Research on the Spatiotemporal Pattern and Influencing Mechanism of Coastal Urban Vitality: A Case Study of Bayuquan

Abstract

Enhancing the spatial vitality of coastal cities is beneficial for the sustainable development of urban construction. However, how to fully utilize coastal resources and boost urban vitality is an important issue. This study takes the coastal city of Bayuquan in China’s cold region as an example. Firstly, we conducted field investigations and data mining in Bayuquan, utilizing Baidu heat map data to measure the spatial–temporal vitality of different areas in Bayuquan. Secondly, we used Moran’s I test to examine the spatial autocorrelation of coastal spatial vitality. Lastly, with the help of the OLS and GWR models, we explored the factors influencing spatial vitality and the urban built environment. The research findings indicate the following: (1) There are spatial–temporal differences in the vitality of different areas in Bayuquan, heavily influenced by the tourist season. (2) The OLS results show that the impact of the built environment on spatial vitality exhibits spatial heterogeneity during different tourist seasons. However, we found no spatial heterogeneity in the influencing factors in the harbor district. (3) The harbor district and the tourism-driven district re quire differentiated construction guidance. Facility functions and block morphology mainly influence the vitality of the harbor district, while the vitality of the tourism-driven district is primarily affected by its aesthetic characteristics. This study can propose differentiated regional construction guidance and specific feasible coastal urban design strategies for seasonally influenced coastal city construction. It holds significant implications for improving urban living quality and is vital to urban decision-makers, planners, and stakeholders.

1. Introduction

The development of modern urban construction has a history of over a hundred years, accompanying the process of the Industrial Revolution [1]. At the end of the 19th century and the beginning of the 20th century, the urbanization processes in many countries experienced acceleration, with large-scale urban infrastructure construction and urban expansion coming to the forefront [2,3]. Although China’s urbanization started relatively late compared with Western countries, it progressed rapidly, leading to numerous new urban areas. However, this also resulted in a series of problems [4,5,6,7]. In 2006, the built-up area of Chinese cities was 32,494.14 square kilometers (https://www.stats.gov.cn/sj/ndsj/ (accessed on 10 May 2023)). By 2021, this area had doubled to 62,290.39 square kilometers over a 15-year construction period. The extensive development model brought about issues for new urban areas, including social segregation [8,9], inadequate supporting facilities [10], environmental degradation [11], and population loss [12,13,14], leading to phenomena like “empty cities” and “ghost cities” [15,16,17]. This threatens the integrity and sustainability of urban systems. As a result, more people advocate for living in more vibrant cities, which enhances convenience and quality of life and promotes human health and safety [18,19,20,21] Therefore, in-depth research on urban vitality and its influencing factors can help better understand the complex relationship between new urban construction dynamics and socio-economic order.

Coastal cities possess unique advantages in marine resources, bringing vitality to the cities and offering significant opportunities for developing the society, economy, ecology, and culture [22]. The coastal zone influences inland urban areas’ construction layout, form, and character [23,24,25]. However, the driving factors of spatial vitality in coastal cities are currently uncertain. This uncertainty arises not only because the guidance and boundary control for coastal city construction style in an urban design context remain undefined and inconsistent but also because the importance of marine resources influencing inland and peripheral social spaces has not been fully recognized [26]. China is surrounded by the sea on three sides, boasting abundant marine resources, with 53 coastal cities. Coastal cities around the Bohai Sea in Northeast China witnessed significant expansion in construction land from 1990 to 2020, with the development intensity increasing from 2.08% to 9.77%. This led to the formation of extensive urban agglomerations and “leapfrogging urban development”. However, constrained by a cold climate, industries, economy, population, and other factors, the high-intensity development model resulted in the homogenization of urban character and lower vitality in these new urban areas [27]. Effective methods need to be proposed for constructing new coastal cities in the Bohai Sea region to reflect the correct positioning and importance of urban marine resources in urban planning and design, promoting local vitality. Meanwhile, urban design suggestions should be proposed for the hinterlands of coastal cities, and the social–spatial connections between spatial vitality and coastal urban spatial construction should be studied.

Enhancing urban vitality is a necessary prerequisite for the sustainable development of cities. A city with high urban vitality is often considered an ideal place for living, working, and tourism, contributing to the well-being of its residents [28,29]. Relevant studies indicate that cities with low urban vitality may hinder urban development, leading to health, safety, and ecological issues [30,31]. Scholars have mainly considered social, economic, cultural, environmental, infrastructural development, and healthcare aspects when characterizing urban vitality. However, this paper believes that human activities are the most direct manifestation of urban vitality [32,33,34]. Regarding the influencing factors of urban vitality, researchers in urban planning [35,36], architecture [37], geography [38,39,40], and urban management have explored various factors such as street layout [41,42], facility accessibility, economic activities, and urban morphology indicators affecting the intensity of urban vitality, establishing the correlation between urban physical attributes and spatial vitality. Spatial construction in coastal cities differs from other cities, with both standard and unique factors influencing spatial vitality compared with other cities. Building on the existing research, this paper will establish the correlation between urban spatial vitality and material environmental elements in coastal cities, explore the influencing factors of spatial vitality, and propose construction suggestions in urban design.

Although the research on spatial vitality in coastal cities is relatively limited, many have attempted to study how to stimulate vitality in waterfront areas through planning and design [43,44,45,46]. Traditional research methods mainly include qualitative and quantitative approaches [47]. Qualitative methods primarily rely on surveys, behavioral mapping, observational methods, and comparisons of activity types and spatial distributions between different populations. For instance, Doga Uzumcioglu et al. [48] utilized surveys to assess the Kyrenia ancient port’s physical, functional, social, economic, cultural, and political suitability based on users’ understanding of waterfront development concepts. T. C. Chang et al. [49] employed in-depth interviews and on-site observations to confirm the significant role local forces play in influencing the development of waterfront vitality. Meanwhile, quantitative methods mainly involve analyzing and describing the construction characteristics of waterfront areas. By collecting a large amount of data, digital information, and statistical techniques, researchers delve into and quantify various factors, explaining waterfront areas’ development and evolutionary processes to measure and compare changes and trends between different waterfront areas, providing robust data support for policy formulation and planning [50,51]. The widespread application of big data in urban development research has provided reliable data support for understanding the characteristics of spatial vitality in coastal cities and its related influencing factors. However, the existing literature still needs to fully address the complexity of social–spatial relationships in coastal cities and the potential spatial autocorrelation interference that may arise when examining factors affecting urban vitality. Therefore, leveraging big data for research on urban spatial vitality in coastal cities holds promise for in-depth exploration of this vital topic.

Based on the above discussion, we identified the following areas for improvement in the interaction mechanisms between spatial vitality and the built environment in coastal cities: (1) More research is needed on the vitality characteristics of coastal cities in cold regions. Existing studies mostly describe the construction around waterfront areas without investigating how marine resources influence urban spatial development. (2) There are some areas for improvement in the exploration methods of influencing factors on urban vitality in coastal cities, and the reasons for changes in spatial vitality characteristics have yet to be interpreted from the perspective of coastal city development. (3) Previous research has yet to explore the relationship between spatial vitality and the built environment in coastal cities at the block scale, and fine-grained research can address urban construction issues more profoundly. In this paper, we chose the basic unit of urban space and social connection—the block—as the research scale. Using Baidu heatmap data, we describe the spatiotemporal patterns of spatial vitality in coastal cities during different tourist seasons.

