Balancing Heritage Conservation and Urban Vitality Through a Multi-Tiered Governance Strategy: A Case Study of Nanjing’s Yihe Road Historic District, China

Abstract

Historic districts face persistent challenges balancing heritage preservation and urban vitality due to fragmented governance and static conservation. This study develops a multi-source data-driven evaluation system coupling spatial quality and urban vitality, focusing on China’s Republican-era historic districts with Nanjing’s Yihe Road as a case study. Integrating field surveys and big data (street view imagery, POI data, heatmaps), we quantitatively assess environmental quality and vitality. Key findings reveal a distinct spatial pattern: “high-quality concentration internally” and “high-vitality concentration externally,” where core areas exhibit functional homogenization and low vitality, while peripheries show high pedestrian activity but lack spatial coherence. Clustering analysis categorizes streets into four types based on quality and vitality levels, highlighting contradictions between static conservation and adaptive reuse. The study deepens understanding of spatial differentiation mechanisms and reveals universal patterns for sustainable development strategies. A multi-tiered governance strategy is proposed: urban-level flexible governance harmonizes cross-departmental policies via adaptive planning, district-level differentiated governance activates spatial value through functional reorganization, and street-level fine-grained management prioritizes incremental micro-renewal. The research underscores the critical need to balance heritage preservation with contemporary functional demands during urban renewal, offering a practical framework to resolve spatial conflicts and reconcile conservation with regeneration.

1. Introduction

With the acceleration of global urbanization, urban regeneration has shifted from new district development to optimizing existing urban fabric [1,2]. Historic and cultural districts, as essential components of urban and cultural heritage, serve as both carriers of regional cultural genes and key areas for enhancing urban vitality [3]. These districts embody a city’s historical features and distinctive local characteristics, representing critical elements in promoting sustainable development and urban competitiveness [4].

Historic and cultural districts have increasingly adjusted their functions during urban renewal to align with modern societal demands. Originally residential areas have transitioned into tourist hubs due to cultural heritage tourism [5], evolving into public spaces for recreation, culture, and leisure involving both residents and visitors [6]. This shift has heightened focus on their communal and public attributes, positioning them as critical components of the “urban community.” With urban regeneration advancing, renewal goals now extend beyond improving living conditions to encompass economic revitalization and urban vitality, ultimately aiming for people-centered sustainable development [5]. This evolution necessitates refined, inclusive spatial interventions to enhance public space quality and human-centric design [7]. The Republican era (1912–1949) marked a pivotal phase of modern urbanization in China, characterized by Western-inspired planning and architectural innovation. As China’s former capital, Nanjing synthesized Beaux-Arts principles with local traditions, producing distinctive mixed-use districts such as Yihe Road, featuring tree-lined boulevards, courtyard residences, and institutional complexes. While Republican-era historic districts in China embody irreplaceable historical and cultural value, China’s urban regeneration policies have historically prioritized tangible developmental outcomes such as infrastructure upgrading and economic growth over cultural heritage conservation [8]. This policy orientation has resulted in resource allocation gaps for conservation efforts, leading to more severe challenges in functional adaptation and spatial reorganization compared to established European practices in historic district preservation [9,10,11].

Republican-era districts exhibit a severe historical awareness gap compared to Ming-Qing streets. First, China’s Regulations on the Conservation of Historic and Cultural Cities lack clarity on protection standards for Republican-era buildings, leaving non-designated structures caught between “preservation-induced decay” and “creative destruction” [12]. Second, public perception remains limited to superficial “nostalgic symbols,” neglecting their role in social transformation [13]. This cognitive gap hinders exploration of internal qualities and vitality mechanisms. These districts, blending residential and public functions, face polarization dilemmas during rapid urbanization. Excessive commercialization generates short-term vitality but erodes historical ambiance, while rigid residential retention diminishes urban dynamism. This contradiction between spatial quality and social vibrancy is particularly acute in residential-dominated Republican-era districts, reflecting a complex dualistic challenge [14].

As a representative Republican-era historic district, the Yihe Road historic district stands as an ideal case for heritage conservation research due to its profound historical significance. However, the district has long grappled with challenges in balancing heritage preservation and urban vitality, particularly amid mixed-use development contexts. Thus, this study selects the district as its research area, integrating multi-source data to assess its spatial quality and vitality. We propose a framework that integrates field surveys with multi-source big data to determine evaluation indicator weights for different street types via the analytic hierarchy process (AHP), constructing a coupled evaluation model of spatial quality and urban vitality to reveal spatial differentiation mechanisms in historic districts. Cluster analysis will be used to classify the coupling types of quality and vitality, revealing key spatial contradictions. The dynamic balance mechanism identified will provide a foundation for flexible governance strategies that can reconcile static heritage protection with constantly changing urban demands. This framework offers actionable insights for adaptive planning, providing a quantifiable foundation to balance preservation and regeneration while addressing the complex interplay between static protection and dynamic urban needs.

2. Literature Review

Research on historic district renewal has increasingly highlighted limitations in traditional methodologies. Conventional approaches, relying on questionnaires, field observations, and expert evaluations [15,16], are constrained by (1) subjectivity in qualitative analysis and lack of standardized criteria [17], and (2) technical challenges in capturing dynamic spatial usage patterns due to small sample sizes [15,16]. These methods also struggle to analyze micro-morphological features of public spaces and interpret intangible perceptual elements. Recent advancements in big data technologies, however, have introduced new methodological opportunities for studying and renewing historic districts [18,19], enabling more precise and scalable assessments of spatial dynamics and functional adaptations.

Recent advancements in big data technologies have opened new pathways for historic district studies. In Europe, virtual reality (VR) and interactive platforms—such as the Virtual Plaza for Culture and Historical Heritage in Bulgaria [20]—enhance public participation and global dissemination of cultural heritage. Romanian scholars used the travel cost method (TCM) to quantify the use value of Bucharest’s historic center, confirming a significant link between tourists’ perception of heritage conservation and their willingness to pay [21]. In Salerno, participatory approaches in circular economy contexts [10] highlight community engagement in sustainable heritage reuse. In the United States, the latent urban natural vitality (IUNV) model integrates census data, street view images, and social media analytics to quantify the influence of land use diversity and population density on urban vitality [22]. Meanwhile, Southeast Asian and Middle Eastern studies focus on digital preservation and tourist experience assessment. For example, the George Town World Heritage Site in Malaysia employs the consumer-based authenticity (C-BA) model and structural equation modeling (SEM) to analyze tourists’ revisit intentions [23], while Saudi Arabia leverages heritage building information modeling (HBIM) for digital heritage transmission [24,25]. Chinese scholars have pioneered the integration of multi-source data with machine learning techniques. Datasets such as street view images, remote sensing data, points of interest (POIs), and social media reviews are combined with algorithms like AHP, GWR, and random forest (RF) to evaluate spatial quality and vitality [26,27,28,29]. These frameworks enable large-scale, high-precision analysis of spatial heterogeneity, providing a scientific basis for urban planning and policy formulation [30].

