Investigating spatial and non-spatial cross-validation techniques
Accurately evaluating predictive models, especially when built for spatial data, remains a challenge due to the limitations of traditional cross-validation techniques. While spatial cross-validation techniques have been developed to address those challenges, there is still a lack of clear guidance o...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/181133 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Accurately evaluating predictive models, especially when built for spatial data, remains a challenge due to the limitations of traditional cross-validation techniques. While spatial cross-validation techniques have been developed to address those challenges, there is still a lack of clear guidance on when a spatial or non-spatial technique would yield the most precise and dependable assessment of a model's performance.
This study investigates the effectiveness of the cross-validation method, spatial or non-spatial, in yielding accurate estimates by comparing three spatial techniques (Spatial K-Fold, Blocked, Buffered) and three non-spatial techniques (Random K-Fold, Bootstrap, Importance-Weighted). Using simulated landscapes, model performance is assessed across a range of metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R-Squared), and Bias. The findings emphasize the importance of considering spatial autocorrelation and covariate shifts, offering practical guidance on the selection of cross-validation techniques in spatial modelling contexts. |
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