GIS6005: Lab 1 - Map Design

I established a clear labeling hierarchy in this map. I made cities the largest and most legible. Next, I labeled the rivers, and lastly, I made the states a muted, less readable, and contrastable Label. I used a curve placement for the rivers again and a centered placement for the cities. State labels were made grey to suppress them against the rivers and cities. I labeled the rivers using dynamic labeling with curved placement. I allowed the text to follow the natural geometry of each of the rivers. I italicized all of the fonts and made them with light halos and a legible but muted color. I made the labels readable and contrasted them with the background.

 

GIS6005: Lab 5 - Analytical data

 

I attempted to keep the colors very basic and continuous throughout the layout. Using a solid background and a green and grey color scheme. I used bold black text to make sure it stood out from the other information on the page. I used neat lines to separate the material and balance the map, making it cohesive. I also separated the maps and other visualization techniques and gave each its own space on the map. I selected two visualization techniques: a scatterplot and a bar chart. The scatterplot was used to examine the relationship between Poor Health (%) and Excessive Drinking (%). This technique is good for identifying correlations and outliers between the two variables. The bar chart was chosen to highlight overall averages for Poor Health and Excessive Drinking. I chose a horizontal bar layout to improve readability and allow easy comparison.

GIS6005: lab 4 - choropleth mapping

I used a choropleth map to represent population change because the data are normalized values that vary by county. This teqnique is good for showing patterns and localized trends. I used sequential color scheme going from light to dark so that increases in population are associated with darker tones, and declines are shown with very light shades. This creates a clear visual hierarchy and allows the viewers to quickly distinguish areas of growth from areas of loss. 

GIS6005: Lab 3 - Terrain Visualization

 

For this map i chose a design that focused on clarity, hierarchy, and visual balance. Base maps, along with thematic layers on top, ensure the data, not the background, remains the focus. A muted color palette with clear contrast helped to contrast the features while remaining readable and visually cohesive. My color choices were consistent across the map to reinforce the meaning. The typography shows readability, using simple fonts with clear distinctions between the titles, labels, and the supporting text. Overall, I think the design balances looks with functionality.

GIS6005 - Module 2: Coordinate Systems

 

For this map, I used NAD 1983 Oregon Albers (Meters). There were multiple for the State, but I decided on this one. Oregon spans multiple state plane zones, so the state plane is unsuitable for Oregon’s statewide mapping. The state also crosses two UTM zones, which would distort if just one zone were used. For this reason, I used a custom statewide conic projection. Oregon is a mid latitude state with a strong east-west orientation, so the conic projection works well. 

GIS5935: Module 4 - TINs and DEMs

 

In this week's lab, we created 3d visualizations of elevation models and compared TINs and DEMs. The TIN followed the original elevation points exactly, resulting in boxy contours that reflected this. In contrast, the DEM produced smoother contours that generalized the original elevation points. The DEM is better for visualization, but doesn't reflect the raw data. The differences between the two models were most noticeable in steeper areas and terrain. The TIN showed sharp bends in the contours while the DEM smoothed them out. In flatter areas, the two models were almost identical. This comparison shows the tradeoffs between the two sets of models.

GIS5935: Module 3 - Assesment

 

Map Layout

The goal of the analysis in this lab was to evaluate the relative completeness of two road network shapefiles in the same county. Street Centerlines and TIGER Roads. To see which data set provided a more complete representation of the county's road system