GIS 6005 Final Project - Data Analysis on UFO Hotspots

View the presentation for this project by clicking here.

GIS 6005 Module 6 - Proportional Symbols and Bivariate Mapping

 The sixth, and final, module of Communicating GIS explored the proportional symbols and the bivariate mapping methodologies employed  throughout the cartographic world; both parts of this assignment also provided opportunities to customize legends in a manner that gives the cartographer complete control over each element within the legend. The first two parts of this assignment used the proportional symbol method to illustrate the magnitude of a provided variable. According to Kimberling, a proportional symbol is 'used to represent an exact data value by scaling the symbol's visual variable to be directly proportional to the value it represents' [Kimberling, 2012, p. 617]. Essentially, the size of the smallest variable is set and the remaining symbols are scaled proportionally. As shown below, the first map was a map displaying cities throughout the country of India, and the magnitude of their population is conveyed by the diameter of that cities point symbol. Due to inadequacies of the human eyes and mind, people tend to visually underestimate the size of the circles as they get bigger, so an appearance compensation algorithm [Flannery] has been applied to these point symbols; more can be read on proportional symbology and the Flannery compensation technique by clicking here.

After the symbology was set, a formatted map was created, and the legend was added. ArcGIS does not currently have a nested legend option [see map above], so the legend had to be converted to graphics to successfully create this type of legend. Once the legend was converted, all elements within the legend are completely editable as graphical elements, giving the cartographer an endless amount of control over the legend's design.

 

The next portion of the lab was also based on proportional symbology, providing more opportunity to explore the capabilities and potential problems of this methodology. The map below is a map of the employment changes from 2007 through 2015 throughout the United States. The difference of data values within this dataset created problematic overlaps throughout the entire map. Once this issue was resolved, the customized legend was created by converting the legend to graphics and manipulating each element individually.

The third portion of the lab explored the bivariate mapping technique; a bivariate map 'displays two variables on a single map by combining two different sets of symbols or colors' [Kimberling, 2012, pg. 456]. The map below is a bivariate map showing the relationship between two individual variables, obesity and inactivity.

 

By examining the map, it is apparent that most U.S. counties fall within the low-low, medium-medium, and high-high classes, thus illustrating a directly proportional relationship with each other. If no correlation between the two classes existed, many of the counties would fall within the low-high or high-low classes. The main challenge of this exercise was to determine the color ramp by choosing colors that were easy to distinguish, were visually complementary, and were also progressively increasing as the values climbed. This took a lot of time and patience, but the final deliverable made the dedication worthwhile. Lastly, a customized legend was created, showing how the qualitative values of a quantitative dataset are represented on the map.

This exercise was a great opportunity to explore some customization options that will be necessary throughout the execution of the final project.

Informational sources:

Environmental Science Resource Institute (2024). Proportional Symbols. 

https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/proportional-symbology.htm#:~:text=Appearance%20compensation%20uses%20an%20algorithm,and%20no%20Maximum%20size%20specified.

Kimberling, A.J. (2012). Map Use: Reading, Analysis, Interpretation (7th edition). ESRI Press Academic.

GIS 6005 Module 5 - Analytical Data

Module 5, Analytical Data was an excellent assignment; it was an opportunity to explore alternative visual elements that worked in conjunction with maps to tell a story; the goal was to create an infographic map, compiled in a manner that was clean, organized, and effectively conveyed a health related issue. For this assignment, we downloaded the 2018 county statistics from the County Health Rankings and Roadmaps website; this is a .CSV [comma separated value] file that contains health related statistics for every county in America. After exploring the dataset, we were tasked with choosing two variables and exploring any relationship between the two. The final deliverable was an infographic map, and mine is displayed below.

 As shown in the map above, the two variables I chose were smoking and fair / poor health; I began by creating two maps, one for each variable; while they are not in perfect conjunction, there are many places between the two maps that show a direct correlation. The scatterplot [top chart] emphasizes this relationship, by plotting each county as a point, using the variables as x-, y- coordinates. A brief study of this scatterplot shows us that as the percentage of smokers rises, the percentage of the population in fair to poor health also rises. The two graphs below the scatterplot are simple bar and pie charts, illustrating how the percentages of the two variables are very closely related. While other factors are definitely in play, it is hard to dispute that smoking is very detrimental to an individual's health. As an ex-smoker and ex-vaper, this information is very interesting to me; it will also be interesting to see what kind of information is available after the long-term effects of vaping are discovered. 