2. Literature Review

Coastal cities are highly sought after due to their unique marine resources. With the emergence and development of port cities globally, tourism has provided strong support for the prosperity and stability of coastal cities. The development of coastal cities can be divided into three main stages. During the Industrial Revolution, coastal cities became important production centers and transportation hubs. The number of coastal cities increased, urban populations expanded, and urban areas sprawled. However, the development of the industrial economy also led to ecological damage, with severe water pollution causing a decline in coastal areas [52]. Secondly, in the mid-20th century, along with economic development and transformation, renewal strategies for port cities promoted the development of coastal cities. Existing ports were preserved and transformed into diversified areas integrating ecological leisure, social interaction, cultural tourism, and economic development. For example, Rotterdam, the largest industrial city in the NL, implemented a “Post-War Reconstruction Plan”, transforming from an “industrial port” to a “cultural capital” [53]. Lastly, there is a greater focus on constructing coastal urban spaces, emphasizing the built environment’s impact on human gatherings in coastal cities.

Research on the spatial environment of coastal cities can be traced back to the 1980s, primarily focusing on sociology, phenomenology, and other disciplines, aiming to explore the relationship between people’s quality of life and daily life [54]. The current research has gradually shifted the focus toward exploring the relationship between coastal built environments and human activities, investigating the factors of their interactions. Researchers believe that the culture, landscape, spatial form, and transportation organization of coastal cities influence the aggregation of human behavior across different times and spaces, affecting the state of spatial vitality to varying degrees. Relevant studies have confirmed that in terms of spatial form, factors such as block size, building density, floor levels, and architectural combinations affect the spatial vitality of coastal cities [55]. Regarding transportation, road width, intersection density, and accessibility are essential in promoting spatial vitality. Regarding land use, the richness and density of commercial facilities also have significant impacts [56]. However, these studies have mostly paralleled factors promoting urban vitality in inland cities rather than starting from the unique characteristics of coastal cities. Specific coastal elements such as skyline, proximity to waterfront areas, and the openness of public spaces have yet to be sufficiently explored from the perspective of coastal city uniqueness.

Researchers have made numerous attempts to explore urban vitality through planning efforts in coastal cities’ construction. Traditional research methods primarily involve survey questionnaires and utilize interviews and observations to investigate coastal spaces, observing their attractiveness to people. They record and compare the distribution of the physical environmental attributes of human activities in different locations. For instance, Woo, Simon S. et al. [57], using MY’s waterfront as an example, distributed 363 questionnaires to explore the impact of waterfront development on cities, finding that waterfront development significantly promotes economic benefits. Yuan Wang et al. [58] conducted field observations and questionnaire surveys on the Qiantang River waterfront area in Hangzhou, China, discovering insufficient public service facilities, necessitating increases in leisure seating and parking spaces. Zannat Ara Dilshad Shangi et al. [59] employed survey interviews to investigate the Surma Waterfront, identifying poor accessibility as a major issue contributing to waterfront degradation, coupled with a lack of barrier-free spaces and diverse functions that meet the needs of different populations. These studies often carry subjectivity and cannot ensure the accuracy and objectivity of their findings. Big data and location-based services provide new perspectives for studying coastal urban spatial vitality, ensuring the sensitivity and rationality of research through quantitative analysis of coastal urban spatial characteristics. For example, Merve Yılmaz et al. [25] used geographical visibility indicators to measure the development patterns of coastal cities, concluding that coastlines are an attractive means for promoting urban development. Dewen Wu et al. [60], using Qingdao, as an example, conducted quantitative evaluations of coastal streets, finding that streets with better intimacy, continuity, and sense of order promote human activities.

We can observe that these quantitative studies often used multiple linear regression models, failing to fully demonstrate spatial process heterogeneity and non-stationarity. The geographically weighted regression (GWR) model can address spatial heterogeneity issues better. The GWR model is increasingly used to explore the relationship between urban spatial vitality and built environments. For example, Guifen Lyu et al. [61] used the GWR model and deep learning to examine building environmental variables related to urban vitality. The study found that built environments promote urban vitality in four dimensions—distance, diversity, design, and destination—and spatial heterogeneity exists. Liu Wangbao [62] analyzed the factors influencing street vitality using the GWR model based on local geographic weights, using the Tianhe District in Guangzhou City as an example. The research showed that considering spatial heterogeneity, the GWR model had a better fit than the OLS model, revealing micro-local characteristics of how built environments affect street vitality. Liwei Qin et al. [63] employed the multi-scale geographically weighted regression (MGWR) model to analyze subtle spatial heterogeneity in 65 parks in Changsha City, finding that park facilities play a crucial role in stimulating local green space vitality. Additionally, studies have covered various areas, such as urban road cycling flow research and the relationship between urban heat island intensity and urban morphology [64,65]. However, research on the correlation between urban environmental characteristics and coastal urban spatial vitality must be more thorough.

In conclusion, despite the development of various academic research methods to study urban waterfront spatial vitality, there are still research gaps: (1) The research on urban spatial vitality often focuses on riverfront or waterfront spaces. In contrast, studies on coastal spatial vitality still need to be completed. (2) Existing research methods, whether Pearson’s correlation analysis or traditional multiple linear regression, must accurately reflect spatial scale-related indicators and spatial processes in exploring coastal urban spatial vitality. This paper employed regression models, including ordinary least-squares (OLS) and geographically weighted regression (GWR), to study the complex relationship between urban vitality and various influencing factors. The findings provide profound insights for the planning and design of coastal cities.

3. Materials and Methods

3.1. Materials

3.1.1. Study Area

Bayuquan is an enclave-type district located more than 50 km south of Yingkou City in the Liaodong Bay area of China’s Bohai Sea region. The administrative area covers 268 square kilometers, including 22.22 square kilometers of marine areas. Due to its tropical monsoon climate, which differs from the cold temperatures experienced in winter in Northeast China, Bayuquan experiences mild winters without severe cold and brief hot summers. The overall climate is suitable for living, making tourism development an important growth strategy for Bayuquan. Figure 1 illustrates the location and scope of the study area, covering an area of approximately 42 square kilometers, with a development trend along the coastline. Initially, Bayuquan’s development relied on port construction and gradually shifted toward the tertiary industry (http://211.159.153.75/download (accessed on 10 May 2023)). Therefore, we divided the study area into two zones using the Honghai River as a boundary: the “harbor district”, developed around the port, and the “residential tourism area”, developed around tourism. Table 1 shows the construction status of the two areas. The total area of the harbor district in Bayuquan is 21.16 square kilometers, with a higher proportion of built-up areas, accounting for 91.87%. The residential tourism area covers a total of 20.1 square kilometers. Compared with the harbor district, the residential tourism area has a lower proportion of built-up areas, at around 41.55%. However, it has a higher concentration of basic coastal tourism service facilities, mainly catering to tourism-oriented external visitors, with approximately 6.93% of the area covered by greenery and good landscape service facilities.

