Analysis of Urban Spatial Morphology in Harbin: A Study Based on Building Characteristics and Driving Factors

Abstract

With the advancement of urbanization, the complexity and diversity of urban spatial forms have become increasingly prominent, profoundly and widely affecting aspects such as urban spatial layout and planning, as well as residents’ quality of life. This paper focuses on the buildings in Harbin City, comprehensively reflecting the spatial form of Harbin through multiple dimensions including building height, volume, and area. This research precisely quantifies three key indicators of urban buildings: building coverage, building expandability, and building staggeredness. Subsequently, these indicators are intertwined with the main driving factors of urban development (including economic development and resident population) to conduct a multidimensional spatial form analysis. The results indicate that the diversity of Harbin’s urban spatial form is the result of the interplay of multiple factors, including economic and demographic influences. These analytical outcomes not only reveal the evolution mechanism of Harbin’s current urban spatial form but also provide data support and theoretical basis for future urban planning and management.

1. Introduction

With the surge in urban population and the scarcity of land resources, urban development has transcended the limitations of horizontal expansion, actively expanding vertically and shaping a unique three-dimensional urban spatial form [1]. Urban spatial form, as a representation of the layout of various elements in both plan and elevation, is a direct reflection of the physical environment in spatial dimensions and serves as a profound imprint of economic and social activities within the region. Today, the importance of urban spatial forms has become increasingly significant, profoundly impacting multiple dimensions such as the urban ecological environment and economic prosperity. The urban ecological environment refers to the unified entity formed by the interaction between residents and the natural and built environments within urban boundaries. As a crucial carrier of the ecological environment, urban spatial forms must be coordinated with ecological considerations to jointly promote sustainable urban prosperity [2]. Strengthening research on urban spatial forms fosters innovative development of green spaces, enhances urban environmental quality, and alleviates spatial pressures [3]; it also enhances understanding of urban development processes, trends, and growth mechanisms, providing references for urban planning and design practices [4,5]. Urban form refers to the spatial characteristics formed by the interactions between economic, social, demographic, and political factors at a specific time [6]. It reflects the organization and development patterns of urban spaces, directly influencing the functionality, efficiency, and environment of cities [7]. An irrational urban form may lead to environmental degradation, exacerbate urban heat island effects, and reduce biodiversity, thereby threatening sustainable urban development and residents’ quality of life [8]. The urban form mainly includes tangible and intangible aspects [9]. Tangible form primarily represents the concrete manifestation of the urban material environment, such as the shape of the city itself, urban surroundings, and various economic and social factors. Intangible form encompasses multiple abstract dimensions, including psychological, cultural, and human activities.

Analysis of urban spatial forms is foundational research for urban development [10]. Currently, research on urban spatial forms primarily focuses on two-dimensional aspects [11,12], such as land use [13], transportation networks [14], and planar analyses of towns [15]. This has led to a predominance of two-dimensional analyses in practical applications, while attention to three-dimensional spatial forms remains relatively insufficient [16,17]. The three-dimensional characteristics of urban spaces are essential, as compared with two-dimensional forms, three-dimensional forms provide a more comprehensive and accurate depiction of urban characteristics and better reveal the patterns of urban evolution and their underlying driving factors [18]. In the in-depth exploration of urban spatial forms, scholars have concentrated on urban buildings [17], geomorphological features [19], regional analyses of three-dimensional forms [20], quantification of residential space forms [21], three-dimensional urban expansion [22], and the evolution of three-dimensional forms across spatial and temporal dimensions [23,24]. Within the development of urban three-dimensional forms, how these forms interact with environmental quality [25,26], the intrinsic relationship between building proximity and urban form [27], the impact of different scales of urban form—from individual buildings to entire communities—on urban vitality [28], and the interactions between buildings and urban expansion influencing changes in urban form [27] have all become research hotspots.