After the data analysis was complete, all elements were compiled onto a layout and adjusted to maintain legibility, organization, and a strong visual hierarchy. I began this assignment feeling unsure and a little underconfident, but I am definitely pleased with the outcome of this final deliverable.

GIS 6005 Module 4 - Color Concepts and Choropleth Mapping

The three color ramps displayed below show a linear progression through six different shades of brown. A sequential color ramp allows the cartographer to illustrate quantitative data, specifically on a choropleth map [generally, the darker the shade, the higher the value]. While this type of thematic mapping can be quite informative, caution must be used so the viewer can differentiate between the colors on the map. For this exercise, the RGB [Red Green Blue] value was given for the darkest shade of brown on the linear progression, the lightest shade of brown was manually chosen, and the RGB values were recorded for each. To obtain the intermediate shades, the RGB values of the lightest shade were subtracted from the RGB values of the darkest shade and divided by five. This integer was progressively added to each RGB value to obtain the next shade of brown; this is how the linear progression color ramp was calculated. The adjusted linear progression color scheme was derived from the linear progression color ramp, but the incremental values were manually adjusted so that they were bigger in the darker shades of brown and progressively reduced as the shade got lighter. This manual adjustment makes it easier for the viewer to distinguish between the darker shades of brown. The third color ramp was obtained from a free internet resource, https://colorbrewer2.org, by selecting three simple options: color scheme, RGB value, and sequential color ramp.

In my opinion, the results of the linear and adjusted progression color ramps are substantially more elegant and visually aesthetic than the ColorBrewer color ramp. The ColorBrewer color ramp utilizes very bright, bold, saturated colors while the customized linear progressions are softer and milder; they are not as visually overpowering. Additionally, the colors generated by ColorBrewer are somewhat scattered within the spectral range, employing hues such from maroon-brown to orange to yellow, while the shades of the linear and adjusted linear progression color ramps are confined within the same hue [brown in this case], only differ in saturation, and possibly lightness, values. Lastly, small modifications were easy to make throughout the construction of the adjusted linear progression ramp, so complete control is in the hands of the cartographer; ColorBrewer has approximately 20 sequential color ramp choices, so there is little room for customization. The ease and convenience of ColorBrewer is a benefit on its own accord, but time permitting, I would prefer to have complete control over the colors within the map.

After experimenting with colors and sequential color ramps, the next portion of the lab assignment was applying these fundamentals by creating a choropleth map. A choropleth map is a thematic map where 'each data collection area is given a particular color lightness, color saturation, or pattern texture depending on its magnitude' [Kimberling, 2012, p. 191]. In the map below, the percentage of Hispanic people is mapped for each county in Texas [the data collection areas] and the shade of the county is determined by the magnitude of the percentage.

To accurately portray the information, the data must be normalized, and an appropriate data classification system must be adopted. Data normalization is standardizing the data so accurate comparisons can be made between each data collection area. To illustrate, a raw count of 1,000 Hispanics residing within a rural county of 5,000 is quite different than 1,000 Hispanics living in Dallas County. Lastly, a data classification system must be employed. While data classification systems are well beyond the scope of this blog post, ESRI [the Environmental Science Research Institute] has a great introductory resource [it can be found by clicking here]. For this specific map, the natural breaks classification system was the best option. 

For the final portion of this assignment, all subjects explored throughout this module were applied in the creation of the map below.

This map is a choropleth map that shows population change between 2010 and 2014 for each county in the state of Colorado. For the diverging color ramp, I chose to employ the natural breaks classification system, slightly modified so they were divided by 0% change. Hence, a red shaded county represents negative growth while a green shaded county represents positive growth. Furthermore, the darker the shade, in either direction, the higher the magnitude of population change. Lastly, for this map, I chose to employ a custom projected coordinate system. This was an intentional decision due to the fact that there were two NAD 1983 State Plane systems [one for the north and one for the south] for the State of Colorado, but not one that encompassed the entire state. Additionally, the state of Colorado intersects UTM Zones 12N and 13N, so the use of a custom projection system minimizes the distortion that would have occurred otherwise. 

I am pleased with the subject matter of this module, and I believe that a fundamental understanding of these concepts are displayed throughout the design of these deliverables.

Sources:

Environmental Science Research Institute. [2024]. Data Classification Methods. https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/data-classification-methods.htm

Kimberling, A.J. (2012). Map Use: Reading, Analysis, Interpretation (7th edition). ESRI Press Academic.