Identification of Urban Renewal Potential Areas and Analysis of Influential Factors from the Perspective of Vitality Enhancement: A Case Study of Harbin City’s Core Area

Abstract

In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution of educational facilities demonstrated a strong positive correlation with economic vitality. By clearly delineating specific areas with urban renewal potential, this study provided a detailed characterization of the urban development pattern in Harbin. Additionally, by depicting the local variations in influencing factors, it established analytical foundations and objective references for urban planning in targeted locations. Ultimately, this research contributes new insights and frameworks for urban renewal analyses applicable to other regions.

1. Introduction

Cities continuously face various challenges during their development, including environmental degradation, inadequate infrastructure, the accumulation of social issues, and economic decline [1,2]. Urban regeneration, as a key urban agenda in most countries, aims to resolve and coordinate efforts to address and reconcile these challenges. After more than three decades of rapid urbanization, China’s urbanization rate has surpassed 50%, profoundly transforming both urban and rural societies and achieving significant developmental successes. However, the traditional model of rapid and extensive development has also resulted in various urban challenges, including environmental degradation, escalating social tensions, and a slowdown in industrial upgrading [3]. The National New Urbanization Plan (2014–2020) proposes that China’s urbanization should pursue a path of ‘new urbanization with Chinese characteristics’ that is people-centered, optimized in spatial layout, ecologically sustainable, and rooted in cultural heritage. It emphasizes the need for urbanization to enter a new stage of transformational development, with a primary focus on enhancing quality [4]. The 14th Five-Year National Plan (2021–2025) officially proposed the implementation of urban renewal actions for the first time, intending to optimize urban spatial structures, enhance environmental quality, and foster sustainable development in both the economic and social realms [5]. Urban renewal initiatives have become one of the important tools for optimizing urban layout and structure, while enhancing urban vitality, in the context of China’s high-quality urbanization. Urban renewal actions involve the functional transformation of urban spaces with poor environmental quality, inefficient land use, and functions misaligned with actual development needs. The primary objectives are to enhance functional structures, optimize environmental quality, stimulate the city’s intrinsic vitality, and foster endogenous development dynamics [6,7]. It is a comprehensive and complex systematic project, facing the emerging new development needs of the city and closely related to the economic and social vitality and spatial structure of the city [8]. The identification and assessment of renewal potential areas constitute the first and most critical step in renewal planning, serving as the foundation for the scientific, rational, and effective advancement of urban renewal efforts [9].

Urban vitality is a crucial component of sustainable urban development and is widely employed in studies evaluating urban performance and development status [7,10]. Kevin Lynch, in the book “Good City Form”, defines urban vitality as the capacity and degree to which urban form supports urban functions, meets biological needs, and facilitates human activities [11]. In her book “The Death and Life of Great American Cities”, Jane Jacobs defines urban vitality as the sustained and diverse range of human activities and active social interactions within urban neighborhoods [12]. It is manifested in urban spaces through the concentration of urban residents or the level of social activity [13]. Urban vitality is regarded as the intrinsic motivation and energy driving urban development, serving as the cornerstone of a livable city [14]. Enhancing the vitality value of urban spaces and optimizing the spatial structure of urban vitality are the primary tasks and objectives of urban renewal initiatives [15]. Different urban regeneration strategies can lead to distinct impacts and transformations on urban vitality [16,17]. Therefore, conducting research on urban regeneration strategies based on vitality assessment is crucial for enhancing the effectiveness of planning decisions [18,19]. In existing research, urban renewal decisions are predominantly based on conceptual discussions, empirical judgments, or case studies [6,20]. Analyses often focus on governance systems [21], modes of participation [22], or stakeholder involvement [23]. However, there remains a lack of targeted quantitative and specific analyses grounded in a people-centered approach and vitality enhancement, as well as an insufficient examination of the fine-grained spatial diagnosis and operational mechanisms of the urban system.

Urban vitality is regarded as being closely linked to the functional elements of a city and its urban form [24,25]. In the book “Creating a Vibrant Urban Centre”, Pamir emphasizes that factors such as location, size, planning, and spatial design play crucial roles in fostering vibrancy in public spaces [26]. Investigating the mechanisms by which elements of the urban physical environment and functional components influence the development of urban vitality is of paramount importance. This understanding can facilitate the formulation of targeted renewal planning strategies that guide the orderly development of cities, thereby contributing significantly to sustainable urban renewal rooted in humanistic principles. Meanwhile, in recent years, with the advancement of open data access technologies, the concept of big data-driven urban research has offered a new research paradigm for analyzing urban problems and uncovering the urban phenomena hidden embedded within the data. The use of big data in research offers new possibilities for assessing urban vitality and analyzing urban spaces. Compared to traditional datasets, big data in urban research enables both macro-scale analyses of urban functional structures and the detailed depiction of micro-scale urban elements [27]. This dual approach significantly enhances the capacity to understand the complexities of urban systems, offering robust support for refined urban governance and the pursuit of high-quality urban development. In previous research, scholars have typically limited their analyses of urban patterns to a singular perspective of urban vitality [14,28], with few efforts to integrate these analyses into the framework of urban regeneration actions for in-depth exploration. This paper seeks to enrich this area of study by investigating urban regeneration strategies that focus on enhancing urban vitality and optimizing spatial patterns.

Therefore, this paper proposes an analytical framework for urban regeneration strategies from the perspective of vitality analysis. It aims to accurately depict the urban vitality patterns and spatial development status of Harbin City from a holistic perspective. The study will quantitatively analyze the influencing factors and underlying mechanisms, ultimately identifying specific areas with urban renewal potential and offering targeted planning strategy recommendations. This paper aims to address the following research questions:

• How can a research framework for refined urban regeneration strategies be established from the perspective of urban vitality analysis?
• What is the spatial pattern of vibrancy in the core area of Harbin City, and which specific areas possess potential for urban renewal and opportunities for enhancing vibrancy?
• How do related urban elements influence or constrain the development of urban vitality?
• How should effective regeneration planning strategies be formulated to better guide the healthy and orderly development of urban vitality?

The rest of the paper is organized as follows: Section 2 reviews relevant studies on the identification of urban renewal areas and the factors influencing urban renewal and vitality; Section 3 specifies the study area, data sources, and methodology; Section 4 analyzes the results of the modeling experiments; Section 5 discusses the findings and proposes targeted recommendations for planning and implementation strategies; and Section 6 presents the conclusion.

2. Literature Review

2.1. Identification of Renewal Potential Areas

Urban renewal potential areas typically refer to various types of construction sites within a town’s development boundary that, after thorough analysis and comprehensive assessment, are deemed unsuitable for the city’s social and economic development needs. These areas often require comprehensive remediation, reconstruction, or functional upgrading [6,29]. It is usually associated with concepts such as the identification of inefficient land in urban areas [1], brownfield redevelopment [30], or the assessment of sustainability vulnerability indices [31]. They manifest as areas afflicted by various urban ailments and development challenges, including deteriorating environments, population decline, traffic congestion, and inadequate facilities. These regions also exhibit a certain degree of decline in social and economic vitality, rendering them inefficient spaces within the urban system. Consequently, they have become negative spaces characterized by poor spatial utilization and outdated functions, which fail to meet the needs of sustainable urban development.

