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The Hawaiian Islands have been employed as a model system to reconstruct agroecological extents of traditional Polynesian agricultural production systems. However, the reliability of previously modeled agricultural extents is unknown due to limitations in empirical evidence to assess accuracy. Utilizing a geospatial database of 8,561 archaeological sites compiled by the Hawaiʻi State Historic Preservation Department (SHPD), this research assessed the accuracy and reliability of three spatial models that estimate the extents of traditional Hawaiian agricultural systems. The results of the model sensitivity assessment indicate the three geospatial models capture the spatial patterns and relative extents of intensive agricultural systems with substantial infrastructure, while additional work is needed to assess reliability of modeled agricultural systems with more indefinite infrastructure.
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Resource Efficient Profiling of Spatial Variability in Performance of Regression Models
Scientists design models to understand phenomena, make predictions, and/or inform decision-making. This study targets models that encapsulate spatially evolving phenomena. Given a model, our objective is to identify the accuracy of the model across all geospatial extents. A scientist may expect these validations to occur at varying spatial resolutions (e.g., states, counties, towns, and census tracts). Assessing a model with all available ground-truth data is infeasible due to the data volumes involved. We propose a framework to assess the performance of models at scale over diverse spatial data collections. Our methodology ensures orchestration of validation workloads while reducing memory strain, alleviating contention, enabling concurrency, and ensuring high throughput. We introduce the notion of a validation budget that represents an upper-bound on the total number of observations that are used to assess the performance of models across spatial extents. The validation budget attempts to capture the distribution characteristics of observations and is informed by multiple sampling strategies. Our design allows us to decouple the validation from the underlying model-fitting libraries to interoperate with models constructed using different libraries and analytical engines; our advanced research prototype currently supports Scikit-learn, PyTorch, and TensorFlow.
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- Award ID(s):
- 1931363
- PAR ID:
- 10448824
- Date Published:
- Journal Name:
- 2022 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 437 to 444
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData55660.2022.10020602