Overall, the evolution of research exhibits two key trends: Firstly, systematic integration of heterogeneous data sources (e.g., street view images, POIs, remote sensing) to assess spatial morphology, vitality dynamics, and cultural significance [27,29,31,32]; secondly, application of advanced analytical techniques (e.g., space syntax, semantic segmentation, GWR) to enhance the precision of quantitative assessments [33,34,35]. Notably, recent studies emphasize context-sensitive interventions that balance heritage conservation with contemporary functional demands [32,36]. These innovations have facilitated the development of targeted renewal strategies, particularly through spatial configuration analyses and transportation accessibility evaluations [28,37].

Republican-era historic districts, with unique historical–cultural significance, present distinctive urban regeneration challenges requiring specialized assessment frameworks. While historic district evaluation has advanced, current research shows limitations in Republican-era contexts. On one hand, existing studies focus on spatial quality or vitality in isolation, causing fragmented assessments [38,39]. For instance, Zhang and Han [4] developed spatial vitality metrics via field surveys but neglected quality, while Wang et al. [29] captured quality with big data yet overlooked spatial vitality. This compartmentalization ignores quality–vitality interdependence in historic districts, where one dimension’s improvements affect the other. On the other hand, despite growing multi-source data use in urban studies, current methodologies underutilize their synergistic potential [28,40]. Recent work by Zhang et al. [17] and Liu et al. [2] showed big data utility in capturing spatial dynamics via street view/POI data, but their frameworks remain descriptive, limiting interpretation of environmental factor–spatial quality causal relationships. In addition, institutional factors in urban governance in China have not been fully explored: local governments often exhibit investment preferences for infrastructure and real estate development, leading to insufficient allocation of resources for cultural protection [8,41]. This study addresses these limitations by developing a coupled quality–vitality evaluation framework integrating traditional field surveys and multi-source big data, enabling more comprehensive dynamic assessment of Republican-era districts. This integrated approach advances existing methodologies by addressing spatial quality and vitality as interdependent dimensions, offering nuanced understanding of complex dynamics in historic district evaluation.

3. Materials and Methods

3.1. Research Framework

This study constructs a multi-source data-driven quality–vitality coupled evaluation model for the Republican-era Yihe Lu historic district in Nanjing. First, the Yihe Road historic district was selected as the case study. Notably, its status as a well-documented, large-scale Republican-era historic district with ongoing urban renewal offers an ideal context for model development. However, existing research lacks integrated frameworks to simultaneously assess spatial quality and vitality in such districts—a critical gap this study aims to fill. Key indicators related to spatial quality and vitality were collected and categorized by integrating field surveys with multi-source big data. Second, streets were classified into typologies based on the Guidelines for Street Design in Nanjing and field conditions. The analytic hierarchy process (AHP) was applied to determine street-type-specific weights for quality evaluation criteria, ensuring localized adaptation of assessment in historical districts. Subsequently, normalization and cluster analysis were conducted. Based on the constructed indicator system and assigned weights, normalization processing standardized quality–vitality scores across all streets. Cluster-based classification revealed spatial differentiation patterns, while radar charts visually displayed dimensional strengths and weaknesses of each street type. Finally, based on the distinct spatial characteristics identified in the results, the study deepened the understanding of spatial differentiation mechanisms in Republican-era historic districts, uncovered their generalizable mechanisms of spatial differentiation and proposed multi-tiered renewal strategies to balance the dynamic equilibrium between heritage conservation and functional regeneration. The research framework is illustrated in the accompanying figure (Figure 1).

3.2. Study Area

This study examines the Yihe Road Historic and Cultural District in Nanjing’s Gulou District, Nanjing’s first nationally designated Historic and Cultural Quarter. Encompassing 37.8 hectares, it represents the largest and best-preserved Republican-era architectural ensemble in the city, bounded by Jiangsu Road–Ninghai Road (east), Xikang Road (west), Beijing West Road (south), and Ningxia Road (north). As the core of the Republican Capital Plan, the district embodies the era’s integrated political, cultural, and architectural legacy, offering an optimal context for analyzing spatial quality–vitality coupling in historic urban fabrics. Its well-documented status, scale, and ongoing regeneration efforts provide a robust foundation for testing the quality–vitality coupling model—a critical gap in existing frameworks. Currently functioning as a mixed-use area, the district faces conservation–regeneration conflicts due to uneven quality–vitality distribution, necessitating data-driven solutions. This case was selected for empirical analysis to address its specific challenges and generate transferable strategies for comparable historic districts.

The study area includes 16 streets, segmented into 45 analytical units using the natural break method, with each segment treated as a basic assessment unit (Figure 2).

3.3. Construction of the Evaluation Indicator System

Due to significant discrepancies in existing studies on urban district quality and vitality assessment, there is a lack of consistency in indicator selection and measurement methodologies, resulting in the absence of a generalizable measurement framework [42]. To address the specific spatial evaluation needs of Republican-era historic and cultural districts, this study constructs a multi-dimensional evaluation matrix that integrates objective indicators with subjective experiences, leveraging multi-source data through a combination of quantitative assessments and human-scale environmental data obtained via traditional survey methods. Based on the literature review and field survey results, the street-level metrics are categorized into two dimensions: quality and vitality.

• The Quality Dimension emphasizes static spatial characteristics, comprising three criterion layers: Element Perception (E), Spatial Form (S), and Functionality (F).

  (1)

  Element Perception (E) focuses on elements in the district that directly influence sensory experiences and cognitive perceptions:

Green View Index (GVI) (E1): A quantitative metric measuring the proportion of green vegetation within pedestrians’ field of view along streets or urban spaces, reflecting the impact of vegetation coverage on visual perception [38]. Special Building Density (E2): The number of historic buildings per unit area with distinctive architectural styles or historical periods, indicating the spatial concentration of buildings and the integrity of the historic urban fabric [43]. Historical and Cultural Experience (E3): Tourists’ cognitive understanding, emotional resonance, and participatory engagement with local history and culture, derived from interactions with architecture, environment, and activities during visits to the historic district [33].