Currently, research on the renewal potential of existing spaces by scholars can be categorized into two aspects. The first focus involves data analysis of spatial elements representing different urban phenomena to identify the spatial patterns of urban development, thereby determining the distribution of urban renewal potential areas. For example, Xia, C. et al. identified local spatial mismatches in five Chinese megacities by calculating the spatial correlation between land use intensity and urban vitality [32]. Pablo et al. used location-based social network data to identify underutilized or vacant urban spaces from a people-based perspective, revealing potential opportunities for urban renewal [33]. Such studies are mostly based on multi-source data and employ spatial statistical analysis methods, such as kernel density analysis [34], Getis-Ord Gi* analysis method [1], and spatial autocorrelation analysis [35], to identify the spatial patterns of urban development and the distribution of anomalous values with renewal potential from a global perspective. The second approach involves establishing evaluation factors that encompass multiple dimensions and developing a comprehensive evaluation model to create a universally applicable framework for identifying urban renewal areas. For example, Della et al. developed a multidimensional evaluation system to assess scarce public resources in cities and identify intervention priorities, aiding decision-makers in planning, designing, and managing complex urban renewal projects [36]. Pérez et al. proposed a spatial decision support system, using indicators based on six sustainable development goals to evaluate community-scale urban renewal projects and support sustainable decision-making [37]. Such studies primarily focus on the construction and refinement of evaluation index systems for renewal potential, with less emphasis on the specific contradictions and spatial patterns within the urban structure. Meanwhile, in existing regeneration practices in China, the approach of delineating regeneration areas around individual projects has become relatively mature. Research on the identification and delineation of regeneration areas primarily focuses on the meso- and micro scales, with less emphasis on the overall urban level and global issues [38,39]. This limited perspective hinders the effectiveness of urban regeneration efforts in promoting the achievement of broader regional development goals and the coordinated integration of urban resources [40]. Therefore, this paper adopts a vitality perspective to comprehensively analyze the urban spatial pattern, development status, and limiting factors from a global perspective. It proposes specific planning strategies based on the global identification and diagnosis of urban renewal areas, aiming to provide recommendations and guidelines for the sustainable and healthy development of the city.

2.2. Urban Renewal Diagnosis Based on Urban Vitality

Urban vitality reflects the degree of communication and interaction among individuals in urban spaces across various levels, including social, cultural, economic, and many other aspects [41]. It serves as the foundation for urban evolution and acts as a driving force for development [14]. Jacobs first introduced the concept of “street vitality” in 1961, emphasizing the importance of street spaces, small-scale blocks, diverse architecture, and mixed-use functions in fostering urban vitality [12]. In today’s society, where urban planning emphasizes a people-centered, sustainable, and high-quality development approach, urban vitality is garnering increasing attention from researchers [42]. Urban vitality, as a key element for measuring regional sustainable development, serves as an important metric for urban issues and a significant indicator of renewal potential [7,43]. At the same time, the development of urban vitality is a complex multidimensional system influenced by various urban physical and functional characteristics [44]. Different forms of urban structures and functional organizations stimulate and guide the distinct spatial distributions of urban dynamism [16,45]. Therefore, employing scientific and effective methods to accurately measure the vitality structure of urban space, identify anomalous areas, and conduct a scientific analysis of the mechanisms underlying urban vitality formation is of great significance for assisting urban renewal decision-making.

Depending on the focus, studies on urban vitality covered different aspects such as social vitality [14], economic vitality [46], cultural vitality [47], network vitality [48], and nighttime vitality [49]. These studies investigated the development levels and conditions of urban stock space from various perspectives, offering corresponding strategic recommendations and planning discussions. Chen et al. measured the vitality levels of urban communities, providing data support and strategic recommendations for the sustainable renewal of the central urban area of Suzhou [50]. Long et al. analyzed the influence patterns of the urban built environment on urban economic vitality, thereby providing policy recommendations and a design basis for renewal planning decision-makers [51]. Shan et al. constructed an evaluation model for the vitality of the historic built environment, considering social vitality, economic development levels, and cultural potential, and exploring regeneration strategies for historic districts [52].

In terms of data acquisition, commonly used datasets for analyzing and measuring urban social vitality levels include Baidu population heatmap data [53], mobile signaling data [14], and Weibo check-in data [54]. Meanwhile, there is a strong correlation between the level of economic development in urban areas and housing prices [28]. Therefore, in a certain sense, the spatial distribution of housing price data can reflect the economic vitality of the city [27,28]. In the construction of environmental indicators influencing urban development, Cervero et al. (1997) were among the first to explore the impact mechanisms of various urban elements on travel behavior from the “3Ds” perspective of the urban built environment [55]. Building on this foundation, Belzer and Autler expanded the framework into a “5D” indicator system by incorporating travel distance and destination accessibility [56]. Gomez-Varo et al. developed a multidimensional index system comprising building density, functional diversity, social opportunities, architectural diversity, accessibility, and boundary voids to evaluate the vibrancy of low-income neighborhoods in Barcelona and offer recommendations for public policy improvements [57]. Although there is some variation in the specific selection of relevant influencing factors, most studies encompass aspects such as building density, functional diversity, traffic conditions, and accessibility to facilities.

In terms of research methodology, existing studies primarily explore the mechanisms influencing urban phenomena through the establishment of econometric models to examine the effects of built environment factors on urban phenomena, such as ordinary least squares (OLS) linear regression models, classic geographically weighted regression (GWR) models [58], binary logistic regression models [7], and geographical detectors [59]. The OLS model fits data from a global perspective, which fails to capture the spatial heterogeneity of urban data. In contrast, the GWR model is a regression model based on spatial variability coefficients and has been shown to possess greater superiority in the analysis of spatial data [60].

From the above review of the literature, it is evident that urban vitality is a significant indicator influencing urban regeneration actions. Furthermore, it is closely related to the elements of the urban physical environment and the functional configurations involved in the regeneration initiatives. However, there is limited research on urban renewal diagnostics and strategies from the perspective of urban vitality. Therefore, this paper aims to explore urban renewal strategies with a focus on enhancing vitality. By employing spatial econometric models from a quantitative perspective, it examined the mechanisms for improving vitality and proposed targeted planning strategies.

3. Methodology

3.1. Overview of the Study Area

With the advancement of urbanization, the core area of Harbin has encountered numerous urban spatial challenges, leading to the formation of an unbalanced spatial pattern between the old and new areas [61]. The old urban areas boast rich urban functions, a deep accumulation of historical and cultural resources, and a strong sense of spatial belonging among residents. However, they face issues such as aging infrastructure, poor environmental quality, and deteriorating public service capacity. On the other hand, the new urban areas suffer from a lack of functional diversity and are also in need of urban renewal.

In the context of refined governance of existing spatial stock, identifying key areas for urban regeneration, coordinating balanced development of spatial elements in both old and new urban areas, and comprehensively enhancing urban vitality have become the primary tasks for urban renewal in Harbin [40]. This paper focuses on the urban core area within the central district of Harbin, characterized by a concentrated distribution of urban elements. This area is primarily located within the city’s Third Ring Road and includes the core regions of Daoli District, Nangang District, Xiangfang District, and Daowai District, covering a total of approximately 188.45 km². It includes the main scope of urban development and key construction projects (Figure 1).

Based on prior investigations [62], this study employed a grid-based approach as the foundation for spatial analysis, dividing the study area into spatial resolution grids of 200 m × 200 m, which serve as the scale units for measuring spatial vitality patterns and exploring the influencing mechanisms of related factors. The selection of the 200 m × 200 m grid as the study scale is justified for several reasons. In Harbin’s core area, block scales range from 80 m to 500 m. Within the Second Ring Road, typical blocks are densely distributed building clusters covering approximately 150 m × 150 m [63]. Employing a 200 m grid as the study unit allows for an accurate representation of small-scale urban blocks while also accommodating larger-scale blocks. Consequently, the study area was delineated into a total of 4515 spatial units.

