Discovering Governing Spatial Interaction Mechanisms in Dynamic Urban Systems

TL;DR

This paper introduces U-Discovery, a framework that uses neural networks and language models to identify governing spatial interaction laws in urban systems from spatiotemporal data, combining hypothesis generation and equation fitting.

Contribution

The paper presents a novel unified differential equation formalism and an integrated framework leveraging large language models for discovering urban governing laws from data.

Findings

Successfully identified governing equations in synthetic experiments.

Demonstrated potential in real-world urban data analysis.

Framework effectively ranks candidate laws based on fit and complexity.

Abstract

Governing equations are fundamental for describing and predicting dynamic urban geographic systems. Unlike physical systems guided by first principles, urban spatiotemporal phenomena emerge from coupled geographic processes that lack deterministic theoretical foundations, making the discovery of governing equations elusive and largely heuristic. Spatiotemporal dynamics in urban systems are often observed as sequential snapshot data of spatial distribution, while the cause of such dynamics is often implied or unknown. In this study, we propose a unified differential equation formalism that decomposes urban dynamics into a time-invariant spatial interaction process and a self-dynamic component. Building on this formalism, we introduce the Urban Discovery Framework (U-Discovery), which integrates hypothesis generation, neural fitting, and governing equation identification for the discovery of governing spatial interaction laws. U-Discovery leverages Large Language Models and literature-based reasoning to propose differential equation candidates. Each candidate was calibrated from the observed spatiotemporal dynamics using a neural fitting method. The candidates are evaluated and ranked based on the fitting error and mathematical complexity. Our synthetic experiments prove that U-Discovery can find the sole governing equation from the simulated dynamics. Empirical experiments in Hennepin County, Minnesota, further demonstrate the potential of U-Discovery in identifying optimal governing laws from real-world human activity dynamics.