These indicators analyze the performance of historic districts in terms of environmental aesthetics, cultural heritage, and tourist interactive experiences

• (2)

  Spatial Form (S) refers to the two-dimensional and three-dimensional morphological characteristics of streets:

Pedestrian Accessibility (S1): The convenience with which residents or tourists can reach public facilities and service nodes via walking, serving as a key metric for evaluating street network connectivity and functional distribution [27]. Pedestrian Flow Potential (S2): The capacity of a district to attract and facilitate pedestrian movement, representing the latent attractiveness of street spaces [27]. Pedestrian Scale (S3) [28], Accessibility (S4), and Spatial Tidiness (S5): These indicators measure pedestrian comfort, the friendliness of the environment toward priority groups (e.g., elderly individuals, children, and people with disabilities), and the overall quality of the spatial environment [28,32,44].

These indicators collectively reflect the spatial organization of street network structure, building interface relationships, pedestrian-scale dimensions, and overall environmental order.

• (3)

  Functionality (F): A critical aspect of a district’s functional layout and diversity:

Functional Density (F1): The number of points of interest (POIs) per unit spatial area, measuring the concentration of functional facilities within the district [45]. Here, “unit spatial area” denotes the 35 m buffer zone along street centerlines (35 m on either side of the street axis), defined based on local street scale characteristics and POI distribution patterns to ensure comprehensive coverage of adjacent functional facilities for POI counting. Functional Diversity (F2): The degree of coexistence of different functional facility types within the district, based on POI categorization [45]. Nighttime Lighting Level (F3): A key indicator of nighttime visibility and safety, reflecting the district’s capacity to support nocturnal activities [46].

These three indicators collectively capture the functional characteristics of the district.

• The Vitality Dimension focuses on dynamic usage patterns, primarily correlating with pedestrian density.

Street Vitality (P): Closely linked to pedestrian density, it is a key indicator of street attractiveness. Pedestrian density is quantified via heatmaps and spatial analysis, reflecting spatial aggregation and dynamic activity patterns [28]. Based on site-specific conditions in Nanjing’s Yihe Road historic district and existing research, workdays and weekends are selected to analyze spatiotemporal heterogeneity in pedestrian density distribution. This approach captures fluctuation patterns of street vitality, providing insights into the temporal dynamics of pedestrian activity within the historic district.

In summary, this study establishes a street evaluation framework for Republican-era historic districts, comprising four dimensions and a total of 27 evaluation indicators. The definitions and data sources for each indicator are specified (

Table 1. Definitions and data sources of street assessment metrics.

Evaluation Dimension	Criterion Layer	Evaluation Indicator	Explanation	Source
Quality Dimension	Element Perception (E)	E1 Green View Index	Proportion of Vegetation Elements Derived from Semantic Segmentation of Street View Images	Baidu Street View Images (2024)
		E2 Special Building Density	Density of Historical and Architectural Building Types Along Streets	Document Review and Field Survey
		E3 Historical–Cultural Experience	Tourist Perceived Cultural Perceptibility in the Historic Block	Perception Questionnaire
	Spatial Form (S)	S1 Pedestrian Accessibility	Pedestrian Road Accessibility was Calculated Using sDNA with a 200 m Radius	OpenStreetMap road network
		S2 Pedestrian Flow Potential	Pedestrian Road Betweenness Centrality was Calculated Using sDNA with a 100 m Radius	OpenStreetMap road network
		S3 Pedestrian Scale	Effective Pedestrian Width	Field Survey
		S4 Accessibility	Tactile Paving and Pavement Smoothness Assessment	Field Survey
		S5 Spatial tidiness	Sanitation Conditions in Urban Public Spaces	Field Survey
	Functionality (F)	F1 Functional Density	Linear Density of Nine POI Categories per Street Segment	Baidu POI Database
		F2 Functional Diversity	Street-Level Functional Diversity of Nine POI Categories	Baidu POI Database
		F3 Nighttime Lighting level	Nighttime Luminance Variability of Streetlights and Architectural Lighting	Field Survey
Vitality Dimension	Population Density (P)	Average street-level heat intensity is measured every 2 h from 8:00 to 22:00 on typical weekdays and weekends	Baidu Maps API-derived Spatiotemporal Human Aggregation Data for Typical Weekdays and Weekends: Analyzing Street-Level Human Vitality via Spatial Unit Averaging	Baidu Heatmap API

).

3.4. Data Collection and Processing

3.4.1. Data Collection

To evaluate spatial quality and urban vitality in Republican-era historic districts, this study integrates multi-source data, with each data type addressing specific evaluation dimensions. Street view data quantifies street vegetation coverage, a key metric for environmental aesthetics and visual comfort. The road network, a core urban fabric element, reflects block connectivity and attractiveness, indicating spatial organization and pedestrian convenience. POI data captures functional facility distribution and diversity, measuring layout and vitality. Heatmap data depicts crowd aggregation; weekday–weekend comparisons capture vitality fluctuations, revealing temporal dynamics. Conventional datasets complement emerging sources via field surveys, providing detailed environmental assessment indicators.

(1)

Street View Data: Street view images were collected at 20 m intervals, yielding 287 sampling points and 1144 panoramic images (four cardinal directions) in 2024. Vegetation coverage was quantified via FCN-based image semantic segmentation. The green view index (GVI) was calculated as the average vegetation proportion across four directions per point, with the street-level GVI derived from the mean of all points along the street [38].

(2)

Road Network Data: Extracted from the open-source platform OpenStreetMap (https://www.openstreetmap.org/), the road network was validated through field surveys and cross-referenced with street view data to exclude narrow alleys and dead-end roads in coverage blind zones. To address excessive complexity in raw network details, cartographic generalization and topological processing were applied [27].

(3)

POI Data: The POI dataset was sourced from Amap LBS API (August 2024), categorized into five functional types with nine subcategories: Commercial Services (shopping, dining, finance), Public Administration, Social Services (healthcare, daily life services), Cultural–Educational–Recreational Facilities (sports, education), and Infrastructure. A 35 m buffer zone along street centerlines, informed by existing studies and local conditions, was used to select 455 POIs for calculating functional density and functional diversity [45].

(4)

Heatmap Data: Spatiotemporal heatmap data were collected via the Baidu Map API to reflect pedestrian aggregation levels and spatial distribution patterns [47]. This study compares and analyzes crowd density on two typical days: 29 May 2024 (Wednesday) as a workday and 1 June 2024 (Saturday) as a weekend day. Data were sampled every 2 h from 08:00 to 22:00, generating 16 heatmap images (8 time points per day) for street vitality analysis. Using ArcGIS 10.8.1, raster heatmap data were converted into vector format through spatial mask analysis and zonal statistics tools to calculate time-segmented average heatmap values for each street unit (Figure 3). Figure 3 presents 8 paired frames (workday vs. weekend) at 2 h intervals (08:00, 10:00, …, 22:00), with normalized heat intensity (0–1) represented by a color scale (blue = low density, red = high density). Dual-day comparison enables quantification of diurnal and weekday–weekend variations in pedestrian flow, providing a basis for analyzing dynamic vitality patterns within the district. It should be noted that although Baidu heatmap data are based on massive mobile terminal densities, they cannot fully represent real-world conditions due to systematic bias from mixed sampling [28]. Therefore, this study focuses on relative spatial distribution features and spatiotemporal comparisons between the two typical days to analyze the dynamic changes in street vitality within the district.