Additionally, this study employs buffer zone analysis to examine the allocation of resources and land use planning within the spatial context [64]. However, there is no widespread consensus on the most suitable buffer zone radius for different studies [65]. According to multiple principles proposed by Clark et al. regarding the determination of buffer zone scales [66,67], the size of the buffer zone should first align with the scale of the analysis area, second comply with relevant policy guidelines, and finally adopt a buffer size that is conducive to effective model analysis. Based on these considerations, this study ultimately determined a buffer radius of 300 m. In the context of Harbin’s core area, a buffer zone with a radius of 300 m is consistent with the densely built urban morphology. Moreover, it aligns with the five-minute living circle defined in The Standard for Planning and Design on Urban Residential Areas (2018), approved by the Chinese government, which pertains to community service facilities [68]. This alignment enables it to serve as an effective metric for assessing the accessibility of urban functions. Furthermore, it has undergone sensitivity analysis to ensure that the model achieves a satisfactory level of fit.

3.2. Data Sources

In terms of data sources, this paper obtained multiple datasets, including Baidu Heatmap data, Anjuke housing price data, Gaode POI data, and OSM road network data, to represent levels of social vitality, economic vitality, and built environment quality. These datasets were used to conduct spatial pattern analysis and identify urban renewal potential (

Table 1. Main data sources of this study.

Data	Data Source	Purpose
BHM Data	Thermal map. Available at: https://lbsyun.baidu.com/ (accessed on 7–8 June 2024)	These data reflect the real-time dynamic population distribution in urban spaces and serve as the source of social vitality data for this study.
Housing price data	Housing price data. Available at: https://www.anjuke.com (accessed on 15 May 2024)	These data reflect the distribution of property values in urban spaces and serve as the source of economic vitality data for this study. Additionally, the construction dates of the built environment were obtained to assess the age and condition of urban spaces.
POI data	Amap. Available at: https://lbs.amap.com/ (accessed on 22 October 2022)	These data reflect the distribution of urban functional facilities, serving as a source of data for the study of urban functions and land use in this research.
Urban form data	OpenStreetMap. Available at: https://www.openstreetmap.org (accessed on 21 April 2022)	These data reflect the quality of the built environment and physical elements of the city, serving as a measure of the urban physical space.

).

As a common type of crowdsourced data in China, BHM offers an effective tool for real-time analysis of population distribution, density, and dynamic changes [53]. Related studies have shown that the distribution and evolution of crowd vitality are similar from Monday to Friday (working days), as well as on Saturday and Sunday (rest days) [69,70]. Therefore, this paper obtained hourly Baidu Heatmap data from 6:00 to 22:00 on 7 June 2024 (Friday) and 8 June 2024 (Saturday) to reflect crowd activity during working days and rest days, resulting in a total of 34 heatmaps with a spatial resolution of 3.5 m. To assess the overall condition of social vitality under typical circumstances, we calculated the average heat density values derived from 34 time points.

Housing price data were obtained from the second-hand housing community page on the Anjuke website (https://heb.anjuke.com/community/, accessed on 5 November 2024) in April 2024. A total of 3124 average housing price entries were filtered from various communities within the core area. By utilizing the Gaode API to match the latitude and longitude values of each community, the integrated information included community names, unit prices, construction years, and geographic coordinates. After data cleaning and filtering out missing items, a total of 2998 valid housing price entries were obtained. The average house price and average construction year of the properties within the study unit were visualized and analyzed separately, resulting in Figure 2. It can be observed that the core area of Harbin generally displays a pattern of new construction areas surrounding the development of the old city. Within the Second Ring Road, most urban construction dates back to before the 20th century, while areas outside the Second Ring Road consist of newly developed districts since the 20th century. The unit housing prices also display a spatial pattern, with higher values on the southwest side and lower prices in the old city and northeast areas.

The POI data were scraped on 22 October 2022, and irrelevant POI data that did not correspond to land use or represent urban functions were filtered out according to urban land use classifications and development planning standards. The remaining data were then organized and restructured, ultimately categorized into eight major types: commercial points of interest, restaurant points of interest, office points of interest, education points of interest, healthcare points of interest, life service points of interest, recreational points of interest, and transportation points of interest. Detailed descriptions of each category are provided in

Table 2. POI data classification in this study.

Data Type	Number	Category
Commercial POI	32440	Convenience stores, supermarkets, shopping centers, shopping streets, department stores, duty-free shops, home appliance and digital appliance retailers, etc.
Restaurant POI	24575	Chinese restaurants, international cuisine establishments, fast food outlets, coffee shops, pastry shops, dessert shops, tea houses, cold beverage outlets, etc.
Office POI	7678	Companies, enterprises, factories, etc.
Education POI	6004	Universities, primary and secondary schools, research institutes, libraries, science museums, cultural centers, exhibition halls, concert halls, training institutions, etc.
Healthcare POI	7349	General hospitals, specialist hospitals, clinics, first aid centers, disease prevention centers, pharmaceutical outlets, pet medical care, etc.
Life service POI	18203	Beauty and barber shops, laundries, logistics centers, photography studios, printing services, post offices, communication service centers, bathing facilities, etc.
Recreational POI	1459	Amusement parks, cinemas, theaters, KTV, chess and card rooms, Internet cafes, resorts, bars, etc.
Transportation POI	6354	Metro stations, bus stations, railway stations and related service points, car parks, coach stations, etc.

, and their spatial distributions are shown in Figure 3. The data related to urban form were obtained from OpenStreetMap, an open GIS data platform, including road networks and building vector data.

3.3. Definitions of Variables

In this paper, the average BHM data are used to represent the city’s social vitality, while house price data reflects the city’s economic vitality. The purpose of this study is to analyze the spatial development structure of Harbin’s core area from the dual perspectives of economic development and social vitality. It assesses the spatial types and distribution patterns of anomalous regions to identify areas with urban renewal potential. To investigate the urban environmental elements that influence urban development and vitality cultivation, this paper constructs an independent variable system based on various indicators of the built environment. This system includes four categories: spatial accessibility, construction intensity, functional mixing, and transportation conditions, with a total of fourteen factors to measure the built environment of the city.

Table 3. Description of indicators for the built environment.

Categories	Variables	Symbol	Description	Mean	Std.	Max	Min
Construction Status	Building Density	BD	floor area of buildings within the study unit divided by the unit area	0.19	0.14	0.84	0.00
	Plot Ratio	PR	total building area within the study unit divided by the unit area	1.03	0.85	5.73	0.00
	Building Age	BA	Average construction year within the study unit obtained through Kriging interpolation.	2004.36	4.41	2013.18	189.09
Diversity	POI Diversity	PD	average Shannon index of POIs within the study unit.	1.68	0.34	2.10	0.00
Transportation Conditions	Road Density	RD	total road length in the research unit divided by the unit area	0.01	0.01	0.06	0.00
	Bus Stop Accessibility	BsA	number of bus stops within the research unit buffer zone	3.79	3.14	19.00	0.00
	Metro Station Accessibility	MsA	number of metro stations within the research unit buffer zone	0.55	1.32	8.00	0.00
Accessibility	Commercial Accessibility	CoA	number of commercial facilities POI within the research unit buffer zone	88.73	122.33	1121.00	0.00
	Restaurant Accessibility	RsA	number of dining facilities POI within the research unit buffer zone	67.54	77.60	499.00	0.00
	Office Accessibility	OfA	number of office facilities POI within the research unit buffer zone	21.02	21.19	165.00	0.00
	Education Accessibility	EdA	number of educational and cultural facilities POI within the research unit buffer zone	16.52	16.71	116.00	0.00
	Healthcare Accessibility	HeA	number of healthcare facilities POI within the research unit buffer zone	20.25	23.14	175.00	0.00
	Life Services Accessibility	LiA	number of life service facilities POI within the research unit buffer zone	49.93	54.09	430.00	0.00
	Recreation Accessibility	RcA	number of recreational facilities POI within the research unit buffer zone	4.00	5.70	54.00	0.00

provides a detailed description of each indicator and its calculation method.

