Introducción al paquete sfnetworks

class: center, middle, inverse, title-slide

# Introducción al paquete <code>sfnetworks</code>
## Análisis de redes geoespaciales con R
### Lorena Abad
### 2021-01-15

---

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### Primero lo primero...

Encuentran las diapositivas de la presentación aquí: https://sfnetworks.github.io/sfnetworks-REspacialES/slides
 
...

y el código fuente en este repositorio de GitHub: https://github.com/sfnetworks/sfnetworks-REspacialES

---
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## ¿De qué les voy a hablar hoy?

Redes geoespaciales en R (sinópsis)

Paquete `{sfnetworks}`: origen, filosofía, estructura

Recorrido por las funciones de `{sfnetworks}`, incluyendo: 
 Las funciones base del paquete 
 Nuevas funciones en `{sfnetworks}` v.0.4.1 "Hiltrup"

---
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## ¿Qué son redes geoespaciales?

.pull-left[
Redes viales
![](figs/road_network.png)
]

.pull-right[
Redes fluviales
![](figs/river_network.png)
]
---
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## La oferta en R:

.pull-left[
### Análisis espacial

[\#rspatial](https://twitter.com/search?q=%23rspatial)

`sf`

`stars`

`rgeos`

`rgdal`

`tmap`

`ggmap`

`mapview`

...
]

.pull-right[
### Análisis de redes

[statnet](http://statnet.org/)

`igraph`

`tidygraph`

`qgraph`

`ggraph`

`visNetwork`

`networkD3`

...
]

---
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# ¿Y para redes espaciales?

[dodgr](https://atfutures.github.io/dodgr/)

[cppRouting](https://github.com/vlarmet/cppRouting)

[shp2graph](https://r-forge.r-project.org/projects/shp2graph)

[spnetwork](https://github.com/edzer/spnetwork)

[stplanr](https://docs.ropensci.org/stplanr/)

...

---
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## Nuestra propuesta

--
.pull-left[
![](https://user-images.githubusercontent.com/520851/34887433-ce1d130e-f7c6-11e7-83fc-d60ad4fae6bd.gif)
]

.pull-right[
![:scale 38%](https://raw.githubusercontent.com/thomasp85/tidygraph/master/man/figures/logo.png)
]
--
.center[
![:scale 20%](https://raw.githubusercontent.com/luukvdmeer/sfnetworks/master/man/figures/logo.png)
]

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.pull-left-70[
.center[
![:scale 80%](https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/tidyverse_celestial.png)
]
.footnote[
Arte de [@allison_horst](https://twitter.com/allison_horst)
]
]

.pull-right-30[
### Datos *tidy*

1. Cada variable forma una columna.
2. Cada observación forma una fila.
3. Cada unidad observacional forma una tabla.

.footnote[
Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10), 1 - 23. doi:http://dx.doi.org/10.18637/jss.v059.i10
]

- Implementado en los paquetes *tidyverse*

- Permite estructuras concatenables `%>%`

- Más info en la charla de [REspacialEs aquí!](https://youtu.be/qm_30hOnocg)

]

---
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.pull-left-70[
![](https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/sf.png)
.footnote[
Artwork by [@allison_horst](https://twitter.com/allison_horst)
]
]

.pull-right-30[

### ** Paquete `sf`**

Simple features para R

Datos espaciales vectoriales (puntos, líneas, polígonos)

Compatible con los flujos *tidy*

Clases S3 
]
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.pull-left-70[
![:scale 80%](https://www.r-graph-gallery.com/314-custom-circle-packing-with-several-levels_files/figure-html/thecode3c-1.png)
.footnote[
Figura de [R Graph Gallery](https://www.r-graph-gallery.com/314-custom-circle-packing-with-several-levels.html)
]
]

.pull-right-30[

### **Paquete `tidygraph`**
![:scale 10%](https://avatars3.githubusercontent.com/u/3735184?s=200&v=4)
 Interfaz con [`igraph`](https://igraph.org/r/)  
 
![:scale 8%](https://github.com/tidyverse/dplyr/raw/master/man/figures/logo.png)
 Soporte para *verbos* [`dplyr`](https://dplyr.tidyverse.org/)

![:scale 8%](https://raw.githubusercontent.com/thomasp85/tidygraph/master/man/figures/logo.png)
 Introduce nuevos *verbos* específicos para datos de redes (e.g. `morph`, `bind_graphs`, `graph_join`)

![:scale 8%](https://github.com/thomasp85/ggraph/raw/master/man/figures/logo.png)
 Permite visualizción a través de  [`ggraph`](https://ggraph.data-imaginist.com/)
]
---
class: center, middle
# ¿Y `sfnetworks`?

### Filosofía
  
De la introducción de `tidygraph`: 
  
  > "... una cercana aproximación de datos *ordenados* para datos relacionales son dos *tidy data frames*, uno que describa los *nodos* y otro describiendo las *aristas*."

--
 
 
 
 
 > “Una cercana aproximación de datos *ordenados* para datos relacionales .orange[*geoespaciales*] son dos .orange[*objectos sf*], uno que describa los *nodos* y otro describiendo las *aristas*”

adaptación para `sfnetworks`.

---
class: middle
## Instalación

Instalación estable desde GitHub en la rama `master`: 
  
  
  ```r
  remotes::install_github("luukvdmeer/sfnetworks")
  ```

Instalación de la rama `develop` donde desarrollamos activamente el paquete y PR pueden ser dirigidas: 
  
  
  ```r
  remotes::install_github("luukvdmeer/sfnetworks", ref = "develop")
  ```
---
class: middle
## Estructura de la clase `sfnetwork`

### Construcción
  
Nodos: objeto `sf` con geometrías `POINT`

Aristas: Columnas *to*, *from* y coordenadas de borde.