The analysis of three-dimensional urban spatial forms has been a focal point of academic attention. Scholars have conducted detailed quantifications of urban spatial organization using diverse indicators and advanced methods, with key metrics such as population density and building density being particularly important [29]. Urban form indicators are quantitative measures used to describe the shape, size, and spatial characteristics of urban entities [30]. These indicators not only aid in understanding the spatial structure and patterns of the built environment amid urban changes [31] but also provide a scientific basis for advancing sustainable urban development. At the same time, indicators for assessing sustainable development encompass multiple aspects of urban life, such as population, housing, economy, transportation, land use, and energy [32], further highlighting the crucial role of the urban form in evaluating resource utilization efficiency and environmental impacts. Traditional spatial form analyses tend to focus on spatial feature indicators, such as shape, compactness, and fragmentation, to reveal the fundamental aspects of urban morphology [33]. To accurately depict the three-dimensional spatial structure of cities, Huang, J. et al. constructed multiple spatial indicators, covering various dimensions such as building height, density, volume, shape, distribution uniformity, and spatial congestion. Through comparative analyses of regional forms, they illustrated the unique characteristics of three-dimensional urban structures [34]. Soliman, A. et al. explored urban sustainability by quantifying the geographic distribution of building coverage, emphasizing the value of investigating the relationships between building coverage and other variables such as population density, housing unit density, and road density for comparing infrastructure across urban areas in the U.S. [35]. Le et al. conducted in-depth and detailed studies on urbanized areas using building density and the building coverage ratio as key urban parameters, effectively revealing the efficiency and patterns of urban spatial utilization [36]. Guo, C. focused on floor height distribution, classifying four urban spatial form indicators including staggeredness, maximum building height, standard deviation of building height, and average building height into five levels from low to high, providing new perspectives and tools for understanding the evolutionary process of urban three-dimensional spaces [37]. Liu, S. et al. explored the relationship between urban form indicators and fractal dimensions in Shenyang City using a three-dimensional fractal approach, examining correlations between 3D values and metrics such as floor area ratio, building density, standard deviation of building floors, and average number of floors, revealing the critical role of form indicators in identifying fractal characteristics of urban morphology and offering new insights for urban design and planning [38]. Lemoine-Rodríguez, R. analyzed the layered impacts of 2D indicators (built-up cover, white sky albedo, night-time lights, and enhanced vegetation index) alongside other 3D indicators (height and volume) on land surface temperature, investigating the relationship between urban morphology and land surface temperature trends in Beijing, Cairo, and Santiago from 2003 to 2019 [39]. Additionally, scholars such as Wen, A., Wang, F., and He, W. utilized the construction of three-dimensional urban indicators to extract features of urban three-dimensional forms, integrating quantitative descriptions, spatial autocorrelation analysis, and trend forecasting methods. This work not only revealed the close spatial relationship between three-dimensional building forms and thermal environments but also predicted the developmental trends in building forms [40,41,42,43]. Lu, X. selected urban three-dimensional indicators such as staggeredness, undulation, expandability, and uniformity to analyze the overall changes in the three-dimensional spatial form of the research area and the structural characteristics of building groups, examining the interplay between three-dimensional forms and surface elements as well as the distribution patterns of urban thermal environments [44]. Rode et al. studied the relationship between urban morphology and residential thermal energy demand in cities such as London, Paris, Berlin, and Istanbul [45]. They employed five urban form metrics, including building height, surface coverage ratio, open space ratio, building density, and the surface-to-volume ratio. These indicators not only reveal the relationship between urban form and energy demand but also provide critical references for sustainable development. Based on the research conducted by scholars in the field of three-dimensional urban spatial form analysis, this paper selects more refined and multidimensional indicators, such as building coverage, building expandability, and building staggeredness. These indicators can comprehensively capture the complexity and diversity of urban forms while accurately quantifying the spatial organization and structure of urban buildings. In summary, using building coverage, expandability, and staggeredness as core indicators of urban form can reveal the characteristics of urban spaces from multiple dimensions and provide solid theoretical and practical support for urban planning and sustainable development.

