Geography 13

# Geography 13
## Lecture 02: Class Roadmap
### Mike Johnson

---

- Today is a broad overview of the content we will cover in class

- The goal is to show you now how all the pieces we will learn fit together

- Again, the code is meant for us just to get comfortable seeing code and listening to it be talked about.

---

# Key points today are: 
--

- tables

- geometries

- binary/boolean conditions (TRUE/FALSE)

- data dimensions (1D, 2D, 3D, 4D)

---

## Whats in a name?

---

### largely this is how 176 has been taught at UCSB... technology --> concepts --> applications

---

### Information _Systems_:

- **Information Systems**: complementary networks of hardware and software used to <span class="code">collect, filter, process, create and  distribute data</span>.

- **Geographic Information Systems**: a framework that provides the ability to capture and <span class="code">analyze spatial and geographic data</span>.
]

---

## ...

### Information _Science_:

- **Information Science**: the study of _storing_ and _retrieving_ information.

- **Geographic Information Science**: studies the techniques to capture, represent, process, and analyze geographic information.

]

---

## ...

### Data Science

- **Data Science**: the discovery and communication of knowledge, patterns, or actionable information in <span class="code">structured</span> and unstructured data.

- **Data Models**: abstract (general) model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. A data model can sometimes be referred to as a <span class="code">data structure</span>, especially in the context of programming languages.

---

# So...

- Data Models tell us _what_ should be stored in an element of data and how it is organized <br><br>
<i class="fas  fa-file fa-2x "></i> <br>

- Information Science tells us _how_ to store and retrieve many elements <br><br>
<i class="fas  fa-file fa-2x " style="color:red;"></i> <i class="fas  fa-file fa-2x "></i> <i class="fas  fa-file fa-2x " style="color:red;"></i> <i class="fas  fa-file fa-2x "></i> <i class="fas  fa-file fa-2x "></i> <i class="fas  fa-file fa-2x "></i> <i class="fas  fa-file fa-2x " style="color:red;"></i> <i class="fas  fa-file fa-2x "></i> <i class="fas  fa-file fa-2x "></i> <br>

- Data Science informs how we _anaylsize_ and _evaluate_ collections of elements <br><br> 
sum( <i class="fas  fa-file fa-2x " style="color:red;"></i> <i class="fas  fa-file fa-2x " style="color:red;"></i> <i class="fas  fa-file fa-2x " style="color:red;"></i> ) = 3

- Systems have been built to support these missions
---

*Geoinformatics* is the science and the technology which develops and uses **information science** infrastructure to address the problems of geography, cartography, geosciences.

_How do we combine the knowledge of <u>computer science</u>, <u>mathmatics</u>, <u>information science</u>, and <u>data science</u> to answer geographic questions?_

To answer this I suggest that we need to address **3**

1. understand our computer and systems for working with digital data in a reproducible, flexable way.
2. Have a strong foundation in standard data manipulation and data visualization
3. Understand what makes "spatial" special including familiarity with dealing with vector objects, raster fields, and time.

---

## Introduction to Geoinformatics

1. You will build habits that will allow your work to be reproducible and shared (**week 1**)
    - R, Github, Markdown (our computer)
 
 2. You will learn to analyze, clean, and communicate structured data in a open-source coding environment (**week 2**)
    - Data wrangling
    - Data Visualization
 
 3. You will learn the underlying data models of spatial data (**week 3-6**)
    - Raster and Vector Data Models and Operations
 
---

## Structured Data

- Generally pre-defined data models
- Usually resides in relational databases ([RDBMS](https://techterms.com/definition/rdbms#:~:text=Terms%20%3A%20RDBMS%20Definition-,RDBMS,format%2C%20using%20rows%20and%20columns.)). 
- Fields store data of a same type (character, boolean, double, factor). 
- Data may be human - or machine - generated as long as the data is created within an a known structure.

## Unstructured Data

- **Unstructured** data either does not have a defined data model or is not organized in a pre-defined manner. 
- typically text-heavy (think Social Media, Webpages, emails), but may contain data such as dates, numbers, and facts. 
- results in ambiguities that require advanced techniques such as semantic tagging or natural language processing (NLP)

---

<img src="lec-img/02-structuredVsUnstructured.png" width="711" />
---

## Data science: the table view

- structured - rows, columns, 
- ["tidy" data](https://vita.had.co.nz/papers/tidy-data.pdf) is ideal (rows = observations; columns = variables/attributes)
---

# A typical approach ...

```r
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-06/us_wind.csv"

us_wind <- readr::read_csv(url) %>% 
  select(t_state, t_county, xlong, ylat)

skimr::skim(us_wind)
```

<table style='width: auto;'
        class='table table-condensed'>
<caption>Data summary</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;">   </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Name </td>
   <td style="text-align:left;"> us_wind </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Number of rows </td>
   <td style="text-align:left;"> 58185 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Number of columns </td>
   <td style="text-align:left;"> 4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> _______________________ </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Column type frequency: </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> ________________________ </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Group variables </td>
   <td style="text-align:left;"> None </td>
  </tr>
</tbody>
</table>

**Variable type: character**

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> skim_variable </th>
   <th style="text-align:right;"> n_missing </th>
   <th style="text-align:right;"> complete_rate </th>
   <th style="text-align:right;"> min </th>
   <th style="text-align:right;"> max </th>
   <th style="text-align:right;"> empty </th>
   <th style="text-align:right;"> n_unique </th>
   <th style="text-align:right;"> whitespace </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> t_state </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 45 </td>
   <td style="text-align:right;"> 0 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> t_county </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 31 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 521 </td>
   <td style="text-align:right;"> 0 </td>
  </tr>
</tbody>
</table>

**Variable type: numeric**

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> skim_variable </th>
   <th style="text-align:right;"> n_missing </th>
   <th style="text-align:right;"> complete_rate </th>
   <th style="text-align:right;"> mean </th>
   <th style="text-align:right;"> sd </th>
   <th style="text-align:right;"> p0 </th>
   <th style="text-align:right;"> p25 </th>
   <th style="text-align:right;"> p50 </th>
   <th style="text-align:right;"> p75 </th>
   <th style="text-align:right;"> p100 </th>
   <th style="text-align:left;"> hist </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> xlong </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> -101.65 </td>
   <td style="text-align:right;"> 12.36 </td>
   <td style="text-align:right;"> -171.71 </td>
   <td style="text-align:right;"> -107.41 </td>
   <td style="text-align:right;"> -100.25 </td>
   <td style="text-align:right;"> -95.51 </td>
   <td style="text-align:right;"> 144.72 </td>
   <td style="text-align:left;"> ▂▇▁▁▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> ylat </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 38.40 </td>
   <td style="text-align:right;"> 5.32 </td>
   <td style="text-align:right;"> 13.39 </td>
   <td style="text-align:right;"> 34.69 </td>
   <td style="text-align:right;"> 37.87 </td>
   <td style="text-align:right;"> 42.69 </td>
   <td style="text-align:right;"> 66.84 </td>
   <td style="text-align:left;"> ▁▃▇▁▁ </td>
  </tr>
</tbody>
</table>

---
  
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
*us_wind
```
]
 
.panel2-wind1-auto[

```
# A tibble: 58,185 x 4
   t_state t_county    xlong  ylat
   <chr>   <chr>       <dbl> <dbl>
 1 CA      Kern County -118.  35.1
 2 CA      Kern County -118.  35.1
 3 CA      Kern County -118.  35.1
 4 CA      Kern County -118.  35.1
 5 CA      Kern County -118.  35.1
 6 CA      Kern County -118.  35.1
 7 CA      Kern County -118.  35.1
 8 CA      Kern County -118.  35.1
 9 CA      Kern County -118.  35.1
10 CA      Kern County -118.  35.1
# … with 58,175 more rows
```
]

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
* filter(t_state == 'CA')
```
]
 