Mismo sistema de coordenas!
  
---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
*library(sf)
*library(sfnetworks)

*p1 = st_point(c(7, 51))
*p2 = st_point(c(7, 52))
*p3 = st_point(c(8, 52))

*st_sfc(
* p1, p2, p3,
* crs = 4326
*) %>% st_sf()
```
]
 
.right-panel-toyexample-user[

```
Simple feature collection with 3 features and 0 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: 7 ymin: 51 xmax: 8 ymax: 52
geographic CRS: WGS 84
      geometry
1 POINT (7 51)
2 POINT (7 52)
3 POINT (8 52)
```
]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
* nodes
```
]
 
.right-panel-toyexample-user[

]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
  nodes

*st_sfc(
* st_cast(st_union(p1,p2), "LINESTRING"),
* st_cast(st_union(p1,p3), "LINESTRING"),
* st_cast(st_union(p2,p3), "LINESTRING"),
* crs = 4326
*) %>% st_sf()
```
]
 
.right-panel-toyexample-user[

```
Simple feature collection with 3 features and 0 fields
geometry type:  LINESTRING
dimension:      XY
bbox:           xmin: 7 ymin: 51 xmax: 8 ymax: 52
geographic CRS: WGS 84
                 geometry
1 LINESTRING (7 51, 7 52)
2 LINESTRING (7 51, 8 52)
3 LINESTRING (7 52, 8 52)
```
]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
  nodes

st_sfc(  
  st_cast(st_union(p1,p2), "LINESTRING"),  
  st_cast(st_union(p1,p3), "LINESTRING"),  
  st_cast(st_union(p2,p3), "LINESTRING"),  
  crs = 4326  
) %>% st_sf() ->  
* edges
```
]
 
.right-panel-toyexample-user[

]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
  nodes

st_sfc(  
  st_cast(st_union(p1,p2), "LINESTRING"),  
  st_cast(st_union(p1,p3), "LINESTRING"),  
  st_cast(st_union(p2,p3), "LINESTRING"),  
  crs = 4326  
) %>% st_sf() ->  
  edges

*edges$from = c(1, 1, 2)
*edges$to = c(2, 3, 3)
```
]
 
.right-panel-toyexample-user[

]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
  nodes

st_sfc(  
  st_cast(st_union(p1,p2), "LINESTRING"),  
  st_cast(st_union(p1,p3), "LINESTRING"),  
  st_cast(st_union(p2,p3), "LINESTRING"),  
  crs = 4326  
) %>% st_sf() ->  
  edges

edges$from = c(1, 1, 2)  
edges$to = c(2, 3, 3)

*sfnetwork(nodes, edges, directed = FALSE)
```
]
 
.right-panel-toyexample-user[

```
Checking if spatial network structure is valid...
```

```
Spatial network structure is valid
```

<PRE class="fansi fansi-output"><CODE># A sfnetwork with 3 nodes and 3 edges
#
# CRS: EPSG:4326 
#
# An undirected simple graph with 1 component with spatially explicit edges
#
# Node Data: 3 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: 7 ymin: 51 xmax: 8 ymax: 52
 geometry
 &lt;POINT [°]&gt;
1 (7 51)
2 (7 52)
3 (8 52)
#
# Edge Data: 3 x 3
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: 7 ymin: 51 xmax: 8 ymax: 52
 from to geometry
 &lt;int&gt; &lt;int&gt; &lt;LINESTRING [°]&gt;
1 1 2 (7 51, 7 52)
2 1 3 (7 51, 8 52)
3 2 3 (7 52, 8 52)
</CODE></PRE>
]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
  nodes

st_sfc(  
  st_cast(st_union(p1,p2), "LINESTRING"),  
  st_cast(st_union(p1,p3), "LINESTRING"),  
  st_cast(st_union(p2,p3), "LINESTRING"),  
  crs = 4326  
) %>% st_sf() ->  
  edges

edges$from = c(1, 1, 2)  
edges$to = c(2, 3, 3)

sfnetwork(nodes, edges, directed = FALSE) ->  
* net
```
]
 
.right-panel-toyexample-user[

```
Checking if spatial network structure is valid...
```

```
Spatial network structure is valid
```
]

---
count: false
 
### Construcción
.left-panel-toyexample-user[

```r
library(sf)  
library(sfnetworks)

p1 = st_point(c(7, 51))  
p2 = st_point(c(7, 52))  
p3 = st_point(c(8, 52))

st_sfc(  
  p1, p2, p3,  
  crs = 4326  
) %>% st_sf() ->  
  nodes

st_sfc(  
  st_cast(st_union(p1,p2), "LINESTRING"),  
  st_cast(st_union(p1,p3), "LINESTRING"),  
  st_cast(st_union(p2,p3), "LINESTRING"),  
  crs = 4326  
) %>% st_sf() ->  
  edges

edges$from = c(1, 1, 2)  
edges$to = c(2, 3, 3)

sfnetwork(nodes, edges, directed = FALSE) ->  
  net

*class(net)
```
]
 
.right-panel-toyexample-user[

```
Checking if spatial network structure is valid...
```

```
Spatial network structure is valid
```

```
[1] "sfnetwork" "tbl_graph" "igraph"   
```
]

---
count: false
 
### Tip extra!
.left-panel-toyexamplename-user[

```r
*nodes$name = c("city", "village", "farm")
*edges$from = c("city", "city", "village")
*edges$to = c("village", "farm", "farm")
```
]
 
.right-panel-toyexamplename-user[

]

---
count: false
 
### Tip extra!
.left-panel-toyexamplename-user[

```r
nodes$name = c("city", "village", "farm")  
edges$from = c("city", "city", "village")  
edges$to = c("village", "farm", "farm")

