GIS 5007 Module 6 - Isarithmic Mapping

 Module 6 of Cartography was based on Isarithmic Mapping. Isarithmic mapping differs from the other types of maps created in this course because it uses raster-based datasets. Raster-based datasets are pixel arrays, arranged in a grid pattern, where each pixel contains a data value. Therefore, each pixel can be individually mapped; this is why raster-based datasets are also called continuous data. Previously, this course has focused on vector datasets, which are comprised of points, lines, and polygons. 

To create raster-based datasets, sample data is taken at specific geographic points [stations] and the areas in between are filled in [interpolated] using various algorithmic methodologies. Essentially, this provides a mathematical estimate for the areas that lie between sampling stations, because it would be impossible to collect data [precipitation amounts in the state of Washington for this exercise] at every geographical point. For the Module 6 lab, we were given a data set that was created by the PRISM [Parameter-elevation Relationships on Independent Slope Model] Group, located out of Oregon State University; this data set included annual precipitation amounts for the state of Washington from 1981 through 2010. For background context, PRISM, unlike conventional interpolation methods, incorporates a regression function into each data cell that considers physiological characteristics of that geographic location, such as elevation, coastal proximity, and other factors. This provided a more accurate interpolated precipitation map than what was previously drawn by hand, and the PRISM model has been continuously evolving, and improving, since its initial introduction in 1991. 

Using ArcGIS Pro, the first half of this exercise was to map the dataset using continuous tones. This means that instead of creating classes of data ranges, there is a continuous color "ramp" between the highest data value and the lowest; each pixel's data value can fall anywhere on the spectrum between the highest and lowest data values. While this method is more accurate on a pixelated basis, it will only give a generalized estimate when viewed with the naked eye.

The second half of this exercise was to map the dataset using hypsometric tinting [using ArcGIS Pro]. This method employs the use of data classes where each pixel falls within the range of a single data class. Each data class is attributed to a different color, allowing the user to easily identify which range each pixel belongs. We also used a geoprocessing tool that created contour lines which outlined each of these data classes, further defining that boundaries of each area. While this method is not as accurate as using continuous tones, it does allow a quicker analysis by giving the user a generalized range in which the data for each area lies. 

This lab exercise was very straightforward, and no issues were encountered during the cartographic process. It was a great opportunity to use various tools included in the ArcGIS Pro software platform, including Hillshade Function, INT Tool, and the Contour List Tool. I was very pleased with the created deliverable, and believe that it effectively portrays the information in an aesthetically pleasing manner. 

Source:

     Daly, C., & Bryant, K. (2013). The PRISM Climate and Weather System—an Introduction. Corvallis, OR: PRISM Climate Group, 2.

GIS 5007 Module 5 - Choropleth and Proportional/Graduated Symbol Mapping

This week's module focused on choropleth and proportional/graduated symbol mapping; choropleth mapping is a mapping technique that uses a graduated shading scheme across predefined enumeration units [typically political boundaries, such as counties, states, or countries in this case]. Proportional/Graduated symbol mapping is a mapping technique that places dots at geographic locations where occurrences take place, and uses progressively bigger symbols to portray differences between classes; also, these dots can be placed at either true or conceptual points during the mapping process. For this exercise, we used the choropleth method to map the population density of many major European countries and we used the proportional/graduated symbol method to map how much wine is consumed in each of these same countries. 

Overall, this assignment was a great introduction to these two mapping schemes, and many challenges were met throughout the entirety of this assignment. ArcGIS has been the software platform used for each module's assignment, and has never presented any issues while running over a virtual desktop environment - until Module 5. For extra credit, we were allowed to use pictorial symbols for our "dots" on this map, and I found a fun little clip art file at Freepik. Once this little .SVG file was introduced to the ArcGIS project file, every action taken to create this map was slowed by an incredible amount.

The process in classifying the data was relatively simple; I chose the natural breaks method, but needed to include a fifth class so detail was not lost having only four classes. However, to create the inset maps, data had to be excluded from the main map. This regenerated new class values, so I ended up selecting the manual intervals option where I could input the original class values. 

Finally, converting all labels to annotations and converting the wine consumption feature class to a point [it was originally a polygonal feature class] also proved to be quite burdensome while working over a virtual desktop. Slowly, I was able to get each label and grape cluster positioned in a manner that was easy to read and understand.