3.4. Research Methodology

3.4.1. Spatial Autocorrelation Analysis

Spatial autocorrelation reflects the degree of correlation between a study unit and its neighboring units regarding a specific attribute. It serves as a spatial statistical method for detecting and quantifying spatial dependence [71], typically calculated using Moran’s I. Moran’s I is divided into global Moran’s I and local Moran’s I and can be analyzed in both univariate and bivariate contexts. Global Moran’s I is typically used to determine whether spatial clustering and outliers are present in the data, while local Moran’s I can identify specific spatial clustering patterns of different variables and conduct spatial cluster analysis. Spatial autocorrelation analysis is one of the effective methods for identifying spatial differentiation patterns and urban spatial configurations [32].

The formula for calculating global Moran’s I is as follows:

$$Moran’s I={\displaystyle \frac{n{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{\left({{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\right){{\displaystyle \sum }}_{i=1}^n{\left(x_i-\overline{x}\right)}^2}},\left(i\ne j\right)$$

(1)

In the formula, $x_i$ and $x_j$ represent the observed values for spatial units $i$ and $j$, while $W_{ij}$ denotes the spatial weight coefficient between units $i$ and $j$. The value range of the global Moran’s I is between [−1,1], with higher absolute values indicating stronger spatial clustering of a variable in a univariate context. In a bivariate context, higher values suggest more similar local spatial patterns and stronger spatial correlations between variables. Negative values indicate a spatial negative correlation between variables, while positive values signify a positive correlation.

Local spatial autocorrelation can be used to accurately capture the spatial characteristics of clustering and differentiation of variables, which can be analyzed using the LISA statistic. Its calculation formula is as follows:

$$I_i=\left[n\left(x_i-\overline{x}\right){\displaystyle \sum }_{j=1}^n{W}_{ij}\left(x_j-\overline{x}\right)\right]{\left[{\displaystyle \sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2\right]}^{-1}$$

(2)

where $W_{ij}$ denotes the row-normalized form of the spatial weight matrix. The value of $I_i$ in a univariate context reflects the similarities or differences in attribute values between the study unit and its neighboring units. In a bivariate context, it indicates the clustering characteristics of similar or dissimilar attribute values between variables, which can be utilized to identify anomalous distributions of spatial states or mismatches in spatial attributes. Based on LISA statistics, four types of cold and hot spot areas can be identified: low-value clustering areas (Low-Low, LL) and high-value clustering areas (High-High, HH), along with anomalous regions represented by low-high (LH) and high-low (HL) types.

3.4.2. Global Regression Model

This study first explored the relationship between urban vitality and building environmental factors from a global perspective. The least squares regression model (OLS) is a commonly used statistical model in the study of urban vitality. It determines the position of the regression line based on the principle of minimizing the sum of squared residuals [72]. The expression is as follows:

$$Y={\alpha }_0+{\alpha }_1{X}_1+{\alpha }_2{X}_2+\cdots +{\alpha }_i{X}_i+\epsilon$$

(3)

where $Y$ stands for the dependent variable, $X_1$ to $X_i$ represent the independent variables of the model, ${\alpha }_1$ to ${\alpha }_i$ are the coefficients of the impact factors to be estimated, ${\alpha }_0$ is a constant, and $\epsilon$ is the random error term.

However, the interrelationships among spatial variables frequently change in accordance with geographic location, which will influence the accuracy of global regression models. The OLS model overlooks the local spatial influences of variables, making it unable to explain the local spatial relationships between spatial variables [73].

3.4.3. Local Regression Model

The geographically weighted regression (GWR) model is commonly employed to analyze the heterogeneity in the spatial distribution of influencing factors. It utilizes geographical weighting factors to examine spatial trends and abrupt changes in the determinants of the dependent variable, facilitating an investigation into the spatial disparities in the impact of these independent factors across various regions. The corresponding equation is as follows:

$$y_i={\beta }_{bw0}\left(u_i,v_i\right)+{\displaystyle \sum }_{j=1}^n{\beta }_{bwj}\left(u_i,v_i\right)x_{ij}+{\epsilon }_i$$

(4)

In this equation, ${\beta }_{bwj}$ represents the regression coefficient for the $j_{th}$ variable, with a smaller bandwidth indicating a smaller spatial influence scale and a higher degree of spatial heterogeneity. ${\beta }_{bw0}\left(u_i,v_i\right)$ denotes the intercept at bandwidth $bw0$, and ${\epsilon }_i$ represents the random error term.

This study employs a fixed Gaussian distance decay function for the geographical weighting mechanism, which asserts that entities located in closer proximity have a more pronounced influence [74]. The bandwidth specifies the range of the spatial weighting function while concurrently affecting the calibration of the local regression model [75]. The optimal bandwidth and the weighting function applied in the model are determined using the corrected Akaike information criterion (AICc), which assesses the model’s alignment with observed data.

4. Results and Analysis

4.1. Spatial Pattern Analysis and Identification of Urban Renewal Potential in the Core Area of Harbin

4.1.1. Analysis of the Spatial Correlation Between Social Vitality and Economic Vitality

A sustainable urban development typically features a reasonable internal spatial structure, a balanced and orderly vitality distribution, and a compatible level of economic and social development [40]. To explore the spatial pattern characteristics and agglomeration distribution status of the core area of Harbin City from the dual perspectives of social vitality and economic vitality levels, this paper first employed the univariate Moran’s I method. The calculated values were 0.707 and 0.967, indicating that both social vitality and economic vitality exhibit significant spatial autocorrelation (Figure 4). Notably, the global Moran’s I for economic vitality exceeds that for social vitality, suggesting a higher degree of spatial clustering associated with economic vitality.

Univariate LISA clustering maps for social and economic vitality were generated using GeoDa software (GeoDa 1.20.0.36, 12 February 2023) (Figure 5). The results indicated that social vitality exhibited significant clustering of high values within the old city, whereas the newly developed urban areas typically demonstrated clustering characteristics of low values. This is attributed to the considerable population concentration within the spatial confines of the old city of Harbin, which still hosts extensive social activities and urban resources, reflecting an enduring appeal of the old city space for urban residents. Conversely, the new district exhibits relatively low regional attractiveness, resulting in an aggregation of low social vitality levels.

In contrast, the aggregation pattern of economic development levels reveals a significantly different state, with notable high-value clustering observed in areas such as the Qunli area and the Haxi area in the southwestern part of the city. Within the old city, only scattered areas, such as the Hexing area, the Aijian area, and the Dongdazhi area, exhibit high-value clustering, while the remaining regions display relatively low levels. This reflects a decline in economic efficiency, stagnation in spatial development, and diminished investment attractiveness in the old city, indicating a degree of hollowing out, as the economic focus shifts toward the new district. This may be associated with the policy orientation of the local government and the deterioration of the livability of the facilities in the old city, reflecting a lack of development momentum and a decline in economic vitality within that area.