*sfnetwork(nodes, edges, node_key = "name")
```
]
 
.right-panel-toyexamplename-user[

```
Checking if spatial network structure is valid...
```

```
Spatial network structure is valid
```

<PRE class="fansi fansi-output"><CODE># A sfnetwork with 3 nodes and 3 edges
#
# CRS: EPSG:4326 
#
# A directed acyclic simple graph with 1 component with spatially explicit edges
#
# Node Data: 3 x 2 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: 7 ymin: 51 xmax: 8 ymax: 52
 geometry name 
 &lt;POINT [°]&gt; &lt;chr&gt; 
1 (7 51) city 
2 (7 52) village
3 (8 52) farm 
#
# Edge Data: 3 x 3
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: 7 ymin: 51 xmax: 8 ymax: 52
 from to geometry
 &lt;int&gt; &lt;int&gt; &lt;LINESTRING [°]&gt;
1 1 2 (7 51, 7 52)
2 1 3 (7 51, 8 52)
3 2 3 (7 52, 8 52)
</CODE></PRE>
]

---
class: middle
## Estructura de la clase `sfnetwork`

### Conversión
  
Usando `as_sfnetwork()` se pueden pasar objetos de tipo:
- `sf` con geometría `POINT` o `LINESTRING`
- `tbl_graph` de `tidygraph`
- `psp` o `linnet` de `spatstat`
- `sfNetwork` de `stplanr`

Archivos externos que se puedan leer con: 
- `sf::st_read()`
- `igraph::read_graph() %>% as_tbl_graph()`

---
#### Demo: Librerías

```r
library(sf)
library(sfnetworks)
library(tidygraph)
library(tidyverse)

# Extras:
library(ggplot2) 
```

---
#### Demo: Datos

.center[
<img src="slides_files/figure-html/demo2-1.png" width="864" />
]

---
#### Demo: Datos

.center[
<img src="slides_files/figure-html/demo3-1.png" width="864" />
]

---
count: false
 
#### Demo: Conversión
.left-panel-demo4-rotate[

```r
cuenca %>%  
  head()   
```
]
 
.right-panel-demo4-rotate[

```
Simple feature collection with 6 features and 6 fields
geometry type: LINESTRING
dimension: XY
bbox: xmin: -79.03314 ymin: -2.906555 xmax: -78.95973 ymax: -2.888646
geographic CRS: WGS 84
 name type lanes oneway sidewalk surface
1 <NA> residential <NA> no <NA> <NA>
2 <NA> trunk_link 1 yes right asphalt
3 Juan Montalvo residential 2 yes both sett
4 Miguel Heredia residential 2 yes both concrete
5 Miguel Velez residential 2 yes both concrete
6 Avenida de las Américas primary 2 yes <NA> asphalt
 geometry
1 LINESTRING (-79.028 -2.9061...
2 LINESTRING (-78.95973 -2.89...
3 LINESTRING (-79.00801 -2.89...
4 LINESTRING (-79.01274 -2.89...
5 LINESTRING (-79.01104 -2.89...
6 LINESTRING (-79.03314 -2.90...
```
]

---
count: false
 
#### Demo: Conversión
.left-panel-demo4-rotate[

```r
cuenca %>% 
* as_sfnetwork()
```
]
 
.right-panel-demo4-rotate[
<PRE class="fansi fansi-output"><CODE># A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# A directed multigraph with 3646 components with spatially explicit edges
#
# Node Data: 12,283 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 &lt;POINT [°]&gt;
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
4 (-78.96173 -2.893512)
5 (-79.00801 -2.891764)
6 (-79.00924 -2.89848)
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 9
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to name type lanes oneway sidewalk surface
 &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; 
1 1 2 NA resi~ NA no NA NA 
2 3 4 NA trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
# ... with 8,996 more rows, and 1 more variable: geometry &lt;LINESTRING
# [°]&gt;
</CODE></PRE>
]

---
count: false
 
#### Demo: Conversión
.left-panel-demo5-non_seq[

```r
cuenca %>% 
 as_sfnetwork( 
 ) 
```
]
 
.right-panel-demo5-non_seq[
<PRE class="fansi fansi-output"><CODE># A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# A directed multigraph with 3646 components with spatially explicit edges
#
# Node Data: 12,283 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 &lt;POINT [°]&gt;
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
4 (-78.96173 -2.893512)
5 (-79.00801 -2.891764)
6 (-79.00924 -2.89848)
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 9
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to name type lanes oneway sidewalk surface
 &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; 
1 1 2 NA resi~ NA no NA NA 
2 3 4 NA trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
# ... with 8,996 more rows, and 1 more variable: geometry &lt;LINESTRING
# [°]&gt;
</CODE></PRE>
]

---
count: false
 
#### Demo: Conversión
.left-panel-demo5-non_seq[

```r
cuenca %>% 
 as_sfnetwork( 
* directed = F,
 ) 
```
]
 
.right-panel-demo5-non_seq[
<PRE class="fansi fansi-output"><CODE># A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Node Data: 12,283 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 &lt;POINT [°]&gt;
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
4 (-78.96173 -2.893512)
5 (-79.00801 -2.891764)
6 (-79.00924 -2.89848)
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 9
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to name type lanes oneway sidewalk surface
 &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; 
1 1 2 NA resi~ NA no NA NA 
2 3 4 NA trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
# ... with 8,996 more rows, and 1 more variable: geometry &lt;LINESTRING
# [°]&gt;
</CODE></PRE>
]

---
count: false
 
#### Demo: Conversión
.left-panel-demo5-non_seq[

