Bivariate Choropleth Mapping

Bivariate choropleth mapping offers a dynamic approach to visualizing the relationship between two variables across geographical regions. Unlike traditional choropleth maps, which depict only one variable, bivariate maps use two color ramps to simultaneously represent two variables, revealing spatial patterns and correlations in a visually intuitive manner. By overlaying data sets, bivariate maps enable users to identify regions with similar trends, disparities, or inverse relationships, empowering researchers, policymakers, and data enthusiasts to gain deeper insights into complex phenomena.

 These maps find applications across diverse fields, including public health, environmental science, urban planning, and social economics. From illustrating the impact of pollution on respiratory illness rates to highlighting disparities in access to transportation infrastructure and socioeconomic status, bivariate choropleth maps facilitate informed decision-making by providing a comprehensive view of spatial data relationships. By following best practices in map design, users can effectively communicate their findings and engage audiences in meaningful discussions, unlocking valuable insights and driving positive changes. 

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.