GIS 6105 Module 6 - Interpolation

For the final map deliverable, I simply modified the map from Module 5 to create a sense of continuity between the two sequential lab assignments. The final map deliverable is displayed below:

The most challenging aspect of this assignment was truly understanding the structure of the SQL queries and what was being accomplished upon their execution; I discovered that the ESRI documentation on IDW provided some useful insight, as well as spending some time examining the calculations that were being solved within the query. This assignment was not the most challenging, but I appreciated using IDW interpolation on a vector dataset, as it is much more commonly used while working with raster datasets.

Souces:

ESRI. [2025]. How Inverse Distance Weighted Interpolation Works. Environmental Systems Research Institute. How inverse distance weighted interpolation works—ArcGIS Pro | Documentation.

GIS 6105 Module 5 - Tessellations

[Obe and Hzu, 2021] define tessellation as the process of 'dividing your polygon into regions using shapes such as rectangles, hexagons, and triangles' [p.268]. Tessellation techniques have gained substantial momentum in Geographic Information Systems to 'represent information about a land's surface within a computer system rather than on the original paper maps' [Gold, 2016, p. 9].

The map below displays Bayou Texar, a hydrologic feature located in east Pensacola, Florida. A local high school's science department has placed four sampling stations throughout the bayou, charged with taking water samples that measure nitrogen and phosphorous levels, tide levels, and amounts of rain at each station's respective location. Since it is impossible to take water samples at every geographic point within the bayou, tessellation is a technique that can be used as a precursor to interpolation or creating a 'continuous surface from sampled point values' [ESRI, 2025].

Tessellation divides the area into discreet units that can be used in various calculations, such as nearest neighbor [Thiessen polygons], to determine estimated values for each unit of the tessellated grid. This process provides an approximated value for every area of the feature class, as opposed to confining the collected data to the sites of the sampling stations. Tessellation can also provide other insight to any areal unit within a GIS, such as systemizing optimal locations for future sampling stations or also creating a spatial "index" to create a frame of reference.  The number of use-cases for tessellation is unquantifiable, but those mentioned in this discussion are among the most common for this function.

Sources:

ESRI. [2025]. An Overview of the Interpolation Toolset. Environmental Science Research Institute. <LINK>https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/an-overview-of-the-interpolation-tools.htm

Gold, C. [2016]. Tessellations in GIS: Part I—putting it all together. Geo-spatial Information Science, 19[1], 9-25.

Obe, R., & Hsu, L. [2021]. PostGIS in Action. Simon and Schuster.

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.

GIS 6005 Module 3 - Terrain Visualization

Module 3 of GIS 6005 - Communicating GIS was an exploration of various methods used to convey three-dimensional terrain on a two-dimensional map. The first exercise was a combination of contour lines and hypsometric tinting to illustrate how the rapidly the elevation changes throughout the mapped study area. Contour lines are 'lines of equal elevation above a datum. If a contour was actually drawn on the earth, it would trace a horizontal path that is constant in elevation [Kimberling, 2012, p. 217]. Additionally, hypsometric tinting is 'a method of "coloring between contour lines" that visually enhances the relative elevation cures for contours while maintaining the absolute portrayal of relief [Kimberling, 2012, p. 220]. A shown in the map below, the shades of the color ramp in the legend directly coincide with the elevation provided [in meters above sea level] at each contour line. For the symbology of the hypsometric tint, a green was used for the lowest elevations, which suggest a valley, and oranges / yellows for mid-elevations, and white for the highest, suggesting white capped mountains; this was the color scheme that was suggested in [Kimberling, 2012].

For the second a third portion of the lab, traditional and multi-directional hillshading effects were employed to actively display dimensionality on a printed terrain. Hillshading, or relief shading, uses a simulated light-source to provide relief on a map; however, the use of a singular light source creates overdeveloped, illuminated surfaces and dark, overbearing shadows. The solution to this problem is to utilize multiple light sources which will effectively reduce these extreme lighting characteristics found in traditional hillshading. The map below is a land classification map of a study area located in Yellowstone National Park. As shown in the legend, there are many various vegetation types that can be found on the map. To give the user a sense of depth and relief, a multi-directional hillshade effect was applied to the Digital Elevation Model and the land classification layer's transparency was set to 50%. This allows the texture of the landscape to be experienced by the viewer as they gather information on vegetation types found throughout the study area.

Part four of the lab was 'draping' a remotely sensed RaDAR imagery over a Triangulated Irregular Network Digital Terrain Model to give three-dimensionality to the orthophotos. The RaDAR imagery was gathered from a portion of Death Valley, California and can be seen in the image below.

This lab was a great opportunity to explore some techniques to give depth / relief to a two-dimensional map. A lot of material was covered in the text that we did not apply in the lab, but this assignment did provide a great foundational knowledge for future applications.

Informational sources:

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