.panel2-wind1-auto[

```
# A tibble: 9,037 x 4
   t_state t_county    xlong  ylat
   <chr>   <chr>       <dbl> <dbl>
 1 CA      Kern County -118.  35.1
 2 CA      Kern County -118.  35.1
 3 CA      Kern County -118.  35.1
 4 CA      Kern County -118.  35.1
 5 CA      Kern County -118.  35.1
 6 CA      Kern County -118.  35.1
 7 CA      Kern County -118.  35.1
 8 CA      Kern County -118.  35.1
 9 CA      Kern County -118.  35.1
10 CA      Kern County -118.  35.1
# … with 9,027 more rows
```
]

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
  filter(t_state == 'CA') %>%
* tally()
```
]
 
.panel2-wind1-auto[

```
# A tibble: 1 x 1
      n
  <int>
1  9037
```
]

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
  filter(t_state == 'CA') %>%
  tally()

*us_wind
```
]
 
.panel2-wind1-auto[

```
# A tibble: 1 x 1
      n
  <int>
1  9037
```

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
  filter(t_state == 'CA') %>%
  tally()

us_wind %>%
* group_by(t_state)
```
]
 
.panel2-wind1-auto[

```
# A tibble: 1 x 1
      n
  <int>
1  9037
```

```
# A tibble: 58,185 x 4
# Groups:   t_state [45]
   t_state t_county    xlong  ylat
   <chr>   <chr>       <dbl> <dbl>
 1 CA      Kern County -118.  35.1
 2 CA      Kern County -118.  35.1
 3 CA      Kern County -118.  35.1
 4 CA      Kern County -118.  35.1
 5 CA      Kern County -118.  35.1
 6 CA      Kern County -118.  35.1
 7 CA      Kern County -118.  35.1
 8 CA      Kern County -118.  35.1
 9 CA      Kern County -118.  35.1
10 CA      Kern County -118.  35.1
# … with 58,175 more rows
```
]

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
  filter(t_state == 'CA') %>%
  tally()

us_wind %>%
  group_by(t_state) %>%
* summarize(n = n())
```
]
 
.panel2-wind1-auto[

```
# A tibble: 1 x 1
      n
  <int>
1  9037
```

```
# A tibble: 45 x 2
   t_state     n
   <chr>   <int>
 1 AK        141
 2 AR          1
 3 AZ        144
 4 CA       9037
 5 CO       2278
 6 CT          3
 7 DE          1
 8 FL          1
 9 GU          1
10 HI        124
# … with 35 more rows
```
]

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
  filter(t_state == 'CA') %>%
  tally()

us_wind %>%
  group_by(t_state) %>%
  summarize(n = n()) %>%
* arrange(-n)
```
]
 
.panel2-wind1-auto[

```
# A tibble: 1 x 1
      n
  <int>
1  9037
```

```
# A tibble: 45 x 2
   t_state     n
   <chr>   <int>
 1 TX      13232
 2 CA       9037
 3 IA       4280
 4 OK       3821
 5 KS       2898
 6 IL       2602
 7 MN       2547
 8 CO       2278
 9 OR       1868
10 WA       1744
# … with 35 more rows
```
]

---
count: false
 
# Data Exploration
.panel1-wind1-auto[

```r
us_wind %>%
  filter(t_state == 'CA') %>%
  tally()

us_wind %>%
  group_by(t_state) %>%
  summarize(n = n()) %>%
  arrange(-n) %>%
* slice(1:5)
```
]
 
.panel2-wind1-auto[

```
# A tibble: 1 x 1
      n
  <int>
1  9037
```

```
# A tibble: 5 x 2
  t_state     n
  <chr>   <int>
1 TX      13232
2 CA       9037
3 IA       4280
4 OK       3821
5 KS       2898
```
]

---

# Table of California ...

## Tables can have ... coordinates as numeric fields

<table class="table table-condensed">
 <thead>
  <tr>
   <th style="text-align:right;"> state </th>
   <th style="text-align:right;"> name </th>
   <th style="text-align:right;"> aland </th>
   <th style="text-align:right;"> X </th>
   <th style="text-align:right;"> Y </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> San Francisco </td>
   <td style="text-align:right;"> 121455687 </td>
   <td style="text-align:right;"> -122.4386 </td>
   <td style="text-align:right;"> 37.75215 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Nevada </td>
   <td style="text-align:right;"> 2480606160 </td>
   <td style="text-align:right;"> -120.7639 </td>
   <td style="text-align:right;"> 39.30437 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Merced </td>
   <td style="text-align:right;"> 5012175306 </td>
   <td style="text-align:right;"> -120.7216 </td>
   <td style="text-align:right;"> 37.18925 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Alpine </td>
   <td style="text-align:right;"> 1912292633 </td>
   <td style="text-align:right;"> -119.8223 </td>
   <td style="text-align:right;"> 38.59672 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Contra Costa </td>
   <td style="text-align:right;"> 1871930816 </td>
   <td style="text-align:right;"> -121.9293 </td>
   <td style="text-align:right;"> 37.91955 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Tulare </td>
   <td style="text-align:right;"> 12495016292 </td>
   <td style="text-align:right;"> -118.8017 </td>
   <td style="text-align:right;"> 36.22045 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Lake </td>
   <td style="text-align:right;"> 3254288286 </td>
   <td style="text-align:right;"> -122.7530 </td>
   <td style="text-align:right;"> 39.10068 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Mono </td>
   <td style="text-align:right;"> 7896839510 </td>
   <td style="text-align:right;"> -118.8847 </td>
   <td style="text-align:right;"> 37.93879 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Imperial </td>
   <td style="text-align:right;"> 10817291640 </td>
   <td style="text-align:right;"> -115.3643 </td>
   <td style="text-align:right;"> 33.04000 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Alameda </td>
   <td style="text-align:right;"> 1914242789 </td>
   <td style="text-align:right;"> -121.8924 </td>
   <td style="text-align:right;"> 37.64661 </td>
  </tr>
</tbody>
</table>
]

## Tables can have ... POINT geometries

- <span class = "code">WKB: Well-Known-Binary (machine and database readable) </span>

<table class="table table-condensed">
 <thead>
  <tr>
   <th style="text-align:right;"> state </th>
   <th style="text-align:right;"> name </th>
   <th style="text-align:right;"> geometry </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> San Francisco </td>
   <td style="text-align:right;"> POINT (-122.4386 37.75215) </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Nevada </td>
   <td style="text-align:right;"> POINT (-120.7639 39.30437) </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Merced </td>
   <td style="text-align:right;"> POINT (-120.7216 37.18925) </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Alpine </td>
   <td style="text-align:right;"> POINT (-119.8223 38.59672) </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Contra Costa </td>
   <td style="text-align:right;"> POINT (-121.9293 37.91955) </td>
  </tr>
</tbody>
</table>

]

<img src="lecture-02_files/figure-html/unnamed-chunk-8-1.png" width="432" />
]

---
count: false
 
#Distance Operators...
.panel1-dist-auto[

