Wednesday, March 29, 2017

Communicating GIS Lab 9

This week for the lab we practiced creating multi-variable maps. In order to do this you have to start with two sets of normalized data that you can represent together in the same area of the map. For our example we have two sets of attribute data associated with the counties in the United States. If you are using a 9-class legend for the choropleth map you should have 3 classes for each of the variables. This will allow you to combine the data into the 9 total classes for the map. The creation of the attribute that you need in the final classification is pretty in-depth into the use of attribute tables in the ArcMAP program, but the basic step is to split each variable into three quantile classes, then add the attributes from each of the variables to crate the final attribute for the classification. In our lab we used 1,2,3 for the obesity variable and A,B,C for the Inactivity variable. So a typical result for a particular county would look like "A3" in the final attribute category.
 From here the counties are labeled using a unique values based on this "final attribute" and you have your bivariate choropleth map! The hardest part of making the map is the selection of your colors to represent your classes. You also have to build the 9-class grid from scratch on ArcMAP since there is no feature to do this. In the end the result is worth the effort and it makes for an aesthetically pleasing map.

Wednesday, March 15, 2017

Communicating GIS Lab 8

This week we worked with infographics. The goal was to produce a product using two sets of national data. The data I chose was the percent of population that had attended college and the percentage of unemployment. The data is represented by the county in the two maps of the U.S. and the information is broken into state averages in many of the infographics. The two base maps allow the user to draw their own basic conclusions about areas where there is a lower college educated population and higher unemployment. Then the information about the state averages introduces the top three and bottom three performers for college education and unemployment in the bar graph. On the left side of the product there is a scatter plot with a trend-line showing that in all of the county data there is a sight trend confirming the basic conclusion that can be drawn. Additionally there is summary information provided in the center of the product referencing the total U.S. averages and year prior statistics.
In finalizing the layout I decided that the design would look cleaner if I separated each infographic with its own “neat line.” This would cut down on confusion of legends and data between infographics and help direct the user where to look for what information. I chose different areas for the infographics based off where the map data was for the two U.S maps. I used the spaces that were best fitted for the infographic to help balance the product as a whole. I chose a dark background color to represent either water or just a neutral background for all of the information to be overlaid on. I was able to use normal legend symbology effectively by curving it around the map features. This helped with the overall balance of the product as well. Finally I added a title describing the data and year of the analysis. 

Wednesday, March 8, 2017

Communicating GIS Lab 7

This week we spent time learning about terrain elevation an how we can visualize it effectively in ArcGIS. We started with elevation data and in this example added land cover polygon data. The elevation data is useful by itself, but it is hard to visualize what is high terrain and what is lower terrain. To give a better picture I executed a hillshade with the standard altitude and elevation to shed a little light on the terrain. With the hillshade you can see the actual ridges and valleys of the area and determine slopes better than the flat gray scale data. Then I took land cover data and adjusted the symbology to show the different types of features in their actual colors (for the most part), some are altered to distinguish between types of vegetation easier on the map.
Finally with both layers displayed correctly I adjusted the transparency of the land cover layer to allow the hillshade layer to be visible underneath it. This creates the illusion that the 3D hillshade is colored with the land cover data. Then by adding the standard map elements and trying to balance the awkwardly shaped area as best as possible, I was able to produce an effective map representing elevation and land cover data in one.