Final Project

Cartography mapped us through a journey of 12 modules that led to this point. For the final project module, I created a bivariate map, where I displayed 2 thematic datasets over one geographic area. The topic of the map was to show the SAT mean composite scores in 2014 in each state coupled with the participation rate of each state. The importance of creating such maps is that it shows the empirical association between two variables. Presenting a map of the SAT scores in each state can be deceptive in how the data is perceived. Other variables can influence why one state has a higher performance than another state. The map that I produced will highlight how the participation rate of persons taking the SAT exam in each state relates to that state’s mean composite score.

To juxtapose the United States’ SAT 2014 mean composite scores next to each state’s participation rate, I chose to display the thematic datasets as a choropleth map for representation of the SAT mean composite scores and employed graduated symbols to signify the participation rate in each state. For the choropleth map depicting the  SAT mean composite scores, I chose a manual 5 class data classification with a graduated color scheme of blue. For the graduated symbols, I selected them over proportional symbols because they are classified data whereas proportional symbols are unclassified. Since the participation rate represented a percentage, I wanted to be able to break it down into classes that represented the data as evenly distributed as possible. Instead of employing 5 classes at Natural Breaks, I manually created 6 classes: 0%-10%, 11%-30%, 31%-50%, 51%-70%, 71-90%, and 91-100%. The intervals for 5 classes was much too large. For the map layout, I chose a landscape orientation because it is best suited for a map of the Unites States. I created 3 inset maps; an inset map each for Alaska and Hawaii so I could zoom on the mainland at an appropriate size without leaving Alaska and Hawaii off. For the third inset map, I zoomed in on an area of the east coast where the states are geographically small but they had the highest participation rate. I wanted the map reader to be able to see that while their test scores were lower than the national average, they also had the highest participation rate. I used Adobe Illustrator to isolate the graduated symbols and move them around each state to create the ideal visual hierarchy and balance. I chose a map projection of North America Albers Equal Area Conic because equal area projections are the ideal choice for thematic maps (Esri).

Every year, students across the country prepare for months for SAT Exams in the hopes of getting accepted into as prestigious a university as possible. If this map had only included the Mean SAT composite scores per state, people would have a skewed perception of what that data truly means. By employing a second dataset that showed the participation rate, the audience can get a better understanding about what those scores mean. The map does a good job depicting that while a cluster of states in the Midwest have the highest scores, they also have the lowest participation rate.

Google Earth

For Module 12, we created a kmz file from our Module 10 Dot Density map for Google Earth and created a tour of Miami, Ft Lauderdale, St Petersburg, and Tampa, FL. Google Earth is an interactive 3D representation of Earth based on satellite imagery. Data can be uploaded into Google Earth and saved as a kml file that is accessible by any ESRI, KML, and Google Earth client. It is a simple way for users who lack GIS knowledge to access geographic data. To create the kmz file, I converted the appropriate features, Surface Waters and Florida Counties, to KML. To prevent ArcPro from crashing, I had to add an attribute field POP10000 on the dot density layer and calculate the field by using the Python expression: !Sheet0_Population!/10000. Once the new field was calculated, I used the Create Random Points tool for the dot density feature class. Then I ran the Layer to KML. I adjusted the legends for the features and dot density in ArcPro and used the snipping tool to save each image and added the image overlay into Google Earth. For the tour, I created placemarks for each location. I created the tour by recording each placemark and zooming in and around the area for an in depth view of the 3D buildings.

3D Mapping

For Module 11, I completed numerous Esri exercises about 3D mapping and converted a 2D map to a 3D scene. 3D view consists of four main elements: surfaces, textures, features, and marginalia and effects. This will include ground surface, aerial imagery/cartographic maps, relative to ground features/know their own absolute z's, and reference aids/atmospheric effects. They can be represented as photorealistic (real world) or cartographic (representative). 3D maps are powerful tools for a user because they are immersive and eye-catching.  Below is the 2D map that was converted to a 3D scene of Downtown Boston. There are two applications of the 3D building layer. The buildings polygons are extruded by height value to create 3-dimensional building shapes. This enables the viewer to visually focus on a specific area and to view the buildings from different perspectives as well as compare building heights. A map user could also focus on a single building and examine the shadow effects and sun exposure and its surrounding landscape. 

