GIS3015 Module 12 Lab: Google Earth

Utilizing Google Earth's mapping capabilities was the focus of this lab exercise. Users of Google Earth can take advantage of the vast amount of information available online or from other users to combine with their own layers. Maps created in Google Earth can be saved and shared with others easily. For this module a Florida counties shape file and a dot density map of south Florida county populations were converted to KMZ files, and then the files were opened in Google Earth. Adjustments were made to the transparency and altitude of the layers to ensure visibility of necessary information after which a Google Earth map was saved in KMZ format. From that point a tour of south Florida was created using placemarks for major cities. The ease of zooming and panning in GE to view areas in 3D was fascinating, even though it did indicate that a new mouse is in order for some of the finer movements. The ability to record a tour was new to me and resulted in a lot of experimenting. A lesson learned by repetition during this module was that only the layer currently selected in Google Earth is saved to My Places. Included below is one view of downtown Miami. If you look closely, you might see Don Johnson in the speedboat.

Screenshot of Downtown Miami in Google Earth

GIS3015 Module 11 Lab: Dot Density Mapping

Module 11 introduced the practice of dot density mapping with ArcMap. During this lab exercise Conversion Tools as well as Join and Relates were utilized to join information between files. Methods for adjusting dot size and dot placement were practiced which enabled a more realistic placement of dots to represent actual concentrations of populations. The exercise was also an opportunity to experience ArcMap at its most finicky level yet. Resuming the use of masking after finishing the map layout created numerous obstacles to a timely completion of this map. 

Population Density of South Florida (2000)

GIS3015 Module 10 Lab: Flow Line Mapping

Mapping with flow lines provides a very simple graphic illustration of geographical movement of some phenomena. In this module a flow map was created to show the origins of and number of persons coming from other regions and obtaining legal permanent United States resident status. A choropleth map of the United States was created to show the percentage of immigrants to each of the fifty states. The graphic representations were based on fiscal year 2007 statistics obtained from the US Department of Homeland Security. Flow lines on flow maps must be hand drawn due to their individual and unique nature. Because of this, Adobe Illustrator was used exclusively to create the flow map.

preliminary sketch - horizontal

Far more than any other map created to date, this flow map took the most preparation to determine the best layout before even starting Adobe Illustrator. First in the process was sketching out two possibilities ~ a horizontal format and a vertical format, each one showing the location of the continents with flow line placement, location of the choropleth map and the legend. For each map a list of pros and cons was also created. The horizontal format allowed plenty of room to place the US in the center with the regions placed around the US with large flow lines directed to it. The horizontal layout seemed less busy, was very clear, and required shorter flow lines. The cons of the horizontal layout were that the regions were not viewed in their normal relationships to each other, and the map was very US-centric giving the impression that the US is of greater importance than the rest of the world. Although the purpose and focus of the map is to show immigration to the US from all other regions, the US-centric format was rejected eventually. On the other hand, the vertical format kept the US and the other regions of the world in their proper relationships, and the US was kept at the same scale as the other countries. 

preliminary sketch - vertical

Negative aspects of this format were that the flow line showing immigration from other parts of North America to the US was split and had to be joined near the arrowhead, and either the South American flow line had to cross the flow line coming from the region of Mexico/Central America or it would have to be placed in the already congested area of the east coast. That would be dealt with later, as this still seemed like the better layout choice.

The next step in the flow map-making process was to print out a preliminary layout for a working sketch. This involved placing the world map and choropleth map in an AI document and printing it out.

working sketch

Questions, notes, ideas, RGB colors for matching purposes, additions/deletions, and information that came up in discussion board postings as well as the flow lines were marked on this map or the initial hand-drawn sketch. This provided a way to keep all the necessary information and lab requirements organized and to minimize the possibility of leaving information out unintentionally. It was also a way to think out different ways of presenting the information prior to committing to making changes in Illustrator. With flow lines sized proportionally and matching the regions by color, the resultant map provides an easy to interpret comparison of immigration from different regions of the world. For specific immigration numbers, viewers can consult the chart included in the map. Ideally this chart would have included the color and size of flow line attributed to each region, but time constraints and other obligations precluded that this time.

Persons Obtaining Legal Permanent
U.S. Resident Status (By Region of Birth, FY 2007)

Choropleth colors were chosen to match the color of North America in the flow map. Normally the bar scale for a map would be placed lower than the legend of the map, but in this particular case it was placed closer to the map for ease of use. The orange color scheme of the choropleth map and the legend along with the size of the rectangles in the legend attract one’s attention directly to the legend from the choropleth map, so it still appears to rank higher visually if not physically.