I am very happy with the quality of this map, and I feel that this exercise proved to quite helpful in grasping an understanding of these mapping methods.

GIS 5007 Module 4 - Data Classification

This week's assignment was based on Data Classification Systems, and how each system can be used to convey information differently. We were tasked with creating two maps of the 2010 Census Tracts [United States Census Bureau] of Miami-Dade County, Florida and graphically portraying 1.) the percentage of Senior Citizens residing in each tract, and 2.) the number of Senior Citizens per square mile in each tract. For each of these maps, we were to display the data utilizing four different data classification methods and displaying the results using graduating color schemes. After final data analysis, we had to decide which map portrayed the information in the most accurate manner. As shown above, I decided that the number of Senior Citizens per square mile was the most accurate depiction, and I will discuss the reasons why toward the end of this post.

The four classification systems that we focused on were Equal Interval, Natural Breaks, Quantile, and Standard Deviation. Below is a brief synopsis of each:

Equal Interval: this data classification system takes the range of values for each feature [or observation] and creates classes that have equal value ranges. For example, if the values ranged from 0 to 100, there would be four classes with a range of 25 or five classes with a range of 20.

Natural Breaks: this data classification system takes the range of values for the entire data set, and creates class ranges that are based on any gaps that occur within the data set. For example, if there is a cluster of observations that range in value from 0 to 10 and the next observation has a value of 14, the computer would create a class that maxes out at 10. The next class would end at the next break that occurs [naturally] in data values, and this would continue until the desired number of classes are created. 

Quantile: this data classification system takes the total number of features [or observations], and creates classes that have approximately equal number of observations. For example, Miami-Dade County includes 521 census tracts [as of 2010]; this equates to four classes of 104 tracts and one class with 105 census tracts. The class ranges are dictated by the values of the 104th, 208th, 312th, and 416th data values.

Standard Deviation: this data classification system takes the entire data set, calculates the statistical mean, and creates classes that are higher and classes that are lower than the mean. There are multiple classes on each side of the mean, and graduated colors are used to visually express how far the values deviate from the calculated average.

After analyzing the data, it was apparent that Standard Deviation and Natural Breaks depicted the values more accurately than the Equal Interval and Quantile methods. The biggest issue with Equal Interval was that the data set was highly skewed to the lower end, so many values were clustered into a single class that should have been divided further. The result was a major loss in detail across the map. Similarly, the Quantile map was also misleading because of the data skew. The major issue with this classification method was that many features with very similar values were placed into different classes, and the fifth class had a range that was far greater than the preceding classes. This creates ambiguity within the map and is potentially misleading to the map viewer; this is the reason that Quantile Data Classification is appropriate for data sets that follow a linear fashion, as opposed to a data set that is highly skewed such as this one.

Finally, after careful consideration between the two maps, it was evident that the number of Senior Citizens per square mile provided a more accurate depiction of census tracts with higher densities of people over the age of 65. To illustrate, a census tract with 95 people over the age of 65 with a total population of 120 yields 79% of Senior Citizens. Conversely, a census tract with 2372 people over the age of 65 with a total population of 9593 yields 24.7% of Senior Citizens. Therefore, it is imperative that the data be normalized against a standard unit of measure to avoid any obscurities. This is why the number of Senior Citizens per square mile depicts higher density levels of Senior Citizens more accurately than mapping percentages of Senior Citizens alone. 

Overall, this lab assignment was an excellent opportunity to dive into different data classification methods and closely analyze how each differs from the others; this assignment allowed us to start making connections on which data classification method is appropriate to use for which kind of scenario. For comparison, I have attached the map containing percentages of Senior Citizens residing in each census tract below.

GIS 5007 Module 3 - Cartographic Design

 Module 3 of Cartography was based on cartographic design; essentially, this consisted of applying the information we have learned in Modules 1 and 2, and applying additional cartographic theories to create an effective map that is aesthetically pleasing and visually harmonious. The principles discussed in this module were as follows: creating a visual hierarchy, contrast, a proper figure-ground relationship, and balance. To demonstrate our understanding of these concepts, we were tasked with creating a map of Washington D.C. that illustrates the location of schools throughout Ward 7, specifically. 

