---
title: "Geoprocessing 1 - Geometric Measurements"
output: 
  html_notebook:
    toc: yes
    toc_float: yes
---

In this Notebook we will use functions from `sf` that measure geometric properties (area, length, distance).

- compute the total area of YNP in meters^2^ and miles^2^  
- compute the area of watersheds   
- compute the total length of roads  
- compute the distance between campgrounds and cell towers    

## Setup

Load the packages we'll need and set tmap mode to 'plot':

```{r chunk01}
library(sf)
library(units)
library(tmap)
tmap_mode("plot")
```

Load `dplyr` and set name conflict preferences:

```{r chunk02}
library(dplyr)

## Load the conflicted package
library(conflicted)

# Set conflict preferences
conflict_prefer("filter", "dplyr", quiet = TRUE)
conflict_prefer("count", "dplyr", quiet = TRUE)
conflict_prefer("select", "dplyr", quiet = TRUE)
conflict_prefer("arrange", "dplyr", quiet = TRUE)
```

## Import the Park Boundary

```{r chunk03}
## Define a convenience variable for UTM Zone 11
epsg_utm11n_nad83 <- 26911

## Import the YNP border
yose_bnd_utm <- st_read(dsn="./data", layer="yose_boundary") |> 
  st_transform(epsg_utm11n_nad83)
```

## Compute the area of the park

Find area with `st_area()`:

```{r chunk04}
yose_area <- yose_bnd_utm |> st_area()
yose_area
```

Find the area in miles^2^.

```{r chunk05}
## View in square miles
set_units(yose_area, mi^2)
```

## Import the Watersheds

Next import the watershed boundaries.

```{r chunk06}
yose_watersheds_utm <- st_read("./data/yose_watersheds.gpkg", layer="calw221") |> 
  st_transform(epsg_utm11n_nad83) |> 
  select(CALWNUM, HRNAME, RBNAME, HUNAME, CDFSPWNAME, CDFPWSNAME, HUC_8, HUC_8_NAME)

yose_watersheds_utm |> slice(1:10) |> as_tibble()

tm_shape(yose_watersheds_utm) +
  tm_borders() +
tm_shape(yose_bnd_utm) +
  tm_borders(col="red", lwd=3)
```

## CHALLENGE: Compute the watershed areas

Compute the watershed areas, and add them as a new column in the attribute table. [Answer](https://bit.ly/3dov9Zw)

```{r chunk07}
# Your answer here

```

## CHALLENGE: Find the largest watershed

Find the largest watershed, and report the area in acres. [Answer](https://bit.ly/3uaY4HE)

```{r chunk08}
# Your answer here

```

## Import the Roads

```{r chunk09}
yose_roads_utm <- st_read("./data/yose_roads.gdb", "Yosemite_Roads") |> 
  st_transform(epsg_utm11n_nad83) |> 
  select(RDNAME, RTENUMBER, YOSE_Surface, YOSE_FIRE_ROAD, YOSE_INPARK, YOSE_Type)
```

Compute the length of roads and save it in the attribute table:

```{r chunk10}
yose_roads_utm <- yose_roads_utm |> 
  mutate(rd_length = st_length(yose_roads_utm))
yose_roads_utm |> slice(1:10) |> as_tibble()
```

## CHALLENGE: Compute Road Lengths

Find the total length for each type of Road (see YOSE_Type) in miles. [Answer](https://bit.ly/3wbbLbb)

```{r chunk11}
# Your answer here

```

## Import the Campgrounds and Cell Towers

```{r chunk12}
yose_campgrounds_utm <- st_read("./data", layer="yose_poi") |> 
  st_transform(epsg_utm11n_nad83) |> 
  filter(POITYPE == 'Campground') |> 
  select(POINAME)
  
yose_celltwrs_utm <- st_read("./data/yose_communications.gdb", "Cell_Towers") |> 
  st_transform(epsg_utm11n_nad83) 
```

Plot them:

```{r chunk13}
tm_shape(yose_bnd_utm) +
  tm_borders() +
tm_shape(yose_campgrounds_utm) +
  tm_symbols(size = 0.3, col = "blue") +
tm_shape(yose_celltwrs_utm) +
  tm_symbols(size = 0.3, col = "red") 
```

## Compute Distances Between Features - 1 sf data frame

`st_distance()` can be used to compute distances between features. If you pass it one sf object, it will return a symmetric distance matrix between all pairs of features. 

Compute the distance between all pairs of campgrounds:

```{r chunk14}
campgrounds_dist_mat <- st_distance(yose_campgrounds_utm)
campgrounds_dist_mat
```


## Compute Distances Between Features - 2 sf data frames

If you pass two sf objects to `st_distance()`, it will return a *n x m* matrix containing the distance between all pairs of features. 

Compute the distance between all pairs of campgrounds and cell towers:

```{r chunk15}
cgct_dist_mat <- st_distance(yose_campgrounds_utm, yose_celltwrs_utm)
cgct_dist_mat
```

## CHALLENGE: Find the closest cell tower

For the first campground (row), which is the closest cell tower? [Answer](https://bit.ly/3webTXp)

Hint: use `which.min()`

```{r chunk16}
# Your answer here

```

## End

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