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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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A simulated 'sandbox' for exploring the modifiable areal unit problem in aggregation and disaggregation.

Jeremiah J Nieves¹, Andrea E Gaughan², Forrest R Stevens²

¹University of Glasgow, School of Geographical & Earth Sciences, Glasgow, UK. jeremiah.j.nieves@gmail.com.

Scientific Data

|February 24, 2024

Summary

This study introduces simulated areal unit data for Guadalajara, Mexico, to analyze the Modifiable Areal Unit Problem (MAUP). The data helps understand how spatial resolution and zoning affect geographic analysis and modeling.

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Area of Science:

Geographic Information Science
Spatial Analysis
Geostatistics

Background:

The Modifiable Areal Unit Problem (MAUP) significantly impacts spatial analysis by introducing uncertainty due to arbitrary zoning and scale.
Understanding MAUP is crucial for accurate geographic modeling, prediction, and data disaggregation/aggregation.

Purpose of the Study:

To create a comprehensive spatial testbed of simulated areal unit data for investigating the MAUP.
To provide a reproducible dataset for assessing the effects of spatial resolution and zonal configuration on geographic applications.

Main Methods:

Simulation of 10 spatial resolution levels (5,515–52,388 units) with 100 zonal configurations each, totaling 1,000 unique datasets.
Utilizing high-resolution census data from Guadalajara, Mexico, as the base for simulations.
Development of a code notebook for data alteration and reproduction.

Main Results:

The generated dataset offers a robust platform for exploring MAUP effects across various spatial scales and zoning schemes.
Facilitates the examination of impacts on model training, prediction, disaggregation, and aggregation.
Enables the production of spatially explicit, non-parametric confidence intervals using bootstrapping.

Conclusions:

The simulated areal unit data testbed provides a valuable resource for researchers studying the MAUP.
This resource aids in developing more robust spatial analytical methods less susceptible to MAUP effects.
The data and associated code promote reproducible research in spatial statistics and geographic information systems.