Module 5 Analytical Data

Assessing the impact of air pollution on public health in the United States involves analyzing key variables such as particulate matter (PM) concentrations and self-reported health status. PM, comprising tiny solid particles or liquid droplets suspended in the air, serves as an indicator of air quality and has been linked to respiratory and cardiovascular diseases. Meanwhile, individuals' perceptions of their health status, categorized as poor or fair health, provide insights into overall well-being and highlight disparities within communities. A better way to model this data would be to hone in on health issues involving respiratory and cardiovascular diseases, as these are most strongly associated with air pollution exposure. By focusing on these specific health outcomes, the resulting mapping outcomes would likely be more accurate, providing valuable insights for policymakers and public health officials aiming to address the adverse effects of air pollution on human health.

Data Source: Rankings Data & Documentation | County Health Rankings & Roadmaps 

Module 4 - Color Concepts & Choropleth Mapping

 

For the legend design, I employed ColorBrewer to select a diverging scheme with six data classes. The use of a diverging color scheme emphasizes both low and high extremes in population change, enabling viewers to easily identify areas experiencing significant shifts. The color choices in the legend were carefully selected to provide a clear visual representation of positive and negative changes, enhancing the map's interpretability. The legend's simplicity ensures that users can quickly grasp the magnitude and direction of population changes in the selected state. This design choice aims to facilitate effective communication of complex spatial information while maintaining a visually appealing and user-friendly map presentation.

Module 3 - Terrain Visualization

In creating the land cover map with terrain visualization for Yellowstone Park, I employed a strategic approach to design. Initially, I thoroughly examined the attributes of the land cover layer to gain a comprehensive understanding of the dataset. Custom symbology was then crafted, manually selecting colors and consolidating similar land cover types into similar categories, enhancing clarity through grouped symbols and edited labels. A choice between traditional and multidirectional hillshade from the DEM was made, with the selected layer positioned beneath the land cover layer to provide supplementary topographic context. Transparency settings for the land cover layer were adjusted to allow the hillshade's influence, and iterative changes were made to symbology based on the combined impact of hillshade and transparency. The map layout design focused on visual hierarchy, ensuring a balanced and clear representation with essential map elements like title, legend (for land cover types only), scale bar, north arrow, and cartographer/source information. 

 

Module 2 - Coordinate Systems

 

Choosing the NAD 1983 Stateplane Maryland FIPS 1900 (Meters) coordinate system for the State of Maryland map is about precision and local alignment. NAD 1983 is widely used in North America, and by focusing on Maryland's Stateplane system (FIPS 1900), the map becomes tailored to the state's unique geography. This ensures accurate and precise location representation, meeting local standards.

Adhering to the Federal Information Processing Standards (FIPS) code 1900 adds consistency and regulatory compliance. By using the FIPS code for Maryland, the coordinate system aligns with local standards, promoting seamless integration of spatial data with other datasets. 

Module 1 - Communicating GIS

 

In designing the map, I prioritized visual contrast by selecting colors for different elements, ensuring essential features stand out. Legibility was achieved through clear, readable fonts and strategic label placement. The figure-ground organization was maintained by creating a clear distinction between foreground and background, guiding the viewer's focus. Hierarchical organization structured map elements based on importance, aiding comprehension. Lastly, balance was achieved through thoughtful distribution of visual elements, preventing any part of the map from appearing cluttered or neglected. 

In creating the map design to optimize legibility, visual contrast, and hierarchy, attention was devoted to the various text elements representing general information, water features, city names, park names, notable topographic features such as Russian Hill, and different area types. For general information, the Arial font type was chosen with a moderate size and strategic placement to provide essential context without overwhelming the map. Water feature labels utilized the Arial font, colored in blue with a white halo for visual contrast, and employed curved text to distinguish water bodies. The San Francisco city name was created using serif font Book Antiqua and was given a larger size and a light gray halo to enhance prominence and legibility. Park names, using the sans-serif Arial font, featured a white halo for clarity against diverse map features. Topographic features like Russian Hill adopted an italicized font with a white halo, striking a balance between visibility and integration. Each area type was characterized by a distinct font style, size, color, and effects, ensuring clear differentiation and contributing to a well-organized visual hierarchy. This comprehensive approach to text elements ensures an effective, legible, and visually appealing map design.

