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Model Evaluation for Geospatial Problems

Research output: Contribution to conference › Paper › peer-review

Standard Standard

Model Evaluation for Geospatial Problems. / Wang, Jing; Hallman, Tyler; Hopkins, Laurel et al.
2023. Paper presented at 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, New Orleans.

Research output: Contribution to conference › Paper › peer-review

HarvardHarvard

Wang, J, Hallman, T, Hopkins, L, Kilbride, JB, Robinson, WD & Hutchinson, R 2023, 'Model Evaluation for Geospatial Problems', Paper presented at 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, New Orleans, 15/12/23.

APA

Wang, J., Hallman, T., Hopkins, L., Kilbride, J. B., Robinson, W. D., & Hutchinson, R. (2023). Model Evaluation for Geospatial Problems. Paper presented at 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, New Orleans.

CBE

Wang J, Hallman T, Hopkins L, Kilbride JB, Robinson WD, Hutchinson R. 2023. Model Evaluation for Geospatial Problems. Paper presented at 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, New Orleans.

MLA

Wang, Jing et al. Model Evaluation for Geospatial Problems. 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, 15 Dec 2023, New Orleans, Paper, 2023.

VancouverVancouver

Wang J, Hallman T, Hopkins L, Kilbride JB, Robinson WD, Hutchinson R. Model Evaluation for Geospatial Problems. 2023. Paper presented at 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, New Orleans.

Author

Wang, Jing ; Hallman, Tyler ; Hopkins, Laurel et al. / Model Evaluation for Geospatial Problems. Paper presented at 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment, New Orleans.

RIS

TY - CONF

T1 - Model Evaluation for Geospatial Problems

AU - Wang, Jing

AU - Hallman, Tyler

AU - Hopkins, Laurel

AU - Kilbride, John Burns

AU - Robinson, W Douglas

AU - Hutchinson, Rebecca

PY - 2023/12/30

Y1 - 2023/12/30

N2 - Geospatial problems often involve spatial autocorrelation and covariate shift, whichviolate the independent, identically distributed assumption underlying standardcross-validation. In this work, we establish a theoretical criterion for unbiased cross-validation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success.

AB - Geospatial problems often involve spatial autocorrelation and covariate shift, whichviolate the independent, identically distributed assumption underlying standardcross-validation. In this work, we establish a theoretical criterion for unbiased cross-validation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success.

M3 - Paper

T2 - 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment

Y2 - 15 December 2023

ER -