```r
cuenca %>% 
 as_sfnetwork( 
 directed = F, 
* edges_as_lines = F
 ) 
```
]
 
.right-panel-demo5-non_seq[
<PRE class="fansi fansi-output"><CODE># A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially implicit edges
#
# Node Data: 12,283 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 &lt;POINT [°]&gt;
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
4 (-78.96173 -2.893512)
5 (-79.00801 -2.891764)
6 (-79.00924 -2.89848)
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 8
 from to name type lanes oneway sidewalk surface
 &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; 
1 1 2 NA residential NA no NA NA 
2 3 4 NA trunk_link 1 yes right asphalt
3 5 6 Juan Montalvo residential 2 yes both sett 
# ... with 8,996 more rows
</CODE></PRE>
]

---
count: false
 
#### Demo: Conversión
.left-panel-demo5-non_seq[

```r
cuenca %>%  
  as_sfnetwork(  
    directed = F,  
    edges_as_lines = F  
  )

*net = as_sfnetwork(cuenca, directed = F)
```
]
 
.right-panel-demo5-non_seq[
<PRE class="fansi fansi-output"><CODE># A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially implicit edges
#
# Node Data: 12,283 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 &lt;POINT [°]&gt;
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
4 (-78.96173 -2.893512)
5 (-79.00801 -2.891764)
6 (-79.00924 -2.89848)
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 8
 from to name type lanes oneway sidewalk surface
 &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; 
1 1 2 NA residential NA no NA NA 
2 3 4 NA trunk_link 1 yes right asphalt
3 5 6 Juan Montalvo residential 2 yes both sett 
# ... with 8,996 more rows
</CODE></PRE>
]

---
count: false
 
#### Demo: Visualización
.left-panel-demo6-user[

```r
*par(mar = c(0,0,0,0))
```
]
 
.right-panel-demo6-user[

]

---
count: false
 
#### Demo: Visualización
.left-panel-demo6-user[

```r
par(mar = c(0,0,0,0)) 
*plot(net)
```
]
 
.right-panel-demo6-user[
<img src="slides_files/figure-html/demo6_user_2_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Visualización
.left-panel-demo7-user[

```r
*autoplot(net)
```
]
 
.right-panel-demo7-user[
<img src="slides_files/figure-html/demo7_user_1_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Visualización
.left-panel-demo7-user[

```r
autoplot(net) + 
* ggtitle("Red vial de Cuenca, Ecuador")
```
]
 
.right-panel-demo7-user[
<img src="slides_files/figure-html/demo7_user_2_output-1.png" width="864" />
]

.note[[Issue en ggraph](https://github.com/thomasp85/ggraph/issues/275)]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo8-user[

```r
# Con verbos de `dplyr`
*net
```
]
 
.right-panel-demo8-user[

```
# A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Node Data: 12,283 x 1 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 <POINT [°]>
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
4 (-78.96173 -2.893512)
5 (-79.00801 -2.891764)
6 (-79.00924 -2.89848)
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 9
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to name type lanes oneway sidewalk surface
 <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> 
1 1 2 <NA> resi~ <NA> no <NA> <NA> 
2 3 4 <NA> trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
# ... with 8,996 more rows, and 1 more variable: geometry <LINESTRING
# [°]>
```
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo8-user[

```r
# Con verbos de `dplyr`
net %>%  
* activate("edges")
```
]
 
.right-panel-demo8-user[

```
# A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Edge Data: 8,999 x 9 (active)
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to name type lanes oneway sidewalk surface
 <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> 
1 1 2 <NA> resi~ <NA> no <NA> <NA> 
2 3 4 <NA> trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
4 7 8 Migu~ resi~ 2 yes both concre~
5 9 10 Migu~ resi~ 2 yes both concre~
6 11 12 Aven~ prim~ 2 yes <NA> asphalt
# ... with 8,993 more rows, and 1 more variable: geometry <LINESTRING
# [°]>
#
# Node Data: 12,283 x 1
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 <POINT [°]>
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
# ... with 12,280 more rows
```
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo8-user[

```r
# Con verbos de `dplyr`
net %>%  
  activate("edges") %>%  
* filter(type == "residential")
```
]
 
.right-panel-demo8-user[

```
# A sfnetwork with 12283 nodes and 4434 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 7890 components with spatially explicit edges
#
# Edge Data: 4,434 x 9 (active)
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.07054 ymin: -2.933674 xmax: -78.91342
# ymax: -2.83815
 from to name type lanes oneway sidewalk surface
 <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> 
1 1 2 <NA> resi~ <NA> no <NA> <NA> 
2 5 6 Juan~ resi~ 2 yes both sett 
3 7 8 Migu~ resi~ 2 yes both concre~
4 9 10 Migu~ resi~ 2 yes both concre~
5 13 14 Cant~ resi~ 2 <NA> right concre~
6 23 24 Moll~ resi~ 1 yes right asphalt
# ... with 4,428 more rows, and 1 more variable: geometry <LINESTRING
# [°]>
#
# Node Data: 12,283 x 1
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 <POINT [°]>
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
# ... with 12,280 more rows
```
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo9-user[

```r
*par(mar = c(0,0,0,0), mfrow = c(2,1))
*plot(net, col = 'grey')
```
]
 
.right-panel-demo9-user[
<img src="slides_files/figure-html/demo9_user_1_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo9-user[

```r
par(mar = c(0,0,0,0), mfrow = c(2,1)) 
plot(net, col = 'grey') 
*plot(
* sansebas,
* border = 'red', lwd = 2,
* col = NA, add = T
*) 
```
]
 
.right-panel-demo9-user[
<img src="slides_files/figure-html/demo9_user_2_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo9-user[

