Final Project -Rise of the budding cartographer

The focus of this project was to create a publishing quality map illustrating the relationship between 2014 Composite Mean SAT Scores and the test participation rates by graduating seniors within the conterminous United States and the non-contiguous states of Alaska and Hawaii.  The map is intended to accompany an article in the Washington Post discussing high school seniors and college entrance scores.

In order to best present the bivariate data, I chose to create a choropleth map with graduated, range-graded symbols.   A choropleth map is ideal for displaying uniformly distributed data within each enumeration unit where change occurs at the enumeration unit boundaries.  The composite SAT scores are shown by applying a color gradient scheme to the States.    I chose to class the composite mean test score values using a defined interval of 100 with six classes.  While this reasonably represented the data, I wanted to better convey the high end outliers.  By tweaking the last break point to cover the range between 1,800 and 1,816 I was able to represent the data more accurately.  I selected a suitable six class color scheme using color brewer. (Brewer, 2002-13)  Superimposed on each state is a graduated, range-graded symbol illustrating the graduating senior test participation rate.  The 2014 participation rates were classed using Natural Breaks (Jenks) with seven classes.  I chose to show the class ranges using the feature values to accurately depict the percentages even though this showed percentage gaps in the legend; the reason for the gaps was explained within the map subtext.  While the other classification options (Equal Interval, Defined Interval, Quantile, Geometrical Interval and Standard Deviation) provided reasonably similar results I chose to use Natural Breaks (Jenks) because this method minimizes the difference between values in the same class and maximizes the difference between classes.  Once I had properly classified the data I shifted my focus to developing my layout and design.  The conterminous states served as the primary map; I included insets for Alaska and Hawaii since the page size restricted their visibility within the primary map.  For clarity, I created an inset map of the Northeastern United States so that the State names and graduated symbols could be easily distinguished.  I added all required map elements, but kept stylization to a minimum within ArcDesktop.  Finishing effects were created using Adobe Illustrator with attention to Gestalt principles.  The Gestalt theory, developed in the 1920s, describes how humans recognize individual components in a graphic image and organize them into a unified whole.  The Northeastern inset map applies closure which is our ability to complete an image even with parts missing; in this instance we visually interpret the dashed line to represent the square in its entirety, without gaps.  In order to apply figure-ground, I made the primary map elements a lighter color than the background.  I also applied stylization effects including feathering and drop shadow so that the objects appeared to be visually closer to the reader.  I feel the feathering effect helped to emphasize the importance of these items and direct the reader’s attention to them.  By making the graduated symbols darker than the states, I was able to apply visual hierarchy so that the symbology was emphasized without detracting or obscuring the underlying states.  While all of the previously discussed items are important to an accurate map and good cartographic design they are all for naught if an incorrect projection is used.  Because we were comparing data by area it was important to choose a projection which preserved area.  There are four main equal area projections:  Globe, Sinusoidal Equal Area, Lambert Azimuthal Equal Area and Albers Equal Area Conic.  By looking at the size and directionality of my subject matter these choices could be further narrowed to those suiting small regions or countries resulting in either Lambert Azimuthal Equal Area or Albers Equal Area Conic.  Narrowing by extent, I selected the Albers Equal Area Conic which is suited for large, mainly east/west areas of mid-latitude.  Additionally, this projection is a good choice for thematic and presentation maps.  ArcDesktop offers further refinement to this projection by setting the longitudinal center to -96, the first standard parallel to 20, the second standard parallel to 60 and the latitudinal center to 40.  I chose this projection, designated as North America Albers Equal Area Conic for my project.  

This project allowed me to utilize the cartographic methods, techniques and skills I had acquired during the semester.  The importance of visualizing the data distribution, defining the map purpose and audience became clear when attempting to design a map with no detailed instructions.  While daunting at first, the task quickly became enjoyable as I was able to recall and apply what I had learned throughout this course.  Because of the experience gained this semester, I was able to create the ArcDesktop portion of the map with relative ease.  Although I had not used Adobe Illustrator in the more recent labs, I was pleased to note that I was able to recall and apply the skills I had learned.  As I was less intimidated by the software, I was able to explore new options and find better ways to manipulate the objects that I had imported from ArcDesktop.  Comparing the .mxd file to the .ai file leaves no doubt that using Adobe Illustrator greatly improves the maps presentation value.  Not only did this project make me an acolyte of Adobe Illustrator, it also strengthened my confidence in the skills learned throughout this semester.  I am more enthusiastic about the remaining coursework and the possibilities that GIS brings to my future.

