Question 1:
Here we will prepare five tesselated surfaces from CONUS and write a function to plot them in a descriptive way.
## function (x, by_feature = FALSE)
## {
## if (by_feature)
## nVerts(sf::st_geometry(x))
## else sum(nVerts(sf::st_geometry(x)))
## }
## <bytecode: 0x7fcd120a53e0>
## <environment: namespace:mapview>
Question 2
In this question, we will write out a function to summarize our tessellated surfaces
| Tesselation | Number of Features | Mean Area | Standard Deviation of Features | Total Area |
|---|---|---|---|---|
| Counties | 1 | 2,521.745 | 3,404.3252 | 7,837,583 |
| Voroni | 1 | 2,521.745 | 2,887.1397 | 7,837,583 |
| Triangulated | 1 | 1,251.808 | 1,575.7996 | 7,756,200 |
| Square Grid | 1 | 3,495.800 | 894.3932 | 7,837,583 |
| Hexagonal | 1 | 3,451.159 | 870.9929 | 7,837,583 |
#2.5 We can see that the voronoi is most similar to the original. Triangulated tessellation has most features. Hexagonal tessellation has least features.
Question 3
We will analyze the distributions of these dams (Q3) and their purpose (Q4) through using a point-in-polygon analysis
#3.6 Again, the voronoi is most similar to original data. I will choose voronoi because I think it has the best coverage. The other ones do not have as much detail.
## [1] 495
Extra Credit
Map the Mississippi River System and show largest/high hazard dam in each state