Geography 13

class: center, middle, inverse, title-slide

# Geography 13
## Lecture 17: Raster Data
### Mike Johnson

---

- Spatial phenomena can be thought of discrete **objects** with clear boundaries or as **continuous** phenomenon that can be observed everywhere, with _no_ natural boundaries.

- We have described these as **objects** and **fields** (Kuhn, 2012)

- Objects are usually represented by vector data consisting of:
  - a geometry (simple features (`sfc`, `sfg`))
  - some attribute information (`data.frame`)

- In R these are unified as a `sf` object

--
****

- Field data is typically represented by raster data

- For this, we will begin our discussions using the `raster` package

- Like `sf`, raster is an implementation of [standard raster data model](https://gistbok.ucgis.org/bok-topics/raster-data-model)

- The model is used by all GIS platforms

- Represented continuous data either as continuous or categorical values

- Regular set of cells in a grid (matrix)

- cells have: resolution, cell coordinate is the center of the cell, the coordinate and value apply to the entire cell area

---

# Recap: R data structures

- Vector:
  - A vector can have dimensions
    - A 1D vector in a collection of values
    - A 2D vector is a matrix
    - A 3D vector is an array

- List: a collection of objects

- data.frame: a list with requirement of equal length column (vectors)

- data.frames and lists (sfc) defined our vector model

- Arrays will define our raster model

---

# Spatial Extent

One last topic with respect to vector data (that will carry us into raster) is the idea of an extent:

```r
(mx = filter(us_states(), name == "New York") %>% 
   st_transform(5070) %>% 
   dplyr::select(name))
```

```
Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 1324221 ymin: 2151298 xmax: 1991064 ymax: 2658558
Projected CRS: NAD83 / Conus Albers
      name                       geometry
1 New York MULTIPOLYGON (((1972243 227...
```

In geometry, the minimum bounding box for a point set (stored as `POINT`, `POLYLINE`, `POLYGON`) in N dimensions is "...the box with the smallest measure within which all the points lie."

---
### We can extract bounding box coordinates with `st_bbox`

- returns: an object of class `bbox` of length 4.

```r
(bb = st_bbox(mx))
```

```
   xmin    ymin    xmax    ymax 
1324221 2151298 1991064 2658558 
```

```r
class(bb)
```

```
[1] "bbox"
```

```r
typeof(bb)
```

```
[1] "double"
```

---

### There is a method for creating an sfc form a `bbox` object

```r
(bb = st_as_sfc(bb) )
```

```
Geometry set for 1 feature 
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 1324221 ymin: 2151298 xmax: 1991064 ymax: 2658558
Projected CRS: NAD83 / Conus Albers
```

```r
class(bb)
```

```
[1] "sfc_POLYGON" "sfc"        
```

```r
typeof(bb)
```

```
[1] "list"
```

---
# Result:

```r
plot(bb, border = rgb(0,0,1))
plot(mx, add = TRUE, col = rgb(1,0,0, .5))
```

<img src="lecture-17_files/figure-html/unnamed-chunk-6-1.png" width="432" />
---

# Extents can be discritized in a number of ways:

.pull-left[

```r
grid = st_make_grid(bb)
plot(mx$geometry)
plot(grid, add = TRUE)
```

<img src="lecture-17_files/figure-html/unnamed-chunk-7-1.png" width="432" />
]

.pull-right[

```r
grid1km = st_make_grid(bb, cellsize = 10000)
plot(mx$geometry)
plot(grid1km, add = TRUE)
```

<img src="lecture-17_files/figure-html/unnamed-chunk-8-1.png" width="432" />
]