To create this map, shapefiles were provided that included many elements of Washington D.C., including interstates, highways, roads, neighborhoods, parks, rivers, Ward 7, and school locations located in Washington D.C. After these were imported into ArcGIS, discretion could be used to decide which elements were necessary, which were not, and what information was needed to effectively convey the required information. 

The first decision that was made was to incorporate all map elements on a landscape page  orientation. I created a layout in portrait and one in landscape to see which map could utilize a larger scale, but there was no significant difference. I chose landscape because I thought it would create an opportunity for a more interesting final product. After this decision had been made, I began playing with color schemes that would begin to establish a visual hierarchy on the page.  For this project, I decided to color the base map with various shades of grey and use a few additional hues to accentuate certain landmarks that exist within the city. For these landmarks, I chose hues of red, blue, and green to identify major highways [roads were left white in color], rivers, and parks that lie within Ward 7 of Washington D.C. While these colors are conventional cartographic standards for these particular types of landmarks, I deliberately chose to incorporate unsaturated values of these colors to avoid confusion within the visual hierarchy I was trying to establish. Next, I chose a dark, burnt red color for the school icons that exist throughout the map. This dark red created a strong presence on the map, telling the reader that school locations were the story being told. Finally, a black san serif font type was used in the title, and the burnt red was used on other map elements to also add to the visual hierarchy on the page.

The color choices incorporated were also chosen to create an appropriate amount of contrast on the map. The greys and other hues used in the base map create a vast amount of contrast with the white space that lies on the outside edges of the layout, while the burnt red color of the icons and legend creates a high contrast with the base map they lie on top of.  Without, this high volume of contrast, the map reader's eyes would not be drawn directly to the school icons and the title block subsequently; this would create visual confusion while looking at the map and the essential information would be lost in translation. This is the reason that contrast plays such an integral role in the cartographic process. 

According to the text we were given, a proper figure-ground relationship is established within areal features by filling the focal area with lighter tones, and using darker tones on the surrounding areas. Conversely, to employ a figure-ground relationship with points that lie on the map, darker colors give the thematic icons a heavier presence, increasing their visual weight on the page. This is also illustrated by the color scheme used throughout the map. The focal point of the map [Ward 7] was filled with a lighter grey [10%], while the remaining Washington D.C. area was filled with a darker shade of grey [20%]. Additionally, the school icons were given the burnt red shade to establish prominence on the map. Not only do these color schemes aid in establishing a visual hierarchy, but they also form a proper figure-ground relationship that accentuates the importance of the schools within Ward 7 to the map viewer. 

Finally, I decided to utilize the angles of the Washington D.C. municipal boundary to create harmony between the map and the outlying map elements. To accomplish this, I arranged to title, subtitle, scale bar, legend, and North arrow in an angular fashion that loosely follows the northeasterly boundary of Washington D.C. For the inset map, I chose to create a polygonal shape that replicated the angle of the southeasterly border. To tie it all in, I chose to align the top edge of the inset map with the major arterial road that dissects Ward 7. With the alignment of all these lines and angles, a visual harmony was created on a page that consists of a very asymmetrical layout. 

This exercise was a great opportunity to expand our knowledge of this complex software platform, and I learned many things while creating this map. The greatest challenge I encountered was finding an efficient way to create the interstate labels. While the shield icons are included in the ArcGIS icon gallery, there was no field in the attribute table for the interstate feature class that included only the interstate number; all fields included the word "interstate". To properly label the shield icons with only the number, I created a new field in the attribute table, executed the 'calculate field' function, and found an Arcade function that extracted the text following a delimiter [a space in this particular case]. This created a field that included ONLY the interstate number, which I used to create labels for these roadways. The other customization that I had to make was to add the word "neighborhood" after the neighborhoods that were being labeled. Without the word 'neighborhood', the labels seemed arbitrary and confusing to anyone who is not from the D.C. area. To remedy this, I added a simple VBscript code to the label properties window that added a line break and the desired text; this added code read as follows: VBcrlf & "Neighborhood".

Overall, I am extremely pleased with the final product of Module 3, and I feel confident that my map exemplifies my understanding of the principles outlined in the text.