Advancements in Urban Planning and Time Series Analysis

The field of urban planning and time series analysis is witnessing significant developments, driven by the integration of machine learning and geographical information systems. Researchers are exploring novel frameworks and techniques to analyze complex nonlinear relationships between urban characteristics and health outcomes, as well as to forecast epidemic spread and player behavior in online games. Notable advancements include the development of explainable and interpretable models, such as graph neural networks and causal spatiotemporal graph neural networks, which are capable of capturing spatial heterogeneity and temporal trends. These models have shown promising results in predicting disease transmission, drug response, and player behavior, and are likely to have a significant impact on public health and urban planning decisions. Some noteworthy papers in this area include:

  • MedGNN, which improved predictions of medical prescriptions by over 25% compared to baseline methods.
  • Unifying Physics- and Data-Driven Modeling, which introduced a novel causal spatiotemporal graph neural network for interpretable epidemic forecasting.
  • Explainable and Interpretable Forecasts, which proposed a novel deep Actionable Forecasting Network for forecasting player behavior in online games.

Sources

Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress

Artificial Geographically Weighted Neural Network: A Novel Framework for Spatial Analysis with Geographically Weighted Layers

Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay

MedGNN: Capturing the Links Between Urban Characteristics and Medical Prescriptions

Concept Extraction for Time Series with ECLAD-ts

Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting

GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction

FORCE: Feature-Oriented Representation with Clustering and Explanation

AI-Driven Consensus: Modeling Multi-Agent Networks with Long-Range Interactions through path-Laplacian Matrices

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