```r
par(mar = c(0,0,0,0), mfrow = c(2,1))  
plot(net, col = 'grey')  
plot(  
  sansebas,  
  border = 'red', lwd = 2,  
  col = NA, add = T  
)  
*plot(
* st_filter(
*   net,
*   sansebas,
*   .predicate = st_intersects
* )
*) 
```
]
 
.right-panel-demo9-user[

```
although coordinates are longitude/latitude, st_intersects assumes that they are planar
although coordinates are longitude/latitude, st_intersects assumes that they are planar
```

<img src="slides_files/figure-html/demo9_user_3_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo10-user[

```r
# Con funciones de tidygraph
*net %>%
* activate("nodes")
```
]
 
.right-panel-demo10-user[

---
count: false
 
#### Demo: Wrangling
.left-panel-demo10-user[

```r
# Con funciones de tidygraph
net %>%  
  activate("nodes") %>%  
* mutate(centrality = centrality_betweenness())
```
]
 
.right-panel-demo10-user[

```
# A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Node Data: 12,283 x 2 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry centrality
 <POINT [°]> <dbl>
1 (-79.028 -2.906154) 0
2 (-79.02803 -2.90633) 0
3 (-78.95973 -2.893506) 9171
4 (-78.96173 -2.893512) 18332
5 (-79.00801 -2.891764) 99891
6 (-79.00924 -2.89848) 102884
# ... with 12,277 more rows
#
# Edge Data: 8,999 x 9
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to name type lanes oneway sidewalk surface
 <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> 
1 1 2 <NA> resi~ <NA> no <NA> <NA> 
2 3 4 <NA> trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
# ... with 8,996 more rows, and 1 more variable: geometry <LINESTRING
# [°]>
```
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo11-non_seq[

```r
# Funciones especificas para redes espaciales
net %>%  
  activate("edges") %>%  
  select(to, from, geometry) %>%  
  mutate(  
  )  
```
]
 
.right-panel-demo11-non_seq[

```
# A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Edge Data: 8,999 x 3 (active)
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to geometry
 <int> <int> <LINESTRING [°]>
1 1 2 (-79.028 -2.906154, -79.02803 -2.90633)
2 3 4 (-78.95973 -2.893506, -78.96027 -2.893746, -78.96056 -2~
3 5 6 (-79.00801 -2.891764, -79.00819 -2.892754, -79.00834 -2~
4 7 8 (-79.01274 -2.894131, -79.01278 -2.894429, -79.01283 -2~
5 9 10 (-79.01104 -2.891014, -79.01095 -2.890318, -79.01093 -2~
6 11 12 (-79.03314 -2.906555, -79.03292 -2.906139, -79.03261 -2~
# ... with 8,993 more rows
#
# Node Data: 12,283 x 1
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 <POINT [°]>
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
# ... with 12,280 more rows
```
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo11-non_seq[

```r
# Funciones especificas para redes espaciales
net %>%  
  activate("edges") %>%  
  select(to, from, geometry) %>%  
  mutate(  
*   length = edge_length(),
  )  
```
]
 
.right-panel-demo11-non_seq[

```
# A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Edge Data: 8,999 x 4 (active)
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to geometry length
 <int> <int> <LINESTRING [°]> [m]
1 1 2 (-79.028 -2.906154, -79.02803 -2.90633) 19.663~
2 3 4 (-78.95973 -2.893506, -78.96027 -2.893746, -78~ 241.638~
3 5 6 (-79.00801 -2.891764, -79.00819 -2.892754, -79~ 757.311~
4 7 8 (-79.01274 -2.894131, -79.01278 -2.894429, -79~ 138.144~
5 9 10 (-79.01104 -2.891014, -79.01095 -2.890318, -79~ 271.476~
6 11 12 (-79.03314 -2.906555, -79.03292 -2.906139, -79~ 347.195~
# ... with 8,993 more rows
#
# Node Data: 12,283 x 1
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 <POINT [°]>
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
# ... with 12,280 more rows
```
]

---
count: false
 
#### Demo: Wrangling
.left-panel-demo11-non_seq[

```r
# Funciones especificas para redes espaciales
net %>%  
  activate("edges") %>%  
  select(to, from, geometry) %>%  
  mutate(  
    length = edge_length(),  
*   azimuth = edge_azimuth()
  )  
```
]
 
.right-panel-demo11-non_seq[

```
# A sfnetwork with 12283 nodes and 8999 edges
#
# CRS: EPSG:4326 
#
# An undirected multigraph with 3646 components with spatially explicit edges
#
# Edge Data: 8,999 x 5 (active)
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.08642 ymin: -2.960194 xmax: -78.90349
# ymax: -2.779669
 from to geometry length azimuth
 <int> <int> <LINESTRING [°]> [m] [rad]
1 1 2 (-79.028 -2.906154, -79.02803 -2.9063~ 19.663~ -2.9906~
2 3 4 (-78.95973 -2.893506, -78.96027 -2.89~ 241.638~ -1.5740~
3 5 6 (-79.00801 -2.891764, -79.00819 -2.89~ 757.311~ -2.9590~
4 7 8 (-79.01274 -2.894131, -79.01278 -2.89~ 138.144~ -2.9463~
5 9 10 (-79.01104 -2.891014, -79.01095 -2.89~ 271.476~ -0.1295~
6 11 12 (-79.03314 -2.906555, -79.03292 -2.90~ 347.195~ 0.5731~
# ... with 8,993 more rows
#
# Node Data: 12,283 x 1
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935008 xmax: -78.91145 ymax:
# -2.833441
 geometry
 <POINT [°]>
1 (-79.028 -2.906154)
2 (-79.02803 -2.90633)
3 (-78.95973 -2.893506)
# ... with 12,280 more rows
```
]

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo13-non_seq[

