General Geospatial Inference with a Population Dynamics Foundation Model

2024-11-11

arXiv.org

AI Summary

To address the lack of general-purpose geospatial foundation models for population dynamics modeling, this paper proposes the Population Dynamics Foundation Model (PDFM)—the first multi-source geospatial foundation model specifically designed for population dynamics. PDFM integrates heterogeneous data including map-based activity intensity, search trends, meteorological variables, and air quality indices, and employs graph neural networks to capture complex inter-regional dependencies, generating transferable geographic embeddings. It supports cross-task and cross-granularity generalization—including interpolation, extrapolation, and super-resolution—and innovatively couples with TimesFM to enable weakly supervised unemployment and poverty forecasting, outperforming fully supervised time-series models. PDFM achieves state-of-the-art (SOTA) performance on all 27 interpolation tasks and on 25 out of 27 extrapolation and super-resolution tasks. All geographic embeddings and source code are publicly released.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even, related tasks. To address this, we introduce a Population Dynamics Foundation Model (PDFM) that aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on all 27 geospatial interpolation tasks, and on 25 out of the 27 extrapolation and super-resolution tasks. We combined the PDFM with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers.

Problem

• Population Dynamics
• Geospatial Factors
• Resource Allocation

Innovation

• Population Dynamic Foundation Model (PDFM)
• Graph Neural Network
• Transparency and Reproducibility