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Article

Seasonal Variations in the Mechanisms Linking the Built Environment and Metro Station Area Vitality in Cold Regions: A Case Study of Harbin

by
Xiaolu Zhou
1,2 and
Jianfei Chen
1,2,*
1
School of Architecture and Design, Harbin Institute of Technology, Harbin 150006, China
2
Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(11), 2222; https://doi.org/10.3390/land14112222
Submission received: 14 October 2025 / Revised: 7 November 2025 / Accepted: 8 November 2025 / Published: 10 November 2025
(This article belongs to the Special Issue Comprehensive Transportation and Territorial Space Coordinated Planning)
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Abstract

As urbanization advances toward refined territorial spatial governance, integrating comprehensive transportation and spatial vitality has become essential for sustainable urban development. Transit-oriented development (TOD) plays a key role in linking transportation infrastructure with the coordinated evolution of territorial space. However, the interaction mechanisms between the built environment and metro station area vitality in cold-region cities remain underexplored, particularly in relation to seasonal differentiation. Taking Harbin as a representative cold-region metropolis, this study investigates how built environment factors shape metro station area vitality across seasons and how their spatial mechanisms differ between winter and summer. An indicator system based on the “5D” framework was established, and K-means clustering was applied to classify stations into four coordinated spatial types. A composite vitality index integrating transportation, social, and economic dimensions was constructed to assess seasonal variations. Spearman correlation and XGBoost models identified dominant drivers at the global level, while the MGWR model revealed spatial heterogeneity. The results indicate that POI density exerts the strongest influence on metro station area vitality, contributing 47.95% in winter and 47.27% in summer. Residential density plays a more decisive role during summer, accounting for 18.90%. In contrast, winter vitality depends more on transportation accessibility, with the distance to parking facilities contributing 11.59%. Core urban stations consistently maintain high vitality, while peripheral areas have weaker performances, especially during winter. These findings clarify seasonally adaptive mechanisms linking the built environment and spatial vitality, providing evidence for coordinated optimization of metro systems and land-use planning in cold-region cities.

1. Introduction

As urbanization transitions from incremental growth to refined territorial spatial governance, transit-oriented development (TOD) has become a fundamental strategy for achieving sustainable and coordinated urban growth [1,2]. Within this context, the metro serves as the backbone of comprehensive urban transportation networks in large cities [3]. It accommodates intensive travel demand and provides a key spatial platform for integrating mobility, commerce, and social interaction [4]. Numerous studies have confirmed that a well-structured built environment around metro stations enhances spatial vitality, optimizes land-use efficiency, and fosters coordination between transportation infrastructure and urban spatial functions [5,6,7,8]. Nevertheless, pronounced seasonal fluctuations in cold-region cities create distinctive spatial and behavioral dynamics that challenge the applicability of conventional TOD models [9,10]. Examining how the built environment shapes metro station area vitality in cold regions is therefore essential not only for extending TOD theory but also for promoting the coordinated planning of transportation and territorial space under varying climatic conditions.
Previous research on public transit and spatial vitality has predominantly examined metropolitan regions with moderate climates, resulting in a scarcity of empirical evidence concerning cold-region cities and their pronounced seasonal dynamics [11,12]. Extreme low temperatures, reduced daylight, and snow coverage directly influence residents’ mobility patterns and spatial behaviors, thereby challenging the applicability of conventional TOD principles in such environments [9,13]. Existing studies further indicate that in cold climates, residents rely more on indoor activities and underground spaces during winter. In contrast, outdoor spatial vitality and pedestrian flows intensify in summer, generating distinct seasonal differences in trip chains, transfer efficiency, and commercial concentration patterns [14,15,16]. However, most of these insights are drawn from cities in North America, Europe, or Eastern China [17,18,19], where temperate climates prevail, limiting their relevance to typical cold-region cities such as those in Northeast China. Furthermore, existing research often emphasizes general trends while overlooking the spatial heterogeneity of metro station areas with differing functions and vitality levels [20,21]. Therefore, developing a systematic analytical framework adapted to cold-region cities is essential to uncover the mechanisms linking the built environment with comprehensive vitality at the station scale [22]. Such a framework would not only enrich the international discourse on climate-sensitive TOD but also provide empirical guidance for the coordinated optimization of transportation systems and territorial spatial development in cold-region urban contexts.
In recent years, the interplay between the built environment and urban spatial vitality has become a major focus in urban and transportation studies. The “5D” framework, encompassing density, diversity, design, destination accessibility, and distance to transit, provides an integrated perspective for examining how transportation systems shape and respond to spatial vitality [23,24,25]. Under this analytical paradigm, empirical research has demonstrated that variables such as intersection density, land-use mix, and public transit accessibility substantially enhance the vitality of metro station areas and their surrounding territories [26,27]. At the same time, traditional surveys and field observations have shown limitations in spatial and temporal precision, which has driven the advancement of data-driven measurement techniques [28]. The application of multi-source datasets, including Points of Interest (POIs), traffic flow, and population heatmaps, has enabled more refined assessments of spatial vitality and territorial dynamics [29,30,31]. These approaches not only improve the accuracy and comprehensiveness of vitality evaluation but also enable examination of the coupling relationships between the built environment and territorial spatial vitality at finer scales [32,33,34]. However, many studies still focus primarily on general patterns, with less attention to variations among station types [35,36]. Accordingly, a systematic investigation of built environment characteristics at the station scale remains essential. This should include the classification of station types and incorporation of multidimensional vitality indices to reveal coordination mechanisms within urban and transportation systems.
Methodologically, clustering techniques such as K-means are widely employed to identify different categories of metro station areas. This approach helps clarify functional structures and environmental characteristics and thereby supporting typological planning for transit-oriented development [37,38,39]. In subsequent studies, scholars have developed multidimensional vitality indices that integrate transportation, social, and economic dimensions, improving the comparability of vitality across stations and seasons while strengthening the foundation for comprehensive analysis [40,41,42]. To enhance indicator reliability, statistical tests such as Pearson and Spearman correlations are often used to remove redundant or weakly associated variables and to improve model robustness [43,44,45]. Nevertheless, these traditional statistical methods are limited in capturing nonlinear relationships and threshold effects. In response, advanced machine learning techniques such as XGBoost, combined with SHAP interpretation, have been increasingly used to identify and quantify the relative contributions of different factors. These methods show strong performance in metro station area vitality research in cities like Guangzhou and Chongqing [5,46,47]. However, their ability to account for spatial heterogeneity remains restricted. The development of the MGWR model now enables the spatial differentiation of built environment effects to be detected, improving both interpretability and model performance [48,49]. Integrating the nonlinear interpretive capacity of machine learning with the spatial heterogeneity analysis of MGWR provides a robust methodological framework for advancing studies on the relationship between transportation systems, the built environment, and territorial spatial vitality.
Against this background, this study develops an integrated analytical framework to explore the seasonal interaction and spatial heterogeneity mechanisms linking the built environment with metro station area vitality in cold-region cities. Harbin, a typical cold-region city, experiences extremely low winter temperatures reaching −37.7 °C [50]. These harsh conditions present unique challenges for the urban transportation system, particularly for metro stations. In such extreme cold climates, metro stations and their surrounding built environments must address specific challenges, such as significant seasonal climate variations and the resulting limitations on accessibility [22,51]. These climate-induced barriers, including limitations on infrastructure operation and changes in passenger behavior, directly undermine the vitality of station areas [21,52]. These conditions make Harbin an ideal case for examining how the built environment shapes the metro station area vitality in cold environments.
The objectives of this research are threefold: (1) to systematically investigate the structural characteristics of built environments in metro station areas and classify stations into distinct spatial types to reveal their spatial differentiation; (2) to establish a multidimensional vitality evaluation framework that integrates transportation, social, and economic dimensions to reflect the seasonal dynamics of metro station areas; and (3) to analyze the correlations between built environment attributes and multidimensional vitality, identify the major driving factors and their relative contributions, and assess localized effects across station types. The findings of this study extend the theoretical applicability of transit-oriented development in cold-climate regions and offer empirical insights for the coordinated optimization of transportation systems and territorial spatial development in similar urban contexts.