3.1.2. Data

We utilized Baidu heatmap data to assess urban vitality, which offers high-quality data and significant potential for revealing human activity dynamics. Since the outbreak of the COVID-19 pandemic, the intensity of human activities in cities, such as travel, consumption, and employment, has significantly decreased [66]. Therefore, to mitigate the impact of the COVID-19 pandemic on urban vitality, this study selected Baidu heatmap data from 2019. Coastal cities in the Bohai Sea region have a ‘migratory bird-like’ flow of residents. Bayuquan has a tropical monsoon climate, with cool summers and no severe cold in winters. From May to October, Bayuquan is cool and suitable for tourism. Foreign tourists come here for short stays, making this period the “peak tourist season”. From November to April in the following year, the weather is relatively cold, and the number of foreign tourists decreases, marking Bayuquan’s “off-peak tourist season”. According to a statistical report from the Bayuquan government, the domestic and foreign tourist arrivals totaled 9.24 million in 2019, peaking in July [67]. Excluding the influence of weather on human activity, we selected the average heatmap data from weekdays, weekends, and holidays in July and November 2019 as representations of Bayuquan’s vitality. To study the characteristics of spatial vitality in coastal cities, data were collected hourly from 8:00 to 21:00, resulting in 84 heatmap images.

Determining the research scale is crucial to rigorously explore the relationship between urban vitality and the urban built environment in coastal cities. Tokyo Bay in Japan, which was developed as Tokyo’s waterfront sub-center, is based on its port. It underwent large-scale comprehensive development, forming an industrial cluster centered around the city, with block scales ranging from 5 to 7 hectares [68]. The redevelopment of the Docklands area transformed Darling Harbor into a pedestrian paradise, creating a unique skyline and harbor view in the bay area, with block scales ranging from 5 to 8 hectares [69]. San Francisco’s waterfront Fisherman’s Wharf redevelopment zone adheres to the sustainable development concept of “3E”, creating an international living and working environment with block scales ranging from 4 to 8 hectares [70]. Manhattan’s waterfront area has created new public spaces through rational extension and connection, with block scales of around 5 to 8 hectares [71]. Therefore, blocks serve as a more suitable research scale for exploring the built environment of coastal cities and pinpointing urban road divisions. This study was conducted at the mesoscale using blocks as the unit, with approximately 5 to 10 hectares block scales, and 165 plots were obtained based on Bayuquan’s urban road division. This study also utilized POI data, building data, road data, and green space data. The specific sources and attributes are detailed in Table 2.

3.1.3. Variables

Based on the characteristics of the migratory bird-like population in coastal cities, this study created three predictive variables for spatial regression analysis: vitality during the tourist off-peak season, vitality during the tourist peak season, and annual average vitality. To ensure the rigor of the results in predicting variables, we divided them into common factors shared by coastal and other cities and specific factors unique to coastal cities. Regarding the selection of common factors, extensive research has been conducted on urban-built elements that promote urban vitality. A review of the relevant literature focused on urban construction characteristics, particularly emphasizing building functional mix, construction age, pedestrian density on streets, and street length. Based on these studies, we compiled indicators and selected 9 common factors shared with other cities. These indicators are categorized as follows: block morphology, building volume ratio, average number of floors, building form integration, building form fragmentation, functional density, functional mix, path coverage density, and road accessibility. Coastal city-specific factors are primarily summarized based on the construction characteristics of typical coastal cities. For example, Australia’s Gold Coast emphasizes the development of coastal public spaces and waterfront skyline construction. Yokohama Bay in Japan features an orderly and staggered waterfront skyline with landmark buildings reaching up to 300 m, focusing on developing coastal nodes. Miami Beach in the United States emphasizes the construction of coastal architectural styles, creating layouts for maritime traffic and compact buildings. Consequently, we selected four elements related to coastal city aesthetics: the hydrological index, public space openness, the dispersal index, and spatial compactness. We constructed 13 variables influencing coastal urban spatial vitality, with the definitions and calculation formulas detailed in Appendix A.

3.2. Framework and Study Design

Figure 2 displays the flowchart of the research methodology. Firstly, primary geographical data within the research area were collected. Secondly, based on the different development characteristics of various zones within the research area, Moran’s I model was employed to compare the spatiotemporal variations in urban vitality during the peak and off-peak seasons of coastal tourism. Furthermore, considering the uniqueness of coastal city constructions, indicators influencing spatial vitality in coastal cities were constructed. The OLS and GWR models examined the relationships between vitality and a series of related factors. Lastly, spatial guidance design strategies were proposed for different construction areas.

3.3. Methods

3.3.1. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is divided into global and local spatial autocorrelation. Global spatial autocorrelation, namely, Moran’s I, is used to analyze the spatial correlation characteristics exhibited by study objects globally. It is generally used to examine whether there is spatial autocorrelation between the entire study object and samples within the entire domain range [72]. We employed Moran’s I test to examine the spatial autocorrelation of urban vitality in coastal cities. The focus was on urban vitality’s spatial clustering characteristics or dispersion trends during different tourist seasons. The primary formula for calculating Moran’s I is as follows:

(1)$I=\frac{N}{W}\frac{{\sum }_{i=1}^N{\sum }_{j=1}^N{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{\sum }_{i=1}^N{\left(x_i-\overline{x}\right)}^2}$

In the formula, $N$ represents the total number of block units; $W$ is the total weight of the weight matrix; $x_i$ and $x_j$ are vitality values; $\overline{x}$ is the average vitality value; $w_{ij}$ is the weight between $i$ and $j$ in the weight matrix, taking a value of 1 when spatial units are adjacent and 0 otherwise. The range of Moran’s I values is between −1 and 1, where 1 indicates a strong positive spatial autocorrelation, and −1 indicates a strong negative spatial autocorrelation. When $I$ > 0, it indicates a positive spatial correlation of vitality values, with a value closer to 1 indicating a more significant positive correlation. When $I$ < 0, it indicates a significant negative correlation in the spatial distribution of vitality, with a value closer to −1 indicating a greater negative correlation effect. If $I$ approaches 0, it indicates no spatial autocorrelation in vitality, manifesting as a random distribution in space.

3.3.2. Spatial Econometric Models

The OLS (ordinary least-squares) regression model is a classic linear regression model used to establish the relationship between independent and dependent variables [73]. We employed the ordinary least-squares (OLS) method of spatial econometric models to study the influencing factors of coastal spatial vitality. Compared with traditional linear regression models, the derived explanatory model in this study is more suitable for interpreting the content. The model estimates the linear relationship between independent (predictive) and dependent (target) variables to elucidate the influencing factors and mechanisms. The formula for the OLS model is as follows:

(2)$Y={\beta }_{0 }+{\beta }_1{X}_{1 }+\dots \dots +{\beta }_{13}{X}_{13}+\epsilon$

In the formula, $Y$ corresponds to the vitality value, and $X_1$ to $X_{13}$ are the 13 explanatory variables that influence the vitality value in this study. They are as follows: block morphology, building volume ratio, average number of floors, building form integration, building form fragmentation, functional density, functional mix, path coverage density, and road accessibility, the hydrological index, public space openness, the dispersal index, and spatial compactness. ${\beta }_1$ to ${\beta }_{13}$ are the coefficients of the influencing factors to be estimated; ${\beta }_0$ is the constant term; and ε is the error term.

