This project called for creating a map displaying the mean ACT/SAT test scores for all 50 states as well as the participation rates for each which will be published in the Washington Post.  The challenge of presenting these two distinct data sets on one map and being made available to a diverse audience required more planning and preparation than we’d faced in earlier assignments.  I chose to work with the ACT data set and decided to create a plan of action before getting started on the actual map.  Thus, I began by investigating the data sets. There were 34 separate ACT average scores and 31 unique rates of participation that would need to be classified to create a coherent, effective map.  Once I felt I understood the data sets and the overall scale of the project I began to put my plan to work which included drawing a preliminary sketch about how to arrange various map elements.  I felt that I could accomplish most of the map work in ArcGIS and use Adobe Illustrator to put on the finishing touches.

I decided that using a choropleth symbology with a diverging color scheme would be most effective for displaying the test scores.  I chose a diverging color scheme over graduated colors because I felt it was easier to distinguish the full range of the data set among the states on the screen.  I used an Equal Interval classification with 5 classes because 6 classes seemed to lack vitality.  I used an orange to purple color scheme since it offered a print friendly & colorblind safe option to accommodate the diverse audience that would see the map.

To display the participation rates I opted to use a Defined Interval classification with a graduated circle symbol broken down to 5 classes.  I felt using 5 classes was easier to view on the screen and created a symmetry with the 5 classes used for the test scores.  Using a bright green circle stood out against the color scheme of the test scores and created a pleasing aesthetic overall. 

Due to a limit on map real estate I chose to use 4 separate data frames to display the data.  One data frame included the contiguous 48 states, another frame for Alaska, another for Hawaii, and finally one for the District of Columbia (D.C.). The final three data frames were not to scale and marked as such on the map.  Despite their reduction in scale, I made sure to use an accurately sized graduated circle representing the participation rates.  I used state abbreviations for labels, again, to save map space.

I included all the other necessary map elements such as a title, sub-title, north arrow, scale bar, neatline, my name, date, projection, and a list of italicized sources along with 2 five class labeled legends.  As requested, there is a descriptive paragraph prominently featured on the bottom center of the map that gives the viewer further information about the subject.

I added my own bit of craftsmanship by using a drop shadow for most of the map elements listed above.  These included the title, sub-title, name, date, projection, sources, the box containing the descriptive paragraph, as well as the insets for Alaska, Hawaii, and D.C.  I couldn't leave the map with a plain white background so I added a light brown vertical gradient to offer the map a subtle figure ground contrast.

For the map title I used letters with a white fill and a solid black stroke.  The sub-title had letters with a black fill and a very slight white stroke.  I also chose to use a narrow version of the font on the sub-title.

Overall, I’m very pleased with the results of my map and feel it gives the viewer an easily interpretable body of information and a pleasing visual set.  There was a lot of trial and error involved in this map’s creation and I feel I could continue to tweak it indefinitely but at some point it needs to be made available for public consumption as long as there are no glaring errors. 

I've learned a great deal in this cartography class, but the one lesson I feel is most important to take away from this project and class overall is that planning out a map is vital to creating something that is both attractive and useful.

Google Earth is a valuable tool for professional cartographers and ambitious amateurs alike.  Knowing the capabilities and options allow anyone with a need and an idea to present rich, compelling mapping products.

The objectives for this lab were to learn how to convert ArcGIS maps and layers into KML files along with creating/sharing maps and tours.

To complete this lab I used the example .mxd file provided. I converted the .mxd and the South Florida shapefile into the KML format that is used by Google Earth.  At this point I was ready to truly dive into the wide ranging options that Google Earth provides its users.

The most exciting part of the lab was creating a tour of the map area.  This feature allows the map maker to zoom in and out to points of interest to highlight relevant locations.  This is something that will allow people to be creative when presenting a spatial topic worth consideration.

The lesson I learned with this lab is that Google Earth is a resource for both novices and the most advanced users.  Anyone with an idea and initiative can make something useful and volunteer it to the wider mapping community.

Dot density maps can be powerful tools in displaying certain types of data.  This week we were asked to map the population density of counties in South Florida.  Using a surface water layer and a modified land cover dataset for urban land, we were able to place the dots in locations where the populations actually existed and not randomly spread throughout counties.  This made the appearance of the map much more realistic and meaningful. 

The assignment was well-conceived and working through it would enable me to accomplish the learning objectives.  Those objectives were assessing the overall design issues for dot density mapping, utilizing the arctool Excel to Table, creating a dot map in ArcGIS, experimenting with dot weights/sizes, and handling dot placement.

The assignment was challenging due to problems with ArcGIS crashing while experimenting with dot placement.

Despite my problems with ArcGIS, viewers should be able to see the effectiveness of dot density mapping to demonstrate the intensity of particular kinds of point data.

Flow line mapping was this week’s focus.  Using Adobe Illustrator, we were asked to create a flow line map reflecting immigration to the United States in 2007.  Another component of the map was a choropleth representation of the US using a five-class symbolization of the percentage of immigrants to each state.  The objectives of this lab was assessing design issues, using proper design techniques, and applying effective style and/or visual effects to create a compelling flow line map.