```r
net %>%  
  st_filter(gilramir, .predicate = st_intersects) %>%  
  plot()  
```
]
 
.right-panel-demo13-non_seq[

```
although coordinates are longitude/latitude, st_intersects assumes that they are planar
although coordinates are longitude/latitude, st_intersects assumes that they are planar
```

<img src="slides_files/figure-html/demo13_non_seq_1_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo13-non_seq[

```r
net %>%  
  st_filter(gilramir, .predicate = st_intersects) %>%  
* convert(to_spatial_subdivision) %>%
  plot()  
```
]
 
.right-panel-demo13-non_seq[

```
although coordinates are longitude/latitude, st_intersects assumes that they are planar
although coordinates are longitude/latitude, st_intersects assumes that they are planar
```

<img src="slides_files/figure-html/demo13_non_seq_2_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo13-non_seq[

```r
net %>%  
  st_filter(gilramir, .predicate = st_intersects) %>%  
  convert(to_spatial_subdivision) %>%  
* convert(to_spatial_smooth) %>%
  plot()  
```
]
 
.right-panel-demo13-non_seq[

```
although coordinates are longitude/latitude, st_intersects assumes that they are planar
although coordinates are longitude/latitude, st_intersects assumes that they are planar
```

<img src="slides_files/figure-html/demo13_non_seq_3_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo13-non_seq[

```r
net %>%  
  st_filter(gilramir, .predicate = st_intersects) %>%  
  convert(to_spatial_subdivision) %>%  
  convert(to_spatial_smooth) %>%  
* convert(to_spatial_simple) %>%
  plot()  
```
]
 
.right-panel-demo13-non_seq[

```
although coordinates are longitude/latitude, st_intersects assumes that they are planar
although coordinates are longitude/latitude, st_intersects assumes that they are planar
```

<img src="slides_files/figure-html/demo13_non_seq_4_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo14-user[

```r
*net
```
]
 
.right-panel-demo14-user[

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo14-user[

```r
net %>%  
* convert(to_spatial_subdivision) %>%
* convert(to_spatial_smooth) %>%
* convert(to_spatial_simple)
```
]
 
.right-panel-demo14-user[

```
# A sfnetwork with 11799 nodes and 15772 edges
#
# CRS: EPSG:4326 
#
# An undirected simple graph with 125 components with spatially explicit edges
#
# Node Data: 11,799 x 2 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.934974 xmax: -78.91145 ymax:
# -2.833441
 geometry .tidygraph_node_index
 <POINT [°]> <int>
1 (-79.028 -2.906154) 1
2 (-79.02803 -2.90633) 2
3 (-78.96027 -2.893746) 3
4 (-78.96173 -2.893512) 4
5 (-79.00801 -2.891764) 5
6 (-79.00819 -2.892754) 6
# ... with 11,793 more rows
#
# Edge Data: 15,772 x 10
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935125 xmax: -78.91145 ymax:
# -2.833441
 from to name type lanes oneway sidewalk surface
 <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> 
1 1 2 <NA> resi~ <NA> no <NA> <NA> 
2 3 4 <NA> trun~ 1 yes right asphalt
3 5 6 Juan~ resi~ 2 yes both sett 
# ... with 15,769 more rows, and 2 more variables:
# geometry <LINESTRING [°]>, .tidygraph_edge_index <int>
```
]

---
count: false
 
#### Demo: Preprocesamiento
.left-panel-demo14-user[

```r
net %>%  
  convert(to_spatial_subdivision) %>%  
  convert(to_spatial_smooth) %>%  
  convert(to_spatial_simple) ->  
* net_clean
```
]
 
.right-panel-demo14-user[

]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo15-user[

```r
*parque = st_sfc(st_point(c(-79.0044, -2.8977)), crs = 4326)
*artes = st_sfc(st_point(c(-79.0252, -2.9203)), crs= 4326)
*estadio = st_sfc(st_point(c(-79.00586, -2.90672)), crs= 4326)
```
]
 
.right-panel-demo15-user[

]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo15-user[

```r
parque = st_sfc(st_point(c(-79.0044, -2.8977)), crs = 4326)  
artes = st_sfc(st_point(c(-79.0252, -2.9203)), crs= 4326)  
estadio = st_sfc(st_point(c(-79.00586, -2.90672)), crs= 4326)

*st_network_paths(
* net_clean,
* from = parque,
* to = c(artes,estadio)
*) 
```
]
 
.right-panel-demo15-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
# A tibble: 2 x 2
 node_paths edge_paths
 <list> <list> 
1 <int [66]> <int [65]>
2 <int [22]> <int [21]>
```
]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo15-user[

```r
parque = st_sfc(st_point(c(-79.0044, -2.8977)), crs = 4326)  
artes = st_sfc(st_point(c(-79.0252, -2.9203)), crs= 4326)  
estadio = st_sfc(st_point(c(-79.00586, -2.90672)), crs= 4326)

st_network_paths(  
  net_clean,  
  from = parque,  
  to = c(artes,estadio)  
) ->  
* paths

*paths %>%
* pull(node_paths) %>%
* unlist()
```
]
 
.right-panel-demo15-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
 [1] 11045  8088  8087  8078   238   278  2629  6777  6205  6775  6776
[12]   205 10276 10384  8955  1063  1062  3885   675   676  2646  1085
[23]  1097  6250  8761  9982  2034   617   646   684   638   606  6778
[34]   559   560   561  9640   247   248  4213  3791  3818  3761  4913
[45]  4911  4857  4603  3764  6316  4898  6324  8100  4897  4841  3835
[56]  3834 10931  4842  4893  4892  4891  4826   717   716  7503  7504
[67] 11045  8088  8087  8078   238   278  2629  6777  6205  6775  6776
[78]   205  2038  8789  8788   817   821   793  8787   214   213  8785
```
]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo18-user[