---

```r
length(grid1km) # how many grid tiles
```

```
[1] 3417
```

```r
mapview::npts(grid1km) # how many points?
```

```
[1] 17085
```

```r
mapview::npts(grid1km) * 2 # how many X and Y?
```

```
[1] 34170
```

```r
mapview::npts(grid1km) / length(grid) # how many points per tile?
```

```
[1] 170.85
```

```r
sqrt(st_area(grid1km)[1]) # length of each tile?
```

```
10000 [m]
```

```r
st_bbox(grid1km) # extent of grid
```

```
   xmin    ymin    xmax    ymax 
1324221 2151298 1994221 2661298 
```

---

```r
cent = st_centroid(grid1km)

plot(mx$geometry)
plot(cent, add = TRUE, pch = 16, cex = .25)
```

<img src="lecture-17_files/figure-html/unnamed-chunk-15-1.png" width="432" />
---
# Equal area from centroid

We can use our voroni diagram the show that the area closest to a cell centroid is the cell itself.

```r
vor = st_union(cent) %>% 
  st_voronoi() %>% 
  st_cast() %>% 
  st_intersection(bb)
```

```r
plot(mx$geometry); plot(vor, add = TRUE)
```

---

The raster model is one of the earliest and most widely used data models within geographic information systems (Tomlin, 1990; Goodchild, 1992, Maguire, 1992).

Typically used to record, analyze and visualize data with a continuous nature such as elevation, temperature ("GIS"), or reflected or emitted electromagnetic radiation ("Remote Sensing")

* Quotes are used because you'll find from a data perspective these differences are artificial and a product of the ESRI/ENVI/ERDAS divide

--
***

The term `raster` originated from the German word for screen, implying a series of orthogonality oriented parallel lines.

- Digital raster objects most often take the form of a _regularly spaced_, _grid-like_ pattern of rows and columns

- Each element referred to as a cell, pixel, or grid point.

- The entire raster is sometimes referred to as an "image", "array", "surface", "matrix", or "lattice" (Wise, 2000).

- The all mean the same thing...
--

Cells of the raster are most often square, but may be rectangular (with differing resolutions in x and y directions) or other shapes that can be tessellated such as triangles and hexagons (Figure below from Peuquet, 1984).

---

### Photos and Computers ...

---

### Aerial Imagery (really just a photo 😄)

---

# What is stored in these cells?

---

.pull-left[
### Categorical Values (integer/factor)
<img src="lec-img/17-lc-pixels.png" width="327" style="display: block; margin: auto;" />
]
.pull-right[
### Continuous Values (numeric)
<img src="lec-img/17-raster-elev.png" width="467" style="display: block; margin: auto;" />
]

---

### Spectral Values

- Either Color, or sensor
 
<img src="lec-img/17-rgb.png" width="340" style="display: block; margin: auto;" />

---
### Any digital image contains an RBG channel for color:

- Red, green, and blue are the three additive colors (primary colors of light)

- In R, colors can be defined using the RBG channels

```r
(rgb(1,0,0)) # red
```

```
[1] "#FF0000"
```

```r
(rgb(0,.54,.96)) # UCSB navy
```

```
[1] "#008AF5"
```

```r
(rgb(254,188,17, maxColorValue = 255)) # UCSB navy
```

```
[1] "#FEBC11"
```
---

```r
par(mfrow = c(1,3), mar = c(0,0,0,0))
plot(mx$geometry, col = rgb(1,0,0)) # red
plot(mx$geometry, col = rgb(0,1,0)) # green
plot(mx$geometry, col = rgb(0,0,1)) # blue
```

---

# RGB and bytes/bits

- The **red**, **green** and **blue** use 8 bits each (1 byte), which each have integer values from 0 to 255.

- This makes 256^3 = 16,777,216 possible colors.

- See more [here](https://www.rapidtables.com/web/color/RGB_Color.html)

--
***
- How does this relate to the selection of 256x256 pixel tiles in web maps?