Module 12 Google Earth - Bringing your map to life, with a tool non-techie laypersons can use!

Our goal this week was to export our Dot Density map to a KMZ file and then use that information in addition to some placemarks to create a recorded tour of South Florida.  Although we had previously created the Dot Density map, we needed to save it to a new map and perform some tweaks.  In order for the dots to be visible, we made the background hollow.  My urban land layer was already off and my surface water was symbolized by category; my legend needed to be recreated in a more simplistic fashion in order to work with Google Earth.  Once those tasks were complete, I exported the Map to a .kmz using the Map to KML tool.  I then exported the Dot Density layer to a .kmz by using the Layer to KML tool.  Once complete I exited my map, opened Google Earth Pro and imported my two .kmz files.  I moved the dot density layer to my map layer, adjusted the color and then saved using the save place as function.

 
I then created a layer for my tour stops making sure to place them in a location that would be suitable for my zoom areas and to label them clearly.  I also made sure to turn on the 3D buidlings layer so that they would be visible.  When I began to record my tour, I found the mouse to be too difficult to control for smooth video; consequently, I did some research and used the information found here https://support.google.com/earth/answer/148115 to obtain the keyboard shortcuts.  When rotating using the mouse, shift and arrow keys, the speed was too fast; I added the alt key simultaneously with the shift key and cut my speed by half.  After a couple of practice runs I recorded my final video.

This lab focused on 3D mapping.  We used ESRI training as the basis of our learning and practice and them implemented some of what we had learned with our own exercise.  Our learning targets were 3D visualization techniques and converting 2D data to 3D.  Using the ESRI tools we learned to create base heights for rater and feature data, how to apply vertical exaggeration, how to improve your map utilizing illumination and back ground color, how to extrude above and below ground features containing elevation values and how to extrude parcel values.  Having completed the ESRI basics it was time to apply some of our skills to converting 2D data to 3D.  We used existing data containing building footprints and a raster surface with elevation data.  We created random points from our building footprint layer using the CID field.  We then added surface information to this sample points layer by using the raster file z values.  We then took our sample points table and used the summary statistics tool to generate a single elevation value for each building by using the z value mean.  We were then able to join the table attributes from our sample point statistics table to our BostonBldgs layer; we exported this data as a file and personal geodatabasefeature class.  We then used ArcScene to extrude our buildings using the Z value.  After completing, we exported this layer to a KMZ file using the Layer to KML tool.  Here was the result in google earth:

Both 2D and 3D have many individualized uses.   2D data can require less expensive software or add-on tools, but can be limiting with respect to ease of understanding when excess layers are incorporated into maps, especially in multivariate situations.  2D also requires the end user to be able to visualize any three dimensional information about the data being conveyed.  For instance, a user unfamiliar with topography might not understand what the contour lines represent.  The same user, however, could readily see the elevation changes with a 3D map.  Creating 3D maps from 2D information can be more expensive and more time consuming, but the ease of visualization, eye-catching depictions, and user interaction can convey more information to the end user.  The ability to better visualize environmental impacts, urban growth, and applied 3D information to value components makes 3D mapping a great tool for planning and presentation.  

Module 10 Dot Mapping.......

        This week’s module introduced us to Dot Mapping.  Dot maps are used to represent the location of one or more instances of a geographic occurrence.  The dot symbol represents a specific quantity of the occurrence and is placed in the relative location of the occurrence.  When creating a dot map, conceptual or raw-count data should always be used.  The dot map allows for a quick visualization of the quantity and location of graphic occurrences.  It is also useful for comparing distributions of related incidences.  Within this lab we also learned how to spatially join tabular data, select the appropriate dot size and unit value and use a masking layer to improve our dot placement.  We were also given the opportunity to improve upon ArcMap’s legend and create a more improved and informative dot legend.   For this task, I exported my map to pdf and then used Bluebeam Revu to create the rectangles and insert the appropriate quantity of dots.  I then exported a snapshot of those rectangles to ArcMap and added the necessary text.