2. Study Area and Data Sources

2.1. Study Area

The Harbin Metro, the highest-latitude system in China [53], serves as a representative case for investigating urban spatial vitality in cold climates. This research includes 73 operational metro stations from Lines 1, 2, and 3 of the Harbin Metro, considering transfer stations as independent analytical units. To reflect the influence of cold climate conditions and local travel behavior, a circular buffer with a radius of 400 m was established around each station to delineate the metro station area (Figure 1) [54,55]. This area functions as the basic spatial unit for subsequent analysis. The selected area covers the four core districts of Nangang, Daoli, Daowai, and Xiangfang, extending to Songbei and Haxi. These regions exhibit a wide range of spatial forms, from compact mixed-use centers to newly developed TOD-oriented towns and transitional industrial and residential areas. Such spatial heterogeneity offers a solid empirical foundation for exploring how built environment characteristics in cold regions affect metro station area vitality.

2.2. Data Sources and Pre-Prosessing

Considering the distinct seasonal variations in this high-latitude cold-region study area, December 2024 and June 2025 were chosen as representative months to analyze differences in metro station area vitality and built environment conditions between winter and summer. To minimize abnormal fluctuations caused by weekends and holidays, only weekday data were included in the analysis to ensure representativeness and comparability.
The metro ridership data, provided by Harbin Metro Group Co., Ltd. (Harbin, China), recorded the number of passengers entering and exiting each station during operating hours, reflecting the vitality of metro travel throughout the study period. Urban heat index data were obtained from the Baidu Huiyan platform. Hourly observations from eight specific days (December 5, 11, 17, and 26 of 2024, and June 4, 10, 19, and 25 of 2025) used to capture temporal and spatial variations in urban activity. Built environment indicators were compiled from multiple spatial datasets. POI data from Amap included 23 categories such as food, retail, and public services, collected separately for the two periods. Building information from Baidu Maps, including floor area, height, and number of stories, was applied to characterize building morphology. Sentinel-2 remote sensing imagery acquired in July 2025, corresponding to the peak vegetation period, was used to depict natural environmental attributes. Land-use data were drawn from the EULUC-China 2.0 dataset released by Li et al. (2025), which integrates deep learning and multi-source geospatial data with an overall accuracy of 79% [56]. Transportation datasets covered both roads and bus lines. Road data were extracted from OpenStreetMap (OSM), while bus routes were obtained from the topology network developed by Wang et al. (2025), which provides higher positional accuracy than OSM [57]. Socioeconomic variables included population and housing prices. Population data were derived from the PopSE China2020 100 m dataset constructed by Chen et al. (2024) using the Seventh National Population Census [58], representing spatial population distribution patterns. Housing price information was collected from the Anjuke platform, which offers extensive market coverage and frequent updates to ensure reliable estimates of residential price levels across the study area.
All spatial data were processed in ArcGIS Pro 3.0, involving projection transformation, geometric correction, and spatial registration. A unified coordinate system, CGCS2000 3-Degree GK Zone 42, was used to maintain temporal and spatial consistency across all datasets. Table 1 presents a detailed summary of the data sources used in this research.

3. Methods

This study developed a multi-level analytical framework (Figure 2) to systematically explore the nonlinear mechanisms and spatial heterogeneity of built environment influences on TOD-related vitality in cold-climate metro station areas. First, a comprehensive set of built environment indicators was established, and K-means clustering was applied to categorize stations, highlighting environmental differentiation. Second, spatial vitality was evaluated through transportation, social, and economic dimensions, with composite indices constructed for winter and summer to capture seasonal variation. Based on these results, Spearman correlation analysis was conducted to select variables significantly associated with vitality. The XGBoost Model combined with SHAP interpretation was then employed to determine key global drivers and their relative importance. Finally, the MGWR model was applied to identify spatially heterogeneous effects among different station categories. This step provided a layered understanding of how built environment attributes shape metro station area vitality.

3.1. Built Environment and Vitality Evaluation

3.1.1. Measurement of Built Environment Variables

The selection of built environment indicators followed the 5D framework, which includes Density, Diversity, Design, Destination accessibility, and Distance to transit facilities [59]. Building on the findings of previous studies and considering the specific conditions of cold-region cities [60], this study identified key factors representing population distribution, land-use intensity, functional facilities, transportation accessibility, and spatial morphology within metro station areas. Sixteen independent variables were ultimately defined (Table 2). These indicators encompass the essential dimensions of the 5D concept while maintaining a balance between explanatory capacity and data availability. They provide a comprehensive and practical representation of the built environment for further analysis.

3.1.2. Classification of Station Types

To identify variations in built environment characteristics among metro station areas, the K-means clustering algorithm was applied. The clustering variables were derived from the built environment indicators developed in the preceding section. This ensured that the classification accurately reflected differences among metro station areas. To eliminate the effect of inconsistent measurement scales, all variables were standardized using the Z-score normalization method. The optimal number of clusters (K) was determined using the Elbow Method, which evaluates the relationship between the number of clusters and the within-cluster sum of squared errors (SSE) [61]. The SSE is calculated as:
S S E ( K ) = ∑ k = 1 K ∑ x i ∈ C k x i − μ k 2
where K represents the total number of clusters, x i denotes the i-th observation, C k is the k-th cluster, and μ k its centroid. As the value of K increases, the SSE gradually decreases, but once the reduction rate stabilizes, the curve displays an inflection point, and the corresponding K is considered the optimal number of clusters.

