Ecosystem Service Value Analysis via Random Forest and SEM
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Solution Overview
Problem
Current methods fail to accurately analyze the driving relationship between ecosystem service value (ESV) and urban agglomeration development, particularly in terms of spatial-temporal evolution characteristics and causal interactions, leading to an incomplete understanding of urban ecological environments and their well-being.
Innovation Solution
A method and system that collect ESV accounting and driving data, calculate ESV using an equivalent factor method, analyze spatial-temporal evolution characteristics with revised coefficients, and employ random forests and structural equation models to quantify driving influences and paths, incorporating human activity and natural condition data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Pearson correlation coefficient is used to determine correlation among variables, then the correlation can be identified, but the definite causal direction among variables cannot be indicated
Solution Approach 1:
The patent introduces structural equation modeling (SEM) as an intermediary method that bridges the gap between correlation analysis and causal inference. SEM allows the system to analyze both the correlation relationships and the directional causal effects among multiple variables simultaneously, thereby recovering the causal direction information that was lost when using only Pearson correlation coefficient.
Solution Approach 2:
The patent combines multiple analytical methods (Pearson correlation coefficient, random forest, and structural equation modeling) into a composite analysis framework. This composite approach leverages the strengths of each method: Pearson for identifying correlations, random forest for handling non-linear relationships and variable importance, and SEM for establishing causal directions, thereby achieving both correlation identification and causal direction determination.
2Measurement precision
If conventional regression analysis method is used, then the relationship between variables can be analyzed, but the interaction relationship between variables cannot be clarified and the importance of each variable cannot be measured accurately
Solution Approach 1:
The patent creates a composite analytical framework that integrates conventional regression analysis with random forest and structural equation modeling. This composite approach allows the system to measure the importance of each variable accurately while also clarifying the interaction relationships among variables, overcoming the limitations of using conventional regression analysis alone.
Solution Approach 2:
The patent introduces random forest as an intermediary analytical tool that complements conventional regression analysis. Random forest provides variable importance measures and handles non-linear interactions, thereby clarifying the interaction relationships that conventional regression analysis cannot capture, while maintaining the interpretability benefits of regression models.
3Measurement precision
If ecosystem service value is calculated using equivalent factor method, then the ESV can be quantized, but the spatial-temporal evolution characteristic of the study area cannot be analyzed
Solution Approach 1:
The patent applies dynamic analysis methods to examine the spatial-temporal evolution of ESV. By incorporating time-series analysis and spatial autocorrelation methods, the system can track how ESV changes across different time periods and spatial locations, transforming the static ESV calculation into a dynamic analysis that reveals evolution patterns and trends.
Solution Approach 2:
The patent adds spatial and temporal dimensions to the ESV analysis framework. By integrating geographic information systems (GIS) and spatial statistics, the system analyzes ESV not only in terms of magnitude but also in terms of spatial distribution patterns and temporal evolution, thereby recovering the spatial-temporal evolution information that was lost in traditional ESV calculations.
Data Source
AI summary
A method and a system to analyze the driving relationship between ecosystem service and urban agglomeration development are provided. The spatial-temporal evolution characteristics of ESV in the Yangtze River Delta urban agglomeration are analyzed based on the revised equivalent value coefficient and land use data, the driving characteristics and the driving influence evolution characteristics of 10 indicators of human activities and natural conditions on ESV are explored through RF and SEM, an interaction relationship among influencing factors of ESV and the direct and indirect driving effect of the influencing factors on ESV are quantitatively measured under a unified framework, and the ESV evolution mode and the driving mechanism of the urban agglomeration are explored. The method can deeply study the interaction relationship among the influencing factors of the ESV, as well as the driving characteristics and driving paths of the driving factors to the ESV.