3.3.3. Geographically Weighted Regression Model

Geographically weighted regression (GWR) is a regression analysis technique for analyzing geographical spatial data [74]. It is an extension of ordinary least-squares (OLS) regression and is employed to analyze the spatial heterogeneity of factors influencing coastal vitality using the GWR model. The GWR model introduces geographical weighting factors to explore the gradual trends and abrupt changes in influencing factors at the same vitality value, understanding the spatial distribution differences in vitality in coastal areas. The primary formula is as follows:

(3)$y_i={\beta }_0\left(u_i,v_i\right)+\sum _{k=1}^n{\beta }_k\left(u_i,v_i\right)x_{ik}+{\epsilon }_i$

In the formula, $y_i$ is the dependent variable (the heat value within the i-block unit), ($u_i$, $v_i$) represents the coordinates of the i-block unit, k indicates the number of independent variables, ${\beta }_k\left(u_i,v_i\right)$ is the regression parameter of the k-th independent variable for the i-th unit, and ${\epsilon }_i$ is the error term. The GWR regression coefficient model is measured using the following formula:

(4)$\beta \left(u_i,v_i\right)={\left(X^{T}W\right(u_i,v_i\left)X\right)}^{-1}{X}^{T}W(u_i,v_i)Y$

where X and Y are the matrices of independent and dependent variables, respectively, $X^T$ is the transpose matrix, and $W(u_i,v_i)$ is the spatial weight matrix of the model, determined by a Gaussian function. Its domain (bandwidth) is the range of neighboring elements or distances used by each local regression equation, controlling the smoothness of the regression model. As the GWR model is a regional spatial weight function, we use the AICC criterion to determine the optimal bandwidth.

4. Results

4.1. Spatiotemporal Analysis of Coastal City Vitality

4.1.1. Vitality Space Difference Analysis

Figure 3 displays the spatial changes in the vitality of the harbor district and tourism-driven districts during the tourism off-peak season and peak season. From these two graphs, it can be visually observed that both areas show a significant enhancement in vitality from the tourism off-peak season to the peak season. Based on the distribution maps of spatial vitality in different regions during the off-peak season and peak season, the vitality value distribution in the harbor district is relatively uniform. Apart from the lower vitality values in units dominated by industrial functions, the differences in vitality intensity between other street blocks are not particularly significant. Generally speaking, the vitality of street blocks during the peak tourist season is higher than during the off-peak season. However, specific street block units show the opposite trend, mainly covering mixed commercial and residential functions. In the tourism-driven district, the vitality of street blocks presents a pattern of high values near the boundary line and lower values further away, showing a trend of continuous development with decreasing vitality values farther away from the central area. Street blocks near the river and seaside in the tourism-driven district have relatively high spatial vitality, influenced by their proximity to the harbor district. The vitality values decrease gradually toward the south, but this distribution pattern is not observed in units closer to the coastline. Moreover, the vitality values of street blocks in the tourism-driven district generally show higher values during the peak tourist season than during the off-peak season. Some street blocks exhibit significant differences in vitality values, with values during the peak season being about 1.5 times higher than during the off-peak season. These street block units are influenced mainly by seasonal coastal tourism. An analysis of these units reveals that there are more high-rise residential buildings within these blocks, with the majority being small-sized economic housing units of one or two bedrooms. Many of these have been developed into guesthouses, indicating that the vitality intensity of this area is significantly affected by seasonality.

In this study, Moran’s I index was calculated using the GeoDa tool to examine the spatial autocorrelation between vitality values in Bayuquan during different tourist seasons. Table 3 presents the global Moran’s I values for Bayuquan during the off-peak season, peak season, and annual average vitality. It can be observed that Moran’s I index for all three time points is around 0.52, with a significance level of 0.001 (<0.05). The observed spatial patterns are unlikely to result from random processes and pass the significance test at the 5% level. Therefore, the null hypothesis can be rejected. The research results indicate a high correlation and spatial clustering phenomenon in the vitality during the off-peak season, peak season, and annual average on a spatial basis. Moreover, Moran’s I index is higher during the peak season than during the off-peak season, suggesting a higher degree of population aggregation during the peak tourist season. Environmental factors influence people in the urban context to varying degrees during different tourist seasons.

4.1.2. Vitality Time Variability Analysis

To better explore the spatial vitality changes in the coastal districts, the vitality intensity was categorized into five types based on the magnitude of vitality values: high heat, medium heat, moderate heat, ordinary, and low. High heat and medium heat reflect areas with a high degree of population aggregation and robust vitality, while ordinary and low indicate areas with lower vitality in the coastal districts. This classification provides a more precise and intuitive way to study the temporal changes in the spatial vitality of coastal urban districts.

Figure 4 illustrates that during the off-peak tourist season, the spatial vitality of the coastal districts in Bayuquan experienced five stages: a rapid increase, stable maintenance, a sudden decline, a slow rise, and a decrease. Combined with Figure 5 for the spatiotemporal analysis, we gained new insights into people’s activity patterns during the off-peak season in Bayuquan. From 8 to 10 in the morning, which is the time for going out, the spatial vitality of the districts gradually increased, and the number of vitality units grew. The vitality characteristics of the districts continued rising during the morning. However, the vitality values declined after 1 pm after lunch and the rest period. In the afternoon, people’s outdoor activities increased, mainly concentrated in public spaces and commercial areas, leading to a slight decrease in vitality in tourism-driven districts. Although the dynamism of the commercial regions declined after 7 p.m., indicating resting behavior, we can infer a mutual growth and decline pattern in urban spatial vitality. This is related to the functional nature represented by different functional blocks. We also found that the highest and lowest vitality values did not align, suggesting a separation of work and residence in the coastal city of Bayuquan.

Figure 6 shows that during the peak tourist season, the spatial vitality of the coastal districts underwent fluctuating wave-like changes: “rise–fall–slow; rise–stable; maintenance–slow; decline–rise–fall”. Combined with Figure 7, we can discern two distinct residential populations in the coastal city. Although the number of transient residents from outside constitutes a small portion of the total population, they still play a significant role in promoting the spatial vitality of the coastal city. They mainly concentrate in districts with well-developed coastal facilities. Due to the increased spontaneity in people’s movements during the peak tourist season, we did not observe complementary patterns among the district units in our study.

The analysis above indicates that both the residents and the transient population in the coastal city of Bayuquan exhibit seasonal differences in urban leisure and lifestyle characteristics during the off-peak and peak tourist seasons. During the off-peak season, the behavioral vitality of urban residents is relatively lower compared with the peak tourist season due to the influence of incoming tourists. During this period, a complementary relationship exists between commercial and entertainment-focused districts and residential districts. However, this complementary effect is less pronounced during the peak tourist season.

4.2. Impact of Coastal Space Construction on Vitality

4.2.1. Modeling Tests of Vitality Effects

We employed the OLS regression model to investigate the overall relationship between the vitality of coastal cities in tourism off-peak season and peak seasons and the built environment characteristics. As shown in Table 4, out of the 13 selected factors, 12 showed significant correlations during both the off-peak season and peak season in Bayuquan. This indicates that these influencing factors have a significant promoting effect on the urban spatial vitality of coastal cities. All these variables were significant at the 0.05 confidence level. The adjusted R-squared values were 0.692 during the off-peak season and 0.715 during the peak season, confirming that the identified independent variables can explain 69.2% and 71.5% of the urban spatial vitality in Bayuquan, respectively. Moreover, the Akaike Information Criterion (AICc) values for the off-peak season and peak season were 888.701 and 923.972, respectively, indicating a normal distribution of the model. The Koenker (BP) tests for different tourist seasons were significant, suggesting spatial heterogeneity issues in the selected variables in Bayuquan, warranting further regression analysis. Additionally, this study examined the variance inflation factor (VIF) values for the architectural environment variables, all of which were below 7.5, indicating no multicollinearity issues among the independent variables and ensuring the reliability and credibility of the experimental results.