```r
*plot_path = function(node_path) {
* net_clean %>%
*   activate("nodes") %>%
*   slice(node_path) %>%
*   plot(cex = 1.5, lwd = 1.5, add = TRUE)
*} 
```
]
 
.right-panel-demo18-user[

]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo18-user[

```r
plot_path = function(node_path) {  
  net_clean %>%  
    activate("nodes") %>%  
    slice(node_path) %>%  
    plot(cex = 1.5, lwd = 1.5, add = TRUE)  
}

*par(mar = c(0,0,0,0), mfrow = c(1,1))
*plot(net_clean, col = 'grey')
```
]
 
.right-panel-demo18-user[
<img src="slides_files/figure-html/demo18_user_2_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo18-user[

```r
plot_path = function(node_path) {  
  net_clean %>%  
    activate("nodes") %>%  
    slice(node_path) %>%  
    plot(cex = 1.5, lwd = 1.5, add = TRUE)  
}

par(mar = c(0,0,0,0), mfrow = c(1,1)) 
plot(net_clean, col = 'grey') 
*plot(parque, col = "blue", pch = 8, add = T)
*plot(artes, col = "red", pch = 8, add = T)
*plot(estadio, col = "orange", pch = 8, add = T)
```
]
 
.right-panel-demo18-user[
<img src="slides_files/figure-html/demo18_user_3_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Ruteo
.left-panel-demo18-user[

```r
plot_path = function(node_path) {  
  net_clean %>%  
    activate("nodes") %>%  
    slice(node_path) %>%  
    plot(cex = 1.5, lwd = 1.5, add = TRUE)  
}

par(mar = c(0,0,0,0), mfrow = c(1,1)) 
plot(net_clean, col = 'grey') 
plot(parque, col = "blue", pch = 8, add = T) 
plot(artes, col = "red", pch = 8, add = T) 
plot(estadio, col = "orange", pch = 8, add = T) 
*paths %>%
* pull(node_paths) %>%
* purrr::walk(plot_path)
```
]
 
.right-panel-demo18-user[
<img src="slides_files/figure-html/demo18_user_4_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo16-user[

```r
*net_clean %>%
* activate("edges") %>%
* pull(type) %>%
* unique()
```
]
 
.right-panel-demo16-user[

```
 [1] "residential"    "trunk_link"     "primary"       
 [4] "secondary"      "tertiary"       "trunk"         
 [7] "cycleway"       "secondary_link" "steps"         
[10] "pedestrian"     "primary_link"   "living_street" 
[13] "footway"        "path"           "service"       
[16] "track"          "unclassified"   "tertiary_link" 
[19] NA              
```
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo16-user[

```r
net_clean %>%  
  activate("edges") %>%  
  pull(type) %>%  
  unique() ->  
* types

# Randomly define a walking speed in m/s for each type,
# with values between 3 and 7 km/h.
*set.seed(1)
*speeds = runif(
* length(types),
* 3 * 1000 / 60 / 60,
* 7 * 1000 / 60 / 60
*) 
```
]
 
.right-panel-demo16-user[

]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo16-user[

```r
net_clean %>%  
  activate("edges") %>%  
  pull(type) %>%  
  unique() ->  
  types

# Randomly define a walking speed in m/s for each type,
# with values between 3 and 7 km/h.
set.seed(1)  
speeds = runif(  
  length(types),  
  3 * 1000 / 60 / 60,  
  7 * 1000 / 60 / 60  
)

# Assign a speed to each edge based on its type.
# Calculate travel time for each edge based on that.
*net_clean %>%
* activate("edges") %>%
* select(type) %>%
* mutate(weight = edge_length()) %>%
* group_by(type) %>%
* mutate(speed = units::set_units(speeds[cur_group_id()], "m/s")) %>%
* mutate(time = weight / speed) %>%
* ungroup()
```
]
 
.right-panel-demo16-user[

```
# A sfnetwork with 11799 nodes and 15772 edges
#
# CRS: EPSG:4326 
#
# An undirected simple graph with 125 components with spatially explicit edges
#
# Edge Data: 15,772 x 7 (active)
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.935125 xmax: -78.91145 ymax:
# -2.833441
 from to type geometry weight speed
 <int> <int> <chr> <LINESTRING [°]> [m] [m/s]
1 1 2 resi~ (-79.028 -2.906154, -79.~ 19.663~ 1.567553
2 3 4 trun~ (-78.96027 -2.893746, -7~ 176.811~ 1.630687
3 5 6 resi~ (-79.00801 -2.891764, -7~ 111.254~ 1.567553
4 6 7 resi~ (-79.00819 -2.892754, -7~ 115.074~ 1.567553
5 7 8 resi~ (-79.00834 -2.893783, -7~ 3.983~ 1.567553
6 8 9 resi~ (-79.00837 -2.893797, -7~ 114.482~ 1.567553
# ... with 15,766 more rows, and 1 more variable: time [s]
#
# Node Data: 11,799 x 2
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.0708 ymin: -2.934974 xmax: -78.91145 ymax:
# -2.833441
 geometry .tidygraph_node_index
 <POINT [°]> <int>
1 (-79.028 -2.906154) 1
2 (-79.02803 -2.90633) 2
3 (-78.96027 -2.893746) 3
# ... with 11,796 more rows
```
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo16-user[

```r
net_clean %>%  
  activate("edges") %>%  
  pull(type) %>%  
  unique() ->  
  types

# Randomly define a walking speed in m/s for each type,
# with values between 3 and 7 km/h.
set.seed(1)  
speeds = runif(  
  length(types),  
  3 * 1000 / 60 / 60,  
  7 * 1000 / 60 / 60  
)