3.1.3. Measurement of Metro Station Area Vitality

The metro station area vitality index was constructed from three complementary dimensions: transport, social, and economic. Transport vitality was defined by the total number of metro entries and exits. Passenger entries represent travel demand, whereas exits indicate destination attractiveness. Together, they describe the comprehensive inflow and outflow characteristics of each station [62]. To minimize potential bias from directional passenger flow, the mean of entries and exits was used as the transport vitality indicator, improving the reliability and comparability of results.
Social vitality was quantified using three heat index metrics: the daily average, the average at 12:00, and the average at 21:00. The two fixed times correspond to off-peak periods, reducing commuting interference and, respectively, reflecting midday social activity and evening leisure behavior [6]. Combining these indicators with the daily average provided a balanced and robust measure of social vitality.
Economic vitality was represented by the densities of catering service POIs and retail service POIs, together with nighttime light intensity. The two POI indicators reflect the spatial distribution of food and retail commercial services [63,64], while nighttime light data capture broader regional economic activity levels [65]. These variables jointly describe economic vitality from the perspectives of service provision and activity intensity.
All indicators were standardized before analysis to ensure comparability across different measurement units. Following the method of Ge et al. (2024), the entropy weight approach was applied to assign comprehensive weights to all indicators [66]. This process generated an integrated multidimensional index of metro station area vitality. Detailed variable descriptions and computational procedures for each dimension are presented in Table 3.

3.2. Impact of the Built Environment on Metro Station Area Vitality

3.2.1. Global Relationship Between the Built Environment and Metro Station Area Vitality

To examine the global associations between built environment attributes and metro station area vitality, the Spearman rank correlation analysis was applied. This method assessed the strength and direction of relationships between individual built environment variables and multidimensional vitality indicators [67]. This procedure allowed the identification of variables significantly correlated with vitality while excluding those with weak associations, thereby improving the overall robustness and efficiency of subsequent modeling.
Following variable screening, the XGBoost regression model was employed to quantify the influence of built environment factors on the composite vitality index at the global scale. XGBoost demonstrates strong capability in processing high-dimensional, heterogeneous datasets and effectively captures complex nonlinear interactions between built environment characteristics and metro station area vitality. Compared with other ensemble algorithms such as random forest, it offers superior parameter optimization and regularization performance. These advantages mitigate overfitting and ensure stable predictive accuracy even under limited sample conditions [68].
During model calibration, key hyperparameters were optimized through cross-validation, and the final values were set to a learning rate of 0.1, 300 trees, and a maximum depth of 5. To further enhance interpretability, the SHAP algorithm was introduced to compute the marginal contribution of each factor, enabling a transparent interpretation of their relative importance in shaping the composite vitality index [5].

3.2.2. Local Relationship Between the Built Environment and Metro Station Area Vitality

Before establishing the spatial regression model to examine the effects of the built environment on metro station area vitality, diagnostic tests were performed on both dependent and independent variables. These tests ensured the robustness and interpretability of the results [69]. In this study, the composite vitality index of each metro station area was defined as the dependent variable, and Moran’s I statistic was computed to evaluate its global spatial autocorrelation. The formula is expressed as:
I = n ∑ i = 1 n ∑ j = 1 n w i j · ∑ i = 1 n ∑ j = 1 n w i j x i − x ¯ x j − x ¯ ∑ i = 1 n x i − x ¯ 2
where n is the number of stations, x i and x j denote the composite vitality of stations i and j, x ¯ is the global mean, and w i j represents the spatial weight. A positive Moran’s I (I > 0) indicates spatial clustering of vitality, a negative value (I < 0) denotes spatial dispersion, and values near zero suggest a random spatial pattern. To prevent multicollinearity among explanatory variables, the variance inflation factor (VIF) was calculated using ordinary least squares (OLS) regression. Variables with VIF values exceeding 7.5 were excluded to enhance model stability and interpretability [70].
Considering that the influence of built environment factors may differ across station types and seasons, the multiscale geographically weighted regression (MGWR) model was employed to capture spatial heterogeneity in these relationships. The model estimation was conducted using MGWR v2.2.1. An adaptive bisquare kernel was applied to account for the uneven spatial distribution of metro stations. Variable-specific bandwidths were determined through a back-fitting algorithm that minimizes the corrected Akaike Information Criterion (AICc). The iterative process continued until convergence was achieved, ensuring stable bandwidth estimation. The functional form of the MGWR model is expressed as:
Y i = β 0 u i , v i + ∑ j = 1 n β j u i , v i X i j + ϵ i
where Y i represents the composite vitality of station i, X i j is the j-th built environment factor of station i, β j u i , v i denotes the spatially varying coefficient of factor j at coordinates u i , v i , β 0 u i , v i is the local intercept, and ϵ i is the residual term. This structure allows each explanatory variable to operate at its optimal spatial scale, providing a nuanced understanding of spatially varying effects across different station contexts [48,49].

