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Challenges in identifying simple pattern-forming mechanisms in the development of settlements using demographic data.
Bartosz Prokop1, Lendert Gelens1, Peter F Pelz2
1Laboratory of Dynamics in Biological Systems, Department of Cellular and Molecular Medicine, KU Leuven, Leuven 3000, Belgium.
Physical Review. E
|July 19, 2023
Summary
Current data resolution and observation times are insufficient to model settlement growth in the Global South using the SINDy (Symbolic Identification of Nonlinear Dynamics) method. Improved data collection is needed to understand pattern-forming mechanisms.
Area of Science:
- Urban studies
- Computational modeling
- Geospatial analysis
Background:
- Rapid population growth and urbanization in the Global South necessitate effective models for settlement formation.
- Previous research suggests simple pattern-forming mechanisms drive settlement evolution.
Purpose of the Study:
- To apply a data-driven white-box approach (SINDy) to discover differential equation models of settlement formation from spatiotemporal demographic data.
- To determine the data requirements for successfully identifying pattern-forming mechanisms in settlement development.
Main Methods:
- Utilized the SINDy algorithm, a data-driven method for discovering governing equations.
- Analyzed spatiotemporal demographic data from three Global South regions.
- Employed synthetic data from the Allen-Cahn equation to establish data resolution and observation time requirements.
Main Results:
- Current demographic data resolution and observation duration are inadequate for uncovering settlement development's pattern-forming mechanisms using SINDy.
- Simulations with synthetic data revealed specific spatial and temporal resolution needs for successful model identification.
Conclusions:
- The study provides a theoretical framework for analyzing large-scale geographical and ecological systems.
- Highlights the need for enhanced data collection strategies and optimization techniques for modeling complex spatial dynamics.
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