The three-dimensional form of urban buildings not only influences various aspects of human activity, such as mobility, accessibility, social interaction, and environmental conditions, but also provides insights into the socio-economic characteristics of urban areas, as the distribution of building height and density can reflect levels of urbanization and economic development [30]. Urban three-dimensional building indicators directly embody urban spatial forms. By analyzing these indicators alongside driving factors, a better understanding of the dynamic changes in cities can be achieved, offering important pathways for optimizing urban layouts. This optimization not only enhances the functionality and livability of cities but also promotes sustainable development in terms of resource conservation, energy efficiency, and environmental protection, laying the foundation for creating more eco-friendly urban spaces. Xu, X. et al. incorporated driving factors into spatial form analysis to delve into the mechanisms of spatial form changes and the impacts of core driving forces [46]. Xiong, G. et al. emphasized the importance of urban form and driving factors in regional development strategies, revealing the characteristics and driving mechanisms of imbalances in regional urban spatial forms [47]. Cai, Z. et al. combined driving factors with the evolutionary characteristics of urban spatial forms and the urban heat island effect for in-depth analysis, highlighting the crucial role of urban planning in regulating these factors [48]. Kucsicsa Gheorghe et al. identified the relationship between urban expansion and driving factors in surrounding areas, asserting that this connection is one of the key elements for effectively predicting the dynamic changes in urban sprawl in spatial and temporal dimensions [49]. Chen, Q. et al. introduced urban spatial form while exploring the driving factors of urban land use eco-efficiency, constructing a new four-dimensional driving system to comprehensively analyze the complex mechanisms behind urban land use eco-efficiency [50]. Tao, Y. et al. conducted an in-depth study of urban expansion in Nanjing, integrating multiple driving factors in their comprehensive analysis. Their research revealed that socio-economic conditions, population distribution, and geographic environment all play crucial roles in the process of urban expansion [51]. This paper focuses on urban three-dimensional building indicators as a direct reflection of urban spatial morphology. By analyzing key driving factors such as resident population and the economic level of the study area, it reveals how these factors interact to influence and shape the spatial morphology and dynamics of cities. The resident population is a crucial foundation for urban expansion and spatial layout, directly reflecting the spatial distribution of housing, employment, and other demands, which significantly impacts urban three-dimensional building indicators. The economic level of the study area not only determines the overall capacity for urban development but also guides the optimization and adjustment of urban spatial structures through economic activities such as investment and consumption, serving as a key driver of changes in urban three-dimensional building indicators. Therefore, this paper integrates factors like population and economic level into the analysis of urban spatial morphology, helping us gain a deeper understanding of the underlying logic of urban development. This analytical approach provides valuable theoretical foundations and practical pathways for optimizing urban layouts and enhancing urban functionality and livability, thus promoting balanced and coordinated development in the context of sustainable urban growth.

In summary, research on urban spatial forms is invaluable for understanding the complexities of urban development, optimizing urban layouts, and promoting economic prosperity. By combining urban three-dimensional building indicators with spatial forms, we can more precisely characterize the organizational structure of urban spaces and reveal their inherent patterns. This refined analytical approach allows us to better understand the relationships between different building characteristics and their impacts on the overall functionality and livability of cities. Furthermore, incorporating driving factors into spatial form analysis not only deepens our understanding of urban dynamic changes but also provides important foundations for scientifically sound urban planning strategies. These driving factors, including population and economic conditions, are crucial in influencing the evolution of urban forms. Overall, three-dimensional urban forms have a significant impact on urban sustainability [52]. They affect not only resource utilization efficiency but also the protection of the ecological environment and the quality of life for residents. Therefore, this research promotes long-term sustainability in cities by exploring the relationships between urban spatial forms and driving factors, enabling us to provide theoretical support and practical guidance for achieving smarter, more sustainable, and livable urban environments. The findings of this research will provide important information for policymakers, urban planners, and relevant stakeholders to advance urban development toward a more sustainable direction.

2. Data

2.1. Study Area

According to information published by the Harbin Municipal Government website, Harbin is located between longitudes 125°41′ and 130°13′ and latitudes 44°03′ and 46°40′, in the northeastern region of China and the center of Northeast Asia. It is the capital of Heilongjiang Province and the political, economic, and cultural center of northern Northeast China. Harbin is known as the “Pearl of the Eurasian Land Bridge”. The total land area of Harbin is approximately 53,100 square kilometers, which includes nine urban districts, seven counties, and two county-level cities. The main urban area of Harbin consists of Daoli District, Daowai District, Nangang District, Xiangfang District, Pingfang District, and Songbei District, covering a total land area of 2398.24 square kilometers. Figure 1 clearly indicates the boundaries of the study area and the distribution of the research region.

As an important city, Harbin’s population distribution and economic development are of great concern. According to the city yearbook published by the Harbin Municipal Government, the total population of the 18 districts and counties under Harbin’s jurisdiction is 10,009,854. Among this large population, 7,067,709 people live in urban areas, accounting for 70.61%, while the rural population stands at 2,942,145, or 29.39%. These data clearly reflect the urbanization process and population distribution characteristics of Harbin. In terms of economic development, Harbin has also achieved significant accomplishments. According to the latest data, in 2023, Harbin’s regional GDP reached CNY 557.63 billion, an increase of 3.1% compared with the previous year when calculated at comparable prices. This growth trend not only demonstrates Harbin’s economic vitality but also lays a solid foundation for its future development.