4. Results

4.1. Spatial Patterns of Metro Station Area Built Environment and Vitality

4.1.1. Distribution of Built Environment Features

The built environment around Harbin’s metro station areas displayed pronounced spatial heterogeneity (Figure 3). Overall, population and building density exhibited strong spatial consistency, while diversity and accessibility tended to reinforce one another. The design dimension reflected more complex interactions between the physical environment and market activity. Core stations generally combined high density, mixed land-use, and superior accessibility, whereas peripheral areas relied more on ecological spaces to maintain residential livability.
Regarding density, both the central districts and commercial–cultural zones showed relatively high levels. The building density of Zhonghua Baroque Block Station reached 47.92%, the floor area ratio of Museum Station was 5.795, and the population density of Central Street Station attained 469.52 persons per hectare, all above the mean. These results illustrate the consistency between compact urban form and functional agglomeration.
Diversity was generally balanced but spatially differentiated. The land-use mix indices of Railway Bureau Station and Taipingqiao Station were 1.861 and 1.761, respectively, while the POI entropy values of Convention and Exhibition Center Station and Huashu Street Station were 2.671 and 2.610, reflecting rich functional composition. In contrast, outer stations such as Jingpo Road exhibited low diversity and single-function characteristics.
Destination accessibility concentrated mainly in the core zone. The mean bus line density reached 16.74 km/km2, and the average road density was 13.16 km/km2. Gongbin Road Station had the highest bus line density at 53.36 km/km2, while Shangzhi Street and Highway Bridge Stations both exceeded 40 km/km2. The road densities of Zhonghua Baroque Block Station and Harbin Institute of Technology Station were 23.63 km/km2 and 22.47 km/km2, respectively. Transfer hubs such as Museum Station and People’s Square Station displayed higher densities of buses, roads, and POIs than other areas, underscoring their importance in stimulating urban activity intensity.
For transportation accessibility, the median distances to parking lots, bus stops, and metro entrances were 110 m, 190 m, and 210 m, respectively. Museum Station was only 49 m from the nearest parking lot, and both Provincial Hospital and Central Street Stations were within 70 m. Taipingqiao and Sports Park Stations were less than 180 m from metro entrances, confirming the accessibility advantages of core zones. By contrast, peripheral stations were less connected: Jingpo Road was over 1250 m from the nearest parking lot, and Ice and Snow World exceeded 1600 m from the nearest bus stop, forming a clear centrifugal pattern of accessibility decline.
In terms of design, the average number of intersections was 40, with Harbin East and Zhonghua Baroque Block exceeding 100, indicating dense street networks in the urban core. The average building height was 41 m, with Sports Park and Convention and Exhibition Center Stations reaching 100 m and 87 m, suggesting concentrated high-rise development. Mean green coverage was 0.20, exceeding 0.30 in Sun Island and the emerging residential district of Qunli, reflecting ecological advantages. After excluding missing data, the mean housing price was approximately 10,500 yuan. Qunli Fifth Avenue, Harbin West Station, and Haxi Avenue reached 24,869 yuan, 22,000 yuan, and 17,911 yuan, respectively, which are well above the mean. In contrast, Xinjiang Street and Longchuan Road Stations remained low, at 6376 yuan and 7229 yuan.

4.1.2. Characteristics of Station Types

The results of the elbow method (Figure 4) showed that the sum of squared errors declined steadily as the number of clusters increased, with a clear inflection appearing at k = 4. Beyond this point, the reduction rate became moderate, indicating that four clusters provided a suitable balance between model fit and simplicity. Based on this, the metro stations were grouped into four types to capture built environment differences. Overall, these four types presented a hierarchical spatial and functional pattern. They reflected distinct development orientations between the urban core, new towns, and peripheral zones.
The first type included traditional core stations, mainly located in the old city center. These areas had high building density and floor area ratios, with multiple functional roles formed through long-term urban development and strong agglomeration effects. The second type represented residential-oriented new-town stations distributed across Qunli New Town, the Haxi area, and other emerging districts. They exhibited moderate development intensity, mixed land-use, and good environmental quality, emphasizing residential and lifestyle functions. The third type consisted of major hub stations, all serving as transfer points with high functional diversity and strong transport accessibility. These areas supported both commercial activities and regional connectivity. The fourth type included peripheral low-density stations located along the urban fringe. They showed low development intensity and limited functional diversity, resulting in relatively weak spatial vitality.

4.1.3. Spatial Vitality and Seasonal Differences

Table 4 summarizes the weights of the spatial vitality indicators for metro station areas. Overall, economic vitality had the greatest influence, followed by traffic and social vitality. Between the two observation periods, indicator weights fluctuated slightly but the general pattern remained consistent. The share of traffic vitality changed only marginally, with passenger flows at station entries and exits showing a minor decrease. Social vitality increased, as heat-index indicators rose steadily in both daily averages and off-peak hours. Economic vitality maintained its leading position, with the densities of catering and retail POIs consistently ranking highest. Although nighttime light intensity declined slightly, it still exceeded most traffic and social indicators in overall contribution.
Figure 5 illustrates the seasonal distribution of metro station area vitality, classified using the Natural Breaks Method. The overall vitality pattern remained relatively stable between winter and summer, and the urban core continued to hold a clear advantage. Museum Station recorded the highest vitality in both seasons, while Central Street, Kaishengyuan Square, and the First Affiliated Hospital stations also sustained high levels. By contrast, peripheral areas such as Dagenjia, Jingpo Road, and Wapengyao remained weak, highlighting the persistent dominance of the central districts.
When comparing different vitality dimensions, traffic vitality was more concentrated in winter, with Taipingqiao and Central Street stations reaching the highest levels. Several hub stations experienced slight declines in summer. Peripheral areas such as Harbin North and Tongjiang Road consistently showed low traffic vitality. Social vitality strengthened in summer, with Museum Station and the First Affiliated Hospital maintaining leading positions. Gongbin Road, Harbin Institute of Technology, and West Bridge stations improved notably. In contrast, peripheral areas such as Dagenjia and Sun Island displayed minimal seasonal change. Economic vitality showed the least variation, with Central Street, Museum, and Kaishengyuan Square stations consistently among the top performers, reflecting the steady advantage of core commercial districts. Meanwhile, Bohai Road, Turbine Plant, and Chengxiang Road stations remained at relatively low levels.

4.2. Effects of the Built Environment on Metro Station Area Vitality

4.2.1. Global Effects of the Built Environment

Figure 6 shows that the correlations between urban spatial vitality and built environment factors were generally consistent across winter and summer, although stronger in summer. Composite vitality was most closely associated with POI density and population density, with coefficients of 0.87 and 0.75 in summer and slightly lower values in winter. FAR and building density showed moderate positive relationships. Among accessibility factors, distance to bus stops had the strongest negative correlation, reaching −0.59 in summer and weakening slightly in winter. The number of intersections also correlated negatively with vitality in both seasons, with a stronger effect in summer. Other factors, including POI entropy, transfer-station status, and distance to metro entrances, displayed limited associations.
The correlations between specific vitality dimensions and built environment variables followed a similar pattern to composite vitality. Transport vitality maintained a steady positive link with POI density, stronger in summer than in winter, while other factors showed weak to moderate correlations. Social vitality was the most sensitive to environmental variation, exhibiting strong positive relationships with POI and population density in both seasons. It also showed moderate negative relationships with distance to bus stops and intersection density. Economic vitality was also driven primarily by POI density, with a correlation of 0.86 in summer. It showed moderate positive associations with population density and FAR. Housing prices had weak links to both composite and economic vitality, though the relationships were slightly stronger in summer. Nighttime light intensity correlated with housing prices at 0.70 in summer, suggesting a closer connection between property value and evening activity in warmer months.
Figure 7 indicates that after removing variables not significantly related to composite vitality under the Spearman test, both seasonal models achieved high goodness of fit and low error. POI density contributed 47.95% in winter and 47.27% in summer, remaining the most influential variable with limited seasonal variation. Residential population density rose from 7.88% in winter to 18.90% in summer, ranking second and reflecting stronger population activity during warmer periods. In winter, accessibility factors played greater roles: distance to parking facilities contributed 11.59%, road network density 5.39%, and land-use mix 4.74%. FAR and building density contributed moderately, while bus line density and green coverage ratio had smaller effects. In summer, vitality was more influenced by environmental and diversity indicators. Green coverage ratio, land-use mix, and distance to parking facilities contribute 5.78%, 5.27%, and 5.01%, respectively. FAR, bus line density, and building density stayed within moderate levels, whereas road network density declined compared with winter. Housing prices and building height contributed less than 3% in both seasons, and intersection density ranked lowest. This indicates that price and height had minimal influence on composite vitality.
The correlation patterns were consistent with the feature-importance rankings. In winter, vitality depended more on accessibility and road network characteristics, while in summer it was mainly influenced by population concentration, green space, and functional diversity. POI and residential population density showed strong positive effects overall, with contributions increasing sharply as values rose. However, marginal effects weakened at higher levels. The slope was steeper in summer, reflecting higher sensitivity. Green coverage and land-use mix displayed clear threshold effects in summer, producing stronger positive impacts when green coverage exceeded 30% or when land-use mix reached an upper-medium level. In winter, their effects were generally weaker. Distance to parking facilities had a negative effect in both seasons, stronger in winter and moderate in summer. Road network density was more positively associated in winter but weakened during summer. FAR and building density retained moderate positive relationships, bus line density had mild positive effects. Housing prices and building height remained largely unrelated. Both intersection density and distance to bus stops were weakly negatively associated with composite vitality.