(5)

Conventional Datasets: This study employed conventional datasets to complement and calibrate emerging data sources, enabling comprehensive assessment of key dimensions, including heritage building density, historical–cultural experience, pedestrian scale, accessibility, spatial tidiness, and nighttime lighting. The specific contents of the resulting datasets are as follows: The heritage building density dataset integrates historical building classification information obtained via document review from the Gulou District Committee official website and Nanjing’s Immovable Cultural Relics Inventory, supplemented by architectural aesthetic records from field surveys. Each building was assigned a score based on its historical value (1 point for outstanding historical buildings, 0.5 points for ordinary historical buildings), ultimately presented as a numerical table of total scores normalized by street length. Data for dimensions such as historical–cultural experience, pedestrian scale, accessibility, spatial tidiness, and nighttime lighting were collected through user perception surveys and direct observations, stored in the form of questionnaires and on-site assessment records. These data are used in the subsequent analyses to supplement and calibrate emerging data sources (e.g., street view imagery and POI data), ensuring the accuracy and comprehensiveness of the evaluation results. The integration of these conventional datasets with emerging data sources provides a robust foundation for subsequent spatial quality assessment.

3.4.2. Normalization Processing

Following existing research, the aforementioned key indicators were normalized using a linear function transformation [40].

The quantification of the GVI(E1) was based on landscape visual evaluation theory [38] with adaptive optimization applied to account for the high vegetation coverage characteristics of the study area (

Table 2. GVI quantitative score.

GVI/%	Explanation	Score
>50	High GVI, strong perception of greenery	1.0
35~50	Relatively high GVI, noticeable perception of greenery	0.8
25~35	Moderate GVI, some perception of greenery	0.6
15~25	Low GVI, limited perception of greenery	0.4
<15	Very low GVI, poor perception of greenery	0.2

). The GVI was calculated using Baidu street view imagery in combination with a semantic segmentation model. The calculation formula is as follows:

$$GV{I}_{\mathrm{street}}={\displaystyle \frac{1}{N}}\sum _{i=1}^N\left({\displaystyle \frac{1}{4}}\sum _{d=1}^{4}GV{I}_{i,d}\right)$$

(1)

where $GV{I}_{i,d}$ denotes the green view index at the i-th sampling point in the d-th direction, with d = 1, 2, 3, 4 corresponding to east (d = 1), west (d = 2), south (d = 3), and north (d = 4), respectively. It is calculated as the ratio of vegetation-covered pixels to total image pixels; N represents the total number of street sampling points.

Special building density (E2) quantifies the distribution density and scoring of outstanding historical buildings and ordinary historical buildings, reflecting differences in historical value and the intensity of cultural heritage transmission along street corridors. The calculation formula is as follows:

$$SBD={\displaystyle \frac{1}{L}}\sum \left(w_i\cdot s_i\right)$$

(2)

where SBD denotes the special building density score, wi represents the weight assigned to the i-th building (with wi = 1 for outstanding historical buildings and wi = 0.5 for ordinary historical buildings), si is the scoring value determined by the cultural relics protection list and field surveys, and L denotes the street length in meters (m).

Pedestrian accessibility (S1) and pedestrian flow potential (S2) are core metrics for evaluating street walkability. Using road network data and typical comfortable walking distances, proximity and betweenness scores were calculated via spatial design network analysis (sDNA) in ArcGIS at radii r = 200 m and r = 100 m, respectively.

Functional density (F1) and the functional diversity (F2) were calculated based on the collected 455 points of interest (POI) data. The formulas are defined as follows:

$$\begin{array}{c}D={\displaystyle \frac{\sum _{i=1}^n{a}_i}{L}}\end{array}$$

(3)

$$\begin{array}{c}H=-{\displaystyle \sum _{i=1}^n}{\displaystyle \frac{a_i}{A}}ln{\displaystyle \frac{a_i}{A}}\end{array}$$

(4)

where D denotes functional density, H represents functional diversity, n is the total number of POI categories, ai indicates the number of POIs in the i-th category, L is the street length (m), and A refers to the total number of POIs.

The population density (P) was standardized using the min–max normalization method applied to crowd density heatmaps obtained from the Baidu heatmap, ensuring spatial and temporal comparability across scales. Raw heat values (intensity values based on pixel density) were normalized to a dimensionless [0, 1] range, where 0 represents the minimum heat value (e.g., no pedestrian activity) observed during the study period, and 1 corresponds to the maximum heat value (e.g., peak population density). The formula is defined as follows:

$$H_{\mathrm{normalized}}={\displaystyle \frac{H_{\mathrm{raw}}-H_{\mathrm{m}\mathrm{i}\mathrm{n}}}{H_{\mathrm{m}\mathrm{a}\mathrm{x}}-H_{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(5)

where $H_{\mathrm{raw}}$ is raw heat intensity value at a specific location and time, $H_{\mathrm{m}\mathrm{i}\mathrm{n}}$ is minimum heat intensity in the study area during the observation period, and $H_{\mathrm{m}\mathrm{a}\mathrm{x}}$ is maximum heat intensity in the study area during the observation period.

The scoring criteria for five indicators—pedestrian scale (S3), accessibility (S4), historical and cultural experience (E3), nighttime lighting level (F3), and spatial tidiness (S5)—were determined based on the overall urban context. Residents and tourists evaluated both sides of street segments using a 5-point Likert scale (

Table 3. Quantitative scores for historical–cultural experience and nighttime lighting level.

Historical and Cultural Experience (E3)	Nighttime Lighting Level (F3)	Score
High historical and cultural experience	Abundant street and building lighting	1.0
Relatively high historical and cultural experience	Bright street lighting, considerable building illumination	0.8
Moderate historical and cultural experience	Bright street lighting, moderate building illumination	0.6
Low historical and cultural experience	Moderate street lighting, sparse building illumination	0.4
Very low historical and cultural experience	Very low or no street lighting, minimal building illumination	0.2

and

Table 4. Quantitative scores for pedestrian scale, accessibility, and spatial tidiness.