Table 5 displays the global relationship between the annual average vitality of different zones in Bayuquan and their built environment characteristics. We can see that X6 is most strongly positively correlated with the vitality of both areas, indicating a sustained significant correlation between the functional density of coastal cities and vitality. This aligns with Jacobs’ view that the diversity and density of urban functions can encourage people to spend more time around urban spaces engaging in various activities. Following this is the block density variable. The p-values from both areas show little difference, suggesting that higher plot ratios are needed in coastal city construction to increase the population density and create vibrant block units. At the same time, block fragmentation is not correlated with the vitality of either area, suggesting it does not need to be considered in coastal city construction. For tourism-driven districts, the waterfront index and fragmentation under the aesthetic features are key factors that promote coastal urban spatial vitality. In contrast, the openness of public spaces and spatial compactness significantly impact the vitality of the port area. The adjusted R² values for the port and tourism-driven districts are 0.685 and 0.689, respectively, indicating high fit and credibility. This study found that the port area’s Koenker (BP) value is 0.173, which is not significant. The tourism-driven district’s Koenker (BP) value is 0.018321 *, indicating significance. This suggests that there is no spatial heterogeneity in the variables of the port area, while there is spatial heterogeneity in the variables of the tourism-driven district.

4.2.2. Vitality in Relation to the Built Environment

After validating with the OLS model, it was found that there are significant correlations and spatial heterogeneities in the spatial variables of the coastal blocks in Bayuquan. Therefore, the GWR model was employed for further research to provide a deeper understanding of the local correlations between urban vitality and the built environment in coastal cities. Table A2 presents the regression results of the GWR model for urban vitality in Bayuquan during the off-peak and peak tourist seasons. We can see that the adjusted R² values for the off-peak and peak seasons are 0.702 and 0.718, respectively. These fit better than the adjusted R² values of 0.692 and 0.715 from the OLS model, indicating that the GWR model provides a better explanatory power for the influencing factors of urban spatial vitality. Figure 8 and Figure 9 display the coefficient distributions of the selected variables in the GWR models for the off-peak and peak tourist seasons. The coefficients show spatial differences in magnitude, consistent with the findings of Wangbao, L [62]. For instance, public space openness is negative during the off-peak season but positive during the peak season. This study suggests that the openness of public spaces in the block significantly impacts coastal urban vitality during the peak tourist season. Road accessibility has a more significant coefficient during the peak tourist season, indicating that people require convenient transportation to meet their travel needs, and accessible road networks can enhance urban vitality.

We also explored the GWR vitality regression results for different zones in Bayuquan. Table A3 shows that the adjusted R² values for the harbor city and residential zone are 0.685 and 0.691, respectively. Additionally, compared with the harbor city zone, public space openness, the hydrological Index, the dispersal index, and spatial compactness have a more substantial promoting effect on vitality in the residential zone. The dispersal index is −0.006 in the harbor city zone and 0.054 in the residential zone. This suggests that when planning coastal urban development, attention should be paid to the staggered coastal skyline and the height differences between buildings within blocks, consistent with Erysheva et al. [75] findings. Furthermore, we identified the influential factors for vitality in the harbor city zone as block integration, block functional density, block functional mix, and path coverage density.

5. Discussion

5.1. The Influencing Factors of Coastal Spatial Vitality Exhibit Differentiation

According to the United Nations survey data in 2021 [76], the population living in coastal areas accounted for about 40% of the world’s total population. In recent years, there has been a trend of population concentration toward coastal cities. The spatial vitality of urban coastal areas can reflect the spatial quality and vibrancy of coastal cities. The study of the factors influencing spatial vitality in different areas of the Bayuquan district reveals differentiated patterns in the impact elements between areas developed around the harbor and those around tourism. This finding is consistent with the exploration of spatiotemporal vitality in Chengdu, China, by Yingying Li et al.‘s study [77], which discovered a clustered, polycentric pattern of spatial vitality with heterogeneous influencing factors. Table 6 presents the vitality impact elements for different areas. We can observe that the primary influencing factors in the harbor area are block integration, functional density, functional mix, and block path coverage density. This is because the harbor area developed around the port has an older construction, with some block units facing redevelopment and environmental improvement. Similarly, port cities like Stockholm have emphasized practical governance and sustainable port planning to develop, utilize, and improve the efficiency of existing port buildings, as indicated by the Port Management Council [78]. For the harbor area, it is essential to consider integrating block units by demolishing abandoned sheds to enhance connectivity. Promoting streetside commerce improves functional mix and encourages gathering and consumption [79]. Increasing the block path coverage density promotes pedestrian activity and interaction by providing resting spaces [18,80]. Coastal cities focus more on ecological and environmental safety [81,82]. We suggest that marine city construction should emphasize certain elements. The research results indicate that the key influencing factors in the residential tourism area are the hydrological index, public space openness, the dispersal index, and spatial compactness. Long Zhou et al. [83] also emphasized these indicators in their study on constructing reclaimed areas in Macau, suggesting that coastal areas should adopt intensive ecological designs with marine elements. For the residential tourism area in our study, with many undeveloped block units, these construction indicators provide innovative guidance for future planning. Development should focus on shaping the coastal skyline. Buildings along the coastline should gradually increase in height, enhancing block diversity and forming an excellent staggered relationship within blocks. Yuting Yin et al. [45] analyzed spatial forms, traffic organization, land use, landscape features, and coastal functions of eight typical coastal cases, emphasizing the importance of the skyline in shaping urban vitality. The per capita possession rate of public spaces within blocks should also be considered to meet outdoor leisure and recreational needs.

5.2. Design-Guided Strategies for Block Spaces

The construction form of block space influences the level of urban spatial vitality [84]. Summarizing and generalizing the block forms in the research area was beneficial for guiding urban design content. By selecting blocks with higher vitality values, the road network structure was clarified, and five types of block road network patterns and ten layout forms were summarized, as shown in Table 7. These include free-form, linear, radial, tributary, and grid-like patterns. Blocks dominated by park green spaces mainly adopt a free layout pattern, which can effectively shape the block structure layout, segment different viewing spaces, and promote the diversity of blocks [85]. Linear grid-like spaces are mainly distributed in the harbor area, with many cul-de-sac roads within tourism-driven districts, which are not conducive to the convenience and accessibility of the tourism-driven districts. However, this type of grid layout is a typical expansion-oriented road network form chosen for new area planning. This road network layout increases connectivity between various functional buildings within the block, facilitating interpersonal interaction. Radial block road network layouts are less common. Although this layout has strong directionality, the orientation of buildings within blocks and wind resistance are crucial for northern coastal cities. This road network structure is not conducive to the layout between buildings, so it is less adopted. Tributary patterns are the leading modern layered road network layouts for modern blocks. The roads within the block form a loop or branching arrangement selected based on the needs of block functions.

Summarizing the architectural layout patterns within block units and the staggered forms of blocks helps understand the variations in block space layout and staggeredness, promoting the presentation of urban style [86]. This study found that residential construction within blocks mainly comprises multi-story, low-rise, and high-rise buildings. Although some blocks have constructed ultra-high-rise buildings, their number is limited and has yet to affect the coordination of urban construction. By summarizing and combining the forms of typical blocks, they are divided into 5 types of block layouts and 15 combinations, as shown in Figure 10. Blocks mainly adopt enclosed layouts, controlling the transformation of block forms through the combination of relationships between buildings. This study found that residential buildings with fewer than nine floors can form enclosed and private-public spaces through the staggered relationships between building facades, providing places for communication between people within the tourism-driven district. However, for buildings with more than nine floors, due to their excessive height, the staggered relationship between building facades weakens. Therefore, enclosed street-level businesses need to form block spaces for interpersonal communication, consistent with the research of Akkelies van Nes [87]. Different layout patterns can also enhance human activity within neighborhoods. In the harbor district, block units primarily use courtyard-style and row-style building layouts, while in residential areas, block building layouts often adopt a small block cluster layout. Cluster-style layouts are conducive to constructing high-rise residential buildings, forming an enclosed pattern by adding low-rise buildings. This residential mode easily creates an enclosed atmosphere, promoting interaction among people [88]. A combination of courtyard-style and row-style layouts can be selected for block units combining large commercial centers and residential areas. This approach avoids monotony in building design within the block and offers greater flexibility and diversity in spatial planning [89].