```r
*st_distance(points)
```
]
 
.panel2-dist-auto[

```
Units: [m]
           [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]      [,8]       [,9]     [,10]     [,11]     [,12]    [,13]     [,14]     [,15]
 [1,]      0.00 225850.30 163939.93 247216.91  48440.37 365137.23 152430.40 312722.21  827620.19  49462.70  87416.90 128629.70 361555.9  86325.99 262464.35
 [2,] 225850.30      0.00 235220.29 113229.65 184292.88 383825.20 172885.53 222941.55  848199.21 208875.60 244500.19 195197.13 152820.6 272586.19  36824.34
 [3,] 163939.93 235220.29      0.00 175271.47 133896.55 202227.52 276988.11 182092.36  670820.25 115227.81  86475.90  47777.44 387473.8 114870.06 266225.04
 [4,] 247216.91 113229.65 175271.47      0.00 198777.83 279176.60 259898.05 109779.29  736878.31 209645.13 223816.18 154648.93 240044.5 257190.09 124793.33
 [5,]  48440.37 184292.88 133896.55 198777.83      0.00 335668.69 149616.19 267029.50  804421.91  30522.90  79256.31  91202.80 326996.8  96280.91 221116.51
 [6,] 365137.23 383825.20 202227.52 279176.60 335668.69      0.00 472717.79 191212.59  473190.83 317168.53 281403.16 245526.23 518987.5 300628.75 402754.94
 [7,] 152430.40 172885.53 276988.11 259898.05 149616.19 472717.79      0.00 360442.67  945458.47 178240.36 227477.79 229859.59 254004.1 236578.72 198986.90
 [8,] 312722.21 222941.55 182092.36 109779.29 267029.50 191212.59 360442.67      0.00  630984.89 266262.92 259964.03 190550.02 337599.1 292350.59 231117.72
 [9,] 827620.19 848199.21 670820.25 736878.31 804421.91 473190.83 945458.47 630984.89       0.00 782543.23 740457.39 716536.29 967331.8 751320.81 861160.72
[10,]  49462.70 208875.60 115227.81 209645.13  30522.90 317168.53 178240.36 266262.92  782543.23      0.00  49334.46  79399.62 354621.1  66458.17 245546.41
[11,]  87416.90 244500.19  86475.90 223816.18  79256.31 281403.16 227477.79 259964.03  740457.39  49334.46      0.00  71662.80 394388.0  33660.67 280325.72
[12,] 128629.70 195197.13  47777.44 154648.93  91202.80 245526.23 229859.59 190550.02  716536.29  79399.62  71662.80      0.00 348012.3 105252.27 228362.68
[13,] 361555.89 152820.61 387473.84 240044.46 326996.83 518987.45 254004.09 337599.13  967331.78 354621.08 394387.98 348012.34      0.0 420348.54 121793.75
[14,]  86325.99 272586.19 114870.06 257190.09  96280.91 300628.75 236578.72 292350.59  751320.81  66458.17  33660.67 105252.27 420348.5      0.00 308895.43
[15,] 262464.35  36824.34 266225.04 124793.33 221116.51 402754.94 198986.90 231117.72  861160.72 245546.41 280325.72 228362.68 121793.8 308895.43      0.00
[16,]  84456.21 162127.25 203668.31 218519.87  74230.06 402471.51  75386.62 307733.77  873976.48 103150.56 152477.25 157442.89 283193.7 163900.73 196496.69
[17,] 159653.98 127289.56 257669.21 218457.65 142503.22 446419.19  45680.24 322114.91  919565.14 172949.38 221604.18 209892.49 217133.0 236905.94 153536.22
          [,16]     [,17]     [,18]     [,19]     [,20]      [,21]     [,22]     [,23]     [,24]     [,25]      [,26]     [,27]     [,28]     [,29]     [,30]
 [1,]  84456.21 159653.98 123393.77 156587.30 223580.05  350099.05 205417.42 173899.57 243026.69 220263.95  336660.23 286170.44 113625.68 426098.62 270361.14
 [2,] 162127.25 127289.56 107338.60  85661.93 205745.84  307027.81 143727.96  95881.25 248047.35 158581.16  195333.57  78104.38 120167.46 475714.97 299478.37
 [3,] 203668.31 257669.21 150510.33 222062.73  84003.34  475943.15 305018.26 139866.33  84446.16 115134.59  413513.45 313198.06 195657.34 272136.36 106540.30
 [4,] 218519.87 218457.65 133268.26 169312.22 113322.98  418309.82 248312.78  74566.94 153720.32  64411.91  306754.56 179298.73 180817.99 374507.51 205043.50
 [5,]  74230.06 142503.22  78205.11 125585.92 181705.65  351343.67 191057.71 125633.70 205914.28 173665.86  316441.89 250266.53  84900.53 404490.91 239292.55
 [6,] 402471.51 446419.19 334597.89 403384.47 180329.21  665366.33 490268.88 296913.06 140200.55 225263.52  578427.60 456904.03 387028.87  97657.83  96596.91
 [7,]  75386.62  45680.24 142007.54  91704.63 300271.00  201737.43  63458.27 195694.10 334709.71 271174.36  194684.26 192444.30  87137.23 549003.28 376654.38
 [8,] 307733.77 322114.91 222220.13 273161.03  98392.77  527566.23 355816.58 164873.96 112011.80  94106.80  415051.16 285092.81 275875.73 288838.53 147758.78
 [9,] 873976.48 919565.14 807743.75 875945.19 651565.14 1138272.77 962946.47 767514.85 612981.04 692478.25 1043305.62 915511.19 860155.03 401577.34 568818.86
[10,] 103150.56 172949.38 101542.15 155223.14 174970.87  379860.89 221440.53 139984.02 193564.19 175499.69  346882.18 277608.24 115218.14 381539.14 221768.02
[11,] 152477.25 221604.18 139137.19 200467.15 162700.90  428988.87 270235.18 163033.49 170717.93 177231.22  393976.97 317262.73 162417.97 338885.25 189160.24
[12,] 157442.89 209892.49 103707.26 174941.30  96119.49  428255.42 257300.90 103101.08 115134.89 105569.87  367588.88 272321.04 147887.69 319164.54 148935.44
[13,] 283193.65 217133.04 255275.57 205036.96 348917.19  276913.47 194130.65 247622.71 390997.18 299304.75  122451.89  77154.20 247971.81 614549.82 442888.79
[14,] 163900.73 236905.94 165711.42 221639.74 194510.96  436031.53 284817.70 194830.21 199145.04 210792.66  412348.77 343231.62 181140.79 350409.66 212041.86
[15,] 196496.69 153536.22 144009.27 117559.40 228578.82  311008.07 160313.55 126443.94 270901.61 179495.54  184394.19  54506.04 155079.27 496753.62 322777.13
[16,]      0.00  75198.08  86388.21  80585.25 236050.80  277115.38 121548.39 145977.08 267021.30 214531.91  252209.12 210391.56  42270.73 475745.60 305935.83
[17,]  75198.08      0.00 111823.13  49205.06 269613.17  219388.17  48730.10 158976.78 306681.39 235964.89  177025.92 150668.73  62185.13 527233.97 351704.32
          [,31]     [,32]     [,33]     [,34]     [,35]     [,36]     [,37]    [,38]     [,39]     [,40]     [,41]     [,42]     [,43]     [,44]      [,45]
 [1,] 298588.85 104306.65 538456.52 475077.12 407288.20  43259.05  647681.3 466933.8  451211.4 192775.12 176213.40 264546.34  427148.5  460826.3  617000.34
 [2,] 368745.89 158668.51 599687.88 560522.75 521438.24 218285.79  641652.5 428060.8  254146.3  50984.82 301575.23 155695.04  295566.1  378912.0  678142.31
 [3,] 147997.15  96035.90 391527.68 338800.20 289048.26 201516.82  485009.5 304493.5  489315.8 237209.22  72144.06 352087.88  513977.4  574868.6  471291.67
 [4,] 280518.00 146543.59 497797.44 466352.17 438886.69 259467.37  529057.5 314903.2  341538.2 151840.16 247407.35 268389.88  405505.9  492026.2  574793.90
 [5,] 278057.21  57567.77 521301.89 462804.86 401561.95  71375.06  618232.3 430034.6  420893.1 157625.88 165013.69 246803.30  411776.9  457016.1  600721.41
 [6,]  92554.65 290463.31 219019.52 199261.68 206241.14 403689.79  282790.4 129055.6  619739.8 406020.47 207297.33 527936.49  679205.1  755287.8  295621.13
 [7,] 424470.73 182759.32 668219.84 611826.77 551069.97 114510.73  751780.5 550865.6  325797.3 121945.67 314625.16 122271.32  277689.0  309255.2  747903.00
 [8,] 223249.52 209463.62 408094.21 389037.53 379236.16 336402.03  421173.6 205816.4  435329.0 260275.63 243752.12 378145.19  512487.2  601451.6  481870.76
 [9,] 529068.61 762783.71 300149.62 378261.80 465674.40 868860.99  213819.2 428568.4 1061746.2 875972.45 654099.40 998032.08 1142375.2 1225225.5  234684.42
[10,] 254333.46  63102.58 496481.96 436124.40 372968.10  86702.67  599928.7 417508.5  449714.6 185845.62 137102.06 277326.20  442295.5  486583.9  575611.43
[11,] 211390.55  86619.39 451163.16 388817.85 324033.96 129828.09  562927.7 389949.5  491802.7 228015.64  89396.67 325326.26  490392.5  535916.2  529855.57
[12,] 195734.87  48260.91 439133.47 386450.72 334856.45 161711.74  527623.7 339432.6  448865.4 192146.94 106444.29 305016.96  467657.6  527180.5  518914.30
[13,] 515776.08 310074.38 737792.46 705064.42 671245.23 342220.57  755506.3 539218.4  102599.6 169530.84 454394.92 151982.06  192634.4  300934.2  814492.12
[14,] 223986.72 117128.99 458012.48 391824.12 321678.12 129271.76  579599.2 414106.4  516050.0 252062.10  97450.30 341993.37  506575.1  545780.5  535637.76
[15,] 394583.58 194067.96 620530.21 584878.28 549516.44 253628.21  652242.9 436827.6  224298.7  79476.96 334287.27 158531.83  281434.0  372621.7  698206.94
[16,] 350321.44 112363.57 593988.92 536681.58 475736.91  59137.24  683575.4 487883.9  369080.7 119404.00 239241.96 180214.10  343570.2  383497.5  673575.26
[17,] 405539.92 161633.69 648498.67 596080.17 540358.44 130258.71  722103.2 517254.0  296956.9  76316.42 304096.78 105328.14  269675.7  317869.1  728293.81
          [,46]     [,47]     [,48]     [,49]     [,50]      [,51]     [,52]     [,53]     [,54]      [,55]      [,56]     [,57]     [,58]
 [1,] 208657.06 202485.58  736297.2 172732.29 201066.50  732303.73 225069.98  37917.41  95246.15  327194.79  205214.51  72331.25 319584.12
 [2,]  27067.52  61895.62  785218.3 123567.16 345676.32  750522.06  82496.41 249714.35 203190.07  249943.83  226339.22 153604.19 436611.86
 [3,] 208335.01 177717.77  587236.8 113942.29 117404.18  574254.35 286091.18 144383.38 241978.76  436743.67  341917.26 160611.07 202283.16
 [4,]  93791.94  64204.05  678175.9  77163.69 292567.55  639408.20 194281.85 255355.18 266970.41  362704.95  322266.83 187685.52 360455.73
 [5,] 164777.45 155474.93  718333.6 124570.39 198812.56  708029.95 196486.95  65423.02 108130.05  320060.13  211461.34  38768.23 312667.20
 [6,] 358533.27 322588.05  401594.4 269754.79 218673.26  375676.65 455185.23 341093.35 443356.54  619125.91  539022.26 358864.58 171467.10
 [7,] 175118.37 196064.51  864224.8 215384.31 347070.00  848205.49 117345.83 190243.97  64476.49  174765.94   66699.38 116618.31 462216.42
 [8,] 203118.13 170792.51  579011.4 149049.86 283225.32  534031.60 303956.97 308338.88 356098.40  472455.01  425228.51 269459.61 314240.52
 [9,] 824697.58 789152.33  127658.7 741244.82 643120.16   97848.69 924212.25 798016.38 912531.63 1090533.28 1011483.53 830510.19 531685.93
[10,] 187523.62 173710.16  693976.4 132856.06 169079.85  686680.34 226188.05  45731.90 131609.42  349960.54  238310.14  69219.07 284211.35
[11,] 220705.26 200546.29  648969.7 147803.61 119746.28  645388.58 271110.57  59708.46 178594.50  399218.92  286730.80 117224.51 235430.67
[12,] 168963.04 141993.20  634512.3  81816.10 150550.40  619640.37 240361.26 118263.34 197975.46  389044.42  295242.58 113997.31 247116.82
[13,] 179333.58 210524.31  915805.0 274355.29 498452.18  870902.61 140803.85 391159.38 309017.58  212883.89  274733.52 291140.87 587799.85
[14,] 249783.56 231646.32  655780.5 180842.98 115558.56  657404.03 292599.79  49720.78 181546.67  410761.74  291463.64 134947.31 234680.17
[15,]  59894.39  88791.75  802759.8 152706.83 378857.82  763821.66  92922.07 286537.43 235360.97  250498.07  246515.21 190161.54 466035.36
[16,] 152452.68 159993.07  790499.5 158722.10 272207.67  777070.92 143680.59 120579.73  48722.54  247287.73  138396.51  43610.37 386863.05
[17,] 130754.85 154260.83  842731.8 181568.62 340594.48  821940.97  76803.69 195454.21  91894.48  179827.81  103809.55 104500.00 451504.74
 [ reached getOption("max.print") -- omitted 41 rows ]
```
]

---
count: false
 
#Distance Operators...
.panel1-dist-auto[