A bivariate Moran’s I analysis was conducted to assess the levels of social vitality and economic vitality. By generating a LISA cluster map, we obtained a local spatial autocorrelation clustering diagram of urban space from a bivariate evaluation perspective (Figure 6). The areas are categorized into high-high (HH) and low-low (LL) agglomeration areas where the distribution of social and economic vitality levels are aligned, as well as low-high (LH) and high-low (HL) agglomeration areas where the distribution levels do not align. It is evident that there exists a significant phenomenon of spatial development mismatch within the core area of Harbin. Large areas with low economic vitality and high social vitality have emerged within the old city, reflecting a degree of economic decline and stagnation. As the physical environment deteriorates and ages, there is insufficient momentum for economic growth, and improvements to the urban landscape remain inadequate. These areas primarily encompass large sections of old residential neighborhoods, such as the Jinyu area (Figure 6a), the Anzi area (Figure 6b), and the Xuanqing area (Figure 6c). They also include partially updated historical and cultural districts like the Chinese Baroque Historical and Cultural District (Figure 6e), as well as old industrial zones, such as the Sandadongli areas in Xiangfang (Figure 6d). These areas occupy favorable urban locations but accommodate functions that are increasingly misaligned with the city’s social development. Over time, they increasingly fail to meet the rising demands for living and production, thus becoming inefficient zones in the process of urban development. In contrast, the Qunli area (Figure 6f) and its northwestern side (Figure 6g,h) exhibit characteristics of clustering with high economic vitality and low social vitality, reflecting that the development and cultivation of vitality in these new areas remain inadequate, preventing the establishment of new comprehensive urban sub-centers. The unbalanced distribution pattern of economic vitality and social vitality presents several challenges for urban development. On one hand, the old urban areas experience issues such as environmental deterioration, aging infrastructure, traffic congestion, and declining efficiency, all of which necessitate urgent environmental optimization and functional renewal. On the other hand, although the new urban districts exhibit a high intensity of development and well-established physical infrastructure, they are characterized by a lack of regional attractiveness. Consequently, these areas have not effectively alleviated the pressure on the old urban areas or contributed to the formation of a new comprehensive urban center.

4.1.2. Analysis of the Spatial Correlation Between Urban Vitality and Land Use Intensity

Land use intensity serves as an indicator of the development and construction intensity in different regions. In high-density urban environments, the distribution of low vitality reflects a decline in urban operational efficiency, which hinders the compactness and high-quality development of the city [1]. To explore the spatial correlation between land use intensity and urban development levels, a spatial correlation analysis was conducted between social vitality values and economic vitality values, respectively, with the plot ratio values of each research unit. The resulting LISA clustering map is illustrated in Figure 7. Specifically, Figure 7A illustrates a notable presence of high-high (HH) and low-low (LL) clustering areas in the relationship between social vitality and the volumetric ratio of the built environment. This indicates that urban areas with high density in the core region are generally associated with a more vibrant concentration of social activities. However, the economic vitality value shows a significant local spatial mismatch with the plot ratio (Figure 7B), where a considerable area within the old city exhibits high built density coupled with low economic vitality clusters. The distribution of these areas closely resembles the anomalous regions depicted in the bivariate LISA map of social and economic vitality (Figure 6), primarily located within the old city, including aging residential neighborhoods, historical districts, and old industrial areas. These areas also reflect a decline in environmental quality and a loss of economic vitality. Aging physical infrastructure and outdated urban functions serve as potential limiting factors for these regions. There is an urgent need for the renewal and improvement of the physical space, as well as for enhancing land use efficiency, positioning them as potential candidates for urban renewal.

4.2. Analysis of Influencing Factors on the Renewal Potential of Harbin’s Core Area

4.2.1. Variable Screening

Urban regeneration efforts aim to revitalize the city’s multidimensional vitality and guide the balanced and sustainable development of urban vitality through urban governance approaches such as environmental remediation and functional enhancement [9]. Based on the 14 influencing factors of regeneration potential mentioned above (Table 3), this study investigates the mechanisms by which these factors influence urban vitality by constructing regression models. Both the OLS model and the GWR model are linear regression models, and multicollinearity among independent variables can significantly affect the accuracy of the model results. Therefore, to prevent bias due to multicollinearity, each factor must be tested before model construction, and the optimal factors should be selected for building the regression models. By calculating the Pearson correlation coefficient, as visualized in Figure 8, Life Services Accessibility (LiA), Healthcare Accessibility (HeA), and Restaurant Accessibility (RsA) show a strong positive correlation (correlation coefficient r > 0.8), indicating the presence of bivariate collinearity issues among these variables. However, the analysis of Pearson’s correlation coefficient only examines the relationship between pairs of variables and is unable to analyze the multicollinearity problem among multiple variables. This study further examined the multicollinearity issues among the independent variable set by calculating the variance inflation factor (VIF) values for each variable. Among them, only the VIF values of Restaurant Accessibility (RsA) and Healthcare Accessibility (HeA) exceed 7.5, indicating the presence of collinear redundancy issues. Therefore, we ultimately excluded the variables Healthcare Accessibility (HeA) and Restaurant Accessibility (RsA) for modeling in order to avoid the above issues.

Additionally, based on the analysis of the spatial patterns of the core area of Harbin City, it is evident that the spatial distribution patterns of social and economic vitality differ significantly. Therefore, this paper constructs separate regression analysis models for each aspect to explore the impact mechanism of built environment factors on urban vitality cultivation from the dual perspectives of social and economic vitality, aiming to provide a refined analysis for planning and decision-making.

4.2.2. Model Regression Results and Comparisons

This paper first employs the OLS model for global regression, with

Table 4. Result of the OLS model.

	Variable	Coefficient	Std. Error	t-Statistic	Probability	Model Diagnosis
SV	CONSTANT	0.000	0.009	0.000	1.000	R2 = 0.605 Adjusted R2 = 0.604 AICc = 8644
	BD	−0.070	0.014	−5.161	0.000 *
	PR	0.234	0.014	16.351	0.000 *
	BA	−0.053	0.012	−4.417	0.000 *
	PD	−0.031	0.011	−2.812	0.005
	RD	0.166	0.010	16.208	0.000 *
	BsA	0.070	0.012	5.821	0.000 *
	MsA	0.113	0.010	11.552	0.000 *
	CoA	0.293	0.016	18.541	0.000 *
	OfA	−0.142	0.014	−10.203	0.000 *
	EdA	0.197	0.013	14.775	0.000 *
	LiA	0.088	0.018	4.909	0.000 *
	RcA	0.141	0.014	10.460	0.000 *
EV	CONSTANT	0.000	0.012	0.000	1.000	R2 = 0.384 Adjusted R2 = 0.382 AICc = 10,657
	BD	−0.269	0.017	−15.873	0.000 *
	PR	0.109	0.018	6.084	0.000 *
	BA	0.519	0.015	34.567	0.000 *
	PD	0.021	0.014	1.552	0.121
	RD	−0.027	0.013	−2.096	0.036 *
	BsA	0.074	0.015	4.906	0.000 *
	MsA	0.079	0.012	6.488	0.000 *
	CoA	0.046	0.020	2.336	0.020 *
	OfA	0.021	0.017	1.191	0.234
	EdA	0.335	0.017	20.149	0.000 *
	LiA	0.002	0.022	0.097	0.923
	RcA	−0.045	0.017	−2.693	0.007 *

* indicates statistical significance.

presenting the results of the linear regression analysis. The R² value of the regression model based on social vitality reaches 0.6053, while the R² value based on economic vitality is comparatively lower at only 0.384. This indicates that the regression results of the OLS model provide a stronger explanation for social vitality. Both regression models have passed the F-test. For social vitality, only the variable PD did not show significance, while among the other significant variables, CoA and PR exhibited a strong positive correlation. This implies that areas with greater commercial concentration and construction intensity tend to have higher social vitality, which aligns with common understanding. Conversely, office facilities exhibit a certain inhibiting effect on social vitality at a global level. This may be attributed to the fact that office areas tend to be more mono-functional, resulting in a lack of attraction for a diverse range of activities among the populace. In the regression model analysis based on economic vitality, the variables PD, OfA, and LiA did not demonstrate significance, and the R² value of this global analysis was relatively low. Given their prominent spatial aggregation observed in the previous spatial autocorrelation analysis, the specific mechanisms of their influence require further investigation. In the OLS regression results, economic vitality demonstrates a strong spatial correlation with the variables BA and EdA, indicating that areas characterized by newer construction years and abundant educational resources are likely to possess superior economic environments and exhibit higher levels of economic vitality. The variable BD, on the other hand, exhibits a certain inhibitory effect on economic vitality. This could be attributed to the densely packed urban space, which typically results in a lack of green spaces and public areas, leading to a decline in environmental quality and hindering the sustainable development of the city.