2.2. Research Data

This study thoroughly explores the three-dimensional morphological characteristics of buildings within Harbin’s main urban area, encompassing 83,555 buildings. This extensive sample offers both comprehensive coverage and strong representativeness, spanning multiple administrative districts. We employed a block-based object simplification method, effectively integrating the two-dimensional building footprint data with height information to automatically generate three-dimensional models, vividly capturing the spatial form of the buildings. For data acquisition, this study fully leverages modern information technologies, drawing not only on authoritative data from urban surveying and planning departments but also actively utilizing freely available, rich 2D vector data from open-source geographic information platforms such as OpenStreetMap (OSM), which provides detailed and accurate building data for cities worldwide. By utilizing advanced tools like ArcGIS v10.8, we extruded the 2D data into 3D models based on building height attributes, constructing a vast and intricate three-dimensional urban environment. To focus specifically on the 3D morphological characteristics of urban buildings, we standardized the elevation data of all buildings to a base level of 0, conducting the analysis on a uniform horizontal plane. This effectively eliminated any potential impact of terrain elevation differences on building height measurements, ensuring the objectivity and comparability of the analysis results. Through in-depth analysis of key data points such as building height, volume, and footprint area, this study aims to reveal the vertical uniformity of development, the scale and morphological diversity of urban building clusters, and the distribution balance of building areas in Harbin’s main urban area. These findings provide valuable insights into the three-dimensional spatial structure of urban buildings, offering important reference points for urban planning, architectural design, and urban regeneration. Figure 2 provides a visual illustration of the white-model visualization of Harbin’s building data, serving as a clear visual reference for related research efforts.

3. Method

3.1. Technical Route

This study utilizes 3D models of urban buildings as experimental data sources, focusing primarily on the three-dimensional morphological characteristics of urban structures. First, the building data undergo preprocessing to eliminate redundant information. Subsequently, each individual building is confirmed based on its spatial location. The height, volume, and area of each determined building are then calculated, extracting the 3D building feature data needed for the study. The height data of the buildings are sourced as specified, while the area data are computed using the field calculator in geometric tools, which iterates over all features and calculates the area based on each feature’s geometric shape, filling in the area data accordingly. After calculating the area data, the building volume is computed, which typically requires height information. This study employs ArcGIS 10.8 software’s spatial analysis capabilities to calculate building volume, utilizing tools from the 3D Analyst extension to create 3D geometries based on area data and height fields. The volume is calculated by combining height data with base area data. Based on the extracted feature values, this study employs scientific computation methods to derive quantitative indicators such as building staggeredness, expandability, and coverage, each expressing different dimensions of urban spatial morphology. Finally, the Pearson correlation coefficient analysis method is used to examine the correlation between these morphological indicators and the economic development status and resident population of the study area. In conducting the correlation analysis, we will focus on building morphology indicators, including building coverage, building expandability, and building staggeredness. These indicators reflect the morphological characteristics of urban spaces and provide important information for understanding the distribution of buildings. At the same time, we will include resident population numbers and GDP in the analysis to explore how these economic and social factors influence building morphology. Specifically, by calculating the correlation between building morphology indicators and socio-economic variables in various regions, we can identify which factors play a key role in driving changes in building morphology. The summary of the aforementioned technical approach is shown in Figure 3. This study not only enriches the theoretical connotation of urban spatial morphology research but also provides strong data support and decision-making basis for urban planning and management practices.

3.2. Calculation of Building Features

In the study of three-dimensional characteristics of urban morphology, the selection of indicators has a significant impact on revealing the dynamic structure and morphology of building spaces. Building height [53], building area [54], and building volume [55] play crucial roles in urban morphology, each with distinct meanings. Given the complexity and diversity of urban morphology, this study selects building height, volume, and surface area as core indicators to comprehensively assess the spatial characteristics of urban buildings. Height, as a measure of the vertical dimension of buildings, reveals the vertical distribution patterns of urban architecture, directly influencing the overall form of the city [56]. Volume reflects the actual occupancy of buildings in a three-dimensional space and their potential impact on the surrounding environment, serving as a key metric for evaluating the efficiency of spatial occupancy and environmental integration [57]. Area indicates the pace of urban expansion, and its distribution reflects urban structure and development direction [58]. By conducting an in-depth analysis of these three indicators, this study reveals the complexity and diversity of urban buildings in terms of spatial layout, morphological evolution, and environmental interaction, providing valuable data support and decision-making basis for urban planners and managers. Figure 4, Figure 5 and Figure 6 illustrate the calculated height, area, and volume of individual buildings segmented based on spatial location.