---

The secondary colors in an RGB color wheel are cyan, magenta, and yellow because these are the three subtractive colors

Think of your printer and the CMYK ink cartridges! Where black, is the absence of color (0,0,0)

```r
par(mfrow = c(1,4), mar = c(0,0,0,0))
plot(mx$geometry, col = rgb(0,1,1))  # cyan
plot(mx$geometry, col = rgb(1,0,1)) # Magenta
plot(mx$geometry, col = rgb(1,1,0)) # Yellow
plot(mx$geometry, col = rgb(0,0,0)) # Key (black)
```

---
class: center, middle, inverse
# Why do we care?

---

# Pixels are the base unit of raster data

---

# Pixels have resolution

This is the X and the Y dimension of each cell in the units of the CRS

---

### Resolution drives image clarity (granulairty)

- Higher resolution (smaller cells) = more detail, but bigger data!

---

### Raster images seek to discritize the real world into cell-based values

- Again either integer (categorical), continuous, or signal

---

# All rasters have an extent!

- This is the same extent as a bounding box
- Can be described as 4 values (xmin,ymin,xmax,ymax)

---

- Unlike vector data, the raster data model stores the coordinate of the grid cells indirectly

- Coordinates are derived from the reference (Xmin,Ymin) the resolution, and the cell index (e.g. [100,150])

- For example: If we want the coordinates of a value in the 3rd row and the 40th column of a raster matrix, we have to move from the origin (Xmin, Ymin) (3 x Xres) in x-direction and (40 x Yres) in y-direction

---
- So, and image (png, tif, gif) can be read as a raster

The raster is defined by the extent and resolution of the cells
To be spatial, the extent (thus coordinates) must be grounded in an CRS

.pull-left[

```r
img = brick('lec-img/17-raster-extent.png')
img
```

```
class      : RasterBrick 
dimensions : 788, 1067, 840796, 4  (nrow, ncol, ncell, nlayers)
resolution : 1, 1  (x, y)
extent     : 0, 1067, 0, 788  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : 17-raster-extent.png 
names      : X17.raster.extent.1, X17.raster.extent.2, X17.raster.extent.3, X17.raster.extent.4 
min values :                   0,                   0,                   0,                   0 
max values :                 255,                 255,                 255,                 255 
```
]

.pull-right[

```r
plotRGB(img, r = 1, g = 2, b = 3)
```

]
---

# Raster Data in R

A `RasterLayer` represents single-layer (variable) raster data.

A `RasterLayer` always stores the fundamental parameters that describe it. 
  - The number of columns and rows, 
  - The spatial extent
  - The Coordinate Reference System.

In addition, a `RasterLayer` can store information about the file where raster values are stored (if there is such a file).

Here we construct an empty raster:

```r
r <- raster(ncol=20, nrow=20, xmx=-80, xmn=-120, ymn=20, ymx=60)
r
```

```
class      : RasterLayer 
dimensions : 20, 20, 400  (nrow, ncol, ncell)
resolution : 2, 2  (x, y)
extent     : -120, -80, 20, 60  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs 
```
---
# Raster Values

```r
getValues(r)
```

```
  [1] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [14] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [27] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [40] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [53] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [66] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [79] NA NA NA NA NA NA NA NA NA NA NA NA NA
 [92] NA NA NA NA NA NA NA NA NA NA NA NA NA
[105] NA NA NA NA NA NA NA NA NA NA NA NA NA
[118] NA NA NA NA NA NA NA NA NA NA NA NA NA
[131] NA NA NA NA NA NA NA NA NA NA NA NA NA
[144] NA NA NA NA NA NA NA NA NA NA NA NA NA
[157] NA NA NA NA NA NA NA NA NA NA NA NA NA
[170] NA NA NA NA NA NA NA NA NA NA NA NA NA
[183] NA NA NA NA NA NA NA NA NA NA NA NA NA
[196] NA NA NA NA NA NA NA NA NA NA NA NA NA
[209] NA NA NA NA NA NA NA NA NA NA NA NA NA
[222] NA NA NA NA NA NA NA NA NA NA NA NA NA
[235] NA NA NA NA NA NA NA NA NA NA NA NA NA
[248] NA NA NA NA NA NA NA NA NA NA NA NA NA
[261] NA NA NA NA NA NA NA NA NA NA NA NA NA
[274] NA NA NA NA NA NA NA NA NA NA NA NA NA
[287] NA NA NA NA NA NA NA NA NA NA NA NA NA
[300] NA NA NA NA NA NA NA NA NA NA NA NA NA
[313] NA NA NA NA NA NA NA NA NA NA NA NA NA
[326] NA NA NA NA NA NA NA NA NA NA NA NA NA
[339] NA NA NA NA NA NA NA NA NA NA NA NA NA
[352] NA NA NA NA NA NA NA NA NA NA NA NA NA
[365] NA NA NA NA NA NA NA NA NA NA NA NA NA
[378] NA NA NA NA NA NA NA NA NA NA NA NA NA
[391] NA NA NA NA NA NA NA NA NA NA
```

