Lecture 03

class: inverse, center, title-slide, middle

# Lecture 03: Spatial Data

## Theory and Tools (a.k.a. GIS Tools Lab.)

### <img src="figs/unibo_logo_white.png" style="width: 15%" /> Bruno Conte

### 25/Sep/2023

---

# Spatial data in economics: schedule

1. ~~Introduction to (spatial) data and programming in `R`~~ [18.Sep.2023]

2. ~~Spatial data basics: vector data + assignment~~ [21.Sep.2023]

3. Basic operations with vector data ~~+ assignment~~ [25.Sep.2023]
 - Attribute operations with vector data (slicing, filtering, aggregating)
 - Spatial operations of vetor data (e.g. intersections, touching, etc.)
 - Spatial merging/joining (based on overlaps and/or distances)

4. Geometry operations and miscelanea + follow-up + assignment [28.Sep.2023]

5. Raster data and operations + assignment [02.Oct.2023]

6. Take-home exam [03.Nov.2023]

---

# Main references for this class

1. Lovelace, R., Nowosad, J. and Muenchow, J., 2019. **Geocomputation with R.** Chapman and Hall/CRC.
 - Chapter 3.2 (attribute data operations)
 - Chapter 4 (spatial data operations)

2. Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446

3. Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".

---

# Vector data operations: attribute and spatial

- Data operations: manipulation of vector data (in terms of geometry and attribute structure). Basic operations are:
 - **Selecting:** restricting the fields (i.e. variables) of a `sf`
 
 - **Slicing:** restricting the features (i.e. observations) of a `sf`
 
 - **Filtering:** restricting based on data attributes
 
 - **Joining/merging:** linking attributes (i.e. data) between different `sf` (or data sets)
 
 - **Aggregating:** processing attributes (i.e. data) within a `sf` based on some fields

- Operations can be either attribute- or spatial-based

---

# Attribute data operations: selecting (choose fields)

```r
*world
```

```
## Simple feature collection with 177 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 × 11
## iso_a2 name_long conti…¹ regio…² subre…³ type area_…⁴ pop
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 FJ Fiji Oceania Oceania Melane… Sove… 1.93e4 8.86e5
## 2 TZ Tanzania Africa Africa Easter… Sove… 9.33e5 5.22e7
## 3 EH Western Sahara Africa Africa Northe… Inde… 9.63e4 NA 
## 4 CA Canada North … Americ… Northe… Sove… 1.00e7 3.55e7
## 5 US United States North … Americ… Northe… Coun… 9.51e6 3.19e8
## 6 KZ Kazakhstan Asia Asia Centra… Sove… 2.73e6 1.73e7
## 7 UZ Uzbekistan Asia Asia Centra… Sove… 4.61e5 3.08e7
## 8 PG Papua New Gui… Oceania Oceania Melane… Sove… 4.65e5 7.76e6
## 9 ID Indonesia Asia Asia South-… Sove… 1.82e6 2.55e8
## 10 AR Argentina South … Americ… South … Sove… 2.78e6 4.30e7
## # … with 167 more rows, 3 more variables: lifeExp <dbl>,
## # gdpPercap <dbl>, geom <MULTIPOLYGON [°]>, and abbreviated
## # variable names ¹continent, ²region_un, ³subregion, ⁴area_km2
```

]

---
counter: false

# Attribute data operations: selecting (choose fields)

```r
*world %>% select(name_long, continent)
```

```
## Simple feature collection with 177 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 × 3
## name_long continent geom
## <chr> <chr> <MULTIPOLYGON [°]>
## 1 Fiji Oceania (((180 -16.06713, 180 -16.55522, 17…
## 2 Tanzania Africa (((33.90371 -0.95, 34.07262 -1.0598…
## 3 Western Sahara Africa (((-8.66559 27.65643, -8.665124 27.…
## 4 Canada North America (((-122.84 49, -122.9742 49.00254, …
## 5 United States North America (((-122.84 49, -120 49, -117.0312 4…
## 6 Kazakhstan Asia (((87.35997 49.21498, 86.59878 48.5…
## 7 Uzbekistan Asia (((55.96819 41.30864, 55.92892 44.9…
## 8 Papua New Guinea Oceania (((141.0002 -2.600151, 142.7352 -3.…
## 9 Indonesia Asia (((141.0002 -2.600151, 141.0171 -5.…
## 10 Argentina South America (((-68.63401 -52.63637, -68.25 -53.…
## # … with 167 more rows
```

