Module 5b--Dot Density Mapping

This lab was very straightforward and just showed how to produce a dot density map. Though this map was optional due to the coronavirus, the professor highly suggested that we complete the project because it builds on future maps.

The map again utilized the Albers Conic Equal Area projection for the same reasons as the choropleth map--to maintain equal areas so that data is not skewed or misinterpreted.

Once all my layers were added, I joined the Excel spreadsheet that included population numbers with a feature class that showed the South Florida counties and boundaries. I then populated the symbology using the dot density feature based on the population density included in the feature class/attribute table.

With the dot density symbology, it was trial and error to determine which looked the best and had the correct size and dot value for the extent of the map. Though I could have used a nomograph, I chose to do this using my own intuition. Because the dots are placed randomly throughout the boundaries, I masked those dots that were inside surface water. This way, people would not be shown as living on the water. In order to show the dots only in urban areas, I clipped the South Florida feature class to the Urban Land feature class in place dots only in the urban areas.

I then finalized the map by placing the essential map elements. To provide a visual anchor for population density, I created three equally sized boxes with a specific number of dots to show an example population density. I did this by created one box with dots, grouping the elements, and then copying until I had the appropriate number of dots. I then ensured the dots were placed randomly in each box.

Again, the map was very straightforward and only took a few hours. I believe it is important to get all the practice one can get, especially when this lab builds upon the next.

Dot Density Map of Southern Florida. One dot equals 5,000 People

Module 5--Choropleth Mapping

For this module, I produced a choropleth map that showed the population density of European countries along with wine consumption per capita. The task was to properly show both variables on the map using either proportional or graduated symbols. For extra points, I chose to use pictures of wine bottles in lieu of the default template (circles).

For the data display, I chose to use graduated symbols and the quantile data method with four classes. I did not need to normalize the data, because the data was already normalized in the attribute table (wine consumption per capita). To me, the quantile data method with four classes provided the best granularity to perceive and understand the differences in each variable. Additionally, I excluded four countries from the data set because they were outliers--high population density, tiny area, insignificant wine consumption. Additionally, the graduated symbols were easier to manipulate than proportional symbols. For proportional symbols, I had to set the lower limit and the sizes were manipulated automatically. For graduated symbols, I could manipulate the size for each class individually to ensure the viewer could perceive differences.

For the actual map, I used the Albers Equal Area Conic Projection because it was important to maintain the area throughout the map when comparing/analyzing data that has an areal perspective. I chose to use a purple color ramp to display population density because it reinforced and complemented the overall theme of the map--wine consumption. I also added an inset map to show the Balkans area, as this was too crowded on the main map to be usable.

Population Density and Wine Consumption (per capita) in Europe

I prepared most of the map in ArcGIS Pro. The biggest challenge was importing the wine bottles. In order to import the bottles, I found free, non-attribution clip arts that were in .svg form. I then imported the bottle and manipulated the size and other aspects to ensure that they would be added just like any other graduated symbol. In order to move the wine bottles, I converted the wine bottle symbols to graphics, which allowed me to move them. I did the same with the text to ensure there was no overlap and everything was sized appropriately.

For my base map, I used the Ocean base map in ArcGIS Pro. I then added bold italics names for each main body of water, though I altered the size based on the magnitude of the body of water. I also curved or bent my text to convey water flow.

Once all my essential map elements were complete, I then saved it as a .pdf because ArcGIS Pro no longer has the functionality to export to Adobe Illustrator. Once saved as a .pdf, I then opened a new project in AI from the .pdf file. I then touched up and added higher level graphics through AI: Moved wine bottles and names, added drop shadows to Atlantic Ocean, added inner halo in legend and other text boxes. I wanted to add an inner halo to the countries; however, this would cause issues with interpreting the population density. If this were just a regular map (not graduated), I would have added an inner halo.

The biggest issue for manipulating features in AI from a .pdf is that there are hundreds of components nested many times that you must manipulate. You must also control/click each component of the feature to ensure all are manipulated the same. For instance, I had a halo around the countries on the main map. In order to resize or move a country's name, I had to click on numerous components so that they were all changed in the same way.

 My advice to everyone is to be meticulous and systematic, especially when manipulating so many different features. I chose to pick a group of features and toggle between visible/not visible to locate it and then I manipulated as needed. Once I finished I went to the next feature in the right pane. Otherwise, it would be very frustrating to find the features by clicking on the map.

Overall, though very tedious, this was a fun exercise and I learned quite a bit. I can definitely tell that my competence and confidence have improved.

Projections

In this lab, I learned to download data (from FGDL) and input it into a project. We then explored projections and learned that ArcGIS Pro requires data sets to be in the same projection (on the fly reprojecting) to be displayed together and have full functionality for analysis.

To demonstrate how different projections alter the orientation, size, compression, etc. of map features, I compared four Florida counties. By transforming the county data set to Albers, UTM, and State Plane, I produced three maps with an associated data comparison of area.

I then worked with raster projections and learned to input my chosen coordinate system. This will ensure the JPEG World File (.jgw) will have the correct coordinate system, resulting in a correct location display. I am very comfortable with UTM, because I am in the military and work extensively with MGRS, which is based on UTM.

Comparison of County Area Outputs Using the Albers, UTM, and the State Plane North Projections

Produced by Terry J. Dokey, January 22, 2020