Mapping numerical data associated with geographic locations is a task all GIS practitioners will eventually face.  One method of presenting such data is by using proportional symbols.  Learning how and when to use proportional symbols was the goal of this week’s lab. 

Some of the more specific objectives of the lab were:

-Getting familiar with using the Query Builder in ArcMap to isolate data

-Using both ArcMap and Adobe Illustrator (AI) to create proportional symbol maps

-Learning how to calculate symbol sizes by means of mathematical scaling

-Practice working with custom symbol templates in ArcMap and AI

-Creating circular labels in AI

The deliverables for the lab were proportionally symbolized maps of wine consumption for Western European countries using 2010 data.  Map 1 covered all Western European countries while Map 2 focused on seven specific countries.

For Map 1 I found it challenging to decide what orientation I wanted to the page to be.  I settled on portrait because of the mostly north/south spread of the countries based on using the Europe Lambert Conformal Conic projection.  Determining an appropriate number of symbol classes was also daunting but I decided that 7 classes gave an accurate representation of the data as opposed to using the 9 classes included in the instructions.  Because of the number of countries I chose to use a verdant tone color scheme so that each nation was easier to see with its associated symbol.  I think the contrast offers the viewer more information.  I chose circular symbols because I didn’t think the wine bottle image displayed consistently for all classes.

Map 2 used the same data set but was limited to seven countries and was created entirely in AI.  I chose an orange color to separate the seven countries from the surrounding nations displayed in a light gray.  A light blue background served as the water.  Label scaling, placement and creation were the most trying aspects to completing this map.  After working through the scaling routine a few times it became easier.  Placement of labels was interesting since not all the symbols fit neatly within the country’s borders.  I challenged myself and created the circular labels and am glad I did.  It’s a useful skill and adds a professional feel to the map.

Proportional symbols can be used to effectively convey map data in an easily digestible format for viewers.  This mapping technique is another valuable tool in the cartographer’s bag of tricks.

Choropleth maps were the subject of this week’s lesson.  Our assignment required that we create two maps reflecting population changes in the United States between the years 1990 to 2000.  One map was focused on percent change by state and utilized a full color approach while the other map was broken down by census divisions using only greyscale.  The lesson objectives were to help students get comfortable with creating choropleth maps and understanding the elements and considerations needed to create a successful map. We also needed to fine tune our maps with the help of Adobe Illustrator.

Quite a bit of thought is required to create an effective choropleth map.  Some of those concerns range from picking and implementing a sensible classification scheme to finding a logical color system to best reflect the underlying message of the map.  Evaluating how each classification method displays the data and determining whether it produces a coherent map is something that demanded a lot of trial and error.  The same can be said of picking a color scheme.  Some color patterns offered better contrasts and were more aesthetically pleasing than others. 

I methodically went through symbolizing the data using each classification scheme until I found one that represented the data most appropriately.  For me, that scheme was the Natural Breaks method.  I then tried a variety of color schemes until I found one that offered the best combination of contrast, clear delineation of data classes and was appealing.

The audience will be able to see how quite a number of states in the west/southwest of the country experienced significant population growth during 1990 -2000.  However, what I think is most useful to viewers is the opportunity to evaluate my design decisions for each map and try to understand why I made certain project choices.  Working through this process was beneficial to me as a student and can also be informative to a diverse audience interested in producing quality choropleth maps.

This week we examined the different classification methods for displaying geospatial data and what advantages and disadvantages come with each method.  The primary focus was on comparing the four most common data classification methods used for mapping.  The different methods are: Equal Interval, Natural Breaks, Quantile, and Standard Deviation. The underlying data centered around the percentage of African-Americans by Census Tract for Escambia County, Florida from the 2000 US Census.

The task called for creating a map with 4 individual data frames displaying the subject data using each different data classification method.  I completed the project by adding the Escambia County shapefile and copying/pasting that data frame three more times.  I then went through each  of the 4 data frames and symbolized them with a different classification method while using the same color scheme and adding all necessary map elements.  A separate map with a single method of classification was also required.  This map reflected my preferred method of data classification for the given dataset.

The examination of these data classifications should give the audience a better understanding of how the same dataset can displayed 4 ways and how each method conveys a message that is slightly different than the other.  Ultimately, the audience needs to be aware of how important it is to choose the right method for presenting data in the best possible light for the purposes of each individual map.

Cartographic Skills – GIS 3015

Module 5: Spatial Statistics

This week we were introduced to spatial analysis and how to determine which method of analysis would be appropriate for a particular set of data. Spatial analysis utilizes spatial statistics to reveal patterns or valuable information that isn’t always obvious after a simple visual inspection of data. This lesson also included tips on best practices and ways to identify potential problems with a data set.

I created the map above utilizing 3 spatial statistics tools from ArcToolbox that are vital when analyzing spatial data. The first tool I used was the Mean Center tool that placed the bright green box in the geographic center of the data set. This process determines the average xy-coordinates for the study data.  This tool is very helpful when you need to know the center point across the entire breadth of spatial data.

The second spatial tool I employed was the Median Center tool that simply picks the xy-coordinate in the middle of the entire list of data points.  This is slightly different from the Mean Center but still very useful in analyzing spatial data.

The last tool I used when making this map was the Directional Distribution feature.  This process examined all data points and created a polygon that reflected the geographic orientation of the data.  Each of the tools is important but I felt this tool gave me the most insight into the data I was working with.  The general east-west nature of the data was helpful in gaining a better understanding of the data that will allow for more intense analysis down the line.

After running each tool and symbolizing the results I went about the task of “owning” my map by adding all those features necessary to make this product complete.  I added the scale bar, north arrow, a title, my name, the date, etc.  The legend was necessary so the audience would understand what was being presented.

I hope you enjoy the map and can see the utility in the tools used to create it.  I certainly do.

This week’s lesson called for us to employ Adobe Illustrator (AI) to label islands and features around Marathon, Florida in the Florida Keys while demonstrating a sound grasp of typographic standards.

First, we had to use a set of online maps to correctly identify where the islands and features are situated.  In total, there were 17 features we needed to label. These included, water bodies, cities, parks and other features, as well as the actual islands. Once we knew what we were mapping we needed to come up with an aesthetically pleasing map product containing all essential elements and a logical color scheme and hierarchy.  Another requirement was exploring the variety of special effects offered by AI and put our own personal touch on the map.

Completing the assignment was both exciting and frustrating. The number of options was a bit overwhelming but the process of experimenting in AI made the lesson fun.  I used different font colors, sizes, and styles to distinguish the different feature labels.  I added a frameline and neatline to the map to create a framed product with the neatline having a blue background to serve as the ocean.  Many of my labels are set at an angle to flow with the orientation of the geography.  I also employed the “Text on a Path” feature to fill in a label exactly where I felt it needed to be.

My three personal touches consisted of utilizing different fonts for label categories, employing a unique color combination with a 0.25 size stroke for the Park & City features, and labelling the water features with an envelope effect.  I also added an extra bit of personal artistry by creating a runway for the Marathon Airport and placing the label inside.  Based on the title of my map, I didn’t think it needed a legend since all of the elements were self-explanatory.

Improving my skillset in AI is still a work in progress as I found out by inadvertently moving my background image while trying to adjust the placement of my text labels time and time again.  I also found the need to zoom in and out of the map tedious and wish there was a more expeditious method to accomplish this.  More practice will surely allow me to better handle the program and master all of its capabilities.  I look forward to our next lesson and the chance to gain a better understanding of all the nuances of AI.