GIS 3015: Dot Mapping

This past week, I created a dot map showing the population density of Southern Florida.  Dot maps are ideally utilized when you have collected conceptual data for enumeration units, and wish to show that the underlying phenomenon is not uniform throughout the enumeration units.  Dot maps are created by letting one dot equal a certain amount of some phenomenon and then placing dots where that phenomenon is most likely to occur.  As seen in the image below, the red dots are showing the population density in Southern Florida.  One of those dots is equivalent to 10,000 people.  I created most of this map by using ArcGIS, and I labeled the major cities and added a legend within Adobe Illustrator.  For my legend, I created a box and copy and pasted it twice, to ensure that all three boxes were of the same dimensions.  I then selected 5, 20, and then 50 dots to place in those three boxes.

GIS 4043: Vector Analysis 2

This past week, I was given an assignment to create buffers of the roads and water within a certain area.  A buffer zone is an area that is within a given distance from a map feature.  Points, lines, and polygon features can all be buffered.  Buffers are used to identify areas surrounding geographic features.  When you buffer on a set of features, the output is a set of polygons.  These polygons define an inside region, which is an area less than the specified buffer distance from the features of interest.  Anything outside of the polygons is the outside region, which is an area more than the specified buffer distance from the features of interest.
I also had some practice with using the overlaying tools, which can be found in the ArcToolbox.  Overlays are another common cartographic modeling operation.  They are the primary way in which information from two separate themes may be brought together in an analysis.  Overlays are most common for polygonal data when we perform a geometric intersection, which results in a new layer with the combined attributes of both initial layers.

GIS 3015: Flow Line Mapping

This past week was all about flow lines.  We were given the option between two different kinds of base maps to create flow lines for. Base Map A, as shown below, provides a choropleth map of immigration per U.S. states in a separate inset map. Whereas in Base Map B (not shown), the choropleth map is overlaid on the world map.  The disadvantage to this, is that it can make the choropleth data difficult to see, since it is being displayed at a global scale. With the base map I have chosen, I had the option to rearrange the positions of each continent so that they are spread out across the map, and direct flow lines toward the stand-alone choropleth map.  I was also able to leave the continents in place, and have the choropleth map remain as a single inset. As seen below, I decided to go with the second option.  I decided against rearranging the continents because I felt it would look less cluttered and more organized.  

I created this map in Adobe Illustrator, using the pen tool to create the flow lines.  While working in Illustrator, I was able to use variations of stylistic effects to make this map more appealing.  For example, I used a drop shadow effect for the flow lines as well as all of the continents. For the continents, I set the mode to “soft light” so it wouldn’t apply a drop shadow for every country within those continents. I also added a box where the title, date and my name are located.  For this, I turned the opacity down to 85% and gave it an inner glow.  I did this because I wanted the colors of the box and the background to have a soft tone and somewhat blend together. 

GIS 3015: Isarithmic Mapping

Our assignment this past week was to create an isarithmic map showing the annual precipitation of the state of Washington.  I created a map showing both continuous tone (not shown) and hypsometric tints (shown below).  The addition of hypsometric tints between contour lines enhances the ability to visualize a 3-D surface because light and dark tints can be associated with low and high values. The problem with this method, though, is that the limited number of tones suggests a stepped surface, rather than a smooth one that occurs in reality.  This issue can be fixed by creating a continuous tone map, which each point on the surface is shaded with a gray tone (or color) proportional to the value of the surface at that point.  However, one problem with interpreting continuous tone maps is that it is difficult to associate numbers in the legend with particular locations, but this can be fixed by using hypsometric tints (as shown below) which overlays continuous tones with traditional contour lines.

GIS 4043: Data Search

For the past couple weeks, we had to create 1-3 maps, showing various things. I created three maps, and this is one of them.  In the map shown below, you will see the different elevation levels of Citrus County, Florida. You will also notice the map shows the main streets and major cities of this county.  The green trees show all of the invasive plants and where they are located within the county.  I created the map by using ArcGIS, and I wrote the names of the major cities in Adobe Illustrator. 

GIS 3015: Choropleth and Proportional Symbol Mapping

My assignment for this past week was to create a choropleth map using either proportional or graduated symbols.  Proportional and graduated symbols are a class of maps that use the visual variable size to represent differences in the magnitude of discrete data, like counts of people.  Just like in choropleth maps, these two symbols allow you to create classed or unclassed versions of this mapping technique.  The classed maps are known as range graded or graduated symbols, and the unclassed are called proportional symbols, where the area of the symbols are proportional to the values of the attribute being mapped.  I decided to use the graduated symbols (as shown below) to compare the population of Europeans and their wine consumption.  I decided to use graduated symbols for my map because unlike proportional symbols, they will likely result in displaying the smallest symbol on the map.  Proportional symbols will likely not display a symbol for the excluded countries due to low consumption qualities.  We also had the option to create our own picture symbol, which after spending hours on trying to figure out, I was unable to achieve.    

The data classification method that I chose for this map is the quantile method.  I chose this because it uses all of the color variations, ensuring that all of the classes are visibly represented on the map.  With this method, it is not difficult for the viewer to ascertain the population dynamics for most of the countries.  The quantile data classification method makes the map look presentable, is appealing, and it also makes things easier to comprehend for the viewers.

I created this map by using both ArcMap and Adobe Illustrator.  I added the inset, the graduated symbols, title, legends and all of the elements within ArcMap.  I then switched over to Illustrator and added the subtext, providing a brief synopsis of what the map is presenting.  I also added all of the country and water names. Finally, I lowered the opacity for the area the inset map shows, making it more visible.

GIS 3015: Data Classification

This week I created four maps showing Miami Dade County, Florida.  These maps show different classification schemes involving the percentage of population within Miami Dade County. In the top left corner, it shows the natural breaks classification method.  In this method, class ranges are determined based on algorithms that attempts to make all values within a class as similar to each other as possible.  It also tries to make these values as different as possible to those values in other classes.  When using natural breaks, the graphs are examined visually to determine any breaks in the data.  Moving onto the next image in the top right, you'll see that this graph shows the equal interval classification method.  This approach creates classes that all have equal ranges in the data.  The range values are determined by dividing the total data range by the number of classes.  On the bottom left, you'll notice the quantile classification method is being used here.  This method divides the distribution into an equal number of observations. Using this approach, data is rank ordered and equal numbers of observation are placed in each class.  Although, using this method could cause gaps in your dataset and could make things difficult to comprehend for the viewer.  Finally, the standard deviation method puts the majority of observations in one class surrounding the average value, while other classes will have fewer and fewer data points as they get further away from the mean.  As you'll notice, this graphic is using a different color scheme then the ones surrounding it.  When using the standard deviation method, you'll want to use a divergent color scheme, so it clearly presents the diverging data.