Scientists use models to further their understanding of phenomena and inform decision-making. A confluence of factors has contributed to an exponential increase in spatial data volumes. In this study, we describe our methodology to identify spatial variation in the performance of classification models. Our methodology allows tracking a host of performance measures across different thresholds for the larger, encapsulating spatial area under consideration. Our methodology ensures frugal utilization of resources via a novel validation budgeting scheme that preferentially allocates observations for validations. We complement these efforts with a browser-based, GPU-accelerated visualization scheme that also incorporates support for streaming to assimilate validation results as they become available.

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.

Geospatial data collections are now available in a multiplicity of domains. The accompanying data volumes, variety, and diversity of encoding formats within these collections have all continued to grow. These data offer opportunities to extract patterns, understand phenomena, and inform decision making by fitting models to the data. To ensure accuracy and effectiveness, these models need to be constructed at geospatial extents/scopes that are aligned with the nature of decision-making — administrative boundaries such as census tracts, towns, counties, states etc. This entails construction of a large number of models and orchestrating their accompanying resource requirements (CPU, RAM and I/O) within shared computing clusters. In this study, we describe our methodology to facilitate model construction at scale by substantively alleviating resource requirements while preserving accuracy. Our benchmarks demonstrate the suitability of our methodology.

Spatial data volumes have grown exponentially over the past several years. The number of domains that spatial data are extensively leveraged include atmospheric sciences, environmental monitoring, ecological modeling, epidemiology, sociology, commerce, and social media among others. These data are often used to understand phenomena and inform decision-making by fitting models to them. In this study, we present our methodology to fit models at scale over spatial data. Our methodology encompasses segmentation, spatial similarity based on the dataset(s) under consideration, and transfer learning schemes that are informed by the spatial similarity to train models faster while utilizing fewer resources. We consider several model fitting algorithms and execution within containerized environments as we profile the suitability of our methodology. Our benchmarks validate the suitability of our methodology to facilitate faster, resource-efficient training of models over spatial data.