Michaela's GIS Journey

 LULC Classification and Accuracy Assessment for Pascagoula, MS Map Summary: This project involved creating a Land Use and Land Cover (LULC) map of Pascagoula, MS, using aerial imagery to identify and classify various land features based on size, shape, color, and other visual elements. Using the USGS Standard Classification System at Level II, I achieved an overall accuracy of 83.33%. Commercial areas had the highest accuracy, easily recognized by their distinct structures and parking lots, while Residential areas presented challenges, primarily due to generalization. This map highlights the nuances of classifying urban and natural landscapes within a diverse region. 

  Understanding the Scale Effects on Vector Data, Basic Resolution Effects on Raster Data, and Gerrymandering Scale Effects on Vector Data : Scale significantly influences how vector data is portrayed. Smaller-scale maps (such as 1:100,000) offer less detail, resulting in the generalization or exclusion of more minor features. Conversely, larger-scale maps (like 1:1,200) provide greater precision, capturing intricate details such as complex boundaries. As the scale reduces, polygons become simplified, and more complex boundaries—like those of irregular coastlines—are smoothed out. This has a direct effect on the accuracy of calculated areas and perimeters. Regarding hydrographic features, moving from a 1:1,200 scale to a 1:100,000 scale demonstrated a notable reduction in detail. Basic Resolution Effects on Raster Data : Raster data divides the world into a grid where each cell holds a single value. The resolution of raster data refers to the dimensions of these cells. A higher re...

 Interpolating Water Quality Data for Tampa Bay In this exercise, we applied various interpolation techniques to explore water quality conditions in Tampa Bay, Florida, using a dataset of samples collected over a short period. The primary focus was on Biochemical Oxygen Demand (BOD) concentrations, measured in milligrams per liter, a key indicator of water quality. By using methods like Thiessen , IDW , and Spline interpolation, I was able to generate surfaces that estimate BOD levels across Tampa Bay, providing insight into the spatial distribution of pollution. Each method offered a different approach—some emphasizing sharp transitions between points and others providing smoother, more continuous surfaces that better represent gradual changes in water quality conditions. Performing Interpolation Analysis  Thiessen interpolation   assigns each location the value of the nearest point, resulting in abrupt boundaries that  may not be suitable for continuous phenomena ...