Investigation into the Distribution Features and Determinants of Underground Commercial Spaces in Qingdao City
Abstract
1. Introduction
2. Research Area and Data Sources
2.1. Research Area
2.2. Data Sources
3. Methodology
3.1. Kernel Density Estimation (KDE)
3.2. Data Gridding
3.3. Spatial Autocorrelation Analysis
3.4. Multi-Distance Spatial Clustering (Ripley’s K) Analysis
3.5. Geographical Detector
3.6. Multi-Scale Geographically Weighted Regression (MGWR)
4. Results
4.1. Spatial Layout Features of Underground Commercial Spaces in the Central Urban Area
4.1.1. The Underground Commercial Space Exhibits a Layout Pattern Characterized by “Multiple Cores–Multiple Levels”
4.1.2. The Agglomeration Features of Underground Commercial Spaces Are Highly Pronounced
- (1)
- In terms of quantity, high–high clustering areas constitute a substantial proportion. Areas with higher underground commercial densities are more likely to cluster and exhibit continuous spatial distributions.
- (2)
- Spatially, underground commercial spaces form point-like clusters in multiple locations. Despite considerable spatial distances between clusters, a limited number of low–high clusters exist, suggesting that high-density areas exert weak radiation effects on surrounding regions, which diminish rapidly with increasing distance.
- (3)
- From a zoning perspective, the east coast urban area features larger high–high clustering areas, where the distribution of underground commercial spaces aligns closely with surface commercial layouts and metro line directions. In the west coast urban area, underground commercial spaces are primarily concentrated along Metro Line 1 and in the JiMiYa business district. In the north coast urban area, only the Zhengyang Road business district contains a relatively concentrated underground commercial space.
4.1.3. The Underground Commercial Space Exhibits a Core Agglomeration Scale of 3.39 km
4.2. Analysis of Influencing Factors Shaping the Layout of Underground Commercial Spaces
4.2.1. Selection of Influencing Factors
4.2.2. The Detection Results of the Influencing Factors
4.2.3. Model Development and Comparative Analysis of Results
4.2.4. Regression Results of the MGWR Model
5. Discussion and Strategies
5.1. Discussion
5.2. Strategies for Optimizing the Layout of Underground Commercial Spaces
5.2.1. Business District Agglomeration-Driven Strategy: Activating Underground Commercial Clusters via Large-Scale Business Districts as Spatial Catalysts
5.2.2. Station–Area Synergistic Development: Building a Passenger–Flow–Business–Space Symbiotic Interface Through Three-Dimensional Transit-Oriented Development
5.2.3. Supply-and-Demand Coupling Measurement: Guiding the Flexible Layout of Underground Commercial Spaces via the “Population-Commercial Supply” Assessment Mechanism
5.2.4. 3D Cadastre Proactivity: Enhance the Top-Level Design by Integrating Forward-Looking Planning with the Development of a Three-Dimensional Cadastre System
6. Conclusions
- (1)
- The underground commercial spaces in the central urban area exhibit a “multi-core–multi-level” distribution pattern, with seven core areas and a hierarchical division into “high-density core–medium-density belt–low-density points–nascent areas”. Areas with higher underground commercial densities are more likely to agglomerate, and overall, they display a “high-high” clustering pattern, with the most significant agglomeration occurring at a scale of 3.39 km.
- (2)
- Commercial supporting facilities, development of underground space, and population heat value serve as the core driving factors for the clustering of underground commercial facilities. The interaction between development of underground space and population heat value exhibits the highest explanatory power, jointly accounting for 52.86% of the spatial layout of underground commercial facilities from the perspectives of underground facility development levels and consumer demand. The explanatory power of other factors significantly increases after interacting with rail transit and central location, indicating that these two factors exhibit the strongest coupling effects with other influencing factors.
- (3)
- The influences of centrality, commercial supporting facilities, development of underground space, shop rent, land cost, and permanent resident population density on the agglomeration of underground commercial space is predominantly localized; the influences of rail transit and population heat value exert a medium-scale influence on underground commercial space agglomeration. The spatial differentiation of various influencing factors is the fundamental cause of the spatial heterogeneity of underground commercial spaces. Therefore, the development of underground commercial spaces requires forward-looking planning to address these challenges. By establishing and improving a three-dimensional land registration management system, obstacles caused by unclear underground space usage rights can be effectively mitigated. Furthermore, specialized development can be achieved by leveraging the radiation and driving effects of regional advantages such as ground-level commercial areas and rail transit systems.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
GWR | Geographically weighted regression |
MGWR | Multi-scale geographically weighted regression |
KDE | Kernel density estimation |
POI | Point of Interest |
Ripley’s K | Multi-distance spatial clustering |