```r
st_distance(points) %>%
* units::drop_units()
```
]
 
.panel2-dist-auto[

```
           [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]      [,8]       [,9]     [,10]     [,11]     [,12]    [,13]     [,14]     [,15]
 [1,]      0.00 225850.30 163939.93 247216.91  48440.37 365137.23 152430.40 312722.21  827620.19  49462.70  87416.90 128629.70 361555.9  86325.99 262464.35
 [2,] 225850.30      0.00 235220.29 113229.65 184292.88 383825.20 172885.53 222941.55  848199.21 208875.60 244500.19 195197.13 152820.6 272586.19  36824.34
 [3,] 163939.93 235220.29      0.00 175271.47 133896.55 202227.52 276988.11 182092.36  670820.25 115227.81  86475.90  47777.44 387473.8 114870.06 266225.04
 [4,] 247216.91 113229.65 175271.47      0.00 198777.83 279176.60 259898.05 109779.29  736878.31 209645.13 223816.18 154648.93 240044.5 257190.09 124793.33
 [5,]  48440.37 184292.88 133896.55 198777.83      0.00 335668.69 149616.19 267029.50  804421.91  30522.90  79256.31  91202.80 326996.8  96280.91 221116.51
 [6,] 365137.23 383825.20 202227.52 279176.60 335668.69      0.00 472717.79 191212.59  473190.83 317168.53 281403.16 245526.23 518987.5 300628.75 402754.94
 [7,] 152430.40 172885.53 276988.11 259898.05 149616.19 472717.79      0.00 360442.67  945458.47 178240.36 227477.79 229859.59 254004.1 236578.72 198986.90
 [8,] 312722.21 222941.55 182092.36 109779.29 267029.50 191212.59 360442.67      0.00  630984.89 266262.92 259964.03 190550.02 337599.1 292350.59 231117.72
 [9,] 827620.19 848199.21 670820.25 736878.31 804421.91 473190.83 945458.47 630984.89       0.00 782543.23 740457.39 716536.29 967331.8 751320.81 861160.72
[10,]  49462.70 208875.60 115227.81 209645.13  30522.90 317168.53 178240.36 266262.92  782543.23      0.00  49334.46  79399.62 354621.1  66458.17 245546.41
[11,]  87416.90 244500.19  86475.90 223816.18  79256.31 281403.16 227477.79 259964.03  740457.39  49334.46      0.00  71662.80 394388.0  33660.67 280325.72
[12,] 128629.70 195197.13  47777.44 154648.93  91202.80 245526.23 229859.59 190550.02  716536.29  79399.62  71662.80      0.00 348012.3 105252.27 228362.68
[13,] 361555.89 152820.61 387473.84 240044.46 326996.83 518987.45 254004.09 337599.13  967331.78 354621.08 394387.98 348012.34      0.0 420348.54 121793.75
[14,]  86325.99 272586.19 114870.06 257190.09  96280.91 300628.75 236578.72 292350.59  751320.81  66458.17  33660.67 105252.27 420348.5      0.00 308895.43
[15,] 262464.35  36824.34 266225.04 124793.33 221116.51 402754.94 198986.90 231117.72  861160.72 245546.41 280325.72 228362.68 121793.8 308895.43      0.00
[16,]  84456.21 162127.25 203668.31 218519.87  74230.06 402471.51  75386.62 307733.77  873976.48 103150.56 152477.25 157442.89 283193.7 163900.73 196496.69
[17,] 159653.98 127289.56 257669.21 218457.65 142503.22 446419.19  45680.24 322114.91  919565.14 172949.38 221604.18 209892.49 217133.0 236905.94 153536.22
          [,16]     [,17]     [,18]     [,19]     [,20]      [,21]     [,22]     [,23]     [,24]     [,25]      [,26]     [,27]     [,28]     [,29]     [,30]
 [1,]  84456.21 159653.98 123393.77 156587.30 223580.05  350099.05 205417.42 173899.57 243026.69 220263.95  336660.23 286170.44 113625.68 426098.62 270361.14
 [2,] 162127.25 127289.56 107338.60  85661.93 205745.84  307027.81 143727.96  95881.25 248047.35 158581.16  195333.57  78104.38 120167.46 475714.97 299478.37
 [3,] 203668.31 257669.21 150510.33 222062.73  84003.34  475943.15 305018.26 139866.33  84446.16 115134.59  413513.45 313198.06 195657.34 272136.36 106540.30
 [4,] 218519.87 218457.65 133268.26 169312.22 113322.98  418309.82 248312.78  74566.94 153720.32  64411.91  306754.56 179298.73 180817.99 374507.51 205043.50
 [5,]  74230.06 142503.22  78205.11 125585.92 181705.65  351343.67 191057.71 125633.70 205914.28 173665.86  316441.89 250266.53  84900.53 404490.91 239292.55
 [6,] 402471.51 446419.19 334597.89 403384.47 180329.21  665366.33 490268.88 296913.06 140200.55 225263.52  578427.60 456904.03 387028.87  97657.83  96596.91
 [7,]  75386.62  45680.24 142007.54  91704.63 300271.00  201737.43  63458.27 195694.10 334709.71 271174.36  194684.26 192444.30  87137.23 549003.28 376654.38
 [8,] 307733.77 322114.91 222220.13 273161.03  98392.77  527566.23 355816.58 164873.96 112011.80  94106.80  415051.16 285092.81 275875.73 288838.53 147758.78
 [9,] 873976.48 919565.14 807743.75 875945.19 651565.14 1138272.77 962946.47 767514.85 612981.04 692478.25 1043305.62 915511.19 860155.03 401577.34 568818.86
[10,] 103150.56 172949.38 101542.15 155223.14 174970.87  379860.89 221440.53 139984.02 193564.19 175499.69  346882.18 277608.24 115218.14 381539.14 221768.02
[11,] 152477.25 221604.18 139137.19 200467.15 162700.90  428988.87 270235.18 163033.49 170717.93 177231.22  393976.97 317262.73 162417.97 338885.25 189160.24
[12,] 157442.89 209892.49 103707.26 174941.30  96119.49  428255.42 257300.90 103101.08 115134.89 105569.87  367588.88 272321.04 147887.69 319164.54 148935.44
[13,] 283193.65 217133.04 255275.57 205036.96 348917.19  276913.47 194130.65 247622.71 390997.18 299304.75  122451.89  77154.20 247971.81 614549.82 442888.79
[14,] 163900.73 236905.94 165711.42 221639.74 194510.96  436031.53 284817.70 194830.21 199145.04 210792.66  412348.77 343231.62 181140.79 350409.66 212041.86
[15,] 196496.69 153536.22 144009.27 117559.40 228578.82  311008.07 160313.55 126443.94 270901.61 179495.54  184394.19  54506.04 155079.27 496753.62 322777.13
[16,]      0.00  75198.08  86388.21  80585.25 236050.80  277115.38 121548.39 145977.08 267021.30 214531.91  252209.12 210391.56  42270.73 475745.60 305935.83