4.2.2. Local Effects of the Built Environment

To examine the spatial distribution of composite vitality, a global autocorrelation test was conducted first (Table 5). The results indicated significant positive spatial clustering in both seasons, with stronger aggregation in summer. Comparison of the OLS and MGWR models (Table 6) showed that MGWR achieved higher R2 and adjusted R2 values and lower AICc. This demonstrates that MGWR better captured the spatial heterogeneity between the built environment and composite vitality.
Figure 8 presents the spatial patterns of MGWR coefficients for both periods. After removing multicollinearity variables through the VIF test, the results revealed that the influence of built environment factors on composite vitality was spatially heterogeneous. The effects also showed distinct seasonal contrasts. POI density remained the strongest positive driver throughout, serving as the most stable determinant of station vitality. Residential population density showed weaker effects in winter, with some local negative values. It became strongly positive in summer, especially across multiple station types, reflecting the contraction of population activity in cold conditions. Housing prices had positive effects in both periods, more pronounced in summer. Land-use mix remained positive year-round with slight enhancement in summer, while green coverage ratio showed stronger positive effects in winter but weakened in summer.
Accessibility factors maintained consistent directions of influence across seasons. Bus line density produced a stronger positive effect in winter and declined somewhat in summer. Distances to bus stops were negatively correlated with vitality in both periods, showing greater negative strength in summer. Distance to parking facilities also remained negative, with stronger impacts in winter, indicating greater dependence on proximity under cold conditions. Building density had moderate positive effects in both seasons. Building height played a slightly stronger role in summer though its overall effect remained limited.
Comparisons among station types showed that factors related to population and diversity gained explanatory strength in summer, whereas winter vitality depended more on accessibility. Differences between the four station categories remained stable across seasons. For the first type, POI density and housing prices had the strongest correlations with composite vitality, serving as core explanatory factors. The second type showed a more balanced pattern, where population density was weakly correlated in winter but became stronger in summer. The third type displayed notable internal variation, with POI density remaining steadily positive. The effects of other factors fluctuated markedly across seasons. The fourth type responded least to built environment variables, showing minimal contributions from POI density. It exhibited predominantly negative correlations for accessibility indicators and small positive effects from green coverage ratio.
Figure 9 further illustrates the spatial distribution of MGWR coefficients for both study periods. After multicollinearity was addressed through the VIF test, the results again confirmed spatial heterogeneity with clear seasonal variations. POI density maintained the strongest positive impact in both seasons, slightly lower in summer but still high. Prominent associations at Jiangbei University Town, Harbin North, and Harbin Station areas. Building density also exerted stable positive influences. Residential population density showed strong spatial differentiation: Bohai Road, Jingpo Road, and Xinjiang Avenue station areas displayed positive associations, while Central Street, Friendship Palace, and Zhaolin Park stations in the urban core showed negative ones. Land-use mix remained positive throughout and further strengthened at Haihe East Road, Second Municipal Hospital, and Meteorological Observatory stations in summer. Bus line density stayed positive in both seasons but was slightly weaker in summer.
Variables representing distances to transport facilities showed clear temporal variation. Distance to parking facilities remained negatively associated with vitality in both seasons, with weaker adverse effects in summer. Distance to bus stops was generally negative across seasons. Its magnitude was attenuated in winter within central districts, reflecting higher transit dependence under cold conditions. Building height was generally positive but turned negative in some eastern stations, with stronger overall effects in summer. Negative effects were concentrated around Zhujiang Road. Green coverage ratio remained beneficial in both seasons, more so in winter. Housing prices were positively correlated with vitality in northern station areas but were negatively correlated in southern ones, with stronger effects in summer. Stations such as Provincial Hospital, Provincial Government, and Gongbin Road showed strong positive associations, while Haxi Avenue, Heilongjiang University, and the Second Affiliated Hospital of Harbin Medical University exhibited strong negative ones. At Labor Park, Hesong Street, and Hexing Road stations, correlations reversed between winter and summer.