5.3. Mechanisms for the Construction and Development of Coastal Cities

Climate is a critical factor affecting coastal cities’ spatial vitality in cold regions. However, it also brings opportunities for the seasonal migration of populations who reside in these areas during hot summers, thereby presenting new prospects for enhancing urban spatial vitality. Shaping a pleasant, comfortable, and distinctive coastal appearance from the perspective of urban construction and development is an essential task for urban planners and designers [90,91]. Rapid urbanization has led to the development of port areas, and the rise of the tertiary industry has shaped the development of coastal residential spaces. As a result, spatial differentiation in urban construction styles has emerged. The main reason behind this is the uneven development of regional functional spaces, a view supported by research by Yuting Yin [45]. How can we guide urban morphological construction during its transformation? How do we develop urban design tailored to specific geographical locations? These questions warrant further exploration. Xian Li proposed methods for integrating new buildings with old urban areas from a micro perspective, conducting research and analysis on the old, traditional, and historical buildings in Bangkok’s Chinatown from three aspects: artistic style, functional injection, and emotional injection. Singapore’s land-use planning and conservation authority has released urban design guidelines, introducing the concept of a “10-min living community”, connecting marine green spaces and commercial and residential districts through pedestrian spaces. The Centre for Liveable Cities (CLCs) has proposed a “conservation plan”, utilizing land-use allocation to carve out the size and function of city blocks based on urban texture and shape, thereby creating pleasant urban spaces. Most studies focus on urban development forms, building heights, open spaces, and street landscape construction. However, micro perspectives directly affecting people’s quality of life, such as urban lighting facilities and street furniture, should also be considered. This is an area where this study could be improved. Urban development and construction are long-term iterative processes. Highly contextualized urban design interventions can only succeed when they meet specific local needs. We need to undertake more research.

6. Conclusions

The increasing demand from urban residents for high-quality living has led to growing attention toward enhancing urban spatial vitality. Coastal cities are no exception, especially when planning and constructing new areas, which requires more extensive research efforts. Therefore, this study selected a special “seasonal migrant” coastal city for investigation. Using the global Moran’s I index, we analyzed the spatial autocorrelation between coastal city vitality during the tourism off-peak season, peak season, and annual average. We also utilized the OLS and GWR models to explore the characteristics and reasons for changes in coastal city vitality across different regions during the tourism off-peak season, peak season, and annual average. In Bayuquan, Moran’s I index during the off-peak and peak tourist seasons is around 0.52, with a significance of 0.001 (<0.05), indicating spatial vitality clustering. Using the OLS model, 12 significant variables were identified, with R² values of 0.692 and 0.715 for the off-peak and peak tourist seasons, respectively. Based on this, further spatial analysis was conducted using the GWR model, with adjusted R² values of 0.702 and 0.718 for the off-peak and peak seasons, showing a better fit than the OLS model’s adjusted R² values of 0.692 and 0.715, which indicates the GWR model better explains the factors influencing urban spatial vitality, providing higher credibility to the research results. This study also found that coastal cities exhibit spatial differentiation during development and construction. Furthermore, the effects of similar environmental influencing factors on urban vitality vary, which is also observed in inland cities.

Our study reveals that multiple factors influence the development of coastal cities in cold regions, and the construction of economically transitioning cities should be given more attention. For the study area, differentiated urban construction is one of the means to improve the spatial vitality of both new and old coastal areas. Contextual urban morphology shaping will also be a key focus for urban designers. Creating attractive coastal cities can also be guided through government intervention policies, such as improving the quality of urban infrastructure and public spaces. Our research focuses on the spatial heterogeneity of urban vitality characteristics and influencing factors in coastal cities during different tourist seasons. Although we obtained various interesting findings, this study still has some limitations that warrant further research. First, this study used Baidu heatmap data, which has potential biases and cannot accurately track people’s movement patterns for more detailed investigations. Mobile signaling data can effectively address this issue, and future research can use mobile signaling data to study the spatial vitality of coastal cities. Second, this study took the cold northern city of Bayuquan, China, as an example, which shows migratory population characteristics due to climate influences. However, not all coastal cities have such characteristics, indicating a limitation in this research. We must strengthen future efforts to contribute to coastal city construction and urban environmental planning.

Author Contributions

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

Data Availability Statement

Relevant datasets and experimental results are available through the corresponding author.

Acknowledgments

We extend our gratitude to Xindong Cai from Shenyang Jianzhu University and Lei Xu from Beijing Union University for their guidance on this paper. We also thank Dongwei Tian from the Beijing University of Architecture and Shao Zhuang from Beijing Forestry University for their meticulous editing.

Conflicts of Interest

The authors declare that they have no competing interests. The funders played no role in the design of this study; the collection, analyses, or interpretation of the data; in the writing of this manuscript; or in the decision to publish the results.

Footnotes

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Figures and Tables

Figure 1. Location map of Bayuquan.

Figure 2. Research framework.

Figure 3. Distribution of tourism vitality in low and high seasons in harbor district and tourism-driven district.

Figure 4. Tourism off-peak season vitality variation.

Figure 5. Spatial distribution status of block vitality over time in tourism off-peak season.

Figure 6. Tourism peak season vitality changes.

Figure 7. Spatial distribution status of block vitality over time in tourism peak season.

Figure 8. Coefficient map of influencing factors during tourism off-peak season.

Figure 9. Coefficient map of influencing factors during tourist peak season.

Figure 10. Block building combination layout patterns.

Table 1

Percentages of areas in the harbor district and tourism-driven district.

Different Zones	Total Area	Built-Up Area	Proportion of Green Area
Harbor district block morphology	21.16 km2	91.81%	7.85%
Tourism-driven district block morphology	20.10 km2	41.55%	6.93%

Table 2

Data sources and attributes.

Data	Format	Attributes	Source
Baidu heat map	.tif	Location, intensity value, and timestamp	China Baidu Map (https://map.baidu.com/ (accessed on 1 July 2019, 12 July 2019, 13 July 2019, 1 November 2019, 8 November 2019 and 9 November 2019))
Bayuquan administrative boundaries and water systems	.shp	Area and name	China Basic Geographic Information System 1:2 million data
Road	.shp	Width and length	Open Street Map (https://openmaptiles.org/ (accessed on 10 May 2019))
POI	.xlsx	Name, type, latitude, and longitude	China Baidu Map (https://map.baidu.com/ (accessed on 10 May 2019))
Building	.shp	Width, length, and grade of roads	Google Earth (https://www.google.cn/ (accessed on 10 May 2019))
Satellite remote-sensing images	.tif	Time and spectral information	Google Earth (https://www.google.cn/ (accessed on 10 May 2019))

Table 3

Global Moran’s I index of coastal tourism off-peak season, peak season, and annual average vitality.

	Off-Peak Season	Peak Season	Annual Average
Moran’s I	0.524878	0.529371	0.516968
Z-value	10.703993	10.762103	10.709803
p-value	0.0001	0.0001	0.0001

Table 4

OLS regression results of urban vitality during tourism off-peak season and peak season.

