Enterprise AI Analysis: The breaking logic and practical path of non-standard commercial renewal from the perspective of spatial production theory based on CatBoost algorithm
Leveraging CatBoost for Spatial Production Theory in Commercial Renewal
Driven by the dual forces of homogeneous competition among urban commercial stock and the upgrading of consumer demand, the renewal of non-standard commercial landscapes has become a key practice to solve the dilemma of spatial reproduction. Based on Lefebvre's spatial production theory, this paper takes the landscape renewal of non-standard commercial projects in Guiyang, Kunming, and Xi'an as research samples, and uses a cross-case comparative method combined with the CatBoost machine learning algorithm to systematically analyze the spatial logic and practical path of non-standard commercial landscape "breaking the circle". The CatBoost method enables us to effectively handle heterogeneous categorical features in complex spatial datasets and evaluate the relative importance of variables such as spatial configuration, functional diversity, and cultural symbolism. The model was explained using SHAP. The study found that the renewal of non-standard commercial land- scapes achieves value leap through three spatial practices: (1) At the spatial representation level, the core IP narrative is constructed with local cultural landscape symbols to form a differentiated cog- nitive anchor point; (2) At the spatial practice level, the consumer behavior experience is reconstructed through the fuzzification of landscape function modules and the integration of business scene design; (3) At the representational space level, the dynamic oper- ation of landscape carriers is used to activate the production of social relations in the place, and promote the transformation of commercial landscape from functional interface to public cultural field. The study expands the application boundary of spatial pro- duction theory in the field of commercial landscape, and provides a methodological framework that combines academic theory, prac-tical operation, and advanced predictive modeling for the renewal of existing commercial stock.
Authors: Zhihong Feng (Zhanjiang University of Science and Technology, Zhanjiang, Guangdong, China), Xin Huang (Land3 (Beijing) Landscape Planning and Design Co, Ltd., Beijing, China)
Journal/Conference: ICAISD 2025: 2025 International Conference on Artificial Intelligence and Sustainable Development
Publication Date: November 14-16, 2025
Executive Impact: Quantifying Renewal Effectiveness
Our analysis, utilizing advanced CatBoost modeling, quantifies the effectiveness of non-standard commercial landscape renewal projects. Key metrics demonstrate the model's high predictive power and the significant impact of the identified spatial practices on user satisfaction.
1.35
Average MAE
4.0
Average MSE
0.97
Average R²
Deep Analysis & Enterprise Applications
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Spatial Production Theory in Urban Planning
Lefebvre's spatial production theory posits that space is a social product, formed by the dynamic interaction of spatial practice, spatial representation, and representational space. This framework is crucial for understanding how commercial landscapes evolve from mere functional interfaces to public cultural fields, driven by capital logic and social needs.
CatBoost: Advanced Machine Learning for Complex Data
The CatBoost algorithm, a gradient boosting method, excels in handling heterogeneous data and categorical features without extensive preprocessing. Its innovations, such as ordered target encoding and symmetric tree structures, prevent overfitting and improve generalization, making it ideal for complex urban spatial datasets.
Data-Driven Commercial Renewal Strategy
Non-standard commercial renewal involves de-standardizing landscape production to embed local cultural symbols, create scene-based experiences, and reshape meaning. The CatBoost-SHAP analysis identified key drivers: visual distinctiveness, boundary blurring, interactive participation, and emotional attachment, which collectively transform commercial spaces.
0.97
Average R² Score
CatBoost model explains 97% of variance in renewal performance, indicating strong predictive reliability.
Enterprise Process Flow
Ordered Target Encoding
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Symmetric Tree Structure
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Efficient Gradient Calculation
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Model Update
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Regularization
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Feature Importance Evaluation
| Feature | CatBoost Advantage | Traditional Limitations |
|---|---|---|
| Heterogeneous Data Handling | Superior performance, effective processing of mixed-type variables | Often requires extensive preprocessing, less robust |
| Categorical Features | Ordered Target Encoding prevents target leakage and bias | Prone to overfitting with one-hot encoding or target leakage |
| Overfitting Control | Integrated regularization (shrinkage, subsampling, leaf smoothing) | Requires careful tuning to prevent overfitting, less intrinsic resistance |
| Model Interpretability | SHAP analysis quantifies feature contributions, revealing underlying mechanisms | Less transparent, harder to interpret complex interactions |
Triple Production Space Application
The study identifies three key spatial practices for commercial renewal: Symbolic Coding for cognitive anchors, Behavioral Reshaping for experiential reconstruction, and Meaning Production for social relations. These practices facilitate the transformation from functional commercial spaces to public cultural fields. For example, the Guiyang project uses 'Colorful Dopamine' IP based on karst imagery and urban trends to attract young people, reconstructing commercial space into an 'urban social living room' during events, increasing online exposure by over 5 million times.
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Estimated Annual Savings
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Implementation Roadmap
Our structured approach ensures a seamless integration of AI-driven spatial analysis into your urban planning and commercial renewal projects, maximizing impact and minimizing disruption.
Phase 1: Discovery & Strategy
Duration: 2-4 Weeks
In-depth analysis of existing commercial landscapes, stakeholder workshops, and CatBoost model training for predictive insights.
Phase 2: IP & Experiential Design
Duration: 6-8 Weeks
Develop unique cultural IPs, design flexible spatial layouts, and integrate scene-based experiences based on model-driven recommendations.
Phase 3: Implementation & Activation
Duration: 8-12 Weeks
Execute landscape renovations, deploy interactive elements, and launch community engagement programs to foster social relations and place identity.
Phase 4: Monitoring & Optimization
Duration: Ongoing
Continuously monitor spatial vitality metrics, collect user feedback, and refine strategies using CatBoost for sustained 'breaking circle' effects.
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