Michaela's GIS Journey

Creating Meaningful Color Ramps Linear Progression  Adjusted progression ColorBrew Comparing the three color ramps reveals distinct approaches to color progression in data visualization. The linear progression maintains mathematically consistent steps (~53.4 units each). However, it does not account for human perception, while the adjusted progression attempts to improve this by decreasing step sizes from ~73.8 to ~36.8 units as values get lighter, acknowledging increased human sensitivity to changes in lighter values. ColorBrewer's progression demonstrates the most sophisticated approach, with carefully calibrated non-linear steps that start large (~85.7 units), peak in the second step (~95.7 units), and then gradually decrease (~41 units), creating a perceptually balanced sequence that best accounts for both human vision characteristics and practical visualization needs. Mapping Change Using Choropleth Mapping Based on the histogram's distribution, Natural Breaks with 6 class...

 Land Cover Map With Terrain Visualization This Yellowstone National Park landcover map employs several effective design strategies to communicate spatial information. The color palette uses distinct tones to represent vegetation types while maintaining good visual contrast and a distinctive blue for water bodies that do not overwhelm the visualization. The typography follows a clear hierarchy with the use of bold text for the title and clean, legible fonts. In addition, the essential map elements like the north arrow, scale bar, and legend are well-placed and uncluttered. Overall, the layout balances the main map and supporting elements, utalizing majority of white space. All of these elements work together to create an accessible and informative visualization.

 Module 2: Coordinate Systems I chose Tennessee as my area of interest. For Tennessee, I selected the Tennessee State Plane (NAD83) coordinate system because it provides optimal accuracy across the entire state through a single zone configuration. Alternative options like UTM were rejected since Tennessee spans zones 16N and 17N, which would introduce edge distortion where the zones meet. A custom state system would be unnecessary given the state's complete coverage by a single State Plane zone that already minimizes distortion across Tennessee's geography

 Map Design & Typography Deliverable 5 Here is how I addressed each of the 5 map design principles: Visual Contrast: Used distinct, high-contrast colors (e.g., dark green golf course, blue waterways) to separate features of interest from supplemental information that was lighter but still darker than the background. Legibility: Clear symbols and appropriate text sizes were chosen to ensure readability across all map elements. Figure-Ground Organization: A lighter background was used to make features pop, creating a strong figure-ground relationship with the important features in darker colors. Hierarchical Organization: Important elements (e.g., recreation centers) were emphasized with larger, darker symbols. Balance: Proper alignment and spacing of map elements (e.g., legend, title) ensured the layout was visually balanced. Deliverable 10 The map's text elements demonstrate a thoughtful implementation of cartographic design principles to achieve legibility, visual contrast, an...

  Land Use Classification: A Study of Germantown, Maryland Introduction Understanding land use is vital for urban planning and environmental management. Using supervised classification techniques in ERDAS Imagine, we analyzed satellite imagery of Germantown, Maryland, to map various land use types and their distribution across the region. The results provide a snapshot of the area's development and natural resources, supporting sustainable growth initiatives like Maryland’s "Smart, Green, and Growing" plan. Methodology The classification process began with collecting spectral signatures for eight land use categories: Urban/Residential Roads Grass Deciduous Forest Mixed Forest Fallow Fields Agriculture Water To ensure accuracy, these signatures were refined using tools like the Region Growing Seed Tool. Using a combination of bands (Red: 6, Green: 5, Blue: 4), we classified the entire image with the Maximum Likelihood algorithm, producing a thematic raster map. A distance ...

Spatial Enhancement and Multispectral Analysis This week's lab assignment included several exercises to further familiarize ourselves with ERDAS Imagine and to switch between programs since both ERDAS and ArcGIS Pro are best used in conjunction. Our primary focus in the lab centered around the Olympic Mountains in Washington State. The Process: A Detailed Look at Exercise 7 The culmination of our project was Exercise 7, where we synthesized the skills learned throughout our remote sensing course. We followed a meticulous four-step process to identify and map distinct environmental features: Histogram Analysis: We began by examining the histograms for each satellite data layer, looking for spikes that denote predominant pixel values. These spikes helped us pinpoint areas of interest by showing where features occur frequently and have consistent reflectance properties. Grayscale and Multispectral Viewing: Next, we analyzed the image in grayscale to identify contrast differences mor...