Pedestrian Scale (S3)	Accessibility (S4)	Spatial Tidiness (S5)	Score
Allows 3 or more people to pass side by side easily	Smooth pavement with tactile guiding path	No litter, stains, or odor in the environment	1.0
Allows 2 people to pass easily, 3 with difficulty	Relatively smooth pavement with tactile guiding path	Minor presence of litter, stains, or odor	0.8
Allows 1 person to pass easily, 2 with difficulty	Relatively smooth pavement, no or obstructed tactile path	Some litter or stains, no noticeable odor	0.6
Allows 1 person to pass with difficulty	Uneven pavement, no or obstructed tactile path	Considerable litter, with noticeable odor	0.4
No pedestrian path available	Very uneven pavement, no or obstructed tactile path	Extensive litter and strong odor	0.2

). A total of 108 questionnaires were collected, with 102 valid responses. The mean method was applied to calculate comprehensive street scores, mitigating subjective bias from unilateral assessments. Notably, due to the interdependence between walkability and accessibility, if the pedestrian scale (S3) score was ≤ 0.4, a deduction of 0.2 points was applied to the accessibility (S4) score [48].

3.5. Weight Determination via Analytic Hierarchy Process (AHP)

Following the Guidelines for Street Design in Nanjing and actual field conditions, the streets within the study area were categorized into six types: Traffic-Dominated Streets, Life-Dominated Streets, Commerce-Dominated Streets, Landscape-Dominated Streets, Life-Landscape Streets, and Commercial-Landscape Streets. Grounded in real-world observations and established research [48,49], a methodology for street-type differentiation was formulated to align with the functional characteristics and spatial patterns of the study region.

The study adheres to the following three criteria to classify street attributes: Firstly, streets categorized as expressways, arterial roads, or those primarily serving through traffic functions are classified as Traffic-Dominated Streets; secondly, streets are evaluated based on the dominance of residential POIs versus commercial POIs: if residential POIs predominate, the street is classified as a Life-Dominated Street, whereas if commercial POIs predominate, it is classified as a Commerce-Dominated Street; thirdly, streets with either distinctive building density scores or green view index (GVI) values in the top 20% of the study area are classified as Landscape-Dominated Streets. Streets satisfying only one attribute are assigned to the corresponding type, while those meeting multiple attributes are classified as composite-type streets, further subdivided into Life-Landscape Streets or Commercial-Landscape Streets (no other composite types were observed in the study area). For streets failing to meet any of the above criteria, manual classification was applied to determine their type, following the workflow illustrated in (Figure 4).

Classification results indicate that Traffic-Dominated Streets semi-encircle the northern and southern peripheries, while Life-Dominated Streets cluster in the central-western block. Commerce-Dominated Streets, the smallest category, align with the block’s periphery. Landscape-Dominated Streets concentrate in the southern area, with Ninghai Road as a typical example. Multi-Function Streets (Life-Landscape and Commercial-Landscape) cluster on the eastern and western sides, exhibiting spatial differentiation (Figure 5).

Due to the functional and morphological diversity among street types, this study employed the analytic hierarchy process (AHP) to ensure objective evaluation of historic district spatial quality. From March to April 2025, 18 experts in urban planning and heritage conservation were consulted to evaluate the weights of three criterion layers (element perception, spatial form, and functionality) for six street types, while also assessing the weights of specific indicators within each criterion layer. Experts assessed each street type by considering its unique characteristics and alignment with the criteria. Through a systematic approach, they provided scores and rankings that reflected the relative importance of each criterion in determining the spatial quality (

Table 5. Weight distribution of quality scores by street type.

Street Type	Element Perception (%)	Spatial Form (%)	Functionality (%)
Traffic-Dominated Street	21.42	50.11	28.47
Life-Dominated Street	31.57	26.31	42.12
Commerce-Dominated Street	25.70	28.69	45.61
Landscape-Dominated Street	61.49	23.86	14.65
Life-Landscape Street	43.45	25.30	31.25
Commercial-Landscape Street	44.66	21.54	33.80

Weights of quality scores are displayed to two decimal places.

and

Table 6. Indicator weights for quality evaluation by criterion layer.

Criteria Layer	Evaluation Indicator	Weight Value
Element Perception (E)	E1 Green View Index	0.35
	E2 Special Building Density	0.42
	E3 Historical–Cultural Experience	0.23
Spatial Form (S)	S1 Pedestrian Accessibility	0.28
	S2 Pedestrian Flow Potential	0.25
	S3 Pedestrian Scale	0.18
	S4 Accessibility	0.15
	S5 Spatial tidiness	0.14
Functionality (F)	F1 Functional Density	0.41
	F2 Functional Diversity	0.34
	F3 Nighttime Lighting level	0.25

). The expert consultation followed a two-stage Delphi process [50], with pairwise comparison matrices validated by consistency ratios (CR < 0.1). This approach aligns with established practices in heritage site assessments, such as AHP applications in the evaluation of historical urban landscapes in Fuzhou, China [51]. By systematically linking expert-derived priorities to empirical analysis, the AHP method ensures the subsequent coupling model and governance strategies are grounded in contextually relevant, empirically validated metrics.

4. Results

4.1. Indicators and Comprehensive Measurement Results

This study evaluates streets based on quality and vitality, with normalized quality indicators weighted by their assigned values. Analysis of quality indicators reveals spatial patterns, with significant variations in individual performance across streets (Figure 6).

In the element perception layer, the GVI exhibits a high and spatially balanced distribution overall, with only a few streets showing slightly lower performance. In contrast, special building density and historical–cultural experience demonstrate marked spatial disparities. Core areas (e.g., the central section of Yihe Road) score notably higher due to well-preserved historic buildings and enriched cultural experiences, whereas peripheral zones (e.g., the southern section of Tianzhu Road) show comparatively lower scores. For the spatial form layer, most streets achieve high levels of pedestrian accessibility and pedestrian flow potential. However, while pedestrian scale, accessibility, and spatial tidiness remain above the baseline threshold for the majority of streets, certain streets exhibit suboptimal pedestrian scale design, inadequate accessibility facilities, or insufficient spatial tidiness. In the functionality layer, significant variations are observed in functional density and functional diversity. Streets along the block periphery, benefiting from diverse business types and comprehensive functional facilities, achieve higher scores. Conversely, interior streets often suffer from functional homogeneity and limited commercial diversity.