While some aspects of using AI are becoming familiar, there is so much more to learn. Creating the flow lines was extremely difficult with each one requiring several attempts. Applying some of the special effects to the flow lines changed either the color, length or position of the flow lines with the longer lines impacted more severely than shorter lines. Because of this, only drop shadow was applied to the lines, and that had to be adjusted where the lines come together at the east coast of the US. Some workarounds for the other issues may be discovered with more practice. Throughout this map-making exercise, numerous copies of the map were saved at different stages to provide backup in case a special effect could not be undone for some reason. This exercise exemplified the extensive capabilities of Adobe Illustrator for creating or enhancing maps.

GIS3015 Module 9 Lab: Isarithmic Mapping

Utilizing contours formed by connecting points of equal values, isarithmic maps are useful for illustrating continuous data such as elevation and topography, barometric pressure, and precipitation. This module’s exercise focused on the average annual precipitation of Washington State and involved the completion of two isarithmic maps, one symbolized with the continuous tone method and the other symbolized with hypsometric tints.

As seen in the first map, the continuous tone technique is a proportionate tonal representation of each surface point’s value. Because this results in a gradual change in color with changes in quantity, distinguishing between quantities can be difficult when trying to relate the values in the legend with distinct locations on the map. That is why I chose to provide contour lines on this type of map. Contour lines provide “break points” in precipitation amounts relative to the legend.

Map 1: Continuous Tone Isarithmic Map
Average Annual Precipitation: Washington State (1981 - 2010)

Hypsometric tints were used for the second map. Hypsometric tinting has discrete breaks between ranges of values. The abrupt changes of color enable a viewer to more easily distinguish the differences between values. Unlike continuous tone maps, maps with hypsometric tinting can be used easily without contour lines. Creating the map from the data involved converting the raster data to integers and then manually forming classes to symbolize.

Map 2: Hypsometric Tints Isarithmic Map
Average Annual Precipitation: Washington State (1981 - 2010)

For both of these precipitation maps I selected a vertical-format legend for a couple of reasons. The first reason is that precipitation is a "vertical" measurement of volume (imagine a rain gauge) with values increasing toward the top. The other reason is that greater precipitation generally is associated with higher elevations. A vertical format legend echoes those relationships (lower/dryer, higher/wetter). Deciding which legend format to use was definitely the easier part of implementing it. Trying to remember where and how to change the symbology and format ended up being a bit time-consuming due to minimal experience.  I’m glad to say that that is improving with every lab exercise.  With their use of hillshading effect and vibrant color ramps, these maps rank among my favorites to create so far.

GIS3015 Module 8 Lab: Proportional Symbol Mapping

The Proportional Symbol Mapping lab walked students through the development of two maps about wine consumption in Europe utilizing proportional symbols. The first map used data for all of Europe with a couple of exceptions and was created after joining the data from Excel with the shapefile in ArcMap. The second map built on the same information, but focused on only seven western Europe nations. Completing the exercise included combining the geographical features of several countries into one feature for each of those countries by the use of the Dissolve tool in Geoprocessing, reprojecting the shapefile, and then adding the wine consumption data. The consumption data was reviewed for discrepancies or omissions that might cause issues, and then was saved as a .csv before it was added to ArcMap through Joins and Relates. After viewing results of using the data as received from the Wine Institute, the quantities were then reduced using the third root with the field calculator in the attribute table and then used for the map. Initially I preferred the open look of hollow symbols for these results, but eventually decided to go with filled circles. I'm still on the fence about both options. What I knew unquestionably from the beginning was that I would use wine-based colors for the maps, reflecting one of my favorites ~ Cabernet Sauvignon. (Initially I had selected the hollow circles to enable the countries' borders to remain visible, and also to give a nod to another European favorite, Champagne.) Unaware at the start that a legend was deliberately not part of this lab, I have a gaping space on my first map and a bit of a queasy feeling about turning in an incomplete map. I stayed true to the instructions for the lab, but I'll fess up to spending some time trying to figure out how to produce a legend for this type of map. That will have to wait for a future project! This first map was completed exclusively in ArcMap.

Wine Consumption in Europe ~ 2010

The second map was begun in ArcMap and completed in Adobe Illustrator. I'll confess right here that I am so looking forward to the day when Illustrator actually feels like an efficient pathway to an awesome map. For now, I spend a lot of time consulting resources and wandering around the menus. The symbolization was a lot of fun, combining the circles and wine bottles and adding labels. Unfortunately, something which I still have not identified (but probably having to do with manipulating files in directory in preparation for uploading), caused all my wine bottles to disappear from the face of my map after I had exported a jpeg. I couldn't figure out where they went or how to get them back, so that part of my .ai document had to be redone. Another thing that was a bit frustrating was having a color scheme started in ArcMap and then opening the exported file in Illustrator to find significant color differences.  The worst was discovering that the North Sea and Atlantic Ocean were lavender instead of the blue I had selected.  Maybe it has been an especially cold year in that region as well, but I changed the color rather than retain the lavender look.