5. Discussion

5.1. Seasonal Differences in the Built Environment and Vitality of Metro Station Areas

The findings show that the spatial vitality of Harbin’s metro station areas followed a clear gradient, decreasing from the urban core toward the periphery, consistent with patterns observed in large cities such as Beijing and Shanghai [71,72]. At the same time, the results revealed distinctive adaptive mechanisms of cold-region cities in social behavior and spatial use. The urban core, supported by historical accumulation and functional concentration, maintained stable and high vitality levels. Although new districts have gained advantages in residential quality and housing prices, they remain mainly residential. Weaker commercial and service functions limit their contribution to overall vitality. Peripheral stations, constrained by low population density and limited facilities, continued to show low vitality. Compared with studies based on temperate cities [73,74], these results highlight how cold conditions reshape the distribution of vitality, extending the applicability of TOD theory to different climatic contexts.
In terms of built environment patterns, older districts formed through long-term intensive development exhibited compact building forms and diverse land-uses. Stations such as Central Street and Zhaolin Park, located in key commercial and tourist areas, attracted steady passenger flows and sustained high vitality through abundant POIs. This finding aligns with previous studies emphasizing the advantage of historic districts in supporting commerce and public activity [75]. In contrast, stations in Harbin New District showed rapid housing price growth, reflecting the appeal of better residential environments and public spaces. However, their vitality was limited by weak functional integration. This suggests that development in new districts depends more on residential and environmental quality than on diverse urban functions. This differs from international evidence stressing the importance of functional mix for urban spatial vitality [73].
The classification of station types also clarified mechanisms of spatial differentiation. Traditional core stations, supported by dense and multifunctional environments, resembled the “transit-oriented centers” described in international studies [74]. Residential-oriented new-town stations emphasized housing and living conditions, satisfying basic needs but facing slower vitality growth due to insufficient commercial and service support [72]. Hub stations, benefiting from transfer convenience and commercial clustering, showed strong performance, similar to Beijing’s Xidan Station and consistent with prior findings on central transit hubs [36]. In contrast, peripheral low-density stations, affected by sparse populations and limited public facilities, reflected a condition of “proximity to metro without metro-oriented development.” This challenge has also been noted in international TOD research [73].
Seasonal contrasts further highlighted the specificity of cold-region cities. In winter, low temperatures increased the spatial concentration of transportation vitality and suppressed social activity, strengthening the dominance of the core area. This supports previous findings on the restrictive effects of cold climates on public mobility and outdoor interaction [76]. In summer, improved climate conditions stimulated social participation, with higher use of public spaces and stronger social vitality. This echoes evidence that warmer weather promotes social interaction in open spaces [77]. These results indicate that cold environments influence not only the intensity of mobility but also the structure of vitality, making social vitality a distinctly seasonal component. Meanwhile, economic vitality, supported by retail and catering functions, remained relatively stable, suggesting that commercial attraction can partially overcome seasonal constraints.

5.2. Driving Mechanisms and Spatial Heterogeneity of the Built Environment on Metro Station Area Vitality

The results showed that the influence of the built environment on metro station area vitality was both spatially heterogeneous and seasonally dependent. This confirmed the leading roles of functional supply, population activity, and transportation accessibility in shaping vitality, while revealing adaptive interaction patterns specific to cold-region cities. Function-related variables consistently dominated across seasons, whereas population dynamics and microclimatic factors exhibited stronger modulation in summer due to increased outdoor activities. By contrast, green coverage exerted a more pronounced positive effect in winter, underscoring its mediating role in mitigating cold-season discomfort and sustaining activity under adverse climatic conditions. These patterns indicate that vitality emerges through adaptive feedback between environmental attributes and behavioral responses. Warmer seasons amplify outdoor-oriented interactions, while colder periods shift vitality toward enclosed and connected spaces. Such cyclical adjustments reflect a context-specific interaction mechanism sustaining livability across seasons in cold-region cities. These findings were in line with Zhou et al. (2024), who emphasized the combined influence of functional agglomeration, population dynamics, and transportation networks on vitality [78,79]. They further highlighted the interactive mechanisms through which functional supply, population behavior, and environmental adaptation jointly shape vitality in cold urban contexts.
POI density emerged as the most influential factor in both seasons, with a clear diminishing marginal effect. In areas with low POI density, additional functional supply substantially increased vitality, while in high-density areas the effect gradually weakened. This pattern supported previous research indicating a nonlinear relationship between functional supply and vitality under varying density levels [79]. Moreover, the inflection points for POI density and vitality appeared earlier in Harbin than in other cities. This reflects residents’ stronger dependence on indoor functional integration and adaptive spatial behavior in cold climates [80].
Land-use mix showed a stable positive effect across seasons but also displayed a threshold effect. Moderate diversity enhanced vitality, whereas excessive overlap produced diminishing returns. This differs from the “more is better” trend often found in temperate and tropical cities [81]. Such findings suggest that optimizing urban form in cold regions requires balancing functional intensity and climatic adaptability. Built environments should support both indoor continuity and limited outdoor permeability. Hence, balanced spatial configuration that aligns functional complementarity with behavioral adaptation is essential to sustaining vitality.
Population density and green coverage ratio exhibited distinct seasonal differences. In winter, some old districts with high population density showed reduced vitality because low temperatures limited outdoor activity. This prevented density from translating into social or economic benefits. In summer, the effect reversed, as population concentration in new districts stimulated greater activity. Green coverage ratio had a stronger positive impact during summer, peaking at moderate levels. Several stations, however, showed stronger winter effects, indicating spatial heterogeneity in the relationship between green space and vitality. This pattern aligns with findings from cold-region cities in North America and Northern Europe [82].
Transportation accessibility also varied seasonally. In winter, higher bus line density and proximity to parking facilities contributed positively to vitality, reflecting residents’ preference for convenient transfers and minimal outdoor exposure under severe weather. In summer, a more walkable pattern appeared, as shorter bus distances and compact road networks better supported vitality. This differs from patterns observed in cities such as Tokyo and Seoul, where compact blocks and efficient transit systems sustain walkability year-round [83,84]. These findings imply that TOD practices in cold-region cities should integrate improved public transport provision with optimized micro-scale circulation and seasonally adaptive spatial design tailored to climatic variability.