An in-depth investigation of street vitality measurements across different time periods within the block reveals distinct differentiation patterns between weekdays and weekends. During weekdays, human activity intensity concentrates along traffic-dominated streets at the block periphery and in the intersection zones of Tianzhu Road, Langya Road, and Lingyin Road, closely aligned with commuting demands. These traffic-oriented streets, serving as critical components of the urban transportation network, accommodate substantial commuter flows. Conversely, on weekends, Yihe Road and Ninghai Road emerge as vitality hotspots due to their unique cultural appeal and commercial attractiveness, drawing both residents and tourists for leisure activities, as illustrated in the accompanying figure (Figure 7). Temporal dynamics further highlight diurnal variations: weekday vitality peaks between 10:00 and 12:00, corresponding to post-commute work initiation, while weekend peaks occur between 16:00 and 18:00, reflecting heightened recreational engagement during afternoon–evening hours. After 20:00, spatial vitality sharply declines across the block, aligning with the vitality decay patterns observed in central Nanjing’s urban areas [52]. Notably, most streets exhibit minimal differences in vitality fluctuations between weekdays and weekends, as illustrated in the accompanying figure (Figure 8).

The study encompasses four layers—element perception, spatial form, functionality, and population density—with measurement results calculated using the mean value method, as illustrated in the accompanying figure (Figure 9). From the perspective of the three quality-related layers, streets in the study area exhibit relatively favorable performance in the element perception layer, except for the northwest and southwest zones (e.g., the Langya Road Primary School area), which show weaker outcomes, reflecting insufficient greening landscape creation and historical building preservation in these regions. The spatial form layer reveals significant regional disparities, with Yihe Road and Guling Road in the block core performing exceptionally well, while most other streets demonstrate moderate levels. In terms of the functionality layer, notable internal differentiation exists: streets near traffic-dominated streets in the northern and southern peripheries (e.g., the southern section of Jiangsu Road and the northern section of Ninghai Road) score significantly higher than interior zones (e.g., the central section of Mogan Road), where functional homogeneity and limited commercial diversity are prevalent.

Regarding the vitality dimension, street vitality exhibits clear spatial agglomeration, with high-activity zones concentrated along the block’s periphery and near transportation hubs (e.g., the southern section of Jiangsu Road, the Tianzhu Road–Langya Road intersection), while core areas generally show lower vitality. This pattern aligns with findings in Shenzhen [27], where commercial districts like Nanshan and Luohu display high vitality, though the vitality spillover effect in Nanjing’s Yihe Road area is more pronounced. Notably, elevated vitality levels observed in the southern sections of Ninghai Road and Mogan Road contradict field survey data, potentially due to inaccuracies from recorded vehicle inflows via Beijing West Road into the block.

Based on the weight distribution of quality indicators across different street types (Table 5), all data were normalized to derive the comprehensive street quality and comprehensive vitality scores. A comparison between the composite quality map and vitality map reveals a distinct spatial pattern characterized by “high-quality concentrated internally” and “high-vitality concentrated externally”. Specifically, comprehensive quality shows an increasing trend from the outer to the inner areas, with the central zone exhibiting a significant advantage in quality performance. The highest quality is concentrated around Yihe Road and the northern section of Ninghai Road. In contrast, comprehensive vitality increases from the interior outward, with high vitality levels prominently observed in peripheral areas, particularly near the Langya Road Primary School. This coexistence of “high quality–low vitality” and “low quality–high vitality” patterns highlights the imbalance in the distribution of quality and vitality across the block. While the core area boasts high historical value, its functional homogeneity constrains vitality. Conversely, peripheral areas, despite their high vitality, face shortcomings in spatial quality.

4.2. Street Clustering Based on Evaluation Matrix

To analyze spatial heterogeneity in the Yihe Road historic district, this study constructed a quality–vitality bivariate coordinate system based on normalized quality (x-axis) and vitality scores (y-axis). Analyzing 45 street segments, streets were classified by comparing median thresholds of quality and vitality, divided into two levels (high vs. low) [53], yielding four clusters: high quality–high vitality, high quality–low vitality, low quality–high vitality, and low quality–low vitality (Figure 10). The terms high and low denote relative performance within the study area. This typology reveals spatial coupling patterns between heritage quality and urban vitality, offering a framework for targeted interventions in historic district management. The characteristics and major issues associated with each street cluster are summarized in

Table 7. Characteristics and distribution of four street cluster types.

Cluster Type	Length Ratio	Spatial Distribution	Characteristics	Major Issues
High Quality–High Vitality	30.64%	Major traffic corridors in the southeastern area	Strong cultural perception, diverse business forms, mixed functions, and high overall vitality	Commercial homogenization and the dilution of historical authenticity
High Quality–Low Vitality	20.62%	Core area	Strong element perception, but functionally homogeneous; low vitality level	Contradiction between static preservation and dynamic utilization; lack of vitality
Low Quality–High Vitality	20.21%	Northern periphery and transitional zones	Located near transportation hubs or schools with high pedestrian density; low cultural perception	Spatial quality shortcomings constrain the sustainability of vitality
Low Quality–Low Vitality	28.53%	Western side streets and internal longitudinal alleys	Aging infrastructure, poor functionality, low overall vitality, and minimal public attention	Vicious cycle of “low investment–limited improvement”; spatial decline and cultural disconnection

Total street length: 6813.98 m.

.

The radar chart provides an intuitive visualization of the strengths and weaknesses of streets across multiple indicator dimensions, offering visual support for targeted analysis of spatial attribute differences and renewal potential (Figure 11). In this study, representative streets were selected from each cluster type, and the quality–vitality radar chart was utilized to visually interpret street conditions, as illustrated in the accompanying figure (Figure 12).

High-quality–high-vitality streets are primarily distributed along major traffic corridors and functionally mixed-use areas on the periphery of the district, such as the southern section of Jiangsu Road, the entire stretch of Beijing West Road, and the northern end of Yihe Road, accounting for 30.64% of total street length. Their spatial characteristics include well-preserved historical buildings, a continuous and coherent streetscape, high cultural value, and strong vitality, with a combination of commercial, cultural, and residential functions. These streets achieve a high standard in both spatial quality and vitality levels, serving as core attraction nodes within the district. However, there exist two major underlying concerns: commercial homogenization and the dilution of historical authenticity. For example, storefronts along these streets tend to be repetitive in business format, and some commercial activities have compromised the historical character, thereby weakening the uniqueness and cultural depth of the streets.

High-quality–low-vitality streets are concentrated in the historically dense core area of the district, with typical examples including the central section of Yihe Road and parts of Ninghai Road, accounting for 20.62% of total street length. These streets exhibit strong element perception and relatively intact spatial form; however, their functional homogeneity limits vitality. Although the architectural style holds significant historical value, this mode of static preservation lacks active commercial activity and diverse functional configurations, resulting in lower than expected foot traffic and vitality levels.

Low-quality–high-vitality streets are mainly located in the northern periphery and transitional zones of the district, such as the intersection area of Lingyin Road, Tianzhu Road, and Langya Road, representing 20.21% of total street length. These streets show clear shortcomings in spatial quality but benefit from proximity to schools and transportation hubs, leading to significantly higher pedestrian density compared to the district average. Functionally, they exhibit a “point-like burst” pattern, where 人流 (foot traffic) concentrates during specific periods (e.g., weekends); yet overall cultural perception remains low. This mismatch between vitality and quality results in a phenomenon of “surface prosperity”—although these streets experience high foot traffic, their poor spatial quality restricts sustained attractiveness.

