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Codes:Understanding of the Predictability and Uncertainty in Population Distributions Empowered by Visual Analytics

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Figshare2024-11-04 更新2026-04-08 收录
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https://figshare.com/articles/dataset/Codes_Undertanding_of_the_Predictability_and_Uncertainty_in_Population_Distributions_Empowered_by_Visual_Analytics/25908820/3
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This is the codes and the example data for the manuscript submitted to IJGIS entitled:<b><i>Understanding of the Predictability and Uncertainty in Population Distributions Empowered by Visual Analytics</i></b><sup>Project Overview</sup><sup>This project aims to perform data preprocessing, population estimation, SHAP explanation of population estimation, uncertainty estimation, SHAP explanation of uncertainty, and result analysis. The source code is organized into multiple modules, and sample data files are provided.</sup><sup>Modules Description</sup><sup>Data Preprocessing</sup><sup>The module contains functions and classes to clean, transform, and prepare the data for further analysis and modeling.</sup><sup>Population Estimation</sup><sup>The module provides methods for estimating population size based on the preprocessed data. This includes various statistical and machine learning techniques.</sup><sup>SHAP Explanation of Population Estimation</sup><sup>The module uses SHAP (SHapley Additive exPlanations) to interpret and explain the results of the population estimation. It helps in understanding the contribution of each feature to the prediction.</sup><sup>Uncertainty Estimation</sup><sup>The module focuses on estimating the uncertainty in the population estimates. This is crucial for understanding the confidence level of the predictions.</sup><sup>SHAP Explanation of Uncertainty</sup><sup>The module applies SHAP to explain the uncertainty estimates, providing insights into the factors contributing to the uncertainty.</sup><sup>Result Analysis</sup><sup>The module is responsible for analyzing the results of the population estimation and uncertainty estimation. It includes various statistical analyses and visualization techniques to interpret the findings.</sup><br>
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Luo
创建时间:
2024-11-04
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