I utilized a drop shadow for both legends as well as for the title and subtitle.  I felt this made the elements more prominent.  I rotated the titles for some of the continents to better flow with how they situated on the map.  The flow lines were placed with graduated shades of black/gray corresponding to the level of immigration from each continent.  On the regional immigration legend I used a dashed line to connect the totals to the name of the continent for easier viewing.

This was a valuable learning experience and another chance to refine my skills in Adobe Illustrator.  Hopefully, the audience is able to quickly grasp the answers to these questions: 

(1)    Where did they come from?

(2)    Which continents provided the most/least immigrants?

(3)    Where did they end up?

If they can, then I've done my job.

Isarithmic mapping is one of the most popular methods of displaying data that is smooth and continuous.  The contour map is the most common style of isarithmic map and is widely used to present data on topography, elevation, and rainfall.  This week’s lab required that I make two isarithmic maps depicting average annual rainfall in the state of Washington.  One map would be symbolized with a continuous tone while the other would employ a hypsometric tint design.  Both maps would also use a hillshade effect to enhance the data contours.

The data for this lab was gathered from the U.S. Dept. of Agriculture through their Geospatial Gateway website but was originally created by researchers at Oregon State University by using an interpolation method called PRISM (Parameter-elevation Relationships on Independent Slopes Model).

Map 1 was titled “Continuous Tone” and was symbolized with the Precipitation color ramp provided by ArcMap.  I added a vertical legend and stretched it so the numbers and colors were easier to read.  I also added some key cities to the map to serve as a reference for viewers unfamiliar with Washington geography. 

Map 2 was titled “Hypsometric Tints” and symbolized in the same manner as Map 1.  For Map 2 I utilized a horizontal legend and manually adjusted the 10 classification values that were called for.  To make the classes easier to display in the legend we used a tool from the Spatial Analyst extension that rounded fractional numbers to whole numbers.  Another difference between the two maps was that for the hypsometric map I added contours at specified intervals related to the amount of average annual rainfall.  To add a bit of craftsmanship and refine the map I decided to make the precipitation layer 20% transparent and placed the contour layer below.  This arrangement offered the benefit of the contours layer without giving the map a cluttered appearance.  I felt this really improved the readability of the map when combined with the hillshade effect.  

I hope the audience will come to appreciate how effective and powerful isarithmic maps can be in understanding our world.

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.

This week's lab assignment was to create a map using Adobe Illustrator to hone our skills in the art of cartographic design principles.  Creating a map that has a sound visual hierarchy, balance, and a distinct figure-ground element were vital to successful completion.

To design a map that effectively communicated the subject matter to the viewer I decided to scale the geographic elements from most important to least important according to size. The south Florida layer carries greater weight than the Florida layer, and the Florida layer is given more prominence than the continental US layer.  Each of these layers helps to orient the viewer.  The progressive color-scheme I utilized allows the audience to easily see which counties have the highest percentage of Hispanic populations.

Viewers of this map can quickly see at a glance those counties with a high density of Hispanics residing in Florida and in what percentage range each falls within.  This type of presentation offers a clear, effective method of delivery for those needing a tool to succinctly convey complex information.

Enhancing maps with Adobe Illustrator

First foray into the world of Adobe Illustrator (AI).  Exported from ArcMap to AI then manipulated, organized, & exported this map to JPEG.  AI gives users much more control and flexibility over map's appearance than with ArcMap. Very cool tool.

Lab 1: Map critique

1.    Example of a well-designed map. (Copy and paste jpg in this document)

2.    One paragraph critique of your well-designed map—critique must be supported with 2-3 map design principles.

This is an example of an impressive looking map due to several well thought our design considerations.  First, the map’s title clearly explains what the map is presenting to the audience.  Second, the map has clear, detailed labelling throughout as demonstrated by the wildlife management area game zones being not only numbered but color coded as well.  Finally, this example of a well-designed map presents interesting data in an aesthetically pleasing layout.  The use of space is managed effectively and fully so that there aren’t any large gaps to distract the viewer.  One other observation that I think makes this an exemplary effort is the use of vital mapping elements, such as, a north arrow, legend, who made it, when, and where to get further information.

3.    Example of a poorly-designed map. (Copy and paste jpg in this document)

4.    One paragraph critique of your poorly-designed map—critique must be supported with 2-3 map design principles.

The map above is easily one of the worst examples I saw in the collection on eDesktop.  I chose this map because it is lacking in the exact qualities where my previous map was strong.  There is no title to give the audience an idea what we are looking at.  Do these points reflect shipwrecks, oil rigs, places of interest?  The lack of labelling is equally frustrating as a viewer.  I’m left to wonder what these points could be and why they are important to the map maker.  There are so many mysterious points that it looks overly cluttered.  Aside from the lack of a title and labels, the designer should have utilized better symbology.  The thick white border just makes this mess even messier.