3.2.1. Calculation of Building Height Characteristics

Building height is a crucial indicator for assessing the vertical development of structures within a region. This study employs representative building staggeredness as a measure of height characteristics. Building staggeredness serves as an indicator to examine the uniformity of vertical development among buildings in different areas. It reflects height differences and the characteristics of three-dimensional spatial layout by comparing the ratio of the standard deviation of building heights to the average height. Specifically, the standard deviation represents the degree of dispersion of building heights, while the average height indicates the central tendency of those heights. Thus, when staggeredness is high, it signifies significant height differences among buildings, resulting in a pronounced sense of spatial layering. Conversely, low staggeredness suggests minimal height differences, leading to a lack of distinct spatial layers. The calculation formula is as follows:

$$\sigma =\sqrt{{\displaystyle \frac{1}{N}}\sum _{i=1}^N(h_i-\overline{h}{)}^2}$$

(1)

$${\mathcal{O}}_{staggeredness}={\displaystyle \frac{\sigma }{{\mathcal{H}}_{\mathrm{Average}}}}$$

(2)

where $\sigma$ is the standard deviation of height, $N$ is the total number of buildings, $h_i$ is the height of the $i$-th building, and $\overline{h}$ is the average height of all the buildings. In practical applications, a higher degree of building staggeredness in high-density urban core areas may enhance the diversity and three-dimensionality of the urban landscape, improving visual appeal and clarity in functional zoning. In low-density areas like residential neighborhoods, appropriate staggeredness can enhance livability and spatial efficiency. Additionally, building staggeredness supports urban environmental and ecological research, as the spatial layers created by varying building heights can significantly impact factors such as ventilation, sunlight exposure, and the urban heat island effect.

3.2.2. Calculation of Building Volume Characteristics

Building volume reflects the scale and morphological diversity of urban clusters, encompassing attributes such as the solid volume and peripheral contours of the buildings. These varying attributes collectively shape the overall characteristics of building forms within a region. Therefore, this study selects expandability as an indicator to measure the volumetric features of the city, aiming to examine the differences in volume between study units and to quantitatively analyze the morphological characteristics of urban buildings in a three-dimensional space. The specific calculation formula is as follows:

$$V_i=s_i\times h_i$$

(3)

$$V=S\times h_{max}$$

(4)

$$Expandability={\sum }_{i=1}^n{V}_i/V$$

(5)

In the formula, ${\mathrm{s}}_{\mathrm{i}}$ and ${\mathrm{h}}_{\mathrm{i}}$ represent the area and height of an individual building, respectively, while $V_i$ denotes the volume of a single building, and $i$ is the number of buildings in the group. $S$ and $h_{max}$ are the footprint area of the building cluster and the maximum building height within that unit, respectively. Building expandability refers to the proportion of space occupied by buildings, specifically the volume they occupy due to their vertical height. In Figure 6, buildings within the same area exhibit varying heights, which in turn affects their expandability. The purple buildings on the left are taller, indicating that they occupy a greater vertical space on the same horizontal footprint. As a result, these taller buildings have a higher expandability, making the overall urban space appear more compact and filled. In contrast, the buildings on the right are shorter; despite maintaining the same horizontal area, their reduced height leads to a lower overall expandability. This creates a sparser appearance, with more open space, resulting in a relatively lower utilization of urban space.

3.2.3. Calculation of Building Area Characteristics

This study selects building coverage as an indicator to measure building area characteristics, aiming to explore the uniformity of building area distribution across different research units. Building coverage is defined as the ratio of the building footprint area to the total area of the measurement unit, providing a clear visual representation of the proportion of built space within the entire area. Analyzing building coverage allows for an understanding of the degree of uniformity in building area distribution across various regions. The calculation formula is as follows:

$$B={\displaystyle \raisebox{1ex}{$A_i$}\!\left/ \!\raisebox{-1ex}{$A$}\right.}$$

(6)

In the formula, $A_i$ represents the total footprint area of buildings within the study unit, and $A$ denotes the area of the study unit. When building coverage is high, it indicates that buildings occupy a significant proportion of the area, suggesting higher building density and extensive coverage of urban space by structures. Conversely, low building coverage implies a smaller proportion of built space, potentially allowing for more open areas or green spaces. Therefore, analyzing building coverage provides crucial insights for urban planning and spatial utilization, facilitating the rational allocation of urban area characteristics and promoting balanced development.

