Module 7- Cartography- Google Earth

 This week focused on converting map layers to KML files and uploading to Google Earth as well as creating a Google Earth Tour. The tour was a neat feature I'd never experimented with. For our map and tour we focused on south Florida.  The map created was a dot density map with surface water layers added. Before we uploaded our layers to Google Earth we practiced using the Layer to KML tool to convert the layer into a format that could be used in Google Earth. We also learned how to organize layers and add images in Google Earth. 

The tour was an overview of major Florida metropolitan areas. After creating placemarks for each area I wanted to feature, I gave an view of the entirety of south Florida and the zoomed in and out on the placemarks then panned and zoomed in, out and around the downtown areas. 

Here is a copy of my Google Earth map.

Module 6- Cartography- Isarithmic Map

 For this week’s exercise we focused on two methods of presenting data on an isarithmic map: continuous and hypsometric. We used PRISM annual precipitation data obtained from the USDA NRCS National Geospatial Management Center in coordination with the PRISM Group at Oregon State University for the state of Washington from 1981-2010.

First, we created a map presenting the precipitation data in continuous form. We also created our own hillshade layer to add to our map display elevation since this effect hasn’t been added to ArcGIS pro yet. Then we created a second map in which we converted the data to a hypsometric tint to better visualize the precipitation totals. To do this we used the Int (Spatial Analysis) tool to convert the raster data to integers. We then created 10 manual intervals to classify the data. We added our hillshade layer and added contour lines using the Spatial Analysis Contour List to add contour values. Finally, we were tasked to create a map layout and to include a description of how the data was interpolated.

When reviewing the differences in the continuous and hypsometric maps and answering the process summary questions I was reminded of a recent map I’d viewed on my local weather station’s website. Sure enough, both the tornado risk and predicted precipitation totals were shown on a hypsometric isarithmic map similar to the one I created.

 

 

 

Module 5- Cartography- Choropleth and Proportional Symbol Mapping

 For lab 5, I was tasked to make a map depicting the population densities of European countries as a choropleth and wine consumption for those countries as a graduated or proportional symbol using ArcGIS Pro. I incorporated data classification and map design principles learned from the past several modules.

Using Data from Eurostat and the Wine Consumption institute, I constructed a map meeting the appropriate parameters. I hit two major snags that took me awhile to resolve. At first, I could not figure out why the labels I wanted to exclude from my map were not excluded. After almost an hour I realized that I was supposed to use an “and” clause rather than the “or” clause we used earlier for data exclusion. This confused me because I understood “and” to require the associated feature to meet all the parameters listed, but I had never worked with a negative statement before and when using “is not equal to” then “and” is the appropriate choice. I anticipate that the programming course offered this summer will help me better understand this aspect of the software.

The second snag I hit was moving my symbols on the map. I didn’t realize that even though the wine consumption data was symbolized using graduated circles, the feature class itself was classed as polygon data. One of the module leaders found an article that helped clear this up for me. To be able to move the symbols I had to convert the data to a point feature. This solved my issue, but it was a headache because all my previous work (setting the classes, excluding the appropriate data, creating my labels, etc.) had to be redone on the new feature class.

I used the histogram to study the intervals made with the classification data. I used Natural Break classification for the population density data, but this posed an issue, one that I foresee would have been an issue for any classification method other than equal interval. For my inset map I needed to exclude the data from the Balkan region on my main map. By excluding this data it “altered” my natural breaks. Then, on my inset map which included those countries, the Natural breaks were representative of Europe as a whole, which meant some of the countries were classified differently and the break points were not the same for both maps. I ended up using the break points of the totality of Europe and set manual break points on the main layout.

I used equal intervals to classify my wine consumption data. Looking at the histogram I felt like this split the data in a way that was meaningful.

Overall, I found this project very insightful and learned many new strategies I feel will be helpful going forward. Even though I had several struggles that added significant time to the process I know that this further reinforced the information I took in.

Here is my map:

 

Module 4- Cartography- Data Classification

     For this week’s lab we compared four different data classification methods and identified the most appropriate method for visualizing the spatial data provided. Our data source was the 2010 US Census Tract in Miami-Dade County obtain by FGDL, and we were tasked with representing the population of seniors (65 and up) by both percentage of the population for each tract and by normalizing the data to show the number of senior citizens per square mile. To do this we created two maps, both comparing four classification methods- Natural Break, Equal Interval, Quantile, and Standard Deviation.

We were tasked to explain how each classification method differs as well as which method of classification (by percentage or per square mile) was a more accurate representation of the distribution of senior citizens. I concluded that since census tracts were specifically designed to have uniform populations the percentage of senior citizens was the best choice for representing this data set. Normalizing the data by square mile favors smaller census tracts even if the senior population is significantly less than a larger tract. In order to obtain a more accurate “per square mile” comparison I proposed that the county should be equally divided by area to get a more accurate visual of population density.

Map 1 depicting the four classification methods representing the distribution of citizens by percentage of the population

 

Of the four classification methods, I believe that the Natural Break is the most accurate representation of the data. The data contains an outlying tract consisting of 79% of the population being 65 or older. This skews the data slightly to the right. The Natural Break method accounted for this outlier while still breaking up the classes into categories that are a cleaner representation of the data. The equal interval method contained an empty class due to the presence of an outlier and grouped the majority of observations in the lower classes. It failed to provide insight into which tracts other than the outlier contained a higher density of senior citizens. The quantile method grouped the outlier with significantly lower population categories which masked its status as an outlier and might lead to inaccurate assumptions about the population represented in those tracts. Standard deviation is not the best representation due to skewedness of the data as well as the presumed audience.

I found this exercise very helpful in solidifying my understanding of the lecture material, how each classification method works, and under which circumstances they are best suited for creating an accurate representation of spatial data.