FAIRification of Geospatial Data

FAIRification (Findability, Accessibility, Interoperability, and Reusability) of geospatial and temporal data from the United States Geological Survey (USGS) is crucial for the continuance of the National Science Foundation (NSF) Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER) whose aim is to aid in the development of eco-friendly fertilizers. The USGS data contains key information about water quality and contaminants, which are important metrics for our geospatial project since our work aims to track the contaminants\u27 flow and work towards their reduction and subsequent elimination. FAIR principles refer to - Findability, Accessibility, Interoperability, and Reusability. This is important for the safeguarding and preservation of the data collected. Often, the data that has been extracted and worked on will be needed by someone else in the same project for a different purpose. The person working on the data will have their philosophies behind naming and storing the data which could become a problem for anyone else trying to use the data down the line if they do not have a guidebook or a manual to the initial person’s pointers. This is why FAIRification of data is crucial in academia and industry. The FAIR principles have been designed to create a standardized and globally recognized nomenclature for the storage of data. Findability refers to assigning the data an identifier that is global and unique and the data is indexed in a searchable database. Accessibility means that the data can be retrieved by its identifier using a universally implementable protocol with authentication procedures wherever required. It also refers to the fact that the metadata will be accessible even when the data is not available. Interoperability means that the metadata uses a formal, accessible, and applicable language for knowledge representation and uses vocabularies that follow FAIR principles. Reusability refers to the fact that the metadata is extensively described with relevant attributes and released with a clear and accessible data usage license

Geospatial Analysis of Hydrologic Nitrogen in Ohio Using Terrain Ruggedness Index (TRI) and Terrain Position Index (TPI)

Hydrologic nitrogen in ecosystems can significantly impact water quality. Excessive nitrogen, often originating from agricultural runoff, wastewater discharge, and industrial activities, can lead to eutrophication – the over-enrichment of water bodies with nutrients, resulting in excessive algal growth and depleted oxygen levels. This study aims to use geospatial analytics to identify areas in Ohio that are more susceptible to high nitrogen levels due to their topographic characteristics. Terrain Ruggedness Index (TRI) and Terrain Position Index (TPI) are two key metrics derived from Digital Elevation Models (DEMs) that can help characterize the landscape. TRI measures the variability in elevation of adjacent parts of a DEM, while TPI compares a data point in a DEM to its neighbors. By analyzing terrain ruggedness and position, we can statistically identify locations that are more likely to have higher nitrogen levels. Nitrogen tends to flow towards areas with lower elevation relative to their neighbors. By using geospatial techniques to identify points on the DEM with lower TPI and TRI values, we can locate areas that could have higher nitrogen runoff compared to others. If left unchecked, hydrologic nitrogen can cause disastrous consequences for ecosystems, as evidenced by the algal blooms in Lake Erie caused by nitrogen runoff from fertilizers. In this study, we propose to use geospatial analytics to estimate areas in Ohio that are more likely to have higher nitrogen levels based on their topographic characteristics. We will visualize our findings using a Shiny App to effectively communicate the spatial distribution of potential high-nitrogen areas