Spatial data visualization

class: center, middle, inverse, title-slide

# Spatial data visualization
## Intro to Data Science
### Shawn Santo

---

## Today's agenda

- Compare `sf` objects and tibbles

- Visualize spatial data

- Wrangle spatial data with `dplyr`

---

## Announcements

- Soft deadline for Lab 03 is February 11 at 11:59pm ET

- Soft deadline for Homework 02 is February 12 11:59pm ET

- Review feedback on past assignments

- Complete the Qualtrics survey (link is available in Slack in `#general`)

---

## Getting started

- Create your personal private repository by clicking 
  https://classroom.github.com/a/3jLTUvyI
   
- Follow the steps as we have done previously to clone this and create a new
  RStudio project in the RStudio Docker containers.
  
- Part of the data used in these slides is given in your repo. The other data
  for these slides is available through 
  [NC OneMap](https://www.nconemap.gov/datasets/e5ddff9b96204c6181be7c022e61d946_0)

---

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# Spatial data

---

## Raster versus vector spatial data

**Vector** spatial data describes the world using shapes (points, lines, 
polygons, etc).

**Raster** spatial data describes the world using cells of constant size.

The choice to use vector or raster data depends on the problem context. We will 
focus on vector data.

*Source:* https://commons.wikimedia.org/wiki/File:Raster_vector_tikz.png

---

## Geometries visualized

Simple features have a geometry type. Common choices are below.

---

## Spatial data plotting needs care

---

## Map Layers

The North Carolina Department of Environment and Natural Resources, 
Wildlife Resources Commission and the NC Center for Geographic Information and 
Analysis has a [shapefile data set](https://www.nconemap.gov/datasets/e5ddff9b96204c6181be7c022e61d946_0) 
available on all public Game Lands in NC.

.tiny[

```r
nc <- st_read("data/nc_covid.shp", quiet = TRUE)
nc_game <- st_read("data/gamelands.shp", quiet = TRUE)
```
]

.tiny[

```r
nc_game %>% print(n = 5)
```

```
#> Simple feature collection with 94 features and 6 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -84.29534 ymin: 33.98542 xmax: -75.54947 ymax: 36.58814
#> geographic CRS: NAD27
#> First 5 features:
#>   OBJECTID         GML_HAB SUM_ACRES GameLandID Shape__Are Shape__Len
#> 1        1           Alcoa 11395.947          1   69931121  549030.42
#> 2        2 Alligator River 24439.089          2  151120825  186792.83
#> 3        3      Angola Bay 34063.447          3  204400526  105421.80
#> 4        4    Bachelor Bay  2786.258          4   17219484   32891.84
#> 5        5   Bertie County  3883.768          5   24044312   83468.94
#>                         geometry
#> 1 MULTIPOLYGON (((-80.07347 3...
#> 2 MULTIPOLYGON (((-76.11832 3...
#> 3 MULTIPOLYGON (((-77.86947 3...
#> 4 MULTIPOLYGON (((-76.73896 3...
#> 5 MULTIPOLYGON (((-76.9209 35...
```
]

---

```r
ggplot(nc_game) +
  geom_sf() +
  theme_bw() +
  labs(title = "North Carolina gamelands")
```

---

```r
ggplot(nc_game) +
  geom_sf(fill = "#ff6700") +
  theme_bw() +
  labs(title = "North Carolina gamelands")
```

---

```r
ggplot(nc) +
  geom_sf() +
  geom_sf(data = nc_game, fill = "#ff6700", alpha = .5) +
  theme_bw() +
  labs(title = "North Carolina gamelands and counties")
```

---

```r
ggplot(nc) +
  geom_sf() +
  geom_sf(data = nc_game, aes(alpha = SUM_ACRES), fill = "#ff6700") +
  theme_bw() +
  labs(title = "North Carolina gamelands and counties", alpha = "Acres")
```