# A user-friendly method for estimating discrete choice land-use model in a panel data setting

## TL;DR

This paper presents a simple method to improve land-use models using panel data, enhancing predictions and handling common statistical issues.

## Contribution

A user-friendly method for integrating panel data into nonlinear land-use models, improving accuracy and scalability.

## Key findings

- The method captures dynamic land-use patterns and improves prediction accuracy.
- It effectively mitigates autocorrelated error terms in panel data analysis.
- The approach is easy to implement and suitable for large datasets.

## Abstract

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.

The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Image, graphical abstract