Variables	Tourism Off-Peak Season				Tourism Peak Season				VIF
	Coefficient	Standard Deviation	T-Value	Robust	Coefficient	Standard Deviation	T-Value	Robust
X1	1.700	0.890	1.910	0.003 *	3.217	0.990	3.248	0.008 *	6.511
X2	−0.165	0.115	−1.434	0.035 *	0.008	0.128	0.064	0.024 *	4.351
X3	0.000	0.000	−0.778	0.023 *	0.000	0.000	0.678	0.014 *	1.388
X4	2.527	2.956	0.855	0.023 *	0.142	3.289	0.043	0.045 *	3.572
X5	0.000	0.000	0.163	0.080	0.000	0.000	0.073	0.094	1.982
X6	1.841	0.210	8.781	0.000 *	1.696	0.233	7.273	0.000 *	2.145
X7	0.436	1.059	0.411	0.034 *	1.129	1.178	0.959	0.024 *	1.275
X8	0.478	0.662	0.722	0.017 *	0.363	0.737	0.493	0.016 *	2.795
X9	0.205	0.079	2.587	0.011 *	0.316	0.088	3.571	0.001 *	2.509
X10	0.000	0.000	−0.447	0.015 *	0.001	0.000	2.483	0.006 *	1.472
X11	−1.548	1.662	−0.931	0.030 *	0.360	1.849	0.195	0.033 *	4.273
X12	0.060	1.087	0.055	0.047 *	0.026	1.209	0.022	0.017 *	2.225
X13	0.629	3.598	0.175	0.020 *	1.134	4.004	0.283	0.016 *	6.080
Performance statistics
R-squared	0.717				0.738
Adjusted R-squared	0.692				0.715
AIC	888.701				923.972
Koenker (BP)	0.000831 *				0.017201 *

* indicates that a p-value (probability) of less than 0.05 indicates that the model is statistically significant.

Table 5

OLS regression results of annual average vitality in different zones of Bayuquan.

Variable	Harbor District					Tourism-Driven District
	Coefficient	Standard Deviation	T-Value	Robust	VIF	Coefficient	Standard Deviation	T-Value	Robust	VIF
X1	3.408	2.066	2.066	0.002 *	7.394	2.244	0.952	2.357	0.001 *	6.836
X2	−0.329	0.425	0.425	0.037 *	6.430	−0.033	0.121	−0.273	0.006 *	5.301
X3	0.000	0.000	0.000	0.049 *	2.043	0.000	0.000	−0.357	0.002 *	1.379
X4	6.527	6.589	6.589	0.010 *	5.319	−1.811	3.669	−0.494	0.016 *	4.218
X5	−0.001	0.002	0.002	0.0758	4.016	0.000	0.000	0.421	0.060	2.462
X6	1.941	0.353	0.353	0.000 *	2.293	1.406	0.272	5.167	0.000 *	1.911
X7	4.305	2.405	2.405	0.008 *	1.632	−0.725	1.148	−0.632	0.003 *	1.304
X8	0.221	1.235	1.235	0.005 *	4.364	1.024	1.012	1.012	0.003 *	2.526
X9	0.289	0.155	0.155	0.041 *	5.155	0.135	0.162	0.833	0.004 *	2.248
X10	−0.001	0.000	0.000	0.046 *	1.831	−0.001	0.000	−1.231	0.002 *	1.741
X11	0.150	3.646	3.646	0.009 *	3.435	0.186	1.990	0.093	0.021 *	6.035
X12	−0.140	2.053	2.053	0.009 *	1.868	0.648	1.321	0.491	0.005 *	2.694
X13	−5.067	6.724	6.724	0.027 *	6.390	3.937	4.773	0.825	0.035 *	6.512
Performance statistics
R-squared	0.744					0.735
Adjusted R-squared	0.685					0.689
AIC	448.766					465.195
Koenker(BP)	0.173					0.018321 *

* indicates that a p-value (probability) of less than 0.05 indicates that the model is statistically significant.

Table 6

Influencing factors of block spatial vitality in different areas.

Different Districts	Main Factors Affecting Spatial Vitality
Harbor district	Integration index, functional density, functional mix index, and path coverage density
Tourism-driven district	Hydrological index, public space openness, dispersal index, and spatial compactness

Table 7

Grading and layout patterns of street networks.

Layout Patterns	Free Form	Linear Form	Radiolucent Form	Branch Form	Grid Form
Tree-shaped	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]
Grid-shaped	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]

Appendix A

Table A1

Variable numbering and schematics.