---

# Assigning a values as a vector

```r
values(r) <- 1:ncell(r)
r
```

```r
getValues(r)
```

```
  [1]   1   2   3   4   5   6   7   8   9  10
 [11]  11  12  13  14  15  16  17  18  19  20
 [21]  21  22  23  24  25  26  27  28  29  30
 [31]  31  32  33  34  35  36  37  38  39  40
 [41]  41  42  43  44  45  46  47  48  49  50
 [51]  51  52  53  54  55  56  57  58  59  60
 [61]  61  62  63  64  65  66  67  68  69  70
 [71]  71  72  73  74  75  76  77  78  79  80
 [81]  81  82  83  84  85  86  87  88  89  90
 [91]  91  92  93  94  95  96  97  98  99 100
[101] 101 102 103 104 105 106 107 108 109 110
[111] 111 112 113 114 115 116 117 118 119 120
[121] 121 122 123 124 125 126 127 128 129 130
[131] 131 132 133 134 135 136 137 138 139 140
[141] 141 142 143 144 145 146 147 148 149 150
[151] 151 152 153 154 155 156 157 158 159 160
[161] 161 162 163 164 165 166 167 168 169 170
[171] 171 172 173 174 175 176 177 178 179 180
[181] 181 182 183 184 185 186 187 188 189 190
[191] 191 192 193 194 195 196 197 198 199 200
[201] 201 202 203 204 205 206 207 208 209 210
[211] 211 212 213 214 215 216 217 218 219 220
[221] 221 222 223 224 225 226 227 228 229 230
[231] 231 232 233 234 235 236 237 238 239 240
[241] 241 242 243 244 245 246 247 248 249 250
[251] 251 252 253 254 255 256 257 258 259 260
[261] 261 262 263 264 265 266 267 268 269 270
[271] 271 272 273 274 275 276 277 278 279 280
[281] 281 282 283 284 285 286 287 288 289 290
[291] 291 292 293 294 295 296 297 298 299 300
[301] 301 302 303 304 305 306 307 308 309 310
[311] 311 312 313 314 315 316 317 318 319 320
[321] 321 322 323 324 325 326 327 328 329 330
[331] 331 332 333 334 335 336 337 338 339 340
[341] 341 342 343 344 345 346 347 348 349 350
[351] 351 352 353 354 355 356 357 358 359 360
[361] 361 362 363 364 365 366 367 368 369 370
[371] 371 372 373 374 375 376 377 378 379 380
[381] 381 382 383 384 385 386 387 388 389 390
[391] 391 392 393 394 395 396 397 398 399 400
```