[17,]  75198.08      0.00 111823.13  49205.06 269613.17  219388.17  48730.10 158976.78 306681.39 235964.89  177025.92 150668.73  62185.13 527233.97 351704.32
          [,31]     [,32]     [,33]     [,34]     [,35]     [,36]     [,37]    [,38]     [,39]     [,40]     [,41]     [,42]     [,43]     [,44]      [,45]
 [1,] 298588.85 104306.65 538456.52 475077.12 407288.20  43259.05  647681.3 466933.8  451211.4 192775.12 176213.40 264546.34  427148.5  460826.3  617000.34
 [2,] 368745.89 158668.51 599687.88 560522.75 521438.24 218285.79  641652.5 428060.8  254146.3  50984.82 301575.23 155695.04  295566.1  378912.0  678142.31
 [3,] 147997.15  96035.90 391527.68 338800.20 289048.26 201516.82  485009.5 304493.5  489315.8 237209.22  72144.06 352087.88  513977.4  574868.6  471291.67
 [4,] 280518.00 146543.59 497797.44 466352.17 438886.69 259467.37  529057.5 314903.2  341538.2 151840.16 247407.35 268389.88  405505.9  492026.2  574793.90
 [5,] 278057.21  57567.77 521301.89 462804.86 401561.95  71375.06  618232.3 430034.6  420893.1 157625.88 165013.69 246803.30  411776.9  457016.1  600721.41
 [6,]  92554.65 290463.31 219019.52 199261.68 206241.14 403689.79  282790.4 129055.6  619739.8 406020.47 207297.33 527936.49  679205.1  755287.8  295621.13
 [7,] 424470.73 182759.32 668219.84 611826.77 551069.97 114510.73  751780.5 550865.6  325797.3 121945.67 314625.16 122271.32  277689.0  309255.2  747903.00
 [8,] 223249.52 209463.62 408094.21 389037.53 379236.16 336402.03  421173.6 205816.4  435329.0 260275.63 243752.12 378145.19  512487.2  601451.6  481870.76
 [9,] 529068.61 762783.71 300149.62 378261.80 465674.40 868860.99  213819.2 428568.4 1061746.2 875972.45 654099.40 998032.08 1142375.2 1225225.5  234684.42
[10,] 254333.46  63102.58 496481.96 436124.40 372968.10  86702.67  599928.7 417508.5  449714.6 185845.62 137102.06 277326.20  442295.5  486583.9  575611.43
[11,] 211390.55  86619.39 451163.16 388817.85 324033.96 129828.09  562927.7 389949.5  491802.7 228015.64  89396.67 325326.26  490392.5  535916.2  529855.57
[12,] 195734.87  48260.91 439133.47 386450.72 334856.45 161711.74  527623.7 339432.6  448865.4 192146.94 106444.29 305016.96  467657.6  527180.5  518914.30
[13,] 515776.08 310074.38 737792.46 705064.42 671245.23 342220.57  755506.3 539218.4  102599.6 169530.84 454394.92 151982.06  192634.4  300934.2  814492.12
[14,] 223986.72 117128.99 458012.48 391824.12 321678.12 129271.76  579599.2 414106.4  516050.0 252062.10  97450.30 341993.37  506575.1  545780.5  535637.76
[15,] 394583.58 194067.96 620530.21 584878.28 549516.44 253628.21  652242.9 436827.6  224298.7  79476.96 334287.27 158531.83  281434.0  372621.7  698206.94
[16,] 350321.44 112363.57 593988.92 536681.58 475736.91  59137.24  683575.4 487883.9  369080.7 119404.00 239241.96 180214.10  343570.2  383497.5  673575.26
[17,] 405539.92 161633.69 648498.67 596080.17 540358.44 130258.71  722103.2 517254.0  296956.9  76316.42 304096.78 105328.14  269675.7  317869.1  728293.81
          [,46]     [,47]     [,48]     [,49]     [,50]      [,51]     [,52]     [,53]     [,54]      [,55]      [,56]     [,57]     [,58]
 [1,] 208657.06 202485.58  736297.2 172732.29 201066.50  732303.73 225069.98  37917.41  95246.15  327194.79  205214.51  72331.25 319584.12
 [2,]  27067.52  61895.62  785218.3 123567.16 345676.32  750522.06  82496.41 249714.35 203190.07  249943.83  226339.22 153604.19 436611.86
 [3,] 208335.01 177717.77  587236.8 113942.29 117404.18  574254.35 286091.18 144383.38 241978.76  436743.67  341917.26 160611.07 202283.16
 [4,]  93791.94  64204.05  678175.9  77163.69 292567.55  639408.20 194281.85 255355.18 266970.41  362704.95  322266.83 187685.52 360455.73
 [5,] 164777.45 155474.93  718333.6 124570.39 198812.56  708029.95 196486.95  65423.02 108130.05  320060.13  211461.34  38768.23 312667.20
 [6,] 358533.27 322588.05  401594.4 269754.79 218673.26  375676.65 455185.23 341093.35 443356.54  619125.91  539022.26 358864.58 171467.10
 [7,] 175118.37 196064.51  864224.8 215384.31 347070.00  848205.49 117345.83 190243.97  64476.49  174765.94   66699.38 116618.31 462216.42
 [8,] 203118.13 170792.51  579011.4 149049.86 283225.32  534031.60 303956.97 308338.88 356098.40  472455.01  425228.51 269459.61 314240.52
 [9,] 824697.58 789152.33  127658.7 741244.82 643120.16   97848.69 924212.25 798016.38 912531.63 1090533.28 1011483.53 830510.19 531685.93
[10,] 187523.62 173710.16  693976.4 132856.06 169079.85  686680.34 226188.05  45731.90 131609.42  349960.54  238310.14  69219.07 284211.35
[11,] 220705.26 200546.29  648969.7 147803.61 119746.28  645388.58 271110.57  59708.46 178594.50  399218.92  286730.80 117224.51 235430.67
[12,] 168963.04 141993.20  634512.3  81816.10 150550.40  619640.37 240361.26 118263.34 197975.46  389044.42  295242.58 113997.31 247116.82
[13,] 179333.58 210524.31  915805.0 274355.29 498452.18  870902.61 140803.85 391159.38 309017.58  212883.89  274733.52 291140.87 587799.85
[14,] 249783.56 231646.32  655780.5 180842.98 115558.56  657404.03 292599.79  49720.78 181546.67  410761.74  291463.64 134947.31 234680.17
[15,]  59894.39  88791.75  802759.8 152706.83 378857.82  763821.66  92922.07 286537.43 235360.97  250498.07  246515.21 190161.54 466035.36
[16,] 152452.68 159993.07  790499.5 158722.10 272207.67  777070.92 143680.59 120579.73  48722.54  247287.73  138396.51  43610.37 386863.05
[17,] 130754.85 154260.83  842731.8 181568.62 340594.48  821940.97  76803.69 195454.21  91894.48  179827.81  103809.55 104500.00 451504.74
 [ reached getOption("max.print") -- omitted 41 rows ]
```
]

---
count: false
 
#Distance Operators...
.panel1-dist-auto[