Following the previous analysis of Moran’s I statistic for urban vitality, which indicated a strong spatial autocorrelation in its distribution, this paper further employs the geographically weighted regression (GWR) model for localized statistical analysis to optimize the model. The analysis results are shown in

Table 5. Result of the GWR model.

	Variable	Mean	Std.	Min	Max	Model Diagnosis
SV	CONSTANT	0.048	0.234	−0.737	1.018	R2 = 0.726 Adjusted R2 = 0.699 LogL = −3488 AICc = 7638
	BD	−0.045	0.133	−0.324	0.491
	PR	0.218	0.134	−0.079	0.655
	BA	−0.028	0.212	−0.900	0.487
	PD	−0.074	0.183	−0.872	0.732
	RD	0.146	0.106	−0.024	0.507
	BsA	0.055	0.099	−0.142	0.623
	MsA	0.077	0.168	−0.605	2.481
	CoA	0.404	0.433	−0.931	2.570
	OfA	−0.079	0.188	−0.712	0.582
	EdA	0.167	0.170	−0.314	0.762
	LiA	0.128	0.307	−1.039	1.114
	RcA	0.177	0.207	−0.588	0.798
EV	CONSTANT	−0.261	0.659	−3.177	2.529	R2 = 0.913 Adjusted R2 = 0.905 LogL = −895 AICc = 2453
	BD	−0.066	0.088	−0.374	0.194
	PR	0.015	0.078	−0.206	0.309
	BA	0.379	0.508	−0.390	3.383
	PD	0.109	0.307	−0.522	1.340
	RD	−0.010	0.060	−0.204	0.290
	BsA	−0.010	0.108	−0.298	0.299
	MsA	0.060	0.138	−0.569	1.677
	CoA	0.045	0.602	−3.572	2.225
	OfA	0.039	0.223	−0.577	0.959
	EdA	0.202	0.292	−1.149	1.271
	LiA	−0.154	0.365	−1.631	1.440
	RcA	0.010	0.265	−0.904	1.536

. It can be observed that by using the spatial regression model, the accuracy of the model has been further optimized. Among them, the R² for social vitality increased to 0.726, and the R² for economic vitality increased to 0.913, indicating that the GWR model has significantly enhanced the explanatory power of the distribution mechanisms of urban vitality. Overall, the impact of each variable on vitality exhibits significant spatial heterogeneity. For social vitality, the variables CoA and PR show a substantial positive impact on average, while for economic vitality, BA and EdA exhibit a strong promoting effect. This is consistent with the results of the OLS regression model, confirming the robustness of the regression findings.

4.2.3. Analysis of the Spatial Pattern of Regression Coefficients

This study visualized the regression coefficients of the independent variables in the GWR models for social and economic vitality (Figure 9 and Figure 10) and compared the spatial differentiation characteristics of the influencing mechanisms of various factors on both forms of vitality. This analysis ultimately aimed to explore effective planning strategies to enhance urban vitality and promote balanced economic and social development within the context of stock renewal.

For the variables BD, PR, and BA, which reflect the state of the physical spatial development in the city, Figure 9a shows that BD mainly exhibits a promoting effect in the western part of the core area. This effect is most pronounced in the Qunli and Haxi areas, where higher building density corresponds to a greater concentration of social vitality. In contrast, BD shows a certain inhibiting effect within the old city area, which is particularly evident in regions with a high concentration of older residential areas, such as Jingyu and Heping areas. This may be due to the fact that areas with high building density often experience a decline in quality of life due to the lack of open space and activity space. In terms of economic vitality, it primarily exhibits an inhibiting effect, with most influence coefficients being less than zero. Positive effects are only observed in certain areas of the Tongjiang area and the Qunli area. For PR, as shown in Figure 9b, social vitality exhibits an inverse relationship with building density. Areas within the new district that have higher plot ratios actually demonstrate lower levels of social vitality. This observation suggests that the functions of various areas within the new district are relatively homogeneous, with residential complexes often exhibiting the highest plot ratios in the entire region. Consequently, a singular residential function does not promote high levels of social vitality. Within the old city, PR has shown a more significant positive effect on social vitality. This may be attributed to the more mixed and diverse urban functions in older areas, where higher development intensity often correlates with a richer variety of urban functions and a concentration of diverse human activities. For BA, as shown in Figure 9c, its impact on social vitality predominantly shows a facilitating effect in most parts of the core area. Areas with newer construction years tend to exhibit higher levels of social vitality. The only exception is the northern part of Xiangfang District, where BA shows a certain inhibiting effect. This may be related to the fact that the newly developed land in this area is primarily designated for educational purposes, such as Northeast Forestry University and Heilongjiang University of Chinese Medicine. Typically, educational land use does not exhibit high levels of social vitality. Regarding economic vitality, the influence coefficients of BA in most areas are also predominantly greater than zero, indicating a strong facilitating effect. However, a negative relationship is observed in the southwestern and northeastern edges of the core area, as well as in the Dongdajizhi area. This may be attributed to the fact that in these areas, traditional urban spaces maintain consistently stronger vitality and appeal, resulting in newly constructed spaces lacking additional attractiveness based solely on their age.

Regarding urban functional diversity, as indicated in Figure 9d, it shows a strong facilitating effect in areas such as Quxian, Tongjiang, and Xuanqing. In contrast, the Jingyu area and regions near the Xuefu area exhibit inhibitory effects, reflecting significant spatial heterogeneity. In terms of economic vitality, it generally demonstrates a strong facilitating effect within the old city area, while no such facilitating effect is observed in the new city areas beyond the Second Ring Road. This may be attributed to the strong positive effects of industrial or business clustering in certain areas, which are more conducive to attracting human activities and fostering economic growth.

For the variables reflecting transportation conditions, namely RD, BsA, and MsA, Figure 10a indicates that the road density (RD) in the Nangang District primarily exhibits a facilitating effect, suggesting that a dense road system is typically associated with higher levels of social vitality. In contrast, the areas along the edges of the core zone and in the Daoli District exhibit the opposite trend, which may be related to the prior development of roads in the periphery of these areas. In new urban areas, infrastructure such as roads is often constructed ahead of schedule due to policy incentives; however, urban vitality remains underdeveloped, thereby reflecting an inverse correlation between urban roads and vitality. In contrast, at the urban periphery, road density demonstrates a certain positive impact on economic vitality. However, its overall effect on economic vitality remains relatively weak, regardless of whether the influence is positive or negative. As illustrated in Figure 10b, for BsA, its impact on social vitality primarily reflects a modest positive effect (impact coefficient ranging from 0.45 to 0.14), while its influence on economic vitality is similarly small (impact coefficient ranging from 0.45 to 0.14). For MsA (Figure 10c), it demonstrates a strong positive effect in regions such as Xuefu and Jingyu, where social vitality is often closely linked to the accessibility of subway stations. Similarly, it demonstrates a certain level of positive influence on economic vitality across a broader range of areas.

