alleys. Plaza A1, with its high W/H ratio (≈1.6), receives ample solar radiation, leading to higher MRT values (18.97–20.33 °C) and functioning as a high-vitality social hub. Conversely, the narrow alleys (e.g., C1/C3, W/H ≈ 0.2) are defined by extensive shading, resulting in lower MRT and their use primarily as transient corridors. This morphological control is further nuanced by street orientation: north–south alleys consistently exhibit lower vitality than their east–west counterparts, a pattern that reflects their distinct thermal comfort, as their alignment provides more pronounced shading and thus holds less appeal for stationary activities.

5.3. Differential Influence of Accessibility Metrics

High connectivity nodes consistently coincide with elevated pedestrian vitality, indicating that, at the micro-scale of a historic district, nodal permeability is more decisive than system-wide integration. Enhancing local connectivity can compensate for low integration and reinforce linkages with adjacent areas, thereby fostering vitality. For instance, Alley C2 exhibits low integration, yet its southern link to the main plaza and the urban arterial road provides high connectivity, transforming the segment into a pedestrian hotspot.

In this study, integration registers a relational grade of 0.674, markedly lower than that of connectivity. This weak association can be attributed to two factors. First, integration, as a global configurational measure, is better suited to large-scale urban networks; at the block scale its explanatory power is attenuated by local morphological features. Second, the prevalence of enclosed courtyards and cul-de-sac paths within the district diminishes the behavioral guidance exerted by global spatial integration.

6. Conclusions

As the preservation of historic and cultural districts enters a deeper stage, enhancing spatial vitality has become a key strategy for promoting their sustainable development. Using the Zhangyuan Historic District in Shanghai as a case study, this research constructs a comprehensive evaluation framework integrating spatial morphology, accessibility, and thermal comfort, and employs Grey Relational Analysis (GRA) to systematically examine the correlations between six spatial evaluation indicators and spatial vitality. The main findings are as follows:

(1)

Width-to-height ratio (W/H) and connectivity are identified as the core drivers of district spatial vitality, exhibiting significant and stable positive effects across different spatial typologies, including plazas and alleys.

(2)

Thermal comfort indicators play a crucial regulatory role in stationary behaviors, with MRT and UTCI showing high correlations with static activities.

(3)

The primary drivers of vitality vary significantly by spatial type. In plaza-type spaces, interface transparency (grey relational grade = 0.870), demonstrating that open sightlines and permeable interfaces promote pedestrian flow and staying. North–south alleys show pronounced associations with thermal comfort (MRT = 0.918; UTCI = 0.874), east–west alleys are dominated by connectivity (0.831) and W/H (0.849), whereas integration shows a low grade (0.512), revealing weaker configurational coherence for this spatial type.

(4)

At the micro-scale of historic districts, connectivity demonstrates greater explanatory power than integration in predicting pedestrian route choices, more effectively reflecting actual movement preferences and behavioral patterns.

This study offers valuable insights into the renewal and activation of historic districts. The primary implication is that renewal should begin by strengthening the pedestrian network, focusing on local connectivity, which our analysis proved to be more influential than macro integration. This means prioritizing intuitive shortcuts and unblocking alleys—even using indoor pass-throughs—to complete the micro-circulation. Building on this enhanced network, interventions should be typology-driven; in plazas, enhancing transparency requires moving beyond adding windows to activating ground floors with a “see–reach–participate” approach—using openable facades, semi-outdoor spillover zones, and cultural windows so visual permeability converts into flow and stay. While the observed MRT in the open plazas (A1 W/H ≈ 1.6; A2 ≈ 1.1) is acceptable in spring, their high solar exposure may pose a risk of thermal discomfort in summer. To ensure year-round comfort and usability, it is crucial that they are paired with partial shading, trees, and cool surfaces to temper the radiative load.

In alleys, the strategic imperative is to stitch a pedestrian spine linking heritage assets and deploy green walls and cool pavements to shape comfortable microclimates that encourage lingering. The case of Alley B2 demonstrates that an abrupt increase in W/H to over 0.8 may increase local thermal stress while simultaneously fracturing the path’s integration. Such open spaces must be intentionally designed as destinations with ample shading and seating, rather than just voids.

While this physical-environment-based assessment provides important practical guidance for the conservation and renewal of historic districts, several limitations should be acknowledged: First, the evaluation framework is primarily based on observable physical environment and behavioral data and does not fully capture the socio-cultural dimensions of vitality, such as subjective satisfaction, sense of place, and cultural identity. The study operates within the boundaries of the historic district and does not account for the macro-scale dynamics and connectivity with the wider city, which can significantly impact the district’s vitality. Second, while GRA effectively reveals the relative associations between variables, it cannot fully capture the causal mechanisms underlying human behavior. Future research could incorporate in-depth interviews or behavioral tracking methods for further validation. Third, the data for thermal comfort analysis were collected during the spring season, which introduces seasonal limitations. Future studies should include multi-seasonal and multi-temporal data to enable dynamic comparative analysis. The research relies on time-specific field surveys. Integrating this with passive, large-scale datasets would offer a more robust and continuous picture of spatial usage patterns. Additionally, the study controlled for construction materials within the Zhangyuan Historic District, which are largely homogeneous—mainly traditional brick-masonry and brick–wood structures—but their exclusion, along with other spatial factors such as vegetation coverage and acoustic environment, represents a limitation. Future research should explicitly consider material diversity, surface albedo, thermal properties, vegetation, and acoustic factors to establish a more comprehensive framework for evaluating microclimatic conditions and spatial vitality.

In conclusion, the spatial vitality of historic districts is not the result of a single factor, but rather a complex product of the interplay among spatial form, behavioral mechanisms, and environmental adaptability. Future urban regeneration efforts should prioritize human-scale design and adopt context-sensitive strategies to optimize spatial morphology, enhance accessibility, and improve microclimatic comfort, thereby achieving a mutually beneficial integration of spatial quality and urban vitality in historic districts.