```r
st_distance(points) %>%
  units::drop_units() ->
* dist_m
```
]
 
.panel2-dist-auto[

]

---
count: false
 
#Distance Operators...
.panel1-dist-auto[

```r
st_distance(points) %>%
  units::drop_units() ->
  dist_m

*dist_mi = 1/dist_m  # qgraph requires a similarity matrix
```
]
 
.panel2-dist-auto[

]

---
count: false
 
#Distance Operators...
.panel1-dist-auto[

```r
st_distance(points) %>%
  units::drop_units() ->
  dist_m

dist_mi = 1/dist_m  # qgraph requires a similarity matrix

*qgraph(dist_mi, layout='spring', vsize=4, labels = points$name)
```
]
 
.panel2-dist-auto[
<img src="lecture-02_files/figure-html/dist_auto_05_output-1.png" width="432" />
]

---
count: false
 
#Geometric Operators...
.panel1-bbox-auto[

```r
*st_bbox(points)
```
]
 
.panel2-bbox-auto[

```
      xmin       ymin       xmax       ymax 
-123.89729   33.03449 -115.36435   41.74200 
```
]

---
count: false
 
#Geometric Operators...
.panel1-bbox-auto[

```r
st_bbox(points) %>%
* st_as_sfc()
```
]
 
.panel2-bbox-auto[

```
Geometry set for 1 feature 
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -123.8973 ymin: 33.03449 xmax: -115.3643 ymax: 41.742
Geodetic CRS:  NAD83
```
]

---
count: false
 
#Geometric Operators...
.panel1-bbox-auto[

```r
st_bbox(points) %>%
  st_as_sfc() %>%
* ggplot()
```
]
 
.panel2-bbox-auto[
<img src="lecture-02_files/figure-html/bbox_auto_03_output-1.png" width="432" />
]

---
count: false
 
#Geometric Operators...
.panel1-bbox-auto[

```r
st_bbox(points) %>%
  st_as_sfc() %>%
  ggplot() +
* geom_sf()
```
]
 
.panel2-bbox-auto[
<img src="lecture-02_files/figure-html/bbox_auto_04_output-1.png" width="432" />
]

---
count: false
 
#Geometric Operators...
.panel1-bbox-auto[

```r
st_bbox(points) %>%
  st_as_sfc() %>%
  ggplot() +
  geom_sf() +
* geom_sf(data = points, col = "darkred")
```
]
 
.panel2-bbox-auto[
<img src="lecture-02_files/figure-html/bbox_auto_05_output-1.png" width="432" />
]

---

## Tables can have ... POLYGON geometries

<table class="table table-condensed">
 <thead>
  <tr>
   <th style="text-align:right;"> state </th>
   <th style="text-align:right;"> name </th>
   <th style="text-align:right;"> geometry </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> San Francisco </td>
   <td style="text-align:right;"> MULTIPOLYGON (((-122.512 37... </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Nevada </td>
   <td style="text-align:right;"> MULTIPOLYGON (((-121.2661 3... </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Merced </td>
   <td style="text-align:right;"> MULTIPOLYGON (((-121.2268 3... </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Alpine </td>
   <td style="text-align:right;"> MULTIPOLYGON (((-120.0725 3... </td>
  </tr>
  <tr>
   <td style="text-align:right;"> CA </td>
   <td style="text-align:right;"> Contra Costa </td>
   <td style="text-align:right;"> MULTIPOLYGON (((-122.4253 3... </td>
  </tr>
</tbody>
</table>
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
*poly
```
]
 
.panel2-area-auto[

```
Simple feature collection with 58 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -124.4096 ymin: 32.53416 xmax: -114.1391 ymax: 42.00925
Geodetic CRS:  NAD83
First 10 features:
   state          name                       geometry
1     CA San Francisco MULTIPOLYGON (((-122.512 37...
2     CA        Nevada MULTIPOLYGON (((-121.2661 3...
3     CA        Merced MULTIPOLYGON (((-121.2268 3...
4     CA        Alpine MULTIPOLYGON (((-120.0725 3...
5     CA  Contra Costa MULTIPOLYGON (((-122.4253 3...
6     CA        Tulare MULTIPOLYGON (((-119.5461 3...
7     CA          Lake MULTIPOLYGON (((-123.0778 3...
8     CA          Mono MULTIPOLYGON (((-119.6392 3...
9     CA      Imperial MULTIPOLYGON (((-116.1056 3...
10    CA       Alameda MULTIPOLYGON (((-122.3337 3...
```
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
poly %>%
* mutate(area = as.numeric(st_area(.) /1e6))
```
]
 
.panel2-area-auto[

```
Simple feature collection with 58 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -124.4096 ymin: 32.53416 xmax: -114.1391 ymax: 42.00925
Geodetic CRS:  NAD83
First 10 features:
   state          name                       geometry       area
1     CA San Francisco MULTIPOLYGON (((-122.512 37...   113.9176
2     CA        Nevada MULTIPOLYGON (((-121.2661 3...  2480.4565
3     CA        Merced MULTIPOLYGON (((-121.2268 3...  5138.2737
4     CA        Alpine MULTIPOLYGON (((-120.0725 3...  1930.8945
5     CA  Contra Costa MULTIPOLYGON (((-122.4253 3...  1956.0298
6     CA        Tulare MULTIPOLYGON (((-119.5461 3... 12510.9144
7     CA          Lake MULTIPOLYGON (((-123.0778 3...  3428.8614
8     CA          Mono MULTIPOLYGON (((-119.6392 3...  8105.4794
9     CA      Imperial MULTIPOLYGON (((-116.1056 3... 11554.3166
10    CA       Alameda MULTIPOLYGON (((-122.3337 3...  1965.7779
```
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
poly %>%
  mutate(area = as.numeric(st_area(.) /1e6)) %>%
* ggplot()
```
]
 