OLS | Ordinary least squares |
L(d) | The observed value |
ExpK | The expected value |
DiffK | The difference between the observed value and the expected value |
EB | Double-factor enhancement |
EN | Nonlinear enhancement |
WU | Single-factor nonlinear weakening |
R2 | The goodness of fit |
Adjusted R2 | The adjusted goodness of fit |
RSS | Residual sum of squares |
AICc | Akaike information criterion with correction for small sample sizes |
VIF | Variance Inflation Factor |
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Type | Underground Commercial Spaces | Catering Services | Shopping Services | Science, Education, and Cultural Services | Accommodation Services | Business Support Services | Life Support Services | Leisure and Entertainment Services |
---|---|---|---|---|---|---|---|---|
Global Moran’s I | 0.819 | 0.501 | 0.470 | 0.479 | 0.088 | 0.511 | 0.550 | 0.105 |
p-Value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Z-Score | 203.1 | 58.07 | 55.39 | 48.05 | 7.06 | 48.74 | 65.99 | 7.959 |
Dimensions | Influencing Factor | Description |
---|---|---|
Location and Transportation | Rail transit (×1) | Distance from the grid centroid to the nearest subway station |
Road network density (×2) | The ratio of road network length to the buffer zone area within a 500 m walking buffer | |
Centrality (×3) | Shortest distance from the grid centroid to a major commercial district 1 | |
Functional Space | Commercial supporting facilities (×4) | The ratio of road network length to the area of the 500 m walking buffer |
Development of underground space (×5) | The ratio of the number of underground POIs to the area of the 500 m walking buffer | |
Development Status | Ground development Intensity (×6) | The ratio of built-up areas to the area of the 500 m walking buffer |
Shop rent (×7) | Average shop rent within the grid | |
Land cost (×8) | Average transaction price per unit area for new housing within the grid | |
Population Factors | Permanent resident population density (×9) | The ratio of permanent resident population to the area of the 500 m walking buffer |
Population heat value (×10) | The average population heat value within the 500 m walking buffer |
Factor | ×1 | ×2 | ×3 | ×4 | ×5 | ×6 | ×7 | ×8 | ×9 | ×10 |
---|---|---|---|---|---|---|---|---|---|---|
q-value | 0.0051 | 0.0104 | 0.0701 | 0.2653 | 0.3890 | 0.0632 | 0.0087 | 0.0176 | 0.1117 | 0.2801 |
p-value | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Factor | ×1 | ×2 | ×3 | ×4 | ×5 | ×6 | ×7 | ×8 | ×9 | ×10 |
---|---|---|---|---|---|---|---|---|---|---|
×1 | 0.0551 | EB | EN | EN | EB | EN | EN | EN | EN | EB |
×2 | 0.0638 | 0.0104 | EN | EN | EB | EN | EN | EB | EN | EB |
×3 | 0.1337 | 0.0926 | 0.0701 | EB | EB | EN | EN | EN | EN | EB |
×4 | 0.3206 | 0.3707 | 0.3210 | 0.2653 | EB | EB | EN | EN | EB | EB |
×5 | 0.4359 | 0.3986 | 0.4286 | 0.5190 | 0.3890 | EB | EN | EN | EB | EB |
×6 | 0.1233 | 0.0823 | 0.1363 | 0.3274 | 0.4453 | 0.0632 | EN | EN | EB | EB |
×7 | 0.073 | 0.0197 | 0.0927 | 0.3000 | 0.4008 | 0.0799 | 0.0087 | EB | EN | WE |
×8 | 0.0751 | 0.0255 | 0.0984 | 0.2899 | 0.4299 | 0.0903 | 0.0222 | 0.0176 | EN | EN |
×9 | 0.1746 | 0.1394 | 0.2340 | 0.3364 | 0.4564 | 0.1598 | 0.1278 | 0.1440 | 0.1117 | EB |
×10 | 0.319 | 0.2886 | 0.3125 | 0.3569 | 0.5286 | 0.3050 | 0.2611 | 0.3013 | 0.3153 | 0.2801 |
Model | OLS | GWR | MGWR |
---|---|---|---|
R2 | 0.418 | 0.677 | 0.859 |
Adjusted R2 | 0.417 | 0.676 | 0.834 |
RSS | 4199 | 2332 | 1016 |
AICc | 16,588 | 12,659 | 8872 |
Explanatory Variables | Mean | Minimum | Maximum | Standard Deviation | Bandwidth (GWR) | Bandwidth (MGWR) | VIF |
---|---|---|---|---|---|---|---|
Rail transit | −0.615 | −1.325 | 0.216 | 0.460 | 722 | 1115 | 1.564 |
Road network density | 0.002 | 0.000 | 0.005 | 0.001 | 722 | 7210 | 1.091 |
Centrality | 0.421 | −1.853 | 5.528 | 1.317 | 722 | 46 | 2.050 |
Commercial supporting facilities | 0.538 | −0.876 | 5.230 | 0.934 | 722 | 47 | 2.654 |
Development of underground space | 0.798 | −4.491 | 5.250 | 1.097 | 722 | 48 | 1.332 |
Ground development intensity | −0.017 | −0.019 | −0.013 | 0.002 | 722 | 7210 | 1.730 |
Shop rent | 0.028 | −1.605 | 3.910 | 0.492 | 722 | 50 | 1.596 |
Land cost | 0.087 | −4.723 | 4.936 | 1.557 | 722 | 145 | 2.149 |
Permanent resident population density | −0.144 | −2.883 | 2.163 | 0.608 | 722 | 48 | 3.085 |
Population heat value | −0.109 | −1.043 | 0.099 | 0.239 | 722 | 406 | 2.393 |
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Zhao, J.; Wang, H.; Sun, Y.; Li, H.; Zhu, Y. Investigation into the Distribution Features and Determinants of Underground Commercial Spaces in Qingdao City. Buildings 2025, 15, 1743. https://doi.org/10.3390/buildings15101743
Zhao J, Wang H, Sun Y, Li H, Zhu Y. Investigation into the Distribution Features and Determinants of Underground Commercial Spaces in Qingdao City. Buildings. 2025; 15(10):1743. https://doi.org/10.3390/buildings15101743
Chicago/Turabian StyleZhao, Jingwei, Heqing Wang, Yu Sun, Haoqi Li, and Yinge Zhu. 2025. "Investigation into the Distribution Features and Determinants of Underground Commercial Spaces in Qingdao City" Buildings 15, no. 10: 1743. https://doi.org/10.3390/buildings15101743
APA StyleZhao, J., Wang, H., Sun, Y., Li, H., & Zhu, Y. (2025). Investigation into the Distribution Features and Determinants of Underground Commercial Spaces in Qingdao City. Buildings, 15(10), 1743. https://doi.org/10.3390/buildings15101743