From the analysis of the correlation between urban functions and vitality, we can infer the distribution and development of urban functions across different areas. As shown in Figure 10d, the accessibility of commercial facilities has a positive impact on economic vitality throughout the core area. Specifically, the southern parts of Xiangfang and Nangang districts exhibit a strong positive effect on both economic and social vitality, indicating that areas with a high concentration of commercial facilities tend to attract greater human activity and demonstrate a better economic environment. Regarding the accessibility of office facilities, Figure 10e indicates that they contribute positively to social vitality, although the correlation is relatively weak. In some areas, a negative correlation is observed, which may be related to the presence of urban green spaces and other open areas where the distribution of office facilities is not representative. For economic vitality, office facility accessibility (OFA) in the Qunli area demonstrates a significant positive effect, highlighting how office facilities in this region enhance urban functions and support the sustainable development of urban sub-centers. In terms of educational facilities, Figure 10f exhibits a significant positive impact on economic vitality across the entire area, as educational resources foster public recognition of the economic environment and urban development. Similarly, educational facilities also contribute positively to social vitality, although a suppressive effect is observed only in the eastern part of the Qunli area and the Tongjiang area. This may be due to the fact that educational facilities typically require a quieter environment conducive to learning, thereby limiting social activities. For the accessibility of living facilities (Figure 10g), there is no significant spatial heterogeneity in their influence on economic and social vitality within the old town area inside the Second Ring Road, and their impact is relatively weak. However, in the new urban areas outside the Second Ring Road, a strong spatial heterogeneity is observed. This may be attributed to the well-developed and mature urban functions related to living facilities in the old town, which do not significantly affect social and economic vitality. In contrast, the clustering of people and the distribution of living facilities in the new urban areas exhibit a strong interrelated influence, resulting in a pronounced pattern of hot and cold spot aggregation. Regarding the spatial distribution of the influence of recreational and entertainment facilities, Figure 10h indicates that their impact on social and economic vitality within the old town area is minimal, demonstrating a slight suppressive effect. Conversely, some hotspot areas are identified along the edges of the core area, reflecting the greater influence of entertainment facilities in the peripheral zones, where they play a more substantial role in promoting vitality.

5. Discussion

5.1. Analysis of Spatial Patterns and Identification of Renewal Areas

Urban renewal is a prominent topic in contemporary urban planning practices; however, most related studies are based on empirical inference or case studies [6,20]. These studies typically adopt a micro-scale perspective centered on specific projects [38,39], with limited analysis of the urban spatial structure and the distribution of renewal potential from a macro-level perspective. Zhang et al. highlighted that, from a macro perspective, conducting quantitative analyses of planning elements and investigating their complex relationships with urban vitality are of significant importance for promoting sustainable urban development [76]. Aligned with this perspective, this paper adopts a perspective of urban vitality and employs an innovative quantitative analysis method to conduct a detailed study and scientific inference of the spatial deployment and planning strategies for urban renewal.

By analyzing the spatial patterns of the relationships among social vitality, economic vitality, and development intensity, a series of spatial areas were identified where urban vitality development is uneven and the intensity of development does not align with vitality growth. It was found that there are widespread areas of underdevelopment or vacancy within urban spaces, which is consistent with the studies by Xia and Jin et al. [32,77]. In Harbin’s old city area, a notable concentration of high social vitality is also commonly observed, whereas newly developed areas exhibit widespread spatial vacancy and a concentration of low social vitality. This phenomenon further corroborates a common occurrence in Chinese cities, where rapid urbanization leads to the swift expansion of new areas while urban functions tend to concentrate in older districts [78]. It has become a widely accepted consensus that promoting stock planning is essential for achieving compact and sustainable urban land development [32].

In the core area of Harbin, these regions specifically manifest as the numerous old residential communities, certain historic districts, and outdated industrial zones within the Second Ring Road. These areas also demonstrate a stronger potential for renewal, which aligns with existing plans and related studies in Harbin [61]. Harbin’s overall plan, established as early as 2011, proposed the development of new urban areas to alleviate pressure on the old city area within the Second Ring Road. This strategy aimed to enhance the environmental quality of the old city area and improve the functional configuration of the new urban areas, thereby optimizing the overall spatial layout of the city and promoting the coordinated development of both the old and new districts [79]. However, in the most recent version of the national land space planning, issues of uneven urban development and the urgent need for urban renewal actions in key areas remain significant difficulties and challenges facing Harbin’s urban planning [80]. The identification of spatial development imbalances in this study further corroborates the existing disparities in urban development within Harbin.

5.2. The Influencing Mechanisms of Related Factors

Through the exploration of the influencing mechanisms of relevant factors, this study further demonstrates that the GWR model exhibits superior performance compared to global regression models such as the OLS model. This further demonstrates the spatial heterogeneity of influencing mechanisms, indicating that distinct urban areas should adopt tailored planning policies. This aligns with the discourse presented by Chen et al. [14]. Additionally, their findings demonstrated that commercial facilities and development intensity contributed to the cultivation of social vitality, while the construction period and educational facilities showed a strong positive correlation with economic vitality, which aligned with the research of Li et al. [71,77]. Populations tend to congregate in areas characterized by diverse commercial activities, and regions with higher development intensity typically exhibit greater social vitality, consistent with the findings of Xia et al. [32]. In contrast, economic vitality demonstrates a stronger correlation with spatial elements that directly influence environmental quality.

Regarding the construction status, the analysis of the influencing mechanisms of various factors within local spaces, conducted using the GWR model, indicates a significant impact of building density on social vitality. This effect is particularly pronounced in the old city area within the Second Ring Road. An appropriate increase in the floor area ratio can help create more urban open spaces, which is beneficial for the cultivation of social vitality. This finding aligns with the research conducted by Zhang et al., further corroborating the notion that compact urban block forms are conducive to the cultivation of urban vitality [81]. The construction period has a significant impact on both economic and social vitality, as it is often closely related to spatial quality. Generally, newer urban spaces tend to offer a better spatial experience [82], which positively contributes to both social and economic vitality. This perspective complements the findings of Li et al., whose research indicates that older urban areas often exhibit higher levels of urban vitality [70]. They attribute this phenomenon to the accumulation of a sense of place, which enhances vitality. Nonetheless, this study identifies that although the historical districts possess significant urban memories and contextual heritage, they are concurrently prone to impeding the development of urban vitality due to substandard living conditions. This finding accentuates the imperative for urban renewal initiatives to prioritize the enhancement of the environmental quality within aging residential areas and the augmentation of supportive infrastructure, thereby facilitating the revitalization of vitality in these historic districts, which aligns with the research of Seyed et al. [83].

Functional diversity has a significant enhancing effect on economic vitality in certain areas, providing evidence that a higher degree of functional mix contributes to vitality improvement. Since the classic theory proposed by Jacobs [12], multiple studies by Li et al. have demonstrated the significant role of mixed-use developments in fostering urban vitality [71]. The findings of this research further corroborate the importance of planning initiatives such as mixed-use development and compact urban design in promoting sustainable urban growth.

The impact of transportation conditions exhibits strong spatial heterogeneity, and the GWR model effectively reveals these local phenomena, aiding in the specific analysis of different regions. Enhanced transportation conditions are often indicative of improved accessibility, which subsequently facilitates higher levels of urban vitality [84]. However, certain studies have indicated a weak negative correlation between the accessibility of transportation facilities and urban vitality [81]. In light of the analysis presented in this study, we contend that the influence of transportation facilities on urban vitality may exhibit significant spatial heterogeneity. Therefore, it is essential to conduct targeted analyses for different regions and cities to discern the specific effects on urban vitality.

Overall, in terms of accessibility to different functional facilities, commercial facilities exhibit the strongest influence on social vitality, while educational facilities show the most significant interrelationship with economic vitality. This finding is consistent with the results of Wen et al., which affirm that educational resources, as a crucial component of urban public goods, significantly impact urban land prices [85].

5.3. Urban Planning and Policy Recommendations

Based on the above analysis, urban renewal efforts in the core area of Harbin can begin by addressing the phenomenon of spatial development imbalance. Targeting areas with abnormal concentrations of urban vitality values and land use intensity, along with varying environmental influences on urban vitality, specific renewal planning strategies should be developed to promote urban renewal actions.