Module 8 - Flow Mapping ....stylized distributive flow....I can go with that

This week’s module was an introduction into flow line mapping.  There are many types of flow maps, Distributive, Network, Radial, Continuous and telecommunications to name a few of the most used.   This lab, depicting immigration into the United States for the year 2007 is a type of distributive flow map.  We mapped the quantitative data found in the U. S. Homeland Security Office of Immigration Statistics 2007 Yearbook of immigration statistics.  Since we did not know the actual migratory paths, we used a stylized placement approach to represent the general direction of flow and indicated quantities through the use of proportional flow arrows.  The line weight of the flow arrows was calculated by taking the square root of the population value of each Continent; we then chose a maximum value line width, in this case 15.  With those two pieces of information we applied the following formula using the highest population value as our denominator, in this case Asia with 619.28 resulting in the following formula for each of our continents where X=continents square root population value: Width of line symbol=15*(X/619.28).  This calculation gave us a line weight for each continent that was proportional to the immigration value for that region.  This made it easy to draw conclusions regarding relative quantity of immigrants per continent. 

Critical to the use of low maps is figure ground.  The flow lines should be the most important element in the visual hierarchy.  I made these a dark color which was separate from any other color on the map, applied appropriate proportional symbology and experimented with stylization effects to find one that provided additional visual hierarchy without being overpowering or distracting; my final choice was a subtle drop shadow effect.  The topic of projection was already solved for us in this case, but none the less is an important decision when creating your flow map.  I had no issue with the provided continent color schemes or with the U.S. Choropleth map and, therefore, chose not to alter them.  I did increase the size of my map area to fill the paper as well as enlarge my inset choropleth map for additional clarity.  In both instances I was careful to group the scales with their associated maps so that they remained proportional.  I labeled the continents for clarity, added a title, author, date, source and projection information.  At this point I decided to apply a fill color to my background.  I used a blue tone that was slightly dark to not only represent the oceans, but to also make the continents and flow arrows stand out more.  I then turned my attention to the legend.  I used a contiguous color range legend for the choropleth inset map to show the percent of total immigrants per state.  I used a second legend for the main map to connect the immigration values by continent to the U.S.  I thought keeping with the “arrow” theme would be attractive; consequently, I created a series of arrows that gradually reduced themselves in length as the quantity of immigrants decreased.  I match the colors of these arrows to their continents and, since I had made them wide enough, included the continent name and immigration value inside each arrow.

Module at Isarithmic Mapping - Come on in the weather is fine....as are the hills, valleys, depressions...

                This week we learned about Isarithmic Mapping.  Within that scope, we were introduced to various methods of interpolation, including PRISM, worked with continuous raster data and learned how to symbolize and produce a complete and accurate legend.  We worked with both continuous tone and hypsometric symbology both utilizing hillshade relief.  Additionally we learned to create contours to overlay on our hypsometric symboloized map.  The map illustrated here was our final product and depicted the annual precipitation for the State of Washington using climate data from 1981-2010.  The Prism method interpolated the data using the 30 year precipitation data and a DEM of the state for elevations.  The various algorithms take location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position and orthographic effectiveness of the terrain in relation to the monitoring locations (stations), to develop a suitable model for our map.  We used the Integer Spatial Analyst tool to convert the floating raster values to integers so that we could create “crisp” contours and be able to truncate values as whole numbers.  Renaming the layer to something less cryptic later facilitated making the legend more functional.  We classed the data using a manual break method with 10 breaks (break values were provided within the lab).  After classing the data, we chose a precipitation color ramp and applied a hillshade effect.  This produced an attractive map with visible relief.  In order to make the relief even more visible, our next task was to add contour lines.  This was accomplished using the Contour List Spatial Analyst tool.  We assigned contour values which matched our break values.  Although one would typically notate the contour interval in the legend, this step was unnecessary in this case since our contours followed our hypsometric steps.  I symbolized the contours using the temperature contour line available within the weather style manager.  The final steps were to add essential map elements to create a finished, easy to understand, visually appealing map.

Module 6 Choropleth and Proportional Symbol Mapping - or, best places in Europe to blend if you're a heavy wine drinker!

     This module was designed to introduce us to Choropleth and Proportional Symbol Mapping.  It was a great exercise to pull together what we have learned with regard to cartographic principles, data classification, SQL queries and additional time with Adobe Illustrator.
   

