Piece of Eden GIS Blog

 This week we explored choropleth and dot mapping. Choropleth is a thematic form of mapping that focuses on color units, whose color intensity is proportional to its corresponding data value. Dot mapping is also thematic. It uses either a proportional or graduated thematic symbol (like a circle), whose size increases due to its data value. Using ArcGIS pro, I analyzed the population densities of countries in Europe (person per square kilometer), as well as their wine consumption (liters per capita) to determine if there was a correlation between the two. In my choropleth map, I decided to use a natural breaks classification. I chose not to use Equal Interval because only 2 classes (with slight 3 rd class) were represented in the map, and it looked like almost just one color in the lower range. The standard deviation classification appeared to be more diverse at first glance but was actually skewed to the top ranges. I was then between Quantile and Natural Breaks. While both t...

 This week we learned about the different map data classification methods: equal intervals, quantile, standard deviation, and natural breaks. Utilizing these different methods, I analyzed how these methods highlight and possible skew different aspects of data while also comparing these same methods to normalized data tracts by area.  1. Equal Interval: In this classification method, you divide your max value by your desired number of categories. When analyzing our data, this method causes a huge skew in the data where most all of the data landed in the first category. 2. Quantile: This method looks to create an equal number of observations within the number of desired categories. This method helped to display the data in the most diverse way possible. 3. Standard Deviation: This method uses the bell curve and standard deviation to create 5 categories with +/- .5, 1.5, >/< 1.5 standard deviation from the mean. This method helps to visually see how the data various fro...

 This week we focused on enhancing elements and dulling out background information to focus on certain details you want to stand out. We implemented this thinking through the Gestalt Principles: visual hierarchy, contrast, balance, and screening.  To implement visual hierarchy I ranked th e importance of a symbol by color and size. I made the school symbols increase in size (Elementary < Middle < High) and color intensity. To achieve contrast,  I created graphic variety by making the streets in Ward7 bubble lines, the other major roads bright red/blue with labels, and other details light plain background colors. For screening, I wanted to create a figure-ground relationship by making  the schools a darker/brighter color than its surroundings. Lastly, I balanced out the map by  removing all but the basic outlining information for the area outside Ward 7 to reduce the busyness. I used light/pastel colors for the background information and brighter primary co...

In Module 2 of Cartography, we learned about map typography and different ways to properly use map elements to highlight land and water data. Utilizing point (cities), line (water ways), and polygon (swamps) data in Florida, I created the map below.  Figure 1: Map of large cities and waterways in Florida. Utilizing ArcGIS pro, I first deleted any data that I did not need from the attribute tables. I then converted each category's symbology from single to unique depending on their data's correct name. Now I was able to create labels for the data I needed. (I originally used the Labels SQL function to narrow down the labels to only those that I needed, but that resulted in a very cluttered legend at the end.) I played around with colors/fonts for each category and saved all my actions. I also changed the positioning of the labels for the rivers to 'river placement." I then converted the data category to annotation (i.e. Rivers). (I learned the hard way to make sure to ch...

 This week in Computer Cartography, we learned about the 20 golden rules, "Tufteisms", of map-making. Using these criteria I chose and analyzed two different maps, one good map and one bad map, and explained why they were good or bad. Below you can see the two maps that I analyzed.  Map A, although very informative, was too busy and hard to read. The map below accurately depicted the data but the color scheme was too bright and would have benefited from light colors that would hurt the eyes less and allow one to read the streets more easily. Additionally, this map did not have any titles or scales; I had to research where/what this map was describing. This map does not follow rule #1 " Graphical excellence is the well-designed presentation of interesting data," or rule #7 " Clear, detailed, and thorough labeling should be used to defeat graphical distortion and  ambiguity." Map A: Bus routes for a city in England.  Map B did a great job of depicting their...

 My name is Eden Santiago Gomez and I am currently a graduate student at UWF, working towards the Master of GIS Administration.  I currently work full-time for a consulting company as an assistant and am also a full-time student. My ultimate goal is to help develop GIS applications in regard to anti-human and sex trafficking initiatives. Through this current Computer Cartography course, I hope to learn modern applications of ArcGIS.   Day to day, I love the outdoors! I try to get out as much as I can and go camping, hiking, and snorkeling.  Below, I created a Story Map of the different Florida waterways I have explored. I initially used the original Story Maps system, but ended up going over to the new ArcGIS Story Map program due to it's customizability.  New https://arcg.is/vvr8a Old https://arcg.is/f8XKq Best regards,  Eden 

 This week we learned how to perform supervised and unsupervised classification on ERDAS Imagine. On the figure 1 map below, you can see my attempt at classifying land use/cover of Germantown, Maryland. This week my map falls short of the assignment goals, as I was not able to get ArcGIS pro to replicate the recoded feature classification outputs I performed in ERDAS. I am not sure what I did wrong, but in the future, I will make sure to analyze signature points more carefully via their histograms and mean plots to identify potential errors.  Figure 1: Supervised classification of Germantown, Maryland with a smaller distance map of the main feature map.