Google Earth and 3D Mapping

The final project for Cartography class was using Google Earth Pro and .kmz files.  The goal of this lab was to familiarize ourselves with presenting 3D depictions of data by creating a multi-city tour of Southern Florida.

We first had to convert our surface water date in ArcPro to a .kmz file so that it could be imported into Google Earth.  This is done with the "Layer to KML" tool.  Next, I launched Google Earth Pro and imported the required .kmz files.  I also placed a legend on the 3D map.

To create the tour itself, I had to locate the points of interest on the map and then create placemark points for each one.  Following that, I used the program's "record tour" feature to make a recording of each location.  This produces a video of each location that shows off the 3D cityscapes.

Google Earth Pro is fairly straightforward to use, though because of its large scale it can be taxing on a lower end computer when the map is zoomed out.  I think this technology could work particularly well for creating virtual tours of places - such as historic neighborhoods - where an audio narrative could accompany the video.

A dot density population map of Southern Florida presented in Google Earth Pro.  Various body of water are also colorized to make it more obvious that the population of Florida is centered near the coasts and away from marshy areas.

Isarithmic Mapping

The focus of the lab this week was isarithmic mapping.  Isarithmic maps depict continuous phenomenon that occur across an area.  The process of interpolation makes it possible for an algorithm to generate data across an entire area even though there are only set locations gathering data..  This type of map is particular common for climatological data and is used in a number of industries such as agriculture and engineering.  

I created a map of the average annual precipitation rates in Washington state using a 30 year data set ranging from 1981-2010.  The interpolation method used on this data was the Parameter-elevation Relationships on Independent Slopes Model, also known as PRISM.  This method takes data from the weather stations and factors in environmental attributes such as elevation and proximity to the coast in order to generate a complete set of data.

To create this map I first map a hillshade effect layer using data from the annual precipitation raster.  This improves the look of the terrain by creating the appearance of depth.  Areas of high elevation tend to have higher rates of precipitation, so this is a logical feature to incorporate into this map.

The next step was to create hypsometric tints.  A hypsometric tint is a set of classed graduated colors useful for showing the range of a environmental phenomenon.  This used the spatial analyst tool "int" to process the annual precipitation raster.  I then symbolized the colors into proper classes that corresponded with precipitation amounts.

The last part of map creation was to create contours.  Contours are lines that show the discrete changes between value ranges.  This process involved the contours tool on the annual precipitation layer at the same values as were assigned for the hypometric tints.  This makes the divisions between the different classes even more obvious.

An isarithmic map of Washington state showing the average annual precipitation rates over 30 years.  The map utilizes hypometric tinting as well as contour lines to display the data.

Choropleth Mapping

In this week's lab we continued to learn about making choropleth maps.  This week we also learned how to work with proportional and graduated symbols.  Choropleth maps are maps with shaded sections to convey the intensity of a phenomenon.  These maps must be normalized by area so when comparing different enumeration units you are making a comparison based off of density and not raw data. 

The goal for this week's lab was to create a map that showed the population density throughout Europe in a choropleth format as well as the consumption of wine per captia.  The map was primarily constructed in ArcGIS Pro with some editing in Adobe Illustrator. 

For the population density, I wrote a SQL exclusion to take the countries of Gibraltar, Jersey, Malta, and Monaco out for when classifying the breaks between populations.  This is because those countries are very small, and thus densely populated - making them outliers in the data.  I classed the remaining countries using the natural breaks method into four different classes.  This created a map with a good amount of contrast between countries. 

The wine consumption was shown through a set of graduated symbols with its associated country.  I decided to take on the extra challenge of using custom symbols for this section.  I downloaded a wine glass icon by Thengakola from the Noun Project.  I then edited this icon in illustrator so that it would have a different fill amount for each of the different classes of consumption.  Again I used natural breaks to divide up the data.  I classed the data into five separate groups to create a range of very low consumption to high consumption.

I used ArcGIS Pro's labeling and annotation feature to generate labels for each country.  Some countries are very small and do not consume significant quantities of wine so they have been omitted.  These include: Gibraltar, Guernsey, Isle of Man, Monaco, Jersey, and San Marino. 

In the final map, I created an inset for the countries near the Adriatic Sea.  This is because in the overview map the information in that area was crowded and difficult to read.  The inset allows for clearer interpretation of the data.

A comparison between the population densities of European countries and their per liter wine consumption.  The population density is shown with choropleth mapping and the wine consumption is showed with overlaid graduated wine glass symbols.

Overall, the take away from this map is that wine consumption correlates a lot with the cultural and agricultural practices of a country.  Vatican City beats all other European countries in wine consumption with a whopping 73.78 liters per person (if an average wine bottle is .75 liters, that's about 100 bottles a year!).  This is in large part to the all-Catholic population of the country that takes part in drinking wine for communion.  Appreciation of wine is also cultural as it is popular in France and Italy.  This is in part because they have a long tradition of growing grapes for wine and have the perfect climate and soil conditions for it.  Other countries may not prefer wine as much but that does not mean they are altogether dry.  Countries with low wine consumption tend to have foodways that put more emphasis on other forms of alcohol such as vodka in Russia and Lithuania.

The most challenging part of this map was placing the wine glass symbols and the country labels just so that it was obvious what they were in reference to without occluding anything else.  I now have great appreciation for cartographers who did this work in the pre-digital age.  As a bonus, I think I now have a much better sense of the geography of Europe too.