Once I imported my data, I used Color Brewer to help select a color ramp for the choropleth portion.  I wanted to use brown tones for the land masses and wanted to make sure the hues were suitable for individuals with colorblindness.  After confirming my choices, I downloaded the style for future use with ArcMap.  My next task was to determine a classification scheme for the population density data.  After previewing several options, I chose a 5 class quantile scheme.  Since we had removed four countries whose small size and large density would have skewed the presentation, I felt that keeping an equal number of features within each class was a good means of presenting the data.  This method allowed me to see density spread among lower ranges much more clearly while still allowing visualization of the higher end densities. With regard to the wine consumption, I chose a deep red/purple color resembling red wine to apply to my graduated symbol choice.  I used graduated symbols because the symbol sizes are discriminated by the range to which they belong making them easily recognizable in the legend.  Classification was a bit trickier here as I did not like the results of the standard options.  While certainly subjective, I chose to use a 5 class manual interval classification to determine a set of ranges which kept consumption levels similar to others in its class (rare, occasional, social, moderate and heavy consumption) while still depicting the high end Vatican City outlier.  Because of the subjectivity of this method, I divided the consumption by weeks, months and days to help determine where to best place my breaks as well as using information on the graph within the classification window.  Before finalizing the map, I inserted a world ocean base layer from ESRI to fill in the blank space where the oceans would be as well as to account for the location of less important, non-European countries.  I placed the basic map elements and labels within ArcMap and then exported to Adobe Illustrator to apply clean-up.  Before beginning any edits in AI, I was careful to organize my layers and move items into more appropriate categories.  I also removed any unnecessary layers, in this case the wine consumption country linework, and clipping planes.  Once my layers were renamed and organized I could begin the clean-up process.  Since I had already translated my country names via my attribute table, all I needed to do with the text in AI was to move and rotate as required to make it legible.  I applied a drop shadow to raise the legend above other items, since space was tight.  I screened the small tightly grouped countries west of Italy and north of Greece on the main map and directed the user to the enlarged, inset map.  The color scheme, symbology an labels for theses countries was only visible in the inset map.  I kept effects to a minimum so as not to shift focus away from the content of the map.  Overall, this was an enjoyable means of implementing choropleth and graduated symbols to compare data.
 

Module 6 Data Classification - At least I'm moving in a positive direction!

This lab was designed to provide practice in not only using data classification methods in ArcDesktop, but also to illustrate the differences between the various methods.  We used Equal Interval, Quantile, Natural Breaks and Standard Deviation in this lab.  Each method needed to be properly symbolized by quantity so that the results were clear to the end user.  All used a color ramp, except for standard deviation which used a divergent color map so that the diverging values above and below the mean were distinguishable.  This lab continued to emphasize the importance of cartographic design to make the information easily interpreted by the end user.  As the map production process evolved, the application of good cartographic design steadily improved the appearance and usability of the map.

I feel the Map presenting the quantity of seniors per square mile is the most accurate means to portray the data.  The population count normalized by area more accurately depicts the distribution of senior citizens because only the senior age group is taken into account with the square mile area, not other age groups.  With this map, I compared the following classification methods:

·        With the Equal Interval method, the range is divided into equal parts along a number line and the data falls within the resulting classes based on its value.  This method did not depict the localized areas of seniors as well because the data was not value grouped.

·        Using the Quantile method, data is rank ordered and an equal number of observations are placed in each class.  Differentiation between data clusters is more visible here, but the high end of the ranges was greatly affected by the outlier.   

·        The Standard Deviation method considers how data is distributed along a number line; classes are created by adding or subtracting the standard deviation from the mean.  This method works very well with normally distributed data.  There is visual evidence of cluster differentiation and the effect of the outlier is visually minimized.

·        In the Natural Break method, similar value data is grouped and algorithms are used to minimize the value difference within classes and maximize the value differences between classes.  I preferred this method above the others as it made the population densities easy to distinguish and reduced the significance of the outlier on the overall data.

This lab certainly helped to make sense of the classification methods.  Combining this with proper symbology really helped to present the information in a clear, easy to understand format.

