Michell's GIS Journey

  This week’s assignment focused on learning how to interpret aerial photographs using different visual cues such as tone, texture, shape, size, shadow, pattern, and association . Even though aerial images may seem like just pictures from above, this lab helped me understand how much information can be extracted when we look closely and intentionally. In Exercise 1 , I worked with a grayscale aerial photo to identify differences in tone and texture . I created a feature class for tone and labeled five areas ranging from very light to very dark. I also created another feature class for texture, identifying areas from very fine to very coarse. This process showed me how brightness and surface roughness can indicate different types of land cover. For example, paved areas appeared very light, while water bodies showed up very dark, and forests had more coarse textures. It made me realize that even without color, we can still interpret the landscape fairly well. In Exercise 2 , I pract...

 Scale Effects on Vector Data: In vector data, the scale at which features are represented can dramatically affect spatial analysis results. Larger scales (zoomed in) capture more detail, while smaller scales (zoomed out) generalize features. For example, analyzing counties versus ZIP codes can produce different statistical outcomes because aggregation over larger areas smooths local variability, a phenomenon known as the Modified Area Unit Problem (MAUP). Resolution Effects on Raster Data: Raster data are made of grid cells, and the cell size determines the spatial resolution. High-resolution rasters capture fine details but require more storage and processing power, whereas low-resolution rasters generalize information and may hide local variation. This affects analyses like slope, elevation, or population density surfaces, where resolution can change both visual and statistical results. Gerrymandering: Gerrymandering is the practice of drawing political district boundaries to fa...

Inverse Distance Weighting (IDW) Interpolation This week’s assignment focused on using surface interpolation methods to visualize water quality in Tampa Bay. The goal was to estimate BOD concentrations across areas where sampling points were sparse, using techniques like IDW, spline, and Thiessen. Interpolation methods are useful for creating continuous surfaces from discrete sampling points. IDW assumes that nearby points are more similar and produces smooth, gradual transitions. Spline generates very smooth surfaces that can sometimes exaggerate peaks and valleys. Thiessen divides the area into polygons where each location is assigned the value of the nearest point, creating distinct zones rather than smooth gradients. Comparing these methods shows that each represents the data differently, highlighting the importance of choosing an approach based on the data characteristics and analysis goals.