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Module 6: Proportional Symbol and Bivariate Choropleth Mapping

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Proportional Symbol Mapping with Positive and Negative Values Proportional symbol mapping effectively displays positive and negative values by combining circle size for magnitude with color for direction (green for gains, red for losses). To implement this, negative values must be exported as a separate feature class, with a new field created using absolute values. The minimum and maximum size thresholds for both positive and negative symbols must be set to the same values to ensure an accurate visual comparison of magnitudes regardless of whether they represent gains or losses. Bivariate Choropleth Mapping The key steps for preparing data for bivariate choropleth mapping include: 1. Data Preparation: Ensure your variables are normalized (in this case, both obesity and physical inactivity are already in percentages) Create new fields in your attribute table to store the classifications (e.g., CLASS_OBESE, CLASS_ACTIVITY, and CLASS_FINAL) Each variable needs to be classified separately ...
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Module 5: Analytical Data

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 Creating an Infographic Visual Hierarchy: I positioned the larger choropleth maps prominently at the left to establish geographic patterns, using contrasting but complementary color schemes (orange for obesity, purple for smoking) to differentiate the health issues while maintaining visual cohesion. The state boundaries and clear legends make these maps immediately interpretable. Data Flow: I arranged the visualizations to tell a progressive story. The scatterplot shows the direct correlation between smoking and obesity, where communities with higher smoke rates also tend to have higher obesity rates. However, the bar charts and trend data provide deeper insights into each health issue, primarily the decrease in smoking rates but the increase in obesity rates. The central title "Weighing the Risks" acts as an anchor point that unifies the various elements. Space Utilization: I balanced the heavier visual weight of the maps on the left with multiple smaller visualizations on ...
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Module 4: Color Concepts & Choropleth Mapping

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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...
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Module 3: Terrain Visualization

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 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.
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Module 2: Coordinate Systems

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 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
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Module 1: Map Design & Typography

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 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...
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Module 5: Unsupervised & Supervised Classification

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  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 ...
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