Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model
Abstract
1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
3. Methodology
3.1. Overview
3.2. GAT-VCA-ABM
3.2.1. GAT
- (1)
- Graph structure construction
- (2)
- Graph Attention Operation
3.2.2. ABM
3.2.3. UESP Model Construction
- (1)
- CA
- (2)
- VCA
3.3. Accuracy Assessment
4. Results
4.1. Application of Model and Results
4.2. Future Scenario Simulation
5. Discussion
5.1. Analysis of Differences in Simulations of Different Scenarios
5.2. Policy Implications
5.3. Uncertainty and Limitations
- (1)
- Policy unpredictability in China:
- In practice, land use policies in China often undergo rapid and unexpected changes. The sudden implementation of environmental redlines, urban renewal projects, or demolition policies can significantly alter urban expansion trends. These abrupt policy shifts are difficult to predict and parameterize within the scenario-based modeling framework.
- (2)
- Simplified representation of human decision-making:
- 2.
- Although the ABM module incorporates agent-level decision rules, these behaviors are simplified and parameterized using aggregated statistical indicators such as GDP, POI density, and accessibility. Such input variables cannot fully capture the nonlinearity, heterogeneity, and contextual dependence of real human decision-making processes, especially under unexpected social, political, or emergency events (e.g., COVID-19 lockdowns or disaster-induced population migration).
- (3)
- Limitations of stochastic disturbance factors:
- 3.
- While stochastic factors are incorporated into the CA transition rules to simulate unknown disturbances in the real world, these random disturbances cannot precisely replicate complex, large-scale external shocks such as pandemics or natural disasters. Therefore, the model’s ability to fully represent sudden and highly dynamic environmental changes remains limited.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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References | DEM | Slope | Primary Roads | Highway | Social Service Facilities | Education | GDP | Population |
---|---|---|---|---|---|---|---|---|
Al-Kheder et al., 2008 [42] | 1 √ | √ | √ | √ | ||||
Guan et al., 2023 [30] | √ | √ | √ | √ | √ | |||
Guan et al., 2024 [48] | √ | √ | √ | √ | √ | |||
He et al., 2006 [43] | √ | √ | √ | |||||
Lau and Kam, 2005 [44] | √ | √ | √ | |||||
Liang et al., 2020 [21] | √ | √ | √ | √ | √ | √ | √ | |
Liu et al., 2020 [31] | √ | √ | √ | |||||
Shen et al., 2008 [45] | √ | |||||||
Shi et al., 2023 [46] | √ | √ | √ | √ | √ | √ | ||
Wu et al., 2022 [47] | √ | √ | √ | √ | √ | √ |
Data | Data Sources | ||
---|---|---|---|
Land use data | 2000 Land use data | Landsat 5 TM, Landsat 7 ETM+ | |
2010 Land use data | Landsat 5 TM, Landsat 7 ETM+ | ||
2020 Land use data | Landsat 8 OLI | ||
Natural environment factors | DEM | Geospatial data cloud | |
Slope | Geospatial data cloud | ||
Socioeconomic factors | School features | OSM | |
Mall features | OSM | ||
Bank features | OSM | ||
Hotel features | OSM | ||
Hospital features | OSM | ||
House features | OSM | ||
Restaurant features | OSM | ||
Main road features | OSM | ||
Highway features | OSM | ||
ABM | Resident agents | Population | NBSC |
GDP | NBSC | ||
Traffic agents | Main road features | OSM | |
Highway features | OSM |
Model | 2000–2010 | 2010–2020 | ||||
---|---|---|---|---|---|---|
OA | Kappa | FoM | OA | Kappa | FoM | |
GAT-VCA | 0.89 | 0.83 | 0.031 | 0.901 | 0.84 | 0.047 |
UESP | 0.92 | 0.84 | 0.036 | 0.925 | 0.878 | 0.048 |
Model | OA | PA | UA | FoM | Kappa |
---|---|---|---|---|---|
LR-VCA | 0.867 | 0.018 | 0.096 | 0.015 | 0.826 |
RF-VCA | 0.915 | 0.016 | 0.081 | 0.013 | 0.863 |
ANN-VCA | 0.896 | 0.056 | 0.111 | 0.041 | 0.835 |
UESP | 0.925 | 0.069 | 0.162 | 0.048 | 0.878 |
Type of Land Use | Land Use Area (km2) | ||||
---|---|---|---|---|---|
2020 | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | |
Cultivated land | 99,964 | 98,004 | 95,104 | 98,900 | 94,212 |
Forest and grassland | 81,180 | 81,136 | 84,278 | 81,344 | 81,256 |
Water area | 7180 | 6368 | 7476 | 6788 | 7476 |
Construction land | 27,932 | 30,748 | 29,400 | 29,224 | 33,312 |
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Gao, Y.; Liu, D.; Zheng, X.; Wang, X.; Ai, G. Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model. Remote Sens. 2025, 17, 2272. https://doi.org/10.3390/rs17132272
Gao Y, Liu D, Zheng X, Wang X, Ai G. Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model. Remote Sensing. 2025; 17(13):2272. https://doi.org/10.3390/rs17132272
Chicago/Turabian StyleGao, Yunqi, Dongya Liu, Xinqi Zheng, Xiaoli Wang, and Gang Ai. 2025. "Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model" Remote Sensing 17, no. 13: 2272. https://doi.org/10.3390/rs17132272
APA StyleGao, Y., Liu, D., Zheng, X., Wang, X., & Ai, G. (2025). Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model. Remote Sensing, 17(13), 2272. https://doi.org/10.3390/rs17132272