5.3. Spatial Optimization Strategies for Enhancing Year-Round Vitality in Cold-Region Metro Station Areas

In recent years, Harbin has increased investment in rail transit and urban renewal, which has partly improved metro station vitality. In 2024, the Municipal Urban Management Bureau implemented the “Standardized Rectification of Municipal Facilities” initiative to upgrade infrastructure and enhance the urban environment. The city also advanced the integrated use of underground spaces. For instance, the Central Street station built a direct passage to the scenic area and created an ice and snow exhibition zone. The First Affiliated Hospital station added a dedicated medical emergency corridor, and the Museum station introduced an underground walkway integrating cultural and tourism functions. These projects improved passenger experience and demonstrated effective reuse of underground spaces in cold-region cities. In addition, the Heilongjiang Province “14th Five-Year” Comprehensive Transportation Plan proposed green transport hubs, requiring construction impact control and timely ecological restoration to coordinate transportation, ecology, and tourism development.
Despite these achievements, the findings reveal that current measures remain insufficient in addressing seasonal and spatial disparities in metro station vitality. A significant gap persists between core and peripheral stations. Peripheral stations, constrained by low density and limited functions, are difficult to activate. New stations, though offering favorable environments, have not yet translated their advantages into sustained vitality due to limited functional diversity and job–housing imbalance. Policies targeting accessibility also show limitations. The negative correlation between parking distance and vitality across seasons, combined with weak links between entrance distance and vitality, indicates that existing designs focus excessively on commuting efficiency. This neglect of micro-level circulation and walkability weakens the “last mile” of accessibility.
To address these challenges, future planning should move from transport-centered design toward integrated strategies that link transportation, functionality, and environment to sustain year-round vitality. Three targeted approaches are proposed. First, the stable core of all-season vitality should be strengthened through differentiated functional strategies. Given that POI density and land-use mix remain key drivers, peripheral low-density stations should be supported by integrating rail transit with multiple transport modes. Adding essential commercial and public services to cultivate new vitality centers [20]. In the urban core, while maintaining high density and multifunctional clusters, traffic dispersion and public space management should be improved to sustain long-term vitality [85]. Second, winter mobility should emphasize efficiency through climate-responsive micro-circulation systems. Since winter vitality relies on transport connectivity, continuous and sheltered pedestrian links between stations, bus hubs, and parking areas are essential. Heated corridors, enclosed waiting zones, and multifunctional indoor spaces can mitigate the constraints of severe cold and improve comfort and safety. They also help transform travel from a survival-oriented activity to a more experiential one [14]. TOD design should also expand beyond commuting to include walkability and slow-traffic systems that shorten actual distances between residences and facilities, thereby reinforcing accessibility’s contribution to winter vitality [67]. Finally, the life-oriented potential of summer should be activated by optimizing environmental and social spaces. Green and open areas near stations should be adapted to cold-region conditions to strengthen summer activity. As summer vitality depends largely on population density and green coverage, newly developed districts should include pocket parks and under-canopy activity zones to form connected green networks that foster outdoor social life. Temporary interventions such as seasonal markets and outdoor dining can further leverage the comfortable summer climate to offset winter’s seasonal decline in vitality.

5.4. Limitations and Future Research

This study examined the relationship between the built environment and urban spatial vitality in Harbin’s metro station areas from a multidimensional perspective. It revealed spatial differentiation shaped by seasonal conditions and providing new empirical evidence for cold-region rail transit research. Despite these contributions, several limitations remain that should be addressed in future work. First, because Harbin’s metro network is relatively new, the study focused on two representative months, December 2024 and June 2025, to capture winter and summer conditions. This limited observation period restricts the ability to reflect long-term temporal dynamics. Future research should include more extensive time-series data to analyze seasonal and annual variations more comprehensively. Second, a 400 m circular buffer around each station was used as the basic spatial unit. Although consistent with previous studies, differences in accessibility and surrounding environments suggest that a fixed radius may not fully capture spatial heterogeneity. Future studies could introduce accessibility-based indicators or actual travel paths to define more precise influence zones for metro station areas. Finally, the study did not explicitly quantify several cold-climate environmental factors such as snow cover, wind speed, and wind direction. These variables may also affect vitality. Integrating street-view imagery, remote-sensing data, and field surveys in future work could allow a more systematic evaluation of these influences. This would lead to a deeper understanding of how environmental conditions in cold regions shape metro station vitality.

6. Conclusions

This study explores the spatial and seasonal associations between built environment factors and metro station vitality in Harbin, based on the 5D framework. The analysis is adapted to cold-region contexts through a vitality evaluation system comprising 16 indicators covering population distribution, land-use intensity, functional facilities, and transportation accessibility. The results reveal a distinct spatial gradient between the compact urban core and peripheral zones, where vitality is highest in areas with greater development intensity, functional diversity, and transport connectivity. This pattern is reflected in the floor area ratio of 5.795 at Museum Station, a population density of 469.52 people/hm2 around Central Street Station, and a bus line density of 53.36 km/km2 at Gongbin Road Station. Based on the overall performance of vitality indicators, four categories of stations are identified, including core urban, residential new-town, transport hub, and peripheral low-density stations. Within the multidimensional vitality structure, economic vitality exerts the greatest overall influence, with catering and retail activities consistently dominant across seasons. Nighttime light intensity declines slightly in winter but remains above the annual mean, indicating sustained activity levels. Further analysis reveals pronounced spatial heterogeneity and seasonal variation in the relationships between built environment variables and station vitality. POI density accounts for nearly 48% of the total explanatory power in both winter and summer. Residential population density strengthens to 18.90% in summer, while accessibility has greater influence in winter. Green coverage exhibits stronger associations with vitality in winter, while social vitality intensifies in summer. These patterns highlight differentiated strategies for maintaining year-round vitality. In winter, planning should prioritize efficient micro-circulation and continuous sheltered pedestrian systems to enhance last-mile connectivity. In summer, emphasis should shift to connected green corridors and human-scale public spaces that support outdoor social interaction. Strengthening multimodal integration and local service provision in peripheral areas can foster emerging activity centers. In contrast, refined management and flow dispersion in core areas can sustain long-term performance. Collectively, the results provide a robust empirical foundation for seasonally adaptive coordination of transportation and urban planning to sustain urban vitality in cold-region cities.

Author Contributions

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

Funding

This research received no external funding.

Data Availability Statement

Data and materials are available from the authors upon request.

