Welcome to the end of GIS 3015: Cartography! For the final project we were asked to create a thematic map that had two data sets. I chose to compare average SAT score with participation rates on a per state basis.
To represent the data, a thematic hierarchy of how it should be read was chosen. The SAT scores are a more attractive component to the reader, so a choropleth map with vibrant colors was chosen to attract the eye and for ease of reading. In order to properly distribute the color scheme throughout the states, the data was analyzed and multiple classification methods were tested, but the classification that gave the reader the easiest way to analyze the data quickly was quantile classification with five classes to divide out the data. This was due to the fairly even spread of the average test score across the states; no single classification range is overloaded and the ranges themselves are somewhat equivalent. Natural breaks or equal interval classification methods would work well with the SAT score data set, but quantile gave the best ranges to display the data on a choropleth map.
The participation rates, while very important but not as an attractive component of the data to the reader, were chosen as a secondary thematic tier and graduated symbols were applied with a congruent but very different color scheme so the reader can easily identify the symbology. Again, multiple data classifications where applied to the participation rates, but what made the most sense was the quantile classification with five classes to divide out the data. This is due the bottom-heavy nature of the data where many states had very low participation rates. If the natural breaks or equal intervals data classification methods were used, then there would be very little dispersion of the symbols where the majority of the states would be within the smallest class of data. If this happened, the reader would not be able to extrapolate any proper correlations between the two data sets.
My favorite design technique is the use of negative space. You can see this in my boarders of the different data frames, texts and legends with the white against the gray background.
This class what definitely quite the challenge, and the workload was not too severe, but it really depends on you and how perfect you want your maps to be. Overall, this was a very fun and rewarding class!
Wednesday, April 19, 2017
This week in GIS 3015: Cartography we explored exporting files from ArcMap to Google Earth. We began with opening our ArcMap file of our dot mapping lab in module 10, turned off background color, and inserted an simple legend. Next we exported the map and dot density layer to separate KML files which can be read by Google Earth. We then opened Google Earth and opened the map KML file first, next we opened the dot density file and formatted it so that it would display the proper density dots. The dot density file not only shows the population density (in pink dots on my map), but has the per county data within it, so when the user clicks on a specific county they can view all of the populous specifics that were in the dot density excel file that was provided and integrated in lab 10. Next we set place markers that were called out in the lab in order to set up our tour of Southern Florida. Finally we opened the record a tour tool, and began recording a tour through the place markers while turning the dot density map on and off as needed depending on how zoomed the camera was to a specific point. An aspect I learning about Google Earth is that when turning the layer map on and off, the user must wait until the camera is stationary.
Thanks for taking a look at my blog post!
Sunday, April 2, 2017
This week in GIS 3015: Cartography we explored dot mapping. Dot mapping is where you assign a unit of data to a dot and distribute the dots over a geographical area. In this lab we dot mapped the general population of Southern Florida. On my map you will see that one dot equals 20,000 people and that the dots begin to coalesce in highly populated areas, such as Miami. In order to create this map we converged a data spread sheet from the US Census bureau with a FGDL geographical map of Southern Florida to create a population density map. Additionally, I overlaid surface water and urban areas to give greater context to the map. One thing to note is that dot map overlays do not adhere to whether or not people reside at the exact location of where the dot is located, instead more to the general surveyed area. This is why we had to use the masking tool to make sure that no dots were residing in the surface water areas. I created my legend by exporting the map from ArcGIS into Adobe Illustrator, copied the boxes and dots, and created a proportional display for the reader to understand densities. A new concept I learned in this lab was to add/display all unique values of a layer and color them properly in ArcGIS. Which is why swamp lands are colored differently than lakes while residing in the same layer.
Sunday, March 26, 2017
Above is my Flowline map I created for Module 9 of GIS 3015: Cartography. The intent of this lab was to create an immigration map that contains proportional flowlines in respect to the amount of people coming to the United States from the different continents. The above flowline map is distributive and shows quantitative data. The lab materials contained at Winkel Tripel projection of the world with a percentage choropleth map of the US. With these materials we were tasked with creating flowlines with desired enhancements for readability. The enhancements I chose were dropped shadows on the choropleth map to draw the reader's eye, flowlines to provide depth, and continent names for ease of reading. Additional, we made a legend for the choropleth map solely in illustrator. The flowlines were created using the Pen and Curvature tools.
Friday, March 17, 2017
Sunday, March 5, 2017
Above is my map for module 7 of GIS 3015: Cartography. It demonstrates the comparison of population densities versus the amount of wine consumed per person within Europe. In this lab we used an Albers Conic Equal Area ESRI file of Europe that contained attribute tables of population totals, population densities, and wine consumption among other data tables. We imported the file to ArcMap and chose which color scheme to show the population densities within a choropleth overlay of the countries. I chose the above color scheme based on Color Brewer, and wanted to convey style while having calming colors. Next we chose to display the wine consumption data points via either graduated symbols or proportional symbols. I chose graduated symbols due to the fact that they do not take up much valuable map space, while conveying information to the reader in the most time-efficient way. Finally in ArcMap, we inserted legends so the reader could understand the data being conveyed. A major point for choropleth legends is that there should be no space between the color bars, this is so the reader can quickly differentiate between the data points/colors. To finish the map, the file was imported into Adobe Illustrator, country names were placed per proper typography rules, and finishing touches were put on the map inset to really make things pop. A new aspect I learned in this lab is to successfully manipulate the grouped times individually in Illustrator by triple clicking, or clicking until you have the specific item isolated. This was needed due to the fact that you do not want any overlap of symbols or country names.
Sunday, February 26, 2017
For module 6 in GIS 3015: Cartography I learned to implement data classification in ArcMap. This was done by opening the data layer in a new blank map, then going into its properties in the table of contents. You must then go to the symbology tab and change what is showing from feature to quantities. From there you can select what field value you want to show and the color ramp. For linear increasing or decreasing data sets a lighter to darker color ramp is the most optimal, rainbow color sets will only confuse the reader. From there you must select your data classification between equal interval, standard deviation, natural breaks, and quantile. These are all acceptable types of data classification methods, but one may work better then the other when dealing with specific data sets.
Above is what I believe to be the best representation of the data set we worked with in module 6. These maps illustrate the population of 65 year olds and above based on a percentage in respect to the surrounding community. I believe the percentage representation depicts a better picture of what the specific areas and communities are like due to the higher amount of senior citizens in respect to the total populations. All of the data classification methods generally have consistent messaging, which is that the Northeast portion of Miami Dade County contains the communities with higher percentages of senior citizens in respect to the overall populations.