In addressing old residential communities, the core task of urban renewal efforts in Harbin is to generally prioritize the enhancement of infrastructure, the upgrading of environmental quality, and the improvement of public services. However, when the renewal process is predominantly driven by the government and implemented in a top-down manner, it places substantial financial demands on public resources, often leading to slower progress, as identified in the study by Li et al. [86]. The research conducted by Zhang et al. demonstrates that local residents’ participation plays a critical role in urban renewal and that participatory planning is an effective approach to revitalizing community vitality [87]. Therefore, to enhance the livability and vitality of aging residential communities in the historic city center, encouraging self-renewal initiatives through the attraction of private capital can alleviate the government’s financial burden while simultaneously improving environmental quality. In this context, the introduction of private capital and self-initiated renewal presents a new opportunity for improving living conditions in older residential areas [88].

For outdated industrial areas, comprehensive renewal and transformation objectives should be established to facilitate industrial restructuring, enhance land use diversity, and promote economic vitality. In the context of revitalizing the outdated industrial zones in Harbin’s core area, it is equally important to promote the transformation of industrial land and foster a diverse range of urban functions. To maximize the locational advantages of these industrial areas and promote integrated urban development, one strategy could be to convert old industrial land for social or service-oriented purposes, such as replacing industrial zones with green spaces or redeveloping them for production-service use [30]. The research by Gu et al. indicates that converting industrial land into creative industry clusters also plays a significant role in reversing the decline in urban regional vitality [89]. Therefore, within the framework of stock planning, the renewal of old industrial zones in core urban areas should adhere to the principles of “controlling total development, reducing new land use, and optimizing existing space”. This approach aims to achieve intensive land use, alleviate spatial expansion pressures, enhance environmental quality, and promote industrial diversification. Meanwhile, conducting a detailed analysis of industrial land and its related influencing factors from a macro perspective, along with formulating targeted industrial transformation plans, will help guide the renewal of outdated industrial land in Harbin [89].

Regarding the historical and cultural sites within this area, Harbin’s efforts to protect its cultural heritage have achieved some progress, such as the construction of the St. Sophia Cathedral Square. However, considering the city’s rich stock of historical buildings, more robust protective measures and development policies are still needed. A large number of historical structures lack appropriate protection and adaptive reuse strategies, leaving room for improvement in shaping the overall urban identity and cultural narrative. As observed in the findings of Wu et al., the preservation of historical heritage requires more flexible policy guidance, while more diverse functional utilization models can contribute to fostering vitality in heritage areas [90]. By creatively repurposing these historical buildings, while respecting their original spatial layouts and functions, their social significance can be revitalized. This will not only protect cultural heritage elements but also unlock their potential and extend the historical and cultural legacy of the city.

The imbalance in the distribution of environmental factors between the old and new urban areas explains, to some extent, the disparity in social vitality and economic development. The old city suffers from overcrowding and environmental degradation, while the new city lacks complete functional facilities. These findings are consistent with those of Jin et al.’s study on representative Chinese cities, reinforcing evidence of the extensive challenges posed by rapid urbanization across various regions in China [77]. To optimize the overall urban structure and promote coordinated development, the old city should focus on relieving pressure by reallocating urban functions, improving environmental quality, reducing population density, and enhancing livability. On the other hand, the new city must address the lack of functional diversity resulting from rapid residential expansion and strengthen commercial and service facilities to ensure a balanced allocation of urban functions.

5.4. Contributions and Limitations

This study still has certain limitations. First, similar to other related research, it only explores the correlation between influencing factors and vitality without establishing or explaining their causal relationships. Second, regarding the research unit, this study employs a grid-based approach, overlooking the differences in block scales across various regions. Finally, in analyzing the influencing factors, the focus was on construction intensity, urban functions, and transportation impacts on vitality, while factors such as urban design and spatial perception were not considered, warranting further investigation in future research.

Despite these limitations, this paper offers an innovative perspective and analytical framework for urban renewal strategy research. It provides a detailed analysis of Harbin’s core area from a macro perspective and offers scientific recommendations. This will facilitate the concrete implementation of urban renewal efforts in Harbin and provide insights and guidance for similar initiatives in other broader urban areas.

6. Conclusions

In the context of enhancing and optimizing existing urban spaces and the refined governance of urban environments, this study takes the core area of Harbin as a case study. By developing a spatial quantitative analysis model based on multi-source data, this research scientifically explores specific spatial development issues and optimization strategies, leading to core conclusions in the following four aspects:

• This study constructs an innovative research framework for identifying renewal areas and analyzing strategies from the perspective of urban vitality. This framework offers a new perspective and scientific guidance for urban renewal analysis by employing a quantitative and holistic approach. A digital analytical model of spatial development in the core area of Harbin was constructed based on multi-source data that reflects urban conditions from a human-centered perspective. Utilizing spatial autocorrelation analysis, the study conducted a detailed assessment of spatial development status from the dual perspectives of economic vitality and social vitality. It identified various regions characterized by uneven vitality development and mismatches between development intensity and vitality cultivation, which also exhibited potential for vitality revival and urban renewal. Furthermore, by constructing a GWR model, the study explored the mechanisms of influence of relevant factors, providing scientific guidance for renewal strategies. Ultimately, based on the aforementioned analyses, the study delineated the main contradictions in spatial development, the primary content of urban renewal, and specific strategies for urban renewal in the core area of Harbin. This research framework also holds implications for spatial diagnosis and urban renewal actions in other regions.
• This study identifies a notable mismatch in vitality development and a spatial development imbalance within the core area of Harbin. Specifically, high concentrations of social vitality are observed in the old city areas within the Second Ring Road, where economic development remains constrained. Conversely, the newly developed areas encounter the phenomenon of elevated economic vitality coupled with low social attractiveness. Across the various urban regions, there exists a pervasive mismatch between the development of economic and social vitality. Furthermore, in the correlation analysis concerning land use intensity, this study identified a prevalent mismatch between economic development and development intensity. This discrepancy indicates inefficiencies in spatial development and a decline in urban operational efficiency. These areas are primarily manifested as outdated residential neighborhoods within the Second Ring Road, certain historical cultural districts, legacy industrial zones, and newly developed urban areas that lack completeness in their development. Collectively, these regions demonstrate significant potential for urban renewal, which is crucial for the overall sustainable development of the city.
• By constructing a GWR model, this study examines the mechanisms through which various factors influence urban vitality and analyzes their spatial heterogeneity. These insights can inform targeted urban renewal and vitality enhancement strategies. The findings reveal that, within the core area of Harbin, the accessibility of commercial facilities and development intensity significantly enhance social vitality, while the age of buildings and the distribution of educational facilities strongly correlate with economic vitality. Moreover, this study concludes that for outdated residential areas, improving environmental quality through increased public and open spaces is essential. Historically underdeveloped districts require a focus on expanding urban functions to enhance diversity and vitality. Additionally, outdated industrial zones should prioritize functional renewal to better integrate into the urban fabric. Finally, in new urban areas, strengthening commercial and service facilities is crucial for completing urban functions and fostering the development of urban centers.
• Based on the diagnostic analysis of spatial patterns and influencing factors, this study offers specific strategic recommendations for addressing the core urban renewal issues in Harbin. For outdated residential areas, historical districts, outdated industrial zones, and underdeveloped urban areas, the study analyzes the constraints on vitality and provides corresponding strategic recommendations for each specific issue. In particular, it recommends the introduction of participatory planning, the guidance of functional transformation, and the optimization of facility configurations. These recommendations aim to provide scientific guidance and reference for urban renewal actions in Harbin’s core area while also establishing an innovative analytical framework for urban renewal strategies from a comprehensive vitality perspective.