---

```r
plot(r)
```

---

## Raster is S4

- Compared to S3, the S4 object system is much stricter, and much closer to other OO systems. 
- What does this mean for us? 
- Data is structured with a "representation" - which is a list of slot (or attributes), giving their names and classes, accessed with @

```r
str(r, max.level = 2)
```

```
Formal class 'RasterLayer' [package "raster"] with 12 slots
  ..@ file    :Formal class '.RasterFile' [package "raster"] with 13 slots
  ..@ data    :Formal class '.SingleLayerData' [package "raster"] with 13 slots
  ..@ legend  :Formal class '.RasterLegend' [package "raster"] with 5 slots
  ..@ title   : chr(0) 
  ..@ extent  :Formal class 'Extent' [package "raster"] with 4 slots
  ..@ rotated : logi FALSE
  ..@ rotation:Formal class '.Rotation' [package "raster"] with 2 slots
  ..@ ncols   : int 20
  ..@ nrows   : int 20
  ..@ crs     :Formal class 'CRS' [package "sp"] with 1 slot
  ..@ history : list()
  ..@ z       : list()
```

---

# Access via Function

.pull-left[

```r
r@extent
```

```
class      : Extent 
xmin       : -120 
xmax       : -80 
ymin       : 20 
ymax       : 60 
```

```r
r@crs
```

```
CRS arguments: +proj=longlat +datum=WGS84 +no_defs 
```

```r
r@ncols
```

```
[1] 20
```

```r
r@ncols
```

```
[1] 20
```

```r
r@data
```

```
An object of class ".SingleLayerData"
Slot "values":
  [1]   1   2   3   4   5   6   7   8   9  10
 [11]  11  12  13  14  15  16  17  18  19  20
 [21]  21  22  23  24  25  26  27  28  29  30
 [31]  31  32  33  34  35  36  37  38  39  40
 [41]  41  42  43  44  45  46  47  48  49  50
 [51]  51  52  53  54  55  56  57  58  59  60
 [61]  61  62  63  64  65  66  67  68  69  70
 [71]  71  72  73  74  75  76  77  78  79  80
 [81]  81  82  83  84  85  86  87  88  89  90
 [91]  91  92  93  94  95  96  97  98  99 100
[101] 101 102 103 104 105 106 107 108 109 110
[111] 111 112 113 114 115 116 117 118 119 120
[121] 121 122 123 124 125 126 127 128 129 130
[131] 131 132 133 134 135 136 137 138 139 140
[141] 141 142 143 144 145 146 147 148 149 150
[151] 151 152 153 154 155 156 157 158 159 160
[161] 161 162 163 164 165 166 167 168 169 170
[171] 171 172 173 174 175 176 177 178 179 180
[181] 181 182 183 184 185 186 187 188 189 190
[191] 191 192 193 194 195 196 197 198 199 200
[201] 201 202 203 204 205 206 207 208 209 210
[211] 211 212 213 214 215 216 217 218 219 220
[221] 221 222 223 224 225 226 227 228 229 230
[231] 231 232 233 234 235 236 237 238 239 240
[241] 241 242 243 244 245 246 247 248 249 250
[251] 251 252 253 254 255 256 257 258 259 260
[261] 261 262 263 264 265 266 267 268 269 270
[271] 271 272 273 274 275 276 277 278 279 280
[281] 281 282 283 284 285 286 287 288 289 290
[291] 291 292 293 294 295 296 297 298 299 300
[301] 301 302 303 304 305 306 307 308 309 310
[311] 311 312 313 314 315 316 317 318 319 320
[321] 321 322 323 324 325 326 327 328 329 330
[331] 331 332 333 334 335 336 337 338 339 340
[341] 341 342 343 344 345 346 347 348 349 350
[351] 351 352 353 354 355 356 357 358 359 360
[361] 361 362 363 364 365 366 367 368 369 370
[371] 371 372 373 374 375 376 377 378 379 380
[381] 381 382 383 384 385 386 387 388 389 390
[391] 391 392 393 394 395 396 397 398 399 400

Slot "offset":
[1] 0

Slot "gain":
[1] 1

Slot "inmemory":
[1] TRUE

Slot "fromdisk":
[1] FALSE

Slot "isfactor":
[1] FALSE

Slot "attributes":
list()

Slot "haveminmax":
[1] TRUE

Slot "min":
[1] 1

Slot "max":
[1] 400

Slot "band":
[1] 1

Slot "unit":
[1] ""

Slot "names":
[1] ""
```
]