Wine Consumption in Western Europe ~ 2010

With the information shown directly below each country's name, I think the second map is easier to read and use. However, that type of symbology does take up a lot of space, so it wouldn't be appropriate for maps with a lot of data. Now that this project is finished, I do believe I'll go relax with a nice red.

Cartographic Skills Module 7: Choropleth Mapping

Module 7 lab continued the data classification experience started in Module 6 and involved the completion of two choropleth maps.  Both ArcMap and Adobe Illustrator were used to complete the maps, one of which was in color (Change in US Population by State) while the other was in gray scale (Change in US Population by Census Division).  The exercise included using Excel for making calculations and utilizing that information to determine appropriate classification method for the data, selecting appropriate color scheme and grays for the different classes of the choropleth maps, labeling states and divisions, and creating legends.  Being quite fond of Excel for numerous purposes both personally and professionally, I used it beyond the lab requirements to determine additional information for different classification methods which I then used to select a data classification method for the second map.

Percent Change in State Populations (1990 to 2000)

Practicing projections in Introduction to GIS (GIS4043) provided the experience needed to change the projections of Alaska and Hawaii to something which would yield more realistic graphics, specifically for Hawaii.  The nearly North-South orientation of the Aloha State was a bit disconcerting, but reprojection took care of that.

Labeling divisions for the second map was an exercise in experimenting effort.  Several font sizes, types, and effects were attempted in trying to achieve the best, most uniform appearance for the division labels.  The appearance of masking was not appealing, and trying to label enough of the states in each division to distinguish between adjacent divisions which were in the same class (West North Central/East North Central and Middle Atlantic/New England) was a challenge.  Finally I settled on allowing labels to straddle some state lines, but shifted the labels so that the lines were generally between letters or words in the label.  Using Transform > Move allowed very fine movement to do this.

Percent Change in Census Division Populations (1990 to 2000)

The results of the lab assignment are two easy-to-read-and-interpret maps, one for the change in US population for individual states as well as the District of Columbia and another for the change in US population by Census Divisions.

Cartographic Skills Module 6: Data Classification

Data classification was the focus of this week's Cartographic Skills Lab.  The creation of a map comparing four different methods of data classification (Equal Interval, Quantile, Standard Deviation, and Natural Breaks) was completed by creating four data frames within one map and then applying a method of data classification to each of the data frames.  As careful as I thought I was being, I learned the hard way that an oversight can produce an erroneous map. Printing out a review copy of the map prior to submitting it helped me to catch a couple of major errors.  Nice-looking maps aren't especially helpful if the information is wrong.

Comparison of Four Methods of Data Classification as Applied to
Black/African-American Percentage of Population
Escambia County, Florida, 2000

After correcting the errors in the data classification method comparison map (involving legends for certain classification methods being applied to the wrong data frames), it was easy enough to save the map as a new document and then make some adjustments to complete a map of the data classification method which I thought best for this particular purpose.  In this situation I felt the Natural Breaks method was best suited.  The Natural Breaks method takes into consideration clusters of information which can be overlooked by using the Equal Interval method.  The Standard Deviation method doesn't seem to be very user friendly for this purpose.  The Natural Breaks method puts more emphasis on groups of similar data and is easy to interpret which seemed appropriate for the audience likely to use a map like this.

Natural Breaks Method of Data Classification Applied to
Black/African-American Percentage of Population
Escambia County, Florida, 2000

Cartographic Skills Module 5: Spatial Statistics

The map below was created while progressing through ESRI's online course, "Exploring Spatial Patterns in Your Data".  This exercise involved several tools from the Spatial Statistics toolbox in ArcMap.  After weeding through some cobwebs to recall my statistics education, I found this to be a fascinating way to visually show the mean center, median center, and directional distribution of the data.  Having this visual representation of the data is helpful in determining whether there are potential issues with the data set which can also be confirmed with additional analysis using some of the other tools in the Spatial Statistics toolbox.

Analysis of Data: Weather Stations in Western and Central Europe

While the creation of this map, the histogram, and the QQ plot were rather straightforward, I found the latter part of the online course to be far more foreign.  Additional work with Voronoi maps, semivariogram clouds, trend analysis, and the like will be necessary before I feel confident working with them.