---

# Attribute data operations: slicing (choose observations)

```r
*world %>% select(name_long, continent) %>% slice(1:2)
```

```
## Simple feature collection with 2 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -18.28799 xmax: 180 ymax: -0.95
## Geodetic CRS: WGS 84
## # A tibble: 2 × 3
## name_long continent geom
## <chr> <chr> <MULTIPOLYGON [°]>
## 1 Fiji Oceania (((180 -16.06713, 180 -16.55522, 179.3641 -16.8…
## 2 Tanzania Africa (((33.90371 -0.95, 34.07262 -1.05982, 37.69869 …
```

---

# Attribute data operations: filtering (based on data)

```r
*world %>% select(name_long, continent) %>% filter(continent=='South America')
```

```
## Simple feature collection with 13 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -81.41094 ymin: -55.61183 xmax: -34.72999 ymax: 12.4373
## Geodetic CRS: WGS 84
## # A tibble: 13 × 3
## name_long continent geom
## * <chr> <chr> <MULTIPOLYGON [°]>
## 1 Argentina South America (((-68.63401 -52.63637, -68.25 -53.…
## 2 Chile South America (((-68.63401 -52.63637, -68.63335 -…
## 3 Falkland Islands South America (((-61.2 -51.85, -60 -51.25, -59.15…
## 4 Uruguay South America (((-57.62513 -30.21629, -56.97603 -…
## 5 Brazil South America (((-53.37366 -33.76838, -53.65054 -…
## 6 Bolivia South America (((-69.52968 -10.95173, -68.78616 -…
## 7 Peru South America (((-69.89364 -4.298187, -70.79477 -…
## 8 Colombia South America (((-66.87633 1.253361, -67.06505 1.…
## 9 Venezuela South America (((-60.73357 5.200277, -60.60118 4.…
## 10 Guyana South America (((-56.53939 1.899523, -56.7827 1.8…
## 11 Suriname South America (((-54.52475 2.311849, -55.09759 2.…
## 12 Ecuador South America (((-75.37322 -0.1520318, -75.23372 …
## 13 Paraguay South America (((-58.16639 -20.1767, -57.87067 -2…
```

---

# Attribute data operations: joining (merging data)

```r
world %>% select(name_long, continent) %>% filter(continent=='South America') %>%
* left_join(coffee_data) # data of coffee production by country (name_long)
```

```
## Simple feature collection with 13 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -81.41094 ymin: -55.61183 xmax: -34.72999 ymax: 12.4373
## Geodetic CRS: WGS 84
## # A tibble: 13 × 5
## name_long contin…¹ geom coffe…² coffe…³
## <chr> <chr> <MULTIPOLYGON [°]> <int> <int>
## 1 Argentina South A… (((-68.63401 -52.63637, … NA NA
## 2 Chile South A… (((-68.63401 -52.63637, … NA NA
## 3 Falkland Islands South A… (((-61.2 -51.85, -60 -51… NA NA
## 4 Uruguay South A… (((-57.62513 -30.21629, … NA NA
## 5 Brazil South A… (((-53.37366 -33.76838, … 3277 2786
## 6 Bolivia South A… (((-69.52968 -10.95173, … 3 4
## 7 Peru South A… (((-69.89364 -4.298187, … 585 625
## 8 Colombia South A… (((-66.87633 1.253361, -… 1330 1169
## 9 Venezuela South A… (((-60.73357 5.200277, -… NA NA
## 10 Guyana South A… (((-56.53939 1.899523, -… NA NA
## 11 Suriname South A… (((-54.52475 2.311849, -… NA NA
## 12 Ecuador South A… (((-75.37322 -0.1520318,… 87 62
## 13 Paraguay South A… (((-58.16639 -20.1767, -… NA NA
## # … with abbreviated variable names ¹continent,
## # ²coffee_production_2016, ³coffee_production_2017
```