.pull-right[

```r
extent(r)
```

```
class      : Extent 
xmin       : -120 
xmax       : -80 
ymin       : 20 
ymax       : 60 
```

```r
crs(r)
```

```
CRS arguments: +proj=longlat +datum=WGS84 +no_defs 
```

```r
nrow(r)
```

```
[1] 20
```

```r
ncol(r)
```

```
[1] 20
```

```r
values(r)
```

---

# Multi-layers

---

In many cases multi-variable raster data sets are used. Variables can related to time or measurements

The raster package has two classes for multi-layer data:
  - `RasterBrick` can only be linked to a single (multi-layer) file. 
  - `RasterStack` can be formed from separate files and/or from a few layers (‘bands’) from a single file.
  
--

A `RasterStack` is a collection of  objects with the same spatial `extent` and `resolution.`

In essence it is a list of  objects, or a 3D array.

A `RasterBrick` is truly a multi-layered object

Processing a `RasterBrick` can be more efficient than processing a `RasterStack` representing the same data. However, it can only refer to a single file.

The main difference is that a `RasterStack` is loose collection of `RasterLayer` objects that can refer to different files (but must all have the same extent and resolution), whereas a `RasterBrick` can only point to a single file.

---

# Remeber our Array data?

```r
(v = 1:27)
```

```
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13
[14] 14 15 16 17 18 19 20 21 22 23 24 25 26
[27] 27
```

```r
(arr = array(v, dim = c(3,3,3)))
```

```
, , 1

[,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

, , 2

[,1] [,2] [,3]
[1,]   10   13   16
[2,]   11   14   17
[3,]   12   15   18

, , 3

[,1] [,2] [,3]
[1,]   19   22   25
[2,]   20   23   26
[3,]   21   24   27
```
---

# An array is a single file so it can be "bricked"

```r
b = brick(arr)
plot(b)
```

---

# Real Data example:

- WorldClim is a set of global climate layers (gridded climate data in GeoTiff format) that can be used for mapping and spatial modeling.

- `raster::getData` provides access to WorldClim Data

```r
# 10 degree resolution, bio variables
(climate <- getData('worldclim', var='bio', res=10))
```

```
class      : RasterStack 
dimensions : 900, 2160, 1944000, 19  (nrow, ncol, ncell, nlayers)
resolution : 0.1666667, 0.1666667  (x, y)
extent     : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs 
names      :  bio1,  bio2,  bio3,  bio4,  bio5,  bio6,  bio7,  bio8,  bio9, bio10, bio11, bio12, bio13, bio14, bio15, ... 
min values :  -269,     9,     8,    72,   -59,  -547,    53,  -251,  -450,   -97,  -488,     0,     0,     0,     0, ... 
max values :   314,   211,    95, 22673,   489,   258,   725,   375,   364,   380,   289,  9916,  2088,   652,   261, ... 
```

What are the properties of the data?

---
# Visual of the stack?
<img src="lec-img/17-bioclim.png" width="340" style="display: block; margin: auto;" />

---

# Define a Region to explore: US West

```r
west = us_states() %>% 
  filter(stusps %in% c("CA", "AZ", "UT", "OR", "WA", "NV", "ID")) %>% 
  st_union() %>% 
  st_transform(crs(climate)) %>% 
  as_Spatial()

plot(west)
```

---
# Crop (clip!) global RasterStack to the West

```r
west_tmp = crop(climate$bio5, west)

plot(west_tmp, main="Max Temperature of Warmest Month")
```

---

# 200+ degree?

```r
summary(west_tmp)
```

```
        bio5
Min.     144
1st Qu.  260
Median   291
3rd Qu.  327
Max.     448
NA's    1309
```

The data for temperature is stored as `$C = bio5 / 10$`

Why?