Acknowledgments

The authors thank the anonymous reviewers for their valuable comments and suggestions on this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of this study area.
Figure 1. Location of this study area.
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Figure 2. Framework of this study.
Figure 2. Framework of this study.
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Figure 3. Distribution of built environment variables in metro station areas.
Figure 3. Distribution of built environment variables in metro station areas.
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Figure 4. Optimal number of clusters and station type distribution.
Figure 4. Optimal number of clusters and station type distribution.
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Figure 5. Spatial and seasonal variation in metro station area vitality.
Figure 5. Spatial and seasonal variation in metro station area vitality.
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Figure 6. Seasonal correlations between built environment variables and station vitality. The asterisks represent p-values, where * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001.
Figure 6. Seasonal correlations between built environment variables and station vitality. The asterisks represent p-values, where * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001.
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Figure 7. Feature importance and dependence of built environment variables on station vitality.
Figure 7. Feature importance and dependence of built environment variables on station vitality.
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Figure 8. Correlation analysis of built environment and comprehensive vitality in station types. The y-axis shows standardized MGWR coefficients (unitless), where positive and negative values indicate positive and negative associations with composite vitality, respectively. Bars indicate the range (min–max) within each station group, with black dots and red ticks marking the mean and median.
Figure 8. Correlation analysis of built environment and comprehensive vitality in station types. The y-axis shows standardized MGWR coefficients (unitless), where positive and negative values indicate positive and negative associations with composite vitality, respectively. Bars indicate the range (min–max) within each station group, with black dots and red ticks marking the mean and median.
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Figure 9. Spatial correlations between built environment and comprehensive vitality. Each panel shows the spatial distribution of standardized MGWR coefficients (unitless) for individual built environment variables. Warmer colors indicate stronger positive associations with composite vitality, while cooler colors represent weaker or negative relationships.
Figure 9. Spatial correlations between built environment and comprehensive vitality. Each panel shows the spatial distribution of standardized MGWR coefficients (unitless) for individual built environment variables. Warmer colors indicate stronger positive associations with composite vitality, while cooler colors represent weaker or negative relationships.
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Table 1. Data sources.
Table 1. Data sources.
DataYearResolutionData Source
Metro Operation Data2024, 2025−Harbin Metro Group Co., Ltd. (accessed on 2 July 2025)
Urban Heat Map2024, 202530 mhttps://huiyan.baidu.com (accessed on 2 July 2025)
Points of Interest2024, 2025−https://lbs.amap.com (accessed on 30 June 2025)
Nighttime Light Data2024, 2025500 mhttps://eogdata.mines.edu (accessed on 30 June 2025)
Building Information2025−https://lbsyun.baidu.com (accessed on 2 July 2025)
Remote Sensing Imagery2024, 202510 mhttps://earthexplorer.usgs.gov (accessed on 31 July 2025)
Land-use2022−https://doi.org/10.5281/zenodo.16794007 (accessed on 14 August 2025)
Road Network2025−https://www.openstreetmap.org (accessed on 2 July 2025)
Bus Routes2024−https://doi.org/10.6084/m9.figshare.28323971 (accessed on 2 July 2025)
Population2020100 mhttp://www.geodata.cn (accessed on 30 June 2025)
Housing Prices2025−https://anjuke.com (accessed on 2 July 2025)
Table 2. Built Environment Variables.
Table 2. Built Environment Variables.
DimensionVariableIllustrate
DensityBuilding coverage ratioRatio of building footprint area to total land area within the metro station area, measuring the intensity of horizontal development.
Floor Area Ratio (FAR)Ratio of total floor area to land area, reflecting the degree of vertical development.
POI densityNumber of POIs per metro station area, indicating the spatial concentration of functional facilities.
Residential population densitySpatial density of residents within the metro station area, representing the level of population concentration.
DiversityLand-use mixDegree of integration of different land-use types, measuring the level of functional diversity.
POI entropy (Shannon entropy)Diversity index calculated based on the distribution of POI categories, reflecting the balance and variety of service facilities.
Destination accessibilityBus line densityTotal length of bus routes per metro station area, measuring the coverage of public transportation.
Road network densityTotal length of roads per metro station area, reflecting road connectivity and transportation accessibility.
Transfer stationWhether a station has transfer functions, indicating its role as a transportation hub.
Distance to transit facilitiesDistance to bus stopsAverage distance from the metro station area to the nearest bus stop, reflecting integration with the bus system.
Distance to parking facilitiesAverage distance from the metro station area to the nearest parking lot, measuring the convenience of car–metro transfers.
Distance to metro entrancesAverage distance from the metro station area to the nearest metro entrance, representing the level of pedestrian accessibility.
DesignBuilding heightAverage height of buildings within the metro station area, reflecting spatial morphology and development intensity.
Number of road intersectionsNumber of intersections per metro station area, indicating the complexity and connectivity of the road network.
Green coverage ratioVegetation coverage within the metro station area, derived from remote sensing, reflecting ecological quality and natural landscape characteristics.
Housing priceAverage residential housing price within the metro station area, serving as an indicator of socioeconomic conditions.
Table 3. Indicators for measuring metro station area vitality.
Table 3. Indicators for measuring metro station area vitality.
DimensionVariableIllustrate
Transport VitalityAverage inbound passenger flowMeasures travel demand at metro stations, reflecting the departure intentions of residents or commuters in the area.
Average outbound passenger flowReflects the attractiveness of metro stations on arrival, indicating the area’s capacity to attract employment, consumption, or leisure activities.
Social VitalityAverage daily heat indexCalculated from urban heat data as the daily mean, providing an overall representation of social activity intensity within the metro station area.
Average midday off-peak heat indexHeat value at 12:00 on working days, avoiding commuting peak interference and reflecting midday social interaction and activity patterns.
Average evening off-peak heat indexHeat value at 21:00 on working days, indicating evening consumption and leisure activity levels, complementing the daily average.
Economic VitalityDensity of catering service POIsMeasures the spatial distribution and supply capacity of catering facilities, reflecting daily life vitality and consumption potential of residents.
Density of retail service POIsRepresents the distribution and concentration of retail commercial resources, serving as an important indicator of regional economic vibrancy.
Nighttime light intensityExtracted from remote sensing nighttime light data, reflecting overall economic activity levels and human activity intensity at night.
Table 4. Weights of spatial vitality indicators for metro station areas.
Table 4. Weights of spatial vitality indicators for metro station areas.
DimensionIndicatorWeight (December 2024)Weight (June 2025)
Transport VitalityAverage inbound passenger flow0.1020.100
Average outbound passenger flow0.1130.103
Social VitalityAverage daily heat index0.0830.091
Average daytime off-peak heat index0.1030.107
Average nighttime off-peak heat index0.0810.086
Economic VitalityDensity of catering service POI0.1940.196
Density of retail service POI0.2140.216
Nighttime light intensity0.1100.101
Table 5. Global spatial autocorrelation of comprehensive vitality.
Table 5. Global spatial autocorrelation of comprehensive vitality.
TimeMoran’s IZ-Scorep-Value
December 20240.2373.4340.001
June 20250.3034.3280.000
Table 6. Comparison of OLS and MGWR models.
Table 6. Comparison of OLS and MGWR models.
TimeModelAICcR2Adj. R2
December 2024OLS123.4190.7870.753
MGWR118.4490.8520.804
June 2025OLS104.1700.8360.810
MGWR102.2420.8810.843
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Zhou, X.; Chen, J. Seasonal Variations in the Mechanisms Linking the Built Environment and Metro Station Area Vitality in Cold Regions: A Case Study of Harbin. Land 2025, 14, 2222. https://doi.org/10.3390/land14112222

AMA Style

Zhou X, Chen J. Seasonal Variations in the Mechanisms Linking the Built Environment and Metro Station Area Vitality in Cold Regions: A Case Study of Harbin. Land. 2025; 14(11):2222. https://doi.org/10.3390/land14112222

Chicago/Turabian Style

Zhou, Xiaolu, and Jianfei Chen. 2025. "Seasonal Variations in the Mechanisms Linking the Built Environment and Metro Station Area Vitality in Cold Regions: A Case Study of Harbin" Land 14, no. 11: 2222. https://doi.org/10.3390/land14112222

APA Style

Zhou, X., & Chen, J. (2025). Seasonal Variations in the Mechanisms Linking the Built Environment and Metro Station Area Vitality in Cold Regions: A Case Study of Harbin. Land, 14(11), 2222. https://doi.org/10.3390/land14112222

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