Module 5 Spatial Statistics ~ Math Mayhem

Whew, I made it through this module.  I started by reading, or attempting to read Chapter 3 in our textbooks during my lunch hours.  During an already hectic, brain scrambling day this was a poor choice!  I shifted my focus that evening to the lecture content and moved on to the lab assignment feeling slightly better and ready to analyze some data.  The ESRI training was well organized, with a nice overview including key terms which I printed and saved for future use.  After each module introduction we were provided step by step instructions to repeat the process using different data so that we could form our own conclusions and check them against the ESRI response.  The first module focused on spatial distribution using Mean Center, Median Center and Directional Distribution tools.  This allowed me to determine that my mean and median were similar and that the majority of my data ran in an east westerly direction.  It was important to note that the Median Center was located southeasterly of the Mean Center, most likely due to the cluster of weather stations in that area.  The remaining lessons in the training expounded on the use of additional Geostatistical Analysis Data Exploration Tools to determine if the data fell into a normal distribution, were there any outliers, was it stationary and did it have autocorrelation.  I'm still a bit confused about some of these tools, but can see that they aid in determining areas that might need further study before performing analysis.  In this case, all the tools pointed to La Fretaz in Switzerland as my outlier.  I look forward to more practice and education with regard to spatial statistics.

Module 4 Cartographic Design - The Ward 7 conundrum

The goal of this lab was to learn basic cartographic design skills employing Gestalt principles, intellectual hierarchy, visual hierarchy to achieve a map which suits the needs of the end user.

Using ArcCatalog, I reviewed the data which had been provided.  This review allowed me to organize my thoughts and list out the intellectual hierarchy.  This task helped me to determine what data I felt was most relevant to my map.  Now that I had an idea of what to use, I began the process of layer organization and symbology to begin my visual hierarchy.  I structured my layers logically so that more important layers were on top such as the schools and Ward 7.  Theses were followed by the transportation and environmental layers, with boundary information at the lowest level.  I then begin the task of applying contrast, figure-ground and balance.  Using prominent symbology for the schools, they stood out as the main feature.  Since Ward 7 was next in importance, I made sure to use figure-ground principles by making the Ward a lighter hue than the surrounding DC area.  My next task was to manipulate the environmental features (parks and rivers) so that they were intuitive to the viewer.  By using standard colors, blue for water, green for parks, they were easily recognizable.  I used color screening to adjust their hues so that they were discernible from the Ward 7 base but not overpowering.  I wanted to address the transportation layers next so that they were not so overwhelming.  I chose to not use the DC streets as they were unnecessary with respect to the map intent and making them visible was far too much clutter. For the remaining transportation data sets, I chose to make them similar in color but apply contrast by weighting each differently using line width.  I created a Ward 7 streets layer with data I had extracted from the main DC streets layer.  I wanted these delineated to help identify locales in Ward 7; I did not, however, want them to dominate.  I used a grey shade to make them visible, but not overpowering.  I made the major streets slightly wider and darker and labeled the ones that appeared to be main arteries both into and through Ward 7.  I made one exception to my transportation color scheme.  In Ward 7  made the state road red to separate it from the others as it was a significant road that traversed the Ward and connected it to the interstate.  This also helped to easily locate where I was in relation to DC using the inset map since this feature was similarly emphasized in both areas.  My next task was to label 7 neighbor hoods.  I chose a moderate text size and font that was easy to read.  By applying a halo effect, the text was easier to read as it stood out from the features below it.  I chose a height that showed these were important, but still secondary to the schools.  Once I was satisfied with the look of my data view, I switched to layout and began adding the inset map, scale, legend, north arrow, title and author/date/source information.  Balancing was an important aspect here, especially due to the oddly shaped base area with which we were working.  I wanted to be sure my information was visible but did not detract from the map nor cover important features.  Space was at a minimum here, again due to the irregular shape of the AOI.  I created the entire map in ArcDesktop.  Adjusting symbology and text were mainstays in my map production.  I found that tweaking was in order as I made changes to one layer, others needed to be adjusted until I was satisfied with the overall product.  Effects such as drop shadow and halo were helpful in emphasizing specific elements and making the map appear tidy without objects overlapping each other.