Low-quality–low-vitality streets are primarily found in the longitudinal side streets and internal alleys on the western side of the district, such as certain sections of Xikang Road and the alleys parallel to the western section of Yihe Road, making up 28.53% of total street length. These areas face challenges in both spatial form and functional configuration, with generally low vitality and minimal foot traffic, failing to attract attention from either residents or tourists. Both spatial quality and vitality levels remain at a low level. The traditional top-down renewal approach has overlooked community participation, trapping these streets in a “low investment–limited improvement” cycle [54]. Additionally, cultural elements are inadequately showcased, lacking distinctiveness and appeal, which leads to spatial decline and cultural disconnection. These streets demonstrate a “depression effect” in spatial terms, sharply contrasting with the surrounding high-vitality areas.

The four clusters exhibit a spatial gradient characterized by “periphery-high–center-low” and “arterial-high–branch-low” patterns, which are closely related to the historical development trajectory of the district. The peripheral traffic corridors, shaped by early commercial development, have formed high-vitality nodes, while the core area has experienced vitality stagnation due to long-term conservation policies that restrict functional renewal.

5. Discussion

5.1. Specificity and Universality of Republican-Era Historic Districts

As a product of modern urbanization in China, Republican-era historic districts exhibit spatial variation patterns that reflect both indigenous institutional and cultural uniqueness, as well as common contradictions in global historic urban renewal.

The specificity of Republican-era historic districts stems from institutional constraints and the duality of cultural symbols. Conservation policies, such as the Regulations on the Conservation of Historic and Cultural Cities, impose institutional constraints via core area enclosures, promoting monofunctionality. This “static preservation model” contrasts with the European “living heritage approach”. For instance, Venice’s historic district [55] illustrates declining residential density and commercial homogenization from overemphasis on architectural conservation, whereas Yihe Road’s core area fragments historical space integration with public life due to its enclosed courtyard structure. Research highlights that such practices may lead to vitality decline in historic districts [27]. Cultural symbols exhibit duality: Republican-era elements (e.g., government residences) function as cultural assets attracting tourism but risk commercial exploitation, potentially causing cultural perception disconnection [56].

The universality of spatial variation patterns in Republican-era districts lies in common functional configuration challenges and the global influence of commercial capital, observed in other historic districts. Whether medieval European cities (e.g., Le Marais in Paris), Chinese Ming–Qing era districts, or colonial ensembles (e.g., Red Square in Malacca), monofunctionality and vitality deficiency remain central conservation challenges. The high quality–low vitality issue in Yihe Road’s core area mirrors functional decline in Venice’s historic district [57], both resulting from prioritizing architectural conservation over streets’ functional roles as “living places”. The logic of commercial capital transformation—chain-based standardization—shows global convergence. Current planning lacks quantitative control over business proportions, leading to commercial development misaligned with historic district characteristics. Policy regulation lags behind market dynamics, failing to balance capital flows with heritage conservation. This phenomenon is evident in San Francisco’s historic districts [22], underscoring the universal “preservation–development” dilemma driven by market profit motives.

The specificity and universality of Republican-era districts are interlinked. Institutional adaptability disparities exist: regulatory constraints in these districts contrast with Europe’s “living heritage approach”, reflecting distinct adaptive strategies under varying cultural contexts. Yet shared challenges—monofunctionality and commercial capital influence—highlight the need for institutional design integrating global experiences. Cultural symbol transferability is evident: Renaissance architecture in Le Marais, Paris, similarly faces commercial homogenization risks, emphasizing the necessity of dynamic governance mechanisms to sustain cultural value.

5.2. Mechanisms of Spatial Differentiation in Historic Districts

The spatial differentiation in historic districts reflects conflicts between preservation and development and functional imbalances during urban renewal. The Republican-era Yihe Road historic district exemplifies a pattern of “high-quality concentration internally” and “high-vitality concentration externally”, a challenge observed globally.

Firstly, the governance tension between static conservation and dynamic adaptation generates the “high quality–low vitality” contradiction. Core areas prioritize architectural preservation over functional diversity, resulting in monofunctionality and vitality decline. The enclosed courtyard structure and homogenized commercialization in Yihe Road’s central section parallel the declining residential density in Venice’s historic district [57]. Studies show that such “static conservation” approaches disconnect historic spaces from contemporary urban life [27], a mechanism prevalent in global heritage policies, revealing systemic conflicts between rigid preservation frameworks and functional renewal demands.

Secondly, spatial functional vacancy and uneven resource allocation generate a “low investment–low improvement” cycle. In Yihe Road’s western longitudinal streets, the lack of basic services sustains underinvestment and stagnated improvement [54]. This aligns with infrastructure deficiencies in Jaipur’s old city, India [58], both reflecting structural imbalances in internal resource distribution. This indicates that protective policies that focus on historical integrity while neglecting adaptive functions may hinder revival. We propose a framework that combines preservation with modern urban needs to provide solutions to systemic conflicts in historical centers such as Cairo’s Islamic old city [59] and Istanbul’s Galata district [60], where similar spatial quality–vitality imbalance patterns have been observed, and historical preservation work often faces challenges from rapid urban development.

Finally, the profit-driven nature of commercial capital drives commercial homogenization and cultural perception disconnection in historic districts. Peripheral vitality stems from market capital intervention, but homogenized commercialization undermines cultural identity. This phenomenon is evident in East London’s historic districts [16] and San Francisco’s historic districts [22], reflecting global convergence in capital-led transformation logic. Profit-driven capital accelerates cultural disconnection, while lagging policy regulation intensifies the trend. Our research enhances understanding of how commercial forces interact with cultural sustainability. It provides insights into aligning policies with cultural and economic goals. This is critical in balancing preservation and development in historic districts.

5.3. A Multi-Tiered Governance Strategy for Sustainable Regeneration

Historic districts face persistent challenges in balancing heritage preservation and urban vitality due to fragmented governance and static conservation. This study proposes a multi-tiered governance strategy to address these conflicts, integrating adaptive policies, differentiated functional reconfiguration, and fine-grained interventions at urban–district–street levels.