3.3. Calculation of Correlation

To comprehensively assess the relationship between building morphology and socio-economic factors as well as population, this study employs the Pearson correlation coefficient analysis method. The Pearson correlation coefficient is a commonly used statistical tool for measuring the linear relationship between two variables. Its coefficient values range from −1 to 1. Specifically, a value of 1 indicates a perfect positive correlation, meaning that when one variable increases, the other variable also increases correspondingly; a value of −1 indicates a perfect negative correlation, meaning that when one variable increases, the other variable decreases; and a value of 0 indicates that there is no linear relationship between the two. The calculation formula is as follows:

$$r={\displaystyle \frac{\sum \left(X_i-\overline{X}{\left)\right(Y}_i-\overline{Y}\right)}{\sqrt{\sum {\left(X_i-\overline{X}\right)}^2\sum {\left(Y_i-\overline{Y}\right)}^2}}}$$

(7)

In the formula, $r$ represents the Pearson correlation coefficient, while $X_i$ and $Y_i$ denote the $i$-th values of variables $X$ and $Y$; $\overline{X}$ and $\overline{Y}$ represent the means of variables $X$ and $Y$. The classification of the correlation coefficients is as follows: a value between 0.8 and 1.0 indicates a very strong correlation, 0.6 and 0.8 indicates a strong correlation, 0.4 and 0.6 indicates a moderate correlation, 0.2 and 0.4 indicates a weak correlation, and 0 and 0.2 indicates a very weak correlation or no correlation.

4. Result and Analysis

4.1. Calculation of Indicators

To comprehensively and accurately assess the architectural characteristics of different functional areas in Harbin, this study has selected three key indicators: building staggeredness, building expandability, and building coverage. These indicators provide essential data for understanding the layout and density characteristics of buildings in each district and are closely linked to urban planning and development. A reasonable building layout can effectively utilize land resources, reduce environmental impact, and enhance residents’ quality of life. By analyzing these data, better urban planning strategies can be formulated to promote the development of Harbin. The following figure, Figure 7,presents the morphological indicator values for Xiangfang District, Daoli District, Daowai District, Nangang District, Pingfang District, and Songbei District.

4.2. Analysis of Building Metrics and Drivers

When comparing the three-dimensional morphological characteristics of buildings in different areas of Harbin, it is essential to explore the driving factors behind the differences in building morphology among these regions. This study will focus on the main urban area of Harbin, calculating the three-dimensional morphological indicators of buildings in these areas and correlating them with resident population data and regional Gross Domestic Product (GDP) (as shown in Figure 8). These two socio-economic data points will be analyzed for correlation with the building morphology using the Pearson correlation coefficient. The resident population reflects the socio-economic conditions, infrastructure needs, and level of public service provision in the area, and it is also one of the key indicators for measuring urbanization and development potential. Meanwhile, the GDP represents the economic strength and development level of each region, directly influencing the scale, type, and evolution of building styles in that area.

By analyzing the correlation between building morphology indicators and socio-economic variables in various regions of Harbin, we can reveal the intrinsic relationship between building morphology and economic and population factors. The results of this analysis are presented in tabular form, as shown in

Table 1. Correlation coefficients for building morphology and economic population data.

Indicator	Correlation Coefficient	Explanation
GDP and Building Expandability	0.47	There is a moderate positive correlation between GDP and building expandability.
GDP and Building Coverage	0.77	There is a strong positive correlation between GDP and building coverage, approaching significance.
GDP and Building Staggeredness	−0.51	There is a moderate negative correlation between GDP and building staggeredness.
Resident Population and Building Expandability	0.32	There is a weak positive correlation between resident population and building expandability.
Resident Population and Building Coverage	0.80	There is a strong positive correlation between resident population and building coverage.
Resident Population and Building Staggeredness	−0.40	There is a moderate negative correlation between resident population and building staggeredness.

, which lists the correlation coefficients between GDP and building expandability, building coverage, and building staggeredness, as well as the correlation between annual resident population and these building morphology indicators. Through these data, we hope to gain a deeper understanding of the differences in building morphology across different regions and provide a scientific basis for urban planning and management.

4.3. Building Staggeredness Analysis

Based on the data calculated in Table 1, the analysis indicates a negative correlation between building staggeredness and both GDP and total annual population. As the central area of Harbin, Nangang District is often referred to as the “Eastern Paris”. Economically, Nangang District leads Harbin in terms of GDP, and it is also one of the areas with the highest population density in the city. Due to the need to consider various factors when designing and constructing buildings to meet diverse user demands, urban planning in Nangang emphasizes overall coordination and unity. To achieve this goal, planners have controlled the height and density of buildings, successfully maintaining the overall esthetic of the urban area, resulting in relatively low staggeredness among buildings. These measures not only keep the staggeredness of buildings in Nangang at a low level but also promote the optimization of the ecological environment.