.panel2-area-auto[
<img src="lecture-02_files/figure-html/area_auto_03_output-1.png" width="432" />
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
poly %>%
  mutate(area = as.numeric(st_area(.) /1e6)) %>%
  ggplot() +
* geom_col(aes(x = reorder(name, -area), y = area))
```
]
 
.panel2-area-auto[
<img src="lecture-02_files/figure-html/area_auto_04_output-1.png" width="432" />
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
poly %>%
  mutate(area = as.numeric(st_area(.) /1e6)) %>%
  ggplot() +
  geom_col(aes(x = reorder(name, -area), y = area)) +
* labs(title = "Area per state",
*      y = "Area Km^2",
*      x = "State")
```
]
 
.panel2-area-auto[
<img src="lecture-02_files/figure-html/area_auto_05_output-1.png" width="432" />
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
poly %>%
  mutate(area = as.numeric(st_area(.) /1e6)) %>%
  ggplot() +
  geom_col(aes(x = reorder(name, -area), y = area)) +
  labs(title = "Area per state",
       y = "Area Km^2",
       x = "State") +
* theme_light()
```
]
 
.panel2-area-auto[
<img src="lecture-02_files/figure-html/area_auto_06_output-1.png" width="432" />
]

---
count: false
 
#Polygon Operators...
.panel1-area-auto[

```r
poly %>%
  mutate(area = as.numeric(st_area(.) /1e6)) %>%
  ggplot() +
  geom_col(aes(x = reorder(name, -area), y = area)) +
  labs(title = "Area per state",
       y = "Area Km^2",
       x = "State") +
  theme_light() +
* theme(axis.text.x = element_text(angle = 90))
```
]
 
.panel2-area-auto[
<img src="lecture-02_files/figure-html/area_auto_07_output-1.png" width="432" />
]

---
count: false
 
#Geometric Operators...
.panel1-dissolve-auto[

```r
*ca
```
]
 
.panel2-dissolve-auto[

```
Simple feature collection with 58 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -124.4096 ymin: 32.53416 xmax: -114.1391 ymax: 42.00925
Geodetic CRS:  NAD83
First 10 features:
    statefp countyfp countyns       affgeoid geoid          name lsad       aland    awater state_name state_abbr jurisdiction_type                       geometry
76       06      075 00277302 0500000US06075 06075 San Francisco   06   121455687 479136515 California         CA             state MULTIPOLYGON (((-122.512 37...
371      06      057 01682927 0500000US06057 06057        Nevada   06  2480606160  41513172 California         CA             state MULTIPOLYGON (((-121.2661 3...
636      06      047 00277288 0500000US06047 06047        Merced   06  5012175306 112509475 California         CA             state MULTIPOLYGON (((-121.2268 3...
714      06      003 01675840 0500000US06003 06003        Alpine   06  1912292633  12557304 California         CA             state MULTIPOLYGON (((-120.0725 3...
717      06      013 01675903 0500000US06013 06013  Contra Costa   06  1871930816 209819213 California         CA             state MULTIPOLYGON (((-122.4253 3...
774      06      107 00277318 0500000US06107 06107        Tulare   06 12495016292  37081410 California         CA             state MULTIPOLYGON (((-119.5461 3...
803      06      033 00277281 0500000US06033 06033          Lake   06  3254288286 188912510 California         CA             state MULTIPOLYGON (((-123.0778 3...
898      06      051 00277290 0500000US06051 06051          Mono   06  7896839510 214694734 California         CA             state MULTIPOLYGON (((-119.6392 3...
953      06      025 00277277 0500000US06025 06025      Imperial   06 10817291640 790230304 California         CA             state MULTIPOLYGON (((-116.1056 3...
982      06      001 01675839 0500000US06001 06001       Alameda   06  1914242789 212979931 California         CA             state MULTIPOLYGON (((-122.3337 3...
```
]

---
count: false
 
#Geometric Operators...
.panel1-dissolve-auto[

```r
ca %>%
*st_union()
```
]
 
.panel2-dissolve-auto[

```
Geometry set for 1 feature 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -124.4096 ymin: 32.53416 xmax: -114.1391 ymax: 42.00925
Geodetic CRS:  NAD83
```
]

---
count: false
 
#Geometric Operators...
.panel1-dissolve-auto[

```r
ca %>%
st_union() %>%
* ggplot()
```
]
 
.panel2-dissolve-auto[
<img src="lecture-02_files/figure-html/dissolve_auto_03_output-1.png" width="432" />
]

---
count: false
 
#Geometric Operators...
.panel1-dissolve-auto[

```r
ca %>%
st_union() %>%
  ggplot() +
* geom_sf()
```
]
 
.panel2-dissolve-auto[
<img src="lecture-02_files/figure-html/dissolve_auto_04_output-1.png" width="432" />
]

---
count: false
 
#Geometric Operators...
.panel1-dissolve-auto[

```r
ca %>%
st_union() %>%
  ggplot() +
  geom_sf() +
* theme_bw()
```
]
 
.panel2-dissolve-auto[
<img src="lecture-02_files/figure-html/dissolve_auto_05_output-1.png" width="432" />
]

---
## Coordinate Reference Systems

A data structure cannot be considered spatial unless it is accompanied by coordinate reference system (CRS) 
 - They describe the reference _origin_ and the reference _units_ so that coordinates become spatially meaningful!

---

```r
st_crs(poly)$input
```

```
[1] "EPSG:4269"
```

```r
st_crs(poly)$wkt
```

```
[1] "GEOGCRS[\"NAD83\",\n    DATUM[\"North American Datum 1983\",\n        ELLIPSOID[\"GRS 1980\",6378137,298.257222101,\n            LENGTHUNIT[\"metre\",1]]],\n    PRIMEM[\"Greenwich\",0,\n        ANGLEUNIT[\"degree\",0.0174532925199433]],\n    CS[ellipsoidal,2],\n        AXIS[\"geodetic latitude (Lat)\",north,\n            ORDER[1],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n        AXIS[\"geodetic longitude (Lon)\",east,\n            ORDER[2],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n    USAGE[\n        SCOPE[\"Geodesy.\"],\n        AREA[\"North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands.  British Virgin Islands.\"],\n        BBOX[14.92,167.65,86.46,-47.74]],\n    ID[\"EPSG\",4269]]"
```
]

```r
st_crs(st_transform(poly, 5070))$input
```

```
[1] "EPSG:5070"
```

```r
st_crs(st_transform(poly, 5070))$wkt
```

```
[1] "PROJCRS[\"NAD83 / Conus Albers\",\n    BASEGEOGCRS[\"NAD83\",\n        DATUM[\"North American Datum 1983\",\n            ELLIPSOID[\"GRS 1980\",6378137,298.257222101,\n                LENGTHUNIT[\"metre\",1]]],\n        PRIMEM[\"Greenwich\",0,\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n        ID[\"EPSG\",4269]],\n    CONVERSION[\"Conus Albers\",\n        METHOD[\"Albers Equal Area\",\n            ID[\"EPSG\",9822]],\n        PARAMETER[\"Latitude of false origin\",23,\n            ANGLEUNIT[\"degree\",0.0174532925199433],\n            ID[\"EPSG\",8821]],\n        PARAMETER[\"Longitude of false origin\",-96,\n            ANGLEUNIT[\"degree\",0.0174532925199433],\n            ID[\"EPSG\",8822]],\n        PARAMETER[\"Latitude of 1st standard parallel\",29.5,\n            ANGLEUNIT[\"degree\",0.0174532925199433],\n            ID[\"EPSG\",8823]],\n        PARAMETER[\"Latitude of 2nd standard parallel\",45.5,\n            ANGLEUNIT[\"degree\",0.0174532925199433],\n            ID[\"EPSG\",8824]],\n        PARAMETER[\"Easting at false origin\",0,\n            LENGTHUNIT[\"metre\",1],\n            ID[\"EPSG\",8826]],\n        PARAMETER[\"Northing at false origin\",0,\n            LENGTHUNIT[\"metre\",1],\n            ID[\"EPSG\",8827]]],\n    CS[Cartesian,2],\n        AXIS[\"easting (X)\",east,\n            ORDER[1],\n            LENGTHUNIT[\"metre\",1]],\n        AXIS[\"northing (Y)\",north,\n            ORDER[2],\n            LENGTHUNIT[\"metre\",1]],\n    USAGE[\n        SCOPE[\"Data analysis and small scale data presentation for contiguous lower 48 states.\"],\n        AREA[\"United States (USA) - CONUS onshore - Alabama; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming.\"],\n        BBOX[24.41,-124.79,49.38,-66.91]],\n    ID[\"EPSG\",5070]]"
```
]

---