Whew, that was a lot of explanation!  Doing took even more effort.  I have to say that I initially created a map and felt it looked pretty good.  I turned away from the computer, and reviewed the principles in the book and realized I did not hit my target.  I returned to the map and assessed my color scheme and weighting.  Although everything was visible and the schools and Ward 7 stood out, it just was not quite right.  I looked to color brewer for guidance and imported a color scheme that I thought would improve my map.  At first having Ward 7 as the lightest feature really bothered me as it did not seem to pop, but as I adjusted the other color schemes and line weighting things really began to take shape.  When I returned to my other map I was astonished at how poor it looked!  Lesson learned for sure.  There were a couple other things I discovered that were helpful.  Throughout the process  I exported the map to a pdf and noted that there was a significant difference from my display to the resulting map.  Using this process throughout helped me to use appropriate colors and weights for printed materials.  I also found that by using the focus data frame feature I could tweak the size of my text so that it looked better in the layout view as it was misleading in the data view.  I learned a lot from this exercise and feel that I will continue to improve my cartographic skills through use of the principles in this module.

Module 3 - Typography

This weeks lab had us create and label Marathon, Florida and surrounding keys using both ArcDesktop and Adobe Illustrator.  In addition to allowing continued practice with data sets, layering and data views in ArcDesktop, this lab allowed for further exploration into Adobe Ilustrator.  The key learning objectives were to improve our skills within Adobe Illustrator and use this software to apply the knowledge gained in the chapter readings and lecture.  Critical to the lab were the ability to define and insert essential map elements.   Applying the general typographic guidelines to map labeling was intrinsic to this lab, including font choices, styles and heights, various subtle type characteristics within the text such as leading, use or non use of serifs and Title Case to mention a few.  Combining this with identifying proper label placement for different feature types.

My map emphasized Marathon and the surround keys.  I used ArcDesktop to import the data sets, create data frames, apply color the the land masses and add the a portion of the essential map elements, scale and north arrow.  Having exported to .ai format, I finalized my map in Adobe Illustrator.  While I am improving, Adobe Illustrator is still a bit clunky for me and I find that many things are trial and error until I learn what each tool does.  Until then, undo will be my buddy.  I enjoyed the ability to fully manipulate text type characteristics to add additional emphasis and distinction to labeled features.  In this way, not only does the symbology and  polylines indicate what I'm conveying, but also the text can be matched to the features so that the reader naturally associates one with the other without it being overbearing and slapping them in the face.  This is a feature I would love to have in Autocad!  By adding a background, it was easy to see that the land masses were in fact island chains.  Using a gradient coloration for my background, I brought the users attention to the full width of the map in a natural left to right progression.  Applying a drop shadow to the land masses helped them to rise above the water and draw the attention to them as well as their associated features.  Using various methods of labeling, I was able to distinguish between hydrographic features, points and areal features.  I customized the feature symbols to align with more standard cartographic guidelines and colors.  The park feature was difficult to see on the small Key so I applied an outerglow to help it pop off the small area without being overbearing.  I used a neat line with a subtler drop shadow effect for both my inset map and my legend.  I made sure to include all essential map elements, balance the page, make the map easy to interpret, yet still reflect some personal style.  Enough rambling, see for yourself:

Taking AI for a spin, or maybe AI took me for a spin! (Module 2 Intro to AI)

The intent of this lab was to help students develop an awareness of the importance of aesthetics as another important component in map production.  While production of an accurate map with ArcGIS desktop is important, it may not be your final production product.  By introducing AI, this lab provided the foundation for future use of AI to finalize and produce not only accurate, but also beautiful maps for many different purposes.  In this lab, I created a basic map of Florida in ArcGIS for desktop and then exported that map into Adobe Illustrator (AI).  The script function was a time saver as it allowed me to change all of my city symbology with one click.  While a little complex at first, I found the layers to be very useful.  I learned a valuable lesson in that sometimes more is less when it comes to what you create in ArcGIS for desktop prior to your export to AI.  I had generated all of my essential map elements prior to export and found that this step was not only unnecessary, but also produced additional layers that became cumbersome to identify and manage.  I won't be doing that again!  Since I'm a seasoned CAD user, I love and embrace the structure layers can provide when implemented properly.  The Window>Image Trace command was quite useful for removing the extra white background behind the state seal; this will be a great tool to remember.  I enjoyed the practice exercises with lines shapes and the extra hints those provided.  I will be referencing these and other videos often as I familiarize myself with AI.  I love the options with text placement, they are much more effective than what I've had to manipulate in Autocad and of course much more pleasing to the eye than labeling within ArcGIS Desktop.  While I feel I've barely scratched the surface of what AI can do, this skeptic has been won over and I'm anxious to learn more and am more confident, having worked through this lab.