Songbei District, as a new area of Harbin, faces certain challenges in building design due to its lower GDP, which constrains economic development. To shape a modern urban image, planners have focused on creating distinct, layered, and staggered building groups to enhance overall urban staggeredness, compensating for potential shortcomings in urban vitality due to weaker economic strength. The height differences and layered layouts of building groups can attract more investment and residents, thereby promoting economic development. Additionally, the lowest resident population figures in Songbei District suggest a relatively low population density, resulting in lower demand for residential and commercial space. A reduced building density naturally leads to less building concentration, further affecting staggeredness between buildings. Compared with areas with high population density and GDP, Songbei may prioritize the creation of ecological environments and public spaces. Therefore, urban planning might retain more green spaces and open areas, providing residents with a good living environment. The presence of these public spaces and green areas creates larger distances between buildings, thereby increasing the potential for staggeredness among them.

4.4. Building Expandability Analysis

Based on the data calculated in Table 1, it is evident that building expandability exhibits a positive correlation with GDP and total annual population. As the central area of Harbin, Nangang District boasts a high total population and regional GDP, which not only reflects the prosperity and vitality of the regional economy but also highlights the positive impact of population concentration and commercial activity on building expandability.

Firstly, the continuous growth in population is a crucial foundation for Nangang District’s economic prosperity. As more people flock to the area, the demand for diverse spaces for living, working, and leisure also increases. This demand directly drives the boom in the real estate market, leading to the rapid emergence of various buildings and significantly enhancing building expandability. The high-density residential population provides a substantial consumer base for commercial activities, further boosting the regional economy. Additionally, the Nangang District government supports the development of high-end industries and modern services through scientifically sound urban planning, providing strong backing for sustained economic growth in the area. In summary, Nangang District’s leading building expandability among various regions vividly illustrates the positive driving effect of population and commercial activities on building expandability. In the future, with the acceleration of urbanization and continued economic development, Nangang District’s building expandability is expected to maintain its leading position, contributing significantly to the prosperity of the regional economy and the sustainable development of the city.

In contrast, Songbei District, as a new area, shows a lower building expandability, indicating that these areas have more open spaces with greater land available for parks, green spaces, and other public areas, providing citizens with pleasant locations for leisure and cultural activities. The vast area of Songbei, combined with relatively low population density and GDP, creates a landscape characterized by sparse building distribution and less surface coverage. This layout preserves regional characteristics to some extent, but it also suggests relatively low land use efficiency, making it difficult to fully realize the potential of land resources.

The relatively low building expandability in Daowai District can be analyzed from both its low GDP and low resident population data. Firstly, a lower GDP often indicates that economic activities in the area are relatively inactive, potentially lacking large-scale commercial development, industrial construction, or the growth of high-end services. The inactivity in economic activities directly restricts investment and construction in new building projects, resulting in a reduced demand for new building space and consequently leading to lower building expandability. Secondly, low resident population data reflects an insufficient level of population concentration. Population is one of the key factors driving urban development and building demand. In conclusion, the low building expandability in Daowai District can primarily be attributed to its low GDP and resident population data, both of which jointly influence the area’s economic activities and population concentration, thereby negatively affecting building expandability.

4.5. Building Coverage Analysis

Nangang District is renowned for its robust economic growth and dense population, with high building coverage directly reflecting the concentrated distribution of skyscrapers and thriving commercial activities, providing solid support for rapid economic expansion. This high concentration of building coverage shows a positive correlation with Nangang’s GDP and population data. As the economy continues to grow and more people flock to the area, the demand for commercial and residential spaces rises, directly driving an increase in building density. However, this high building coverage also presents challenges: a significant number of buildings occupy limited ground space, leading to a reduction in green areas and open spaces, which in turn limits residents’ opportunities to enjoy nature and leisure activities. In the long term, this situation may negatively impact the city’s ecological balance and climate regulation, hindering sustainable urban development.

Xiangfang District, as an old urban area, is characterized by active economic development. Its high GDP level indicates substantial economic volume, providing ample financial support for building expansion. High population density further drives an increase in building coverage. With sustained GDP growth and a large influx of residents, the number of buildings in Xiangfang District has rapidly increased, resulting in a more concentrated building distribution and further enhancing the region’s building coverage.

