Assessing Spatial Consistency using Spatio-Temporal Interactions with Generalized Additive Models

Crop yield is spatially consistent when the high-yield areas of a field (or low-yield areas) occur in the same places from year to year. Using precision ag data from previous years to design a management plan for the current year assumes there is spatial consistency, but there are few statistical methods to assess this consistency, especially for data from more than two years. We use generalized additive models with tensor-product splines to smooth spatially-explicit annual yield data and project each year’s data onto the same grid of locations. Each year’s yield data is then centered and standardized to adjust for annual differences in overall yield and within field variability. The interaction between space and time tests the overall lack of spatial consistency. This can be implemented by comparing the fit of annual spatial-trend models to the fit of a model with a single spatial trend common to all years. Consistency at individual locations can be quantified by the standard deviation of annual adjusted yields at each location; this can be mapped to identify consistent and inconsistent areas of the field. We illustrate the approach using five years of data from a field in central Iowa

Bio-Surveillance of COVID-19 Data using Statistical Process Control

The ability to predict virus outbreaks is important for assessing the spread of the virus and handling the impact of the spread on the population. The COVID-19 pandemic has provided data that can be studied at the county level that contributes to the knowledge and research surrounding the eradication of the virus; at the county level, the ability to track the spatial dependence of COVID-19 spread between counties across the United States can be done using the geospatial autocorrelation statistic, Moran\u27s I. Using Moran\u27s I we have been able to track the spatial dependence of the COVID-19 cases throughout the pandemic and visualize spikes in Coronavirus case rates to predict outbreaks. This study will present methods for tracking incident type data using Moran\u27s I and statistical process control techniques to predict outbreaks