At the urban level, policy design must transcend the dichotomy between static conservation and dynamic development through flexible planning mechanisms that integrate multi-departmental resources. For instance, Suzhou’s Pingjiang historic district successfully enhanced vitality by introducing temporary cultural installations such as pop-up exhibitions and immersive theaters, offering a stark contrast to the functional neglect seen in Nanjing’s earlier rigid top-down policies [61]. Unlike East London’s fixed quotas for cultural businesses, which may not suit areas lacking digital infrastructure [62], our strategy advocates for adaptive governance. This includes using dynamic evaluation frameworks like real-time pedestrian density monitoring and machine learning-driven spatial analysis, such as POI clustering and street vitality modeling, to refine policy thresholds. These approaches allow for greater flexibility than static zoning models [63] and align with the growing emphasis on “living heritage” frameworks [64], which prioritize adaptability to local contexts over rigid regulatory standards. For example, in European cities like Amsterdam [65], similar adaptive strategies have revitalized historic canals areas by balancing heritage preservation with new cultural and commercial functions. In Islamic cities such as Fez [66], Morocco, the methodology could be applied by using pedestrian density data to guide the sensitive integration of new economic activities within medinas, ensuring that historical authenticity is maintained while urban vitality is enhanced.

At the district level, differentiated governance strategies require functional reconfiguration based on cluster analysis. This approach contrasts sharply with Venice’s experience, where monofunctional decline occurred due to overemphasis on architectural preservation, ultimately leading to residential depopulation [67]. In our methodology, we prioritize mixed-use activation through micro-courtyard transformations, such as repurposing enclosed courtyards into community plazas [68]. This strategy aligns with Shanghai’s micro-renewal initiatives but uniquely integrates participatory governance to actively involve residents and reduce commercial homogenization. Unlike Venice’s preservation model, this approach ensures that historical districts remain vibrant and relevant to contemporary urban life. However, the scalability of such strategies is constrained by institutional support in Chinese districts. Similar challenges are observed in Lima, Peru, where mixed-use interventions faced significant resident resistance due to a lack of participatory mechanisms [69]. While our participatory model resonates with successful cases like Barcelona’s El Born, where community involvement has been crucial in balancing heritage conservation with urban vitality [70], it also highlights the tension between functional innovation and community acceptance in more rigid institutional contexts such as China [63]. By embedding local knowledge and practices, our strategy improves upon conventional top-down micro-renewal approaches. This method aligns with the principles of relational urbanism [71], which advocates for a more flexible and community-oriented approach to urban renewal.

At the street level, fine-grained management and micro-renewal practices must prioritize small-scale interventions. Data-driven prioritization using GVI and POI density [38] targets high-vitality areas, improving efficiency over generic micro-renewal [72]. For high-vitality streets, dynamic business regulation (e.g., real-time monitoring of pedestrian density and turnover) prevents commercial homogenization, extending East London’s admission checklist [62]. However, as the Hungarian Tourism Agency has pointed out, mobile digital data may misclassify residents and tourists [73]; this method faces limitations in mixed-use districts where resident–tourist dynamics are complex. To address this, future implementations could integrate real-time monitoring with participatory feedback mechanisms [74]. For instance, in Islamic cities like Istanbul, this methodology could be adapted by using pedestrian density data and local feedback to guide the integration of new economic activities within historic districts, ensuring that historical authenticity is maintained while urban vitality is enhanced.

The multi-tiered renewal strategies, when integrated through systemic governance frameworks, enable adaptive responses to evolving urban dynamics. By embedding dynamic feedback mechanisms [22] and participatory governance models, such approaches not only mitigate the surface prosperity phenomenon but also establish a resilient paradigm for balancing heritage conservation with sustainable urban vitality. This layered framework offers both theoretical clarity and actionable scalability for historic districts globally.

5.4. Limitations and Future Research Directions

This study integrates multi-source datasets to bridge environmental objective indicators with user subjective perceptions, overcoming the data fragmentation in spatial coverage and anthropocentric depth. Although exploratory efforts were made in constructing a Republican-era street evaluation system, methodological limitations persist due to data acquisition constraints. The thermal data employed primarily captures macro-level population trends but, owing to its statistical characteristics based on mobile user density, it fails to distinguish behavioral differences among tourists, residents, and transit vehicles. This may introduce biases in localized street vitality assessments. Future research will integrate mobile signaling data with field observation datasets to decouple vehicle and pedestrian impacts. Furthermore, multi-source data fusion will be pursued to unravel dynamic coupling mechanisms between spatial quality and vitality, enhancing the adaptability of the evaluation framework to diverse regional contexts and meeting the high-precision design demands of urban renewal.

Furthermore, data timeliness and update frequency may compromise the accuracy and applicability of research findings. While the constructed evaluation system and governance strategies have demonstrated efficacy in the case study, their applicability across diverse regions and historic district types requires further validation. Historic districts exhibit distinct historical, cultural, social, and economic contexts, and thus the application of this study’s methods and strategies in other contexts necessitates further refinement and adaptation.

6. Conclusions

Aiming at the conservation challenges of Republican-era historic districts, this study develops a coupled evaluation system integrating environmental quality and urban vitality through multi-source data fusion. By integrating street view semantics, POI density, heatmap dynamics, and conventional field surveys, this methodology enhances assessment precision. The analytic hierarchy process weighted indicators (such as landscape-dominated streets prioritizing element perception with a weight of 61.49%) ensure differentiated evaluation of street types and quantify the differences in spatial quality standards under different functional backgrounds. Focusing on Nanjing’s Yihe Road historic district, four-dimensional analysis (element perception, spatial form, functionality, population density) reveals a spatial structure of “high-quality concentrated internally” and “high-vitality concentrated externally”. The core area maintains architectural integrity but suffers functional homogenization, while peripheral zones exhibit vitality driven by transport hubs yet face spatial quality deficits. This tension between static preservation and dynamic use underscores the limitations of enclosed courtyard structures in integrating historical spaces with contemporary urban life.

Cluster analysis identifies four quality–vitality coupling types: high quality–high vitality, high quality–low vitality, low quality–high vitality, and low quality–low vitality. Radar charts further contextualize these patterns, enabling intuitive interpretation of spatial differentiation across the district. Findings indicate that high-quality–high-vitality streets benefit from functional diversity and mixed-use characteristics, whereas low-quality–low-vitality streets are trapped in a “low investment–limited improvement” cycle.

A multi-tiered governance strategy is proposed: urban-level governance must promote policy integration and interdepartmental coordination; district-level differentiated strategies activate historic values through functional reorganization; and street-level fine-grained management prioritizes incremental micro-renewal to enhance environmental quality. This research advances understanding of spatial differentiation in historic districts, offering a globally applicable framework for balancing heritage conservation and urban vitality. By integrating policy design, technical implementation, and social participation, the framework provides actionable pathways for cultural heritage reuse. Future work should leverage cross-disciplinary approaches and emerging technologies to refine precision in historic district protection, aligning conservation with sustainable urban development goals.