AI-Powered Research Analysis
Sustainable Development of Public Cultural Service Supply in Beijing's Suburbs: A Study Based on the Entropy-Weighted TOPSIS-DGM Model
Authored by Wen Zhang and Minan Yang
This study leverages advanced analytical models to evaluate and forecast public cultural service supply in Beijing's suburbs, offering critical insights for urban-rural sustainable development and resource allocation.
Executive Impact
Unlock strategic insights derived from a novel integrated Entropy-Weighted TOPSIS-DGM model, designed to optimize public cultural service provision and drive sustainable urban-rural development in complex environments.
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Mean MAPE for DGM(1,1)
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Cultural Effects Dimension Weight
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Transportation Infrastructure Weight
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Suburban Population Analyzed
The DGM (1,1) model achieved a mean MAPE of 6.90%, outperforming traditional methods and proving effective for small-sample, non-linear systems. This robust methodology reveals critical spatial disparities in cultural service supply across Beijing's suburbs, with inner regions showing higher development levels and outer regions lagging. Understanding these dynamics allows for targeted policy interventions, ensuring equitable resource allocation and fostering integrated urban-rural growth.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Model Performance
Regional Analysis
Strategic Implications
Integrated Evaluation & Prediction Process
The study introduces a novel integrated methodology combining the Entropy-Weighted TOPSIS method and the Dynamic Grey Prediction Model (DGM (1,1)). This approach overcomes limitations of traditional methods like AHP (subjectivity) and regression analysis (small-sample issues), providing objective weight allocation, precise ranking, and accurate forecasting for complex urban-rural systems.
Enhanced Predictive Accuracy
Validation against 2023 data showed that the DGM (1,1) model achieved a mean MAPE of 6.90%, significantly outperforming ARIMA (9.91%) and GM(1,1) (8.46%). This demonstrates its superior capability for robust forecasting in small-sample, non-linear data sets, critical for reliable future trajectory projections in sustainable development planning.
Spatial & Temporal Trends in Beijing's Suburbs
Analysis of Beijing's ten suburban districts from 2012-2023 revealed a fluctuating upward trend in public cultural service supply, but with persistent significant spatial differentiation. Inner suburbs generally lead (e.g., Mentougou, Tongzhou showing upward trends), while outer suburbs (e.g., Pinggu, Miyun) tend to lag, indicating an unbalanced developmental pattern.
Informing Targeted Policy & Resource Allocation
The research provides an evidence-based framework for optimizing the allocation of urban-rural cultural resources. Findings highlight the importance of factors like cultural effects (34.41% weight) and transportation infrastructure (9.16% weight). Future policies should be tailored to regional realities, transforming distinctive resources and addressing the inner-outer suburb disparity to better fulfill residents' needs.
Integrated Evaluation & Prediction Process
Data Preprocessing & Normalization
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Entropy Weight Method (Objective Weights)
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TOPSIS Model (Ranking by Ideal Solution)
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Dynamic Grey Prediction Model (DGM)
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Comprehensive Assessment & Forecasting
| Model | Mean MAPE | Key Advantages / Benefits |
|---|---|---|
| ARIMA | 9.91% |
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| GM(1,1) | 8.46% |
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| DGM(1,1) | 6.90% |
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6.90%
Mean Absolute Percentage Error (MAPE) for DGM (1,1) Model (2023 Validation)
This exceptional accuracy signifies the DGM (1,1) model's robustness in forecasting complex, small-sample data, crucial for sustainable development planning and strategic resource allocation in dynamic urban-rural systems.
Case Study: Beijing's Suburbs - Sustainable Cultural Service Development
Context: This study analyzed the sustainable development of public cultural service supply across Beijing's ten suburban administrative districts from 2012 to 2023, utilizing a hybrid Entropy-Weighted TOPSIS-DGM model.
Key Findings:
- Overall Trend: The supply level in Beijing's suburbs demonstrated a fluctuating but generally upward trend over the study period, reflecting sustained investment and development efforts.
- Spatial Disparity: Significant regional variations were observed. Inner suburbs (such as Mentougou, Tongzhou, Changping, Fangshan, Shunyi, Daxing) generally exhibited leading or steadily improving performance. Outer suburbs (Huairou, Pinggu, Miyun, Yanqing) tended to lag, with areas like Pinggu showing short-term declines, highlighting an unbalanced developmental pattern.
- Influencing Factors: The "cultural effects" dimension, particularly the number of characteristic cultural villages, received the highest weight, underscoring its significant role. "Transportation infrastructure" and "population density" were also critical, emphasizing the need for spatial balance and efficient service allocation.
- Future Projections: Projections indicate that the existing unbalanced pattern between inner and outer suburbs is likely to persist in the short term, driven by cumulative advantages in inner suburbs and structural constraints in outer areas.
Enterprise Relevance: This case study demonstrates how an advanced analytical framework can provide granular, evidence-based insights for regional planning and resource optimization. It enables policymakers and urban planners to identify specific areas requiring intervention, develop tailored strategies to address disparities, and foster more equitable and sustainable public service provision in urbanizing regions globally. The application of such models supports Intelligent Decision-Making for balanced urban-rural integration.
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