---

```r
(raw = format(object.size(west_tmp), units = "auto"))
```

```
[1] "52 Kb"
```

```r
(formed = format(object.size(west_tmp /10), units = "auto"))
```

```
[1] "90.9 Kb"
```

```r
90.2 / 51.2
```

```
[1] 1.761719
```

---
# Data Volume!

- With raster data the scale of the stored data is important not only for use but for data creation
- Often this factor is called the data scale or the scaling factor

- Basic operators work directly on the data vector/slot of raster objects

```r
west_tmp_c = west_tmp / 10
summary(west_tmp_c)
```

```
          bio5
Min.      14.4
1st Qu.   26.0
Median    29.1
3rd Qu.   32.7
Max.      44.8
NA's    1309.0
```

```r
west_tmp_f = (west_tmp_c * 9/5) + 32
summary(west_tmp_f)
```

```
           bio5
Min.      57.92
1st Qu.   78.80
Median    84.38
3rd Qu.   90.86
Max.     112.64
NA's    1309.00
```

---

# Our subsetting techniques also work across RasterStack objects

```r
west_tmp = crop(climate[[1:4]], west)

plot(west_tmp, nrow = 1)
```

---

# Wrap up... raster vs. vector

- Lab 04 is about making object tessellations of the CONUS

- We needed an object view to perform PIP counts of dams

---

```r
r = raster(count_pts, res = 10000)
fr = fasterize::fasterize(count_pts, r, field = "n")

format(object.size(count_pts), units = "auto")
```

```
[1] "3.9 Mb"
```

```r
format(object.size(fr), units = "auto")
```

```
[1] "1 Mb"
```

```r
plot(fr, axes = FALSE)
```

<img src="lecture-17_files/figure-html/unnamed-chunk-57-1.png" width="432" />
---

# Where are we going?

```
class      : RasterBrick 
dimensions : 340, 346, 117640, 7  (nrow, ncol, ncell, nlayers)
resolution : 30, 30  (x, y)
extent     : 594075, 604455, 4652535, 4662735  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=15 +datum=WGS84 +units=m +no_defs 
source     : memory
names      : coast,  blue, green,   red,   NIR, SWIR1, SWIR2 
min values :  8566,  7721,  6670,  6057,  5141,  4966,  5041 
max values : 18482, 19079, 19592, 21965, 24772, 23896, 20687 
```

---

<img src="lec-img/17-rgb-stack.jpg" width="600" style="display: block; margin: auto;" />
---

```r
par(mfrow = c(1,3))
plotRGB(r, r = 4, g = 3, b = 2, stretch = "lin") 
plotRGB(r, r = 5, g = 4, b = 3, stretch = "lin")
plotRGB(r, r = 5, g = 6, b = 4, stretch = "lin")
```

<img src="lecture-17_files/figure-html/unnamed-chunk-60-1.png" width="864" />
---

# Daily Assignment

In an Rmd:

- install `elevatr` from Github **NOT** CRAN (`remotes::install_github("jhollist/elevatr")`)
  - Recent improvements (using GDAL) have been added
  
- Read your `uscities.csv` data into R, make it a `sf` object, and filter to Goleta

- Transform to `CRS:5070` and create a 5 km buffer (`st_buffer(5000)`)

- Get the bounding box of that buffer (`st_bbox`) and make the `bbox` object a `sfc` and then `sf` object

- Pass that bounding box sf object to `elevatr::get_elev_raster`

- In addition to the bbox you will need to define a Z. This is the same zoom as in webmaps!

- Crop the returned elevation object to the bounding box (`raster::crop`)

- Plot the raster and describe its properties.

---

## Hints to get started ...

---

# Submission:

Submit your knit HTML file to Gauchospace.