Journal of Data Science

The U.S. Rehabilitation Services Administration (RSA) has partnered with state vocational rehabilitation (VR) agencies since 1973 to improve employment outcomes for individuals with disabilities. A critical resource in this effort is the RSA-911 dataset, a quarterly collection of standardized participant data. However, its complex structure, including high rates of missing or ambiguous values, poses significant challenges for effective analysis. We address these challenges by developing an R package designed to streamline the cleaning and analysis of RSA-911 data, as well as the newly introduced Transition Readiness Toolkit (TRT) scores data (R Core Team, 2021). The TRT assesses participants’ improvement across services and offers a critical measure of VR program effectiveness. Using this R package, our work offers the first analysis of the relationship between TRT pre-post scores and RSA-911 demographic data, providing insights into program outcomes. Additionally, we deliver a user-friendly online dashboard, built with the shiny framework, to allow VR counselors and researchers to independently analyze RSA-911 and TRT data (Chang et al., 2024). This dashboard features intuitive visualizations and workflows, making it easier to generate reproducible analyses without requiring extensive technical expertise. By automating data preparation and providing accessible analysis tools, this project contributes to the field of vocational rehabilitation by facilitating more efficient research and empowering VR professionals with data-driven insights. The tools presented offer a framework for future studies, enhancing the consistency, flexibility, and reproducibility of VR data analysis.

Land use land cover (LULC) change in the agriculture, is a critical area of concern as it directly impacts food security, environmental health, and economic stability. One of the leading LULC data products is the U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL). Produced annually by the USDA National Agricultural Statistics Service (NASS) using satellite imagery, the CDL provides crop-specific data with an estimated classification accuracy of 85% to 95% for major crop types across the U.S. However, several limitations inherent to the CDL, such as crop underestimation bias, pixel misclassification, and difficulty distinguishing certain vegetation types, have raised questions about the accuracy of LULC change estimates derived from this dataset. In this paper, we introduce the R package cdlsim, designed to quantify the sensitivity of CDL-derived metrics through simulations of CDL data at the patch level using NASS published accuracy statistics. We present a case study utilizing landscape metrics calculated with the popular landscapemetrics R package to demonstrate the utility of cdlsim in quantifying the sensitivity of metrics to random perturbations in the data. The case study examines a mixed agricultural and grassland landscape in South Dakota, illustrating how our package enables researchers to achieve a more nuanced representation of land-use change.

The programming overhead required to implement machine learning workflows creates a barrier for many discipline-specific researchers with limited programming experience. The stressor package provides an R interface to Python’s PyCaret package, which automatically tunes and trains 14-18 machine learning (ML) models for use in accuracy comparisons. In addition to providing an R interface to PyCaret, stressor also contains functions that facilitate synthetic data generation and variants of cross-validation that allow for easy benchmarking of the ability of machine-learning models to extrapolate or compete with simpler models on simpler data forms. We show the utility of stressor on two agricultural datasets, one using classification models to predict crop suitability and another using regression models to predict crop yields. Full ML benchmarking workflows can be completed in only a few lines of code with relatively small computational cost. The results, and more importantly the workflow, provide a template for how applied researchers can quickly generate accuracy comparisons of many machine learning models with very little programming.

The potential weight of accumulated snow on the roof of a structure has long been an important consideration in structure design. However, the historical approach of modeling the weight of snow on structures is incompatible for structures with surfaces and geometry where snow is expected to slide off of the structure, such as standalone solar panels. This paper proposes a “storm-level” adaptation of previous structure-related snow studies that is designed to estimate short-term, rather than season-long, accumulations of the snow water equivalent or snow load. One key development associated with this paper includes a climate-driven random forests model to impute missing snow water equivalent values at stations that measure only snow depth in order to produce continuous snow load records. Additionally, the paper compares six different approaches of extreme value estimation on short-term snow accumulations. The results of this study indicate that, when considering the 50-year mean recurrence interval (MRI) for short-term snow accumulations across different weather station types, the traditional block maxima approach, the mean-adjusted quantile method with a gamma distribution approach, and the peak over threshold Bayesian approach tend to most often provide MRI estimates near the median of all six approaches considered in this study. Further, this paper also shows, via bootstrap simulation, that the peak over threshold extreme value estimation using automatic threshold selection approaches tend to have higher variance compared to the other approaches considered. The results suggest that there is no one-size-fits-all option for extreme value estimation of short-term snow accumulations, but highlights the potential value from integrating multiple extreme value estimation approaches.