```r
*us_wind
```
]
 
.panel2-wind2-auto[

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
* filter(!t_state %in% c("AK", "PR", "HI", "GU"))
```
]
 
.panel2-wind2-auto[

```
# A tibble: 57,856 x 4
   t_state t_county    xlong  ylat
   <chr>   <chr>       <dbl> <dbl>
 1 CA      Kern County -118.  35.1
 2 CA      Kern County -118.  35.1
 3 CA      Kern County -118.  35.1
 4 CA      Kern County -118.  35.1
 5 CA      Kern County -118.  35.1
 6 CA      Kern County -118.  35.1
 7 CA      Kern County -118.  35.1
 8 CA      Kern County -118.  35.1
 9 CA      Kern County -118.  35.1
10 CA      Kern County -118.  35.1
# … with 57,846 more rows
```
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
* ggplot()
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_03_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
* aes(x = xlong, y = ylat)
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_04_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
  aes(x = xlong, y = ylat) +
* borders("state")
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_05_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
  aes(x = xlong, y = ylat) +
  borders("state") +
* geom_point(col = "darkred", alpha = .5)
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_06_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
  aes(x = xlong, y = ylat) +
  borders("state") +
  geom_point(col = "darkred", alpha = .5) +
* geom_density_2d(col = "steelblue")
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_07_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
  aes(x = xlong, y = ylat) +
  borders("state") +
  geom_point(col = "darkred", alpha = .5) +
  geom_density_2d(col = "steelblue") +
* coord_map(projection = "mercator")
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_08_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
  aes(x = xlong, y = ylat) +
  borders("state") +
  geom_point(col = "darkred", alpha = .5) +
  geom_density_2d(col = "steelblue") +
  coord_map(projection = "mercator") +
* labs(title = "Wind turbines in the CONUS")
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_09_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table
.panel1-wind2-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  ggplot() +
  aes(x = xlong, y = ylat) +
  borders("state") +
  geom_point(col = "darkred", alpha = .5) +
  geom_density_2d(col = "steelblue") +
  coord_map(projection = "mercator") +
  labs(title = "Wind turbines in the CONUS") +
* theme_void()
```
]
 
.panel2-wind2-auto[
<img src="lecture-02_files/figure-html/wind2_auto_10_output-1.png" width="432" />
]

---

```r
*us_wind
```
]
 
.panel2-wind3-auto[

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
* filter(!t_state %in% c("AK", "PR", "HI", "GU"))
```
]
 
.panel2-wind3-auto[

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
* group_by(t_state)
```
]
 
.panel2-wind3-auto[

```
# A tibble: 57,856 x 4
# Groups:   t_state [41]
   t_state t_county    xlong  ylat
   <chr>   <chr>       <dbl> <dbl>
 1 CA      Kern County -118.  35.1
 2 CA      Kern County -118.  35.1
 3 CA      Kern County -118.  35.1
 4 CA      Kern County -118.  35.1
 5 CA      Kern County -118.  35.1
 6 CA      Kern County -118.  35.1
 7 CA      Kern County -118.  35.1
 8 CA      Kern County -118.  35.1
 9 CA      Kern County -118.  35.1
10 CA      Kern County -118.  35.1
# … with 57,846 more rows
```
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
* summarise(n = n())
```
]
 
.panel2-wind3-auto[

```
# A tibble: 41 x 2
   t_state     n
   <chr>   <int>
 1 AR          1
 2 AZ        144
 3 CA       9037
 4 CO       2278
 5 CT          3
 6 DE          1
 7 FL          1
 8 IA       4280
 9 ID        541
10 IL       2602
# … with 31 more rows
```
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
  summarise(n = n()) %>%
* ggplot()
```
]
 
.panel2-wind3-auto[
<img src="lecture-02_files/figure-html/wind3_auto_05_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
  summarise(n = n()) %>%
  ggplot() +
* aes(x = reorder(t_state, -n), y = n)
```
]
 
.panel2-wind3-auto[
<img src="lecture-02_files/figure-html/wind3_auto_06_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
  summarise(n = n()) %>%
  ggplot() +
  aes(x = reorder(t_state, -n), y = n) +
* geom_col()
```
]
 
.panel2-wind3-auto[
<img src="lecture-02_files/figure-html/wind3_auto_07_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
  summarise(n = n()) %>%
  ggplot() +
  aes(x = reorder(t_state, -n), y = n) +
  geom_col() +
* labs(title = "Wind turbines By State",
*      x = "State",
*      y = "Total Turbines")
```
]
 
.panel2-wind3-auto[
<img src="lecture-02_files/figure-html/wind3_auto_08_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
  summarise(n = n()) %>%
  ggplot() +
  aes(x = reorder(t_state, -n), y = n) +
  geom_col() +
  labs(title = "Wind turbines By State",
       x = "State",
       y = "Total Turbines") +
* theme_bw()
```
]
 
.panel2-wind3-auto[
<img src="lecture-02_files/figure-html/wind3_auto_09_output-1.png" width="432" />
]

---
count: false
 
#Extending the Wind Table...
.panel1-wind3-auto[

```r
us_wind %>%
  filter(!t_state %in% c("AK", "PR", "HI", "GU")) %>%
  group_by(t_state) %>%
  summarise(n = n()) %>%
  ggplot() +
  aes(x = reorder(t_state, -n), y = n) +
  geom_col() +
  labs(title = "Wind turbines By State",
       x = "State",
       y = "Total Turbines") +
  theme_bw() +
* theme(axis.text.x = element_text(angle = 90))
```
]
 
.panel2-wind3-auto[
<img src="lecture-02_files/figure-html/wind3_auto_10_output-1.png" width="432" />
]

---

```r
*USAboundaries::us_states(resolution = "low")
```
]
 
.panel2-wind4-auto[

```
Simple feature collection with 52 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1743 ymin: 17.91377 xmax: 179.7739 ymax: 71.35256
Geodetic CRS:  WGS 84
First 10 features:
   statefp  statens    affgeoid geoid stusps                 name lsad         aland       awater           state_name state_abbr jurisdiction_type
1       23 01779787 0400000US23    23     ME                Maine   00   79885221885  11748755195                Maine         ME             state
2       15 01779782 0400000US15    15     HI               Hawaii   00   16634100855  11777698394               Hawaii         HI             state
3       04 01779777 0400000US04    04     AZ              Arizona   00  294198560125   1027346486              Arizona         AZ             state
4       05 00068085 0400000US05    05     AR             Arkansas   00  134771517596   2960191698             Arkansas         AR             state
5       10 01779781 0400000US10    10     DE             Delaware   00    5047194742   1398720828             Delaware         DE             state
6       13 01705317 0400000US13    13     GA              Georgia   00  149169848456   4741100880              Georgia         GA             state
7       27 00662849 0400000US27    27     MN            Minnesota   00  206232257655  18929176411            Minnesota         MN             state
8       02 01785533 0400000US02    02     AK               Alaska   00 1477946266785 245390495931               Alaska         AK             state
9       06 01779778 0400000US06    06     CA           California   00  403501101370  20466718403           California         CA             state
10      11 01702382 0400000US11    11     DC District of Columbia   00     158364992     18633403 District of Columbia         DC          district
                         geometry
1  MULTIPOLYGON (((-68.92401 4...
2  MULTIPOLYGON (((-156.0497 1...
3  MULTIPOLYGON (((-114.7997 3...
4  MULTIPOLYGON (((-94.61792 3...
5  MULTIPOLYGON (((-75.77379 3...
6  MULTIPOLYGON (((-85.60516 3...
7  MULTIPOLYGON (((-97.22904 4...
8  MULTIPOLYGON (((179.4813 51...
9  MULTIPOLYGON (((-118.594 33...
10 MULTIPOLYGON (((-77.11976 3...
```
]