Dot Mapping

This week's Module 10 lab we learned about the dot mapping method. Dot maps are a type of thematic map used to help visualize distribution and densities of a large number of discrete numerical points. Dot maps rely on visual scatter to show spatial patterns and are commonly used for population maps.They display a good visual representation of variations and can also be used for statistical analysis. Dot maps have good spatial representation. Dot maps are easy for map readers to interpret. Dot maps visually match phenomena that changes smoothly over a space. There are a few disadvantages to using dot maps. The clustering can make it impossible to plot and interpret dot maps. Large numbers of dots are difficult to count and calculate actual figures. The size of the dot has to be carefully selected to display data clearly. The areas with no data give a false sense of emptiness. 
For our assignment this week, I mapped the population density of South Florida. I chose a size 3pt for the the dot symbol and the color red. I opted to no color for the county borders and the bodies of water for the main portion of the map because it made the map look cleaner. For the urban areas, I chose a mauve color and used a 50% transparency. I created an inset map for the state of Florida that included the county border outline for reference. I created two legends for more clarity for the map reader. One of the legends depicted the dot symbols and the proportional count. I created this by using the rectangle tool to make one square and copied it to make two more. I then used the circle tool and added the dot symbols. 

Flowline Mapping

For the Module 9 lab, we employed a radial flow map to depict the migration of immigrants from around the world to the United States. The map has a spoke like pattern in nodal form with all of the flow lines ending at the same destination, the United States. For my map, I opted to go with the Flowline Basemap A with the choropleth map located in the inset map. I chose to leave the continents in place and made all of the flow lines the same red color. I used the weight point that I calculated in excel to show the proportion of the migration count from each country. I set the opacity to 75% and added a drop shadow to all of the flow lines. I chose a brown to light tan graduated color scheme for the choropleth inset map. These colors were noted in the legend in the manual breaks method with the lightest colors correlating to the lowest immigration percentage. The legend also contained the immigration totals per country in descending order to match the color legend. I changed the color scheme for the countries and chose slightly vibrant colors since it was a world map and my map was not heavily stylized.

Isarithmic Mapping

Module 8 introduced the class to Isarithmic mapping. Isarithmic maps depict smooth, continuous phenomena across an area using varying symbology methods. The phenomena are measured at control points and interpolated using the appropriate method. We mapped the state of Washington depicting the annual average precipitation from 1981-2010. The precipitation data was derived by measuring at control points and interpolated using PRISM (Parameter-elevation Relationships on Independent Slopes Model). This interpolation method accounts for major physiological factors (location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position, and orthographic effectiveness of the terrain) influencing climate patterns. As precipitation generally increases with elevation, PRISM integrates elevation into the surface by utilizing a digital elevation model (DEM). I created two maps with one using continuous tone symbology and the other using hypsometric tinting. The first map is a continuous tone map showing the annual average rainfall in Washington state from 1981-2010 using smooth stretch symbology. The second map implements hypsometric tinting and utilized classified symbology with the data manually divided into 10 different classes. Relief was incorporated into both maps by employing the hillshade effect but the hypsometric tint map also shows contour lines. The hypsometric tint map was ultimately used for the end product because it is ideal for geographically smaller areas such as a state where the continuous tone map would have been more appropriate if the map was of the United States.

Choropleth Mapping

This week's lab we produced a choropleth map denoting the population density of European countries and their wine consumption. A choropleth map is a thematic map in which enumeration units are shaded by intensity proportional to the data values associated with those units. The data needed to be normalized by using population density versus raw population count because the latter can be deceptive when land polygons are not the same size. The eye naturally follows areas of the same color giving larger polygons undue ranking and minimizes the significance of smaller polygons. For this map, I used the Europe Albers Equal Area Conic because equal area projections are ideal for choropleth maps especially when choosing to map population density. It shows the map is displayed in true proportions to its size on Earth. For the data classification, I chose the quartile method because it included all of the classes and contained a clear breakdown of the data displayed. Some of the other classifications did not include the darkest color class. I chose a graduated color ramp of brown light to dark that is color blind friendly. Since choropleth maps display alot of data by using color, I wanted my map to be easily read by every potential user. For the wine consumption, I chose the graduated symbols because the proportional symbols overlapped my entire map. I did not normalize this data because graduated/proportional symbology represents  numerical data associated with point locations, not area.