I mapped the State of Florida for use in a children's encyclopedia.  The map provides basic information about the state, highlights a few major cities and displays some state symbols.  I tried to keep the map simple since my target audience is of a young age group.  I wanted to use color and imagery to get their attention and hopefully inspire them to study the map more closely and engage in discussion with their teacher and peers.

Module 1 Map Critique

This lab was designed to help us learn basic map elements and to evaluate maps using these criteria.  This will become a foundation for creating our own maps as we work through the courses.  This was a fun exercise for me.  I enjoyed looking at all the maps and honestly had trouble selecting which one was the worst!  There were quite a few!  Any way, the choices I went with are shown here:

For my well designed example I used SC Wildlife Zones:

Here is the critique I wrote for the map:

While not complex and multivariate, this map is well designed for its purpose.  The cartographer certainly placed concept before compilation and clearly delineated the requisite data.  The colors on the map are engaging without being too bright.  The depth of color is clear within appropriate zones while allowing for underlying features and text to be visible.  This along with the well placed title, subtitles, north arrow, scale bar, legend and date make the map layout attractive.  While the Zone number, county boundaries and names are not specified in the legend, the objective is clear and helps to keep the clutter to a minimum by not over-weighting the legend with extraneous information that can be surmised by the user.  The combination of all of these elements successfully engaged my emotions causing me to want to stop and study the map rather than giving it a cursory glance.  As a hunter, I would be able to use this map to quickly determine the game zone in which I was hunting.  Using the hyperlink on the map, I would be able to quickly determine specific regulations for the zone in which I was hunting as well as other pertinent information.

For my poorly designed example I used Hurricanes_1851-2005:

Here is the critique I wrote for the map:

This map makes me frustrated and sad.  The cartographer had an intent to display data concerning hurricanes from 1851-2005 in the Atlantic Ocean.  Having seen well done versions of these maps I am aware of how much data can be gleaned from using similar map features but providing thorough and detailed labeling.  Because of its lack of labeling and no use of data explanation for important events with the data, the map utterly fails to convey complex ideas with clarity, precision and efficiency.  If I did not already have knowledge of the subject matter, I would not have any idea as to the intent of this map.  In my opinion, the cartographer should have conceptualized exactly what he wanted to convey. Was it hurricane quantity, hurricane strength, common pathways?  Did he want to include possible cause from water temperatures and climatic events from other nearby continents and ocean currents?  Many tracks could have been taken to produce a map that would be visually appealing yet allow for quantitative information to be determined through use of better scale, labeling, and explanations of critical events.  Because of the denseness of hurricane data along the eastern seaboard, Panama, Cuba, the Bahamas, Virgin Islands and South America it would be important to narrow the scope of the data to be conveyed.  One might need to do a series of Map Plates in order to graphically convey all the data for analysis.  Another approach could produce graphical representations of important events and other data being conveyed that could be used in conjunction with the main theme of this map.  Thus a large amount of information could be provided in a logical efficient manner, allowing the user to focus on the main theme and use the additional information to glean insight and analyze the data.  Critical to either style would be to produce a scale suitable for the information conveyed and the area represented.  Including a north arrow and scale bar with appropriate units would also be helpful.  Depending on the theme determined, the legend should include logical explanations of the symbology and colors.  Labeling the continents and possibly the states would be helpful.  If labeling the states becomes too cluttered due to hurricane tracks, a small “cut-out” could be included in one of the blank areas such as northern California and Canada as they are not affected by hurricanes according to this map. Again, depending on the intent the above mentioned information may or may not be necessary.  While the map is currently poorly designed, it has tremendous potential to be reworked and become a visually appealing, useful map. 

Orientation - Cartographic Skills GIS 3015

Hi everybody!  I've been in the AEC industry for about twenty years and have had some exposure to GIS.  I'm looking forward to formalizing my training.  In my free, (yea right), time I love to travel, hike, indoor rock climb, hunt, fish, cook and bake.  I have a great partner and two dogs...a chihuaua who is close to ten and a 9 month old German Shepherd.  I work full time with the City of Port Saint Lucie in the Utility Engineering department.  I am adventerous, enthusiastic and oftentimes quirky.

Here's a link to my story map link:  http://arcg.is/1Tg3nwV