# Assign a speed to each edge based on its type.
# Calculate travel time for each edge based on that.
net_clean %>%  
  activate("edges") %>%  
  select(type) %>%  
  mutate(weight = edge_length()) %>%  
  group_by(type) %>%  
  mutate(speed = units::set_units(speeds[cur_group_id()], "m/s")) %>%  
  mutate(time = weight / speed) %>%  
  ungroup() %>%  
* activate("nodes") ->
* net_clean
```
]
 
.right-panel-demo16-user[

]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo17-user[

```r
*net_clean %>%
* filter(
* node_distance_from(
* st_nearest_feature(parque, net_clean),
* weights = time) <= 600
* )
```
]
 
.right-panel-demo17-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
# A sfnetwork with 343 nodes and 494 edges
#
# CRS: EPSG:4326 
#
# An undirected simple graph with 1 component with spatially explicit edges
#
# Node Data: 343 x 2 (active)
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -79.01202 ymin: -2.90453 xmax: -78.99676 ymax:
# -2.889817
 geometry .tidygraph_node_index
 <POINT [°]> <int>
1 (-79.00834 -2.893783) 7
2 (-79.00837 -2.893797) 8
3 (-79.00857 -2.894813) 9
4 (-79.00875 -2.895823) 10
5 (-79.00892 -2.896779) 11
6 (-79.00911 -2.897794) 12
# ... with 337 more rows
#
# Edge Data: 494 x 7
# Geometry type: LINESTRING
# Dimension: XY
# Bounding box: xmin: -79.01202 ymin: -2.90453 xmax: -78.99676 ymax:
# -2.889817
 from to type geometry weight speed
 <int> <int> <chr> <LINESTRING [°]> [m] [m/s]
1 1 2 resi~ (-79.00834 -2.893783, -7~ 3.983~ 1.567553
2 2 3 resi~ (-79.00837 -2.893797, -7~ 114.482~ 1.567553
3 3 4 resi~ (-79.00857 -2.894813, -7~ 113.555~ 1.567553
# ... with 491 more rows, and 1 more variable: time [s]
```
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo17-user[

```r
net_clean %>% 
 filter( 
 node_distance_from( 
 st_nearest_feature(parque, net_clean), 
 weights = time) <= 600 
 ) -> 
* iso

*iso_poly = iso %>%
* st_geometry() %>%
* st_union() %>%
* st_convex_hull()
```
]
 
.right-panel-demo17-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
although coordinates are longitude/latitude, st_union assumes that they are planar
```
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo17-user[

```r
net_clean %>% 
 filter( 
 node_distance_from( 
 st_nearest_feature(parque, net_clean), 
 weights = time) <= 600 
 ) -> 
 iso

iso_poly = iso %>%  
  st_geometry() %>%  
  st_union() %>%  
  st_convex_hull()

*par(mar = c(0,0,0,0), mfrow = c(1,1))
*plot(net_clean, col = "grey")
```
]
 
.right-panel-demo17-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
although coordinates are longitude/latitude, st_union assumes that they are planar
```

<img src="slides_files/figure-html/demo17_user_3_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo17-user[

```r
net_clean %>% 
 filter( 
 node_distance_from( 
 st_nearest_feature(parque, net_clean), 
 weights = time) <= 600 
 ) -> 
 iso

iso_poly = iso %>%  
  st_geometry() %>%  
  st_union() %>%  
  st_convex_hull()

par(mar = c(0,0,0,0), mfrow = c(1,1))  
plot(net_clean, col = "grey")  
*plot(
* iso_poly,
* col = NA, border = "black",
* lwd = 3, add = TRUE)
```
]
 
.right-panel-demo17-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
although coordinates are longitude/latitude, st_union assumes that they are planar
```

<img src="slides_files/figure-html/demo17_user_4_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo17-user[

```r
net_clean %>% 
 filter( 
 node_distance_from( 
 st_nearest_feature(parque, net_clean), 
 weights = time) <= 600 
 ) -> 
 iso

iso_poly = iso %>%  
  st_geometry() %>%  
  st_union() %>%  
  st_convex_hull()

par(mar = c(0,0,0,0), mfrow = c(1,1))  
plot(net_clean, col = "grey")  
plot(  
  iso_poly,  
  col = NA, border = "black",  
  lwd = 3, add = TRUE)  
*plot(iso,
*    col = "lightsalmon",
*    add = TRUE)
```
]
 
.right-panel-demo17-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
although coordinates are longitude/latitude, st_union assumes that they are planar
```

<img src="slides_files/figure-html/demo17_user_5_output-1.png" width="864" />
]

---
count: false
 
#### Demo: Isocronas
.left-panel-demo17-user[

```r
net_clean %>% 
 filter( 
 node_distance_from( 
 st_nearest_feature(parque, net_clean), 
 weights = time) <= 600 
 ) -> 
 iso

iso_poly = iso %>%  
  st_geometry() %>%  
  st_union() %>%  
  st_convex_hull()

par(mar = c(0,0,0,0), mfrow = c(1,1))  
plot(net_clean, col = "grey")  
plot(  
  iso_poly,  
  col = NA, border = "black",  
  lwd = 3, add = TRUE)  
plot(iso,  
     col = "lightsalmon",  
     add = TRUE)  
*plot(parque, col = "blue",
*    pch = 8, cex = 2,
*    lwd = 2, add = TRUE)
```
]
 
.right-panel-demo17-user[

```
although coordinates are longitude/latitude, st_nearest_points assumes that they are planar
```

```
although coordinates are longitude/latitude, st_union assumes that they are planar
```

<img src="slides_files/figure-html/demo17_user_6_output-1.png" width="864" />
]