02-data_acquisition.qmd

---
bibliography: references.bib
---
# Data Acquisition

## Import from lat / long file

The function `st_as_sf()` makes it possible to transform a `data.frame` container of geographic coordinates into an object `sf`. Here we use the `data.frame` `places2` created in the previous point.

```{r build_sf, eval = TRUE, echo = TRUE, nm = TRUE, fig.width=8, class.output="code-out", warning=FALSE, message=FALSE}
library(sf)
place_sf <- st_as_sf(read.csv("data_cambodia/adress.csv"), coords = c("long", "lat"), crs = 4326)
place_sf
```

To crate a `sf` POINT type object with only one pair of coordinate (WGS84, longitude=0.5, latitude = 45.5) :

```{r build_sf2, eval = TRUE, echo = TRUE, nm = TRUE, fig.width=8, class.output="code-out", warning=FALSE, message=FALSE}
library(sf)
test_point <- st_as_sf(data.frame(x = 0.5, y = 45.5), coords = c("x", "y"), crs = 4326)
test_point
```

We can display this object `sf` on an [OpenStreetMap](https://www.openstreetmap.org/) basesmap with the package maptiles `maptiles` [@maptiles].

```{r display_sf, eval = TRUE, echo = TRUE, nm = TRUE, out.width="788px", warning=FALSE, message=FALSE}
library(maptiles)
osm <- get_tiles(x = place_sf, zoom = 12)
plot_tiles(osm)
plot(st_geometry(place_sf), pch = 2, cex = 2, col = "red", add = TRUE)
```

## Online databases

## OpenStreetMap

::: {style="float: right"}
<img src="img/Openstreetmap_logo.svg" width="150px" padding="1em"/>
:::

[OpenStreetMap (OSM)](https://www.openstreetmap.org){target="_blank"} is a participatory mapping project that aim s to buil a free geographic database on a global scale. OpenStreetMap lets you view, edit and use geographic data around the world.

**Terms of use**

> OpenStreetMap is open data : you are free to use it for ant purpose as long as you credit OpenStreetMap and its contributers. If you modify or rely data in any way, you may distribute the result only under the same license. (...)

**Contributors**

> (...) Our contributors incloude enthusiastic mapmakers, GIS professional, engineers running OSM servers, humanitarians mapping disaster-stricken areas and many mmore.(...)

### Display and interactive map

The two main packages that allow to display as interactive map based on OSM are `leaflet` [@leaflet] and `mapview` [@mapview].

#### `leaflet`

<img src="img/logo_leaflet.png" align="right" width="200"/> `leaflet` uses the javascript library Leaflet [@JS-Leaflet] to create interactive maps.

```{r leaflet, eval=TRUE, cache = FALSE, class.output="code-out", warning=FALSE, message=FALSE}
library(sf)
library(leaflet)

district <- st_read("data_cambodia/cambodia.gpkg", layer = "district", quiet = TRUE)
hospital <- st_read("data_cambodia/cambodia.gpkg", layer = "hospital", quiet = TRUE)


banan <- district[district$ADM2_PCODE == "KH0201", ]     #Select one district (Banan district: KH0201)
health_banan <- hospital[hospital$DCODE == "201", ]      #Select Health centers in Banan

banan <- st_transform(banan, 4326)                       #Transform coordinate system to WGS84
health_banan <- st_transform(health_banan, 4326)

banan_map <- leaflet(banan) %>%                          #Create interactive map
  addTiles() %>%
  addPolygons() %>%
  addMarkers(data = health_banan)
banan_map
```

::: {.callout-note appearance="minimal" icon="false"}
Website of `leaflet`  
[Leaflet for R](https://rstudio.github.io/leaflet/)
:::

#### `mapview`

<img src="img/logo_mapview.gif" align="right" width="150"/> `mapview` relies on `leaflet` to create interactive maps, its use is easier and its documentation is a bit dense.

```{r mapview, cache=FALSE, class.output="code-out", warning=FALSE, message=FALSE}
