Kelsey Yoder-Ostroski's Portfolio of Geographic Information Science

Spatial Interpolation Comparing Exact Interpolators Interpolation is a fundamental concept in spatial analysis, where we aim to predict and construct new data within a range of known data points. In an ideal world, we could do field studies on every square centimeter of the Earth and collect unlimited data. Since this not (yet) possible, we can strategically plan where to collect samples and use these points to mathematically predict ground truth. But before that, here's a joke. Did you know there are two kinds of people in the world? (1) Those who can extrapolate from incomplete data What are Exact Interpolators? For this analysis, we compared several methods of exact interpolation techniques. Exact interpolators are methods where functions pass through known data points. We compared the outcomes of three differing methods: Thiessen polygons, Inverse Distance Weighting (IDW), and Spline (regularized and tension). While these methods guarantee accuracy at known points, choosing the

Surfaces - TINs and DEMs Triangulated Irregular Networks (TINs) and Digital Elevation Models (DEMs) provide models critical to understanding and analyzing our planet. Through ArcGIS Pro, the GIS platform developed by Esri, we can create both these model types. Understanding TINs and DEMs What is a TIN? A Triangulated Irregular Network (TIN)  is a vector-based surface representation, represented through the connection of elevation points. These irregular points are connected with non-overlapping triangles. The face of each triangle is interpolated for values such as slope.  TINs are effective models for representing rugged terrain while also being data light. Because values are derived from a finite number of points, the facet values are assumed. Subtle changes in the landscape are lost in exchange for efficiency. Close up of TIN model What is a DEM? A Digital Elevation Model (DEM)  is a raster-based representation of the earth's surface, where each raster cell (also called pixel) r

  Comparing Completeness In continuing our investigations of data quality, this week we look at data "completeness". How can we ensure that the information we are given is not just accurate but actually covers the areas we need? Going out to the field, making measurements, and taking the records ourselves would be time-consuming and exorbitantly expensive. Instead, we can compare our dataset with a higher-quality dataset.  For this project, we were tasked with comparing the completeness of Jackson County, Oregon's road network to the federally recognized network. The US Census Bureau's TIGER (Topologically Integrated Geographic Encoding and Referencing) linefiles are a reliable dataset, which we will use as the control variable in this analysis. The method for this analysis is in reference to Haklay's 2010 study, " How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance survey datasets".           County road n