In addressing the challenge of effectively labeling multiple features in the map, a strategic approach was taken to balance numerous labels while maintaining legibility and communicative integrity. Several key label options and strategies were employed. Mexico City, as the capital, was assigned a unique symbol, ensuring it stands out prominently. Other cities share a common symbol for simplicity, while state capitals share a distinct symbology, clearly differentiating them. Distinct font types and styles were chosen for cities, rivers, and states to avoid confusion. Bold and clear fonts were selected for cities, ensuring they were easily distinguishable. Italicized fonts were used for rivers, providing a visual contrast, while states were labeled with regular fonts. Leveraging automatic labeling for cities and states helped streamline the process, avoiding manual placement complexities and allowing for efficient handling of numerous labels while maintaining readability. Prioritization was crucial to managing conflicts among labels. Mexico City, being a focal point, was labeled with the highest priority. State capitals emphasize population centers of importance. Rivers were labeled with a slightly lower priority but maintained visibility. Recognizing the challenge of labeling all features dynamically, a selective approach was taken. Weights and overlapping settings were adjusted using the Labelling toolbar to control label placement. City labels were allowed to overlap rivers but not other cities, ensuring a balanced visual hierarchy.

Final Project

     For my final project, I chose to focus on the shipwrecks within the Olympic Coast National Marine Sanctuary.  This particular area spans 2,408 square nautical miles off of the coast of Washington State. The weather conditions of the coastline are known to be fierce and contain extremely rugged terrain. 

"The combination of fierce weather, isolated and rocky shores, and heavy ship commerce established, early on, the Olympic Coast as a graveyard for ships. More than 180 wrecks have been historically documented in the vicinity of the Olympic Coast National Marine Sanctuary, an amount proportional to the commercial development in the region and the region's significance in the economic lives of the United States and Canada. However, due to the destructive forces of wave and current, very few ships remain intact, particularly near the shore" (NOAA.gov).

     For the purpose of this project, I will focus on the 19 remaining wrecks in the sanctuary and try to show any correlations that suggest that the terrain of the area has influenced shipwrecks as well as show any areas that should be further researched.

Map #1 shows the Olympic Coast National Marine Sanctuary Boundary. I chose this particular ocean basemap, as it highlights differences on the ocean floor. 

Map #2 shows both the sanctuary boundary and the 19 shipwrecks found within. 

 Map #3 is a digitized Electronic Navigational Chart (ENC), boundary, and shipwrecks. 

Map #4 is a basic map with a 300m buffer zone added around the shipwrecks. It was hard to show the buffers with such a large space.

 Map#5 is a digitized historical map from 1853.

Map #6 is a benthic map.  I had a tough time locating this data, but had some luck after contacting USGS. They were able to provide me with everything necessary to continue my project.   Most of the shipwrecks were found in the areas classified as nearshore and shelf.   These areas contain most of the rugged anomalies found along the coast and prove extremely hazardous for any ships passing in the area. 

Map #7 is a benthic reclassification.  I found it interesting that the shelf is at the highest range on the map.  

Map #8 shows a weighted overlay.  Weighted overlays can help provide useful information that depicts areas that are more likely to have shipwrecks. Red and orange areas should be further researched, green areas are unlikely to hold any wrecks. 

 Map#9 shows a 25m contour map that has been clipped to the park boundary. 

Map #10 displays a Kernel Density map.  The densest areas of shipwrecks are found near the shoreline. This area is known to have many dangerous obstacles that would prove to be too much for vessels traveling in harsh weather conditions that are known to plague the area.

The final project was definitely time-intensive, but I enjoyed using the skills I learned to perform data analyses. 

Biscayne Bay – Analyze Week 9

This week we continued our work on shipwrecks within Biscayne Bay.  The first map shows buffer of 300 meters around each of the 5 known wrecks.  We also created and clipped benthic bottom types that show various types of bottoms in the 300 meter buffers. 

The second map explores benthic bottom and bathymetric layers and shows them as newly reclassified layers. 5 groups were shown in each map that depict the likelihood of wrecks as well as depths. 

The last map was the creation of a combination of two reclassified layers. Additionally, we weighted one layer showing 30% weight in bathymetric and 70% in benthic bottom type.   The benthic bottom type shows areas that are more likely to have wrecks. Areas highlighted in red should be further explored.