Major Categories of Influencing Factors	Number	Category	Formulas	Interpretation
Block morphology	X1	Floor area ratio	$\mathrm{FAR}=\frac{A_b}{A_l}$$\mathrm{FAR} \mathrm{stands} \mathrm{for} \mathrm{the} \mathrm{floor} \mathrm{area} \mathrm{ratio} \mathrm{of} \mathrm{the} \mathrm{block}; A_b$$\mathrm{represents} \mathrm{the} \mathrm{total} \mathrm{building} \mathrm{area} \mathrm{of} \mathrm{the} \mathrm{plot}; \mathrm{and} A_l$ represents the site area.	It refers to the ratio of a building’s total floor area to the area of the land it occupies.
	X2	Average floor level	$AS=\frac{A_b}{A_c}$$AS$$\mathrm{stands} \mathrm{for} \mathrm{the} \mathrm{floor} \mathrm{area} \mathrm{ratio} \mathrm{of} \mathrm{the} \mathrm{block}; A_b$$\mathrm{represents} \mathrm{the} \mathrm{total} \mathrm{building} \mathrm{area} \mathrm{of} \mathrm{the} \mathrm{plot}; \mathrm{and} A_c$ represents the site area.	It describes the vertical stacking of building spaces within a plot, serving as a way to reflect the degree of spatial aggregation of buildings.
	X3	Building envelope form	The combinations of buildings within the block can be roughly divided into free-style combinations, row-style combinations, cluster-style combinations, courtyard-style combinations, detached combinations, and mixed combinations.	Architectural spatial composition includes various types of individual buildings, group buildings, and their enclosed spaces.
	X4	Integration index	$\mathrm{NSI}=\frac{2\ln l_i}{\ln a_i}$$\mathrm{NSI} \mathrm{stands} \mathrm{for} \mathrm{the} \mathrm{integration} \mathrm{index}; \mathrm{L} \mathrm{represents} \mathrm{the} \mathrm{perimeter} \mathrm{of} \mathrm{the} \mathrm{block}; \mathrm{and} a$ represents the spatial area of the block.	The block shape integration degree reflects the regularity of an individual block’s form. The smaller the block shape integration degree, the more uniform and regular the block’s texture tends to be.
	X5	Form fragmentation index	${FRA}_i=\frac{(A_i-\overline{x_a})}{{\sigma }_a}$${FRA}_i$$\mathrm{represents} \mathrm{the} \mathrm{block} \mathrm{form} \mathrm{fragmentation} \mathrm{index}; \overline{x_a}$$\mathrm{represents} \mathrm{the} \mathrm{average} \mathrm{area} \mathrm{of} \mathrm{buildings} \left({\mathrm{m}}^2\right); \mathrm{and} {\sigma }_a$ represents the standard deviation of building areas (m2).	This measure reflects the spatial form generated within the block, assessing the morphological characteristics exhibited by the overall layout of buildings within the block.
Facility function	X6	Functional density	${POI}_i$$=\frac{{APOI}_i}{A_i}$${POI}_i$$\mathrm{stands} \mathrm{for} \mathrm{the} \mathrm{functional} \mathrm{density}; {APOI}_i$ represents the number of POIs; and Ai represents the area of the block.	The density measurement of points of interest (POIs) within the block.
	X7	Functional mix index	${BFM}_i=-\frac{\left({\sum }_{i=1}^M{p}_{i,j}{Inp}_{i,j}\right)}{InM}$${BFM}_i$$\mathrm{represents} \mathrm{the} \mathrm{functional} \mathrm{mix} \mathrm{index}; p_{i,j}$ refers to the proportion of the j-th type of function within block i; and M refers to the number of functional categories within the current block.	The proportion of points of interest (POI) data within the block.
Road network structure	X8	Path coverage density	$DL=\frac{L_i}{l_i}$$DL$$\mathrm{stands} \mathrm{for} \mathrm{the} \mathrm{path} \mathrm{coverage} \mathrm{density}; L_i$$\mathrm{represents} \mathrm{the} \mathrm{total} \mathrm{length} \mathrm{of} \mathrm{paths} \mathrm{within} \mathrm{block} \mathrm{i}; \mathrm{and} l_i$ represents the perimeter of block i.	The ratio of the total length of pathways within a block to the perimeter of the block, which can evaluate the richness of the block’s road network structure and road variations.
	X9	Road accessibility	$\mathrm{AI}=\frac{(NC \times NC)}{TD}$AI stands for the road accessibility; NC represents the total number of nodes (intersections) of roads; and TD represents the total depth value of roads.	Quantitative analysis of the convenience of residents reaching the block.
Architectural features	X10	Hydrological index	${SHI}_N=D_0-Min\left(D_{iw}\right)$${SHI}_N$$\mathrm{stands} \mathrm{for} \mathrm{the} \mathrm{hydrological} \mathrm{index}; D_0$$\mathrm{is} \mathrm{a} \mathrm{constant}; \mathrm{and} D_{nw}$ represents the spatial linear distance between block unit i and the surrounding water system w.	The nearest distance from spatial units to adjacent water bodies serves as an indicator to measure waterfront accessibility.
	X11	Public space openness	$\mathrm{OSR}=\frac{(1 - {GSI}_x)}{{FSI}_x}$$\mathrm{OSR} \mathrm{stands} \mathrm{for} \mathrm{public} \mathrm{space} \mathrm{openness}; {FSI}_x$$\mathrm{is} \mathrm{the} \mathrm{floor} \mathrm{space} \mathrm{index}; \mathrm{and} {GSI}_x$ is the ground space index.	The size of public spaces within the block.
	X12	Dispersal index	$O=\frac{\sigma }{\mathrm{H}}$$O$$\mathrm{stands} \mathrm{for} \mathrm{dispersal} \mathrm{index}; \sigma$ is the standard deviation of heights; and H is the average height of buildings within the block.	The difference between the average and maximum building heights within the plot.
	X13	Spatial compactness	$C=\frac{2\sqrt{\pi \times A}}{P}$C stands for spatial compactness; A is the total building footprint area within the block; and P is the perimeter of the total building footprints within the block.	The geometric distribution of total buildings within the block, analyzing the compactness of the block.

Table A2

GWR model regression results of urban vitality in Bayuquan during tourism off-peak and peak seasons.

Variable	Tourism Off-Peak Season					Tourism Peak Season
	Mean	Standard Deviation	Min.	Median	Max.	Mean	Standard Deviation	Min.	Median	Max.
X1 (floor area ratio)	0.211	0.001	0.21	0.211	0.212	0.346	0.001	0.344	0.346	0.348
X2 (average floor level)	−0.13	0.001	−0.131	−0.13	−0.128	0.006	0	0.006	0.006	0.007
X3 (building envelope form)	−0.04	0	−0.041	−0.04	−0.039	−0.033	0	−0.034	−0.033	−0.033
X4 (integration index)	0.07	0.001	0.067	0.07	0.073	0.003	0.002	0	0.003	0.006
X5 (fragmentation index)	0.01	0	0.01	0.01	0.011	0.005	0.001	0.003	0.005	0.006
X6 (functional density)	0.557	0	0.556	0.557	0.557	0.444	0	0.443	0.444	0.444
X7 (functional mix index)	0.02	0.001	0.019	0.02	0.021	0.045	0.001	0.044	0.045	0.047
X8 (path coverage density)	0.052	0.001	0.05	0.052	0.053	0.033	0	0.033	0.033	0.034
X9 (road accessibility)	0.177	0.001	0.177	0.177	0.178	0.236	0	0.235	0.236	0.237
X10 (hydrological index)	−0.023	0.001	−0.024	−0.023	−0.022	−0.126	0	−0.126	−0.126	−0.125
X11 (public space openness)	−0.084	0.001	−0.086	−0.084	−0.082	0.017	0	0.016	0.017	0.017
X12 (dispersal index)	0.004	0	0.003	0.004	0.004	0.001	0.001	0	0.001	0.003
X13 (spatial compactness)	0.019	0.001	0.017	0.019	0.021	0.03	0.002	0.026	0.029	0.033
Performance statistics
R-squared	0.73					0.744
Adjusted R-squared	0.702					0.718
AICc	289.723					279.343
BIC	337.869					325.355
Number of observations	165

Table A3

GWR model regression results of urban vitality in Bayuquan in harbor district and tourism-driven district.

Variable	Harbor District					Tourism-Driven District
	Mean	Standard Deviation	Min.	Median	Max.	Mean	Standard Deviation	Min.	Median	Max.
X1 (floor area ratio)	0.29	0	0.289	0.29	0.29	0.356	0.01	0.339	0.356	0.375
X2 (average floor level)	−0.125	0.001	−0.127	−0.125	−0.124	−0.027	0.001	−0.029	−0.027	−0.026
X3 (building envelope form)	−0.058	0	−0.059	−0.058	−0.057	−0.024	0	−0.024	−0.024	−0.024
X4 (integration index)	0.146	0	0.145	0.146	0.147	−0.046	0	−0.047	−0.046	−0.046
X5 (fragmentation index)	−0.066	0	−0.066	−0.066	−0.065	0.036	0.001	0.035	0.036	0.038
X6 (functional density)	0.534	0	0.534	0.534	0.534	0.42	0	0.42	0.42	0.421
X7 (functional mix index)	0.148	0	0.148	0.148	0.149	−0.046	0	−0.046	−0.046	−0.045
X8 (path coverage density)	0.025	0.001	0.023	0.025	0.026	0.09	0	0.089	0.09	0.09
X9 (road accessibility)	0.27	0	0.27	0.27	0.271	0.072	0	0.072	0.072	0.073
X10 (hydrological index)	−0.154	0	−0.155	−0.154	−0.153	−0.08	0	−0.08	−0.08	−0.079
X11 (public space openness)	0.007	0	0.007	0.007	0.007	0.017	0.001	0.016	0.018	0.019
X12 (dispersal index)	−0.006	0	−0.007	−0.006	−0.005	0.054	0	0.054	0.054	0.054
X13 (spatial compactness)	−0.122	0	−0.122	−0.122	−0.122	0.109	0.001	0.107	0.109	0.11
Performance statistics
R-squared	0.745					0.743
Adjusted R-squared	0.685					0.691
AICc	150.292					170.109
BIC	177.370					201.761
Number of observations	76					89

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