---

# Attribute data operations: aggregating (based on attributes)

```r
*world %>% select(name_long, continent, pop)
```

```
## Simple feature collection with 177 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 × 4
## name_long continent pop geom
## <chr> <chr> <dbl> <MULTIPOLYGON [°]>
## 1 Fiji Oceania 885806 (((180 -16.06713, 180 -16…
## 2 Tanzania Africa 52234869 (((33.90371 -0.95, 34.072…
## 3 Western Sahara Africa NA (((-8.66559 27.65643, -8.…
## 4 Canada North America 35535348 (((-122.84 49, -122.9742 …
## 5 United States North America 318622525 (((-122.84 49, -120 49, -…
## 6 Kazakhstan Asia 17288285 (((87.35997 49.21498, 86.…
## 7 Uzbekistan Asia 30757700 (((55.96819 41.30864, 55.…
## 8 Papua New Guinea Oceania 7755785 (((141.0002 -2.600151, 14…
## 9 Indonesia Asia 255131116 (((141.0002 -2.600151, 14…
## 10 Argentina South America 42981515 (((-68.63401 -52.63637, -…
## # … with 167 more rows
```

---
count: false

# Attribute data operations: aggregating (based on attributes)

```r
world %>% select(name_long, continent, pop) %>% 
* group_by(continent) %>%
* summarise(total_population = sum(pop, na.rm = T))
```

```
## Simple feature collection with 8 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 8 × 3
## continent total_population geom
## <chr> <dbl> <MULTIPOLYGON [°]>
## 1 Africa 1154946633 (((40.43725 -11.76171, 40.…
## 2 Antarctica 0 (((-48.66062 -78.04702, -4…
## 3 Asia 4311408059 (((120.295 -10.25865, 118.…
## 4 Europe 669036256 (((-53.77852 2.376703, -54…
## 5 North America 565028684 (((-78.21494 7.512255, -78…
## 6 Oceania 37757833 (((171.9487 -41.51442, 172…
## 7 Seven seas (open ocean) 0 (((68.935 -48.625, 69.58 -…
## 8 South America 412060811 (((-57.75 -51.55, -58.05 -…
```

---

# Attribute data operations: aggregating (based on attributes)

.pull-left[
![](00_class03_files/figure-html/unnamed-chunk-8-1.png)
]
.pull-right[
![](00_class03_files/figure-html/unnamed-chunk-9-1.png)
]

---

# Spatial data operations

- Same intution, but now spatial aspects determine the operations
 - Before: based on the underlying attributes
 
- Spatial relationship of `sf` objects: determined by different **topological relations**
 - Examples: intersection, containing, touching, etc.
 
- Intuition (and workflow with data): the same as with attribute data

- **Detailed exposition:** on class material `01_class03.R`

- Next: types of topological relationships

---

.center[
<img src="https://r.geocompx.org/04-spatial-operations_files/figure-html/relations-1.png" style="width: 75%" />
]

---

# Hands-in: your turn! (1/2)

.pull-left[
* Combine `world` (`sf`) and `worldbank_df` (`data.frame`)

* Filter only countries in America

* Calculate urban rate by `subregion`

* urban rate = urban population/total population

* Plot of Americas by subregions' urban rates:
]

.pull-right[
.center[
<img src="00_class03_files/figure-html/unnamed-chunk-10-1.png" width="85%" />

]
]

---

# Hands-in: your turn! (2/2)

.pull-left[
* Combine `lnd` (Great London) and `cycle_hire` (location of bike stations)

* Filter London regions with bike stations, **plot the two together**

* Join both datasets, plot bike stations by London neighborhood

* Aggregate datasets, plot London neighborhoods by number of bikes

]
.pull-right[
<img src="00_class03_files/figure-html/unnamed-chunk-11-1.png" width="90%" />

]

---
counter: false

# Hands-in: your turn! (2/2)

.pull-left[
* Combine `lnd` (Great London) and `cycle_hire` (location of bike stations)

* Filter London regions with bike stations, plot the two together

* Join both datasets, **plot bike stations by neighborhood**

* Aggregate datasets, plot London neighborhoods by number of bikes

]
.pull-right[
<img src="00_class03_files/figure-html/unnamed-chunk-12-1.png" width="120%" />

]

---
counter: false

# Hands-in: your turn! (2/2)

.pull-left[
* Combine `lnd` (Great London) and `cycle_hire` (location of bike stations)

* Filter London regions with bike stations, plot the two together

* Join both datasets, plot bike stations by London neighborhood

* Aggregate datasets, plot **London neighborhoods by number of bikes**

]
.pull-right[
<img src="00_class03_files/figure-html/unnamed-chunk-13-1.png" width="90%" />

]