In contrast, Daoli District, Daowai District, Pingfang District, and Songbei District exhibit relatively low building coverage. These areas have lower levels of economic development and population concentration, leading to a sparser distribution of buildings and more abundant green spaces and open areas, providing residents with a better living environment. Although these regions have advantages in ecological resources and living comfort, their lower building coverage indicates that there is still room for improvement in land use efficiency. These areas can enhance land use efficiency by exploring diversified land utilization models, such as developing green buildings, introducing transit-oriented development, and strengthening the development and utilization of underground spaces. Therefore, through reasonable planning and effective management, these areas can achieve a better balance between ecological protection and economic growth, continually optimizing land use efficiency while ensuring sustainable urban development and improving residents’ quality of life.

5. Conclusions

5.1. Research Findings

The urban spatial morphology indicators encompass key factors such as building expandability, building coverage, and building staggeredness, which comprehensively reflect the efficiency of urban space utilization, environmental impact, and quality of living. In the process of conducting three-dimensional morphological analysis across different regions, we observed significant differences primarily driven by factors such as economic development levels and urban population. The research findings clearly reveal a positive correlation between GDP and both building expandability and building coverage, suggesting that economic growth trends often drive increases in these metrics. Meanwhile, there is a negative correlation between GDP and building staggeredness, indicating that economic growth may be accompanied by a certain decline in the regularity of building distribution. Regarding resident population, there is a positive correlation with both building expandability and building coverage, further illustrating that an increase in population leads to corresponding rises in these metrics. Additionally, there is a certain degree of negative correlation between resident population and building staggeredness, although this relationship is relatively weak. Based on these findings, we propose the following:

• Economically developed cities must balance high-density development to meet population and commercial demands with the ecological environment and building coverage. Excessively high building coverage may exacerbate urban heat island effects, reduce green spaces, and degrade air quality. Therefore, urban planning should rationally control building coverage, increase green spaces and open areas, promote ecological cycles, and enhance the overall environmental quality of the city.
• In economically developed and densely populated areas, low building staggeredness is primarily due to efficient land use planning, standardized building design, advanced construction technology, robust market demand, rapid urbanization, and government policy guidance and regulatory constraints. These factors collectively lead to a more compact and orderly layout of buildings, reducing the irregularity caused by disorderly development and chaos, thereby meeting the demands of limited land resources and rapid economic development.
• High-rise buildings and commercial complexes have become symbols of economically developed cities, providing more living and working spaces while reflecting urban economic prosperity. In contrast, relatively underdeveloped areas, especially older cities, struggle with slow population growth and lagging infrastructure development, lacking the funds and resources for large-scale renewal and renovation. As a result, these regions retain a significant number of low-rise and densely packed old buildings, leading to a lower overall building expandability.

5.2. Future Research Directions

In deepening the study of urban spatial morphology, we particularly focus on core driving forces such as economic development and resident population. Behind these forces lies a complex web of interwoven factors, including policy orientation, cultural values, and technological innovation. These factors interact with each other, collectively shaping and continuously influencing the development trajectory of urban spatial morphology. In future research, to more accurately grasp this dynamic process, we recommend enhancing the richness and reliability of the research data, particularly by incorporating a time series analysis. By tracking historical data, we can reveal the evolution patterns, long-term trends, and potential cyclical changes in urban spatial morphology over time.

Given the dynamic nature of urban spatial morphology, research data need to be updated regularly to reflect the latest urban development status. In addition to traditional indicators such as building height, volume, and area, subsequent studies could include additional parameters that reflect the quality of urban spaces, such as the green space ratio, transportation accessibility, and distribution of public facilities. These additional data points would not only help construct a more comprehensive and multidimensional evaluation system but also enhance the reliability of the research, making it more effective in capturing the complexity and variability of urban spatial morphology.

At the same time, we recognize the interdisciplinary nature of urban spatial morphology research and call for strengthened collaboration and communication among various disciplines, including urban planning, geography, environmental science, and sociology. By integrating the expertise and unique perspectives of different fields, we can collectively advance the depth and breadth of research, revealing the complex mechanisms and patterns behind urban spatial morphology.

Moreover, we should actively embrace an international perspective, drawing on advanced experiences and methods from global research in urban spatial morphology. These experiences and methods can provide valuable references and insights for our research, and through diverse data support and validation, further enhance the scientific rigor and reliability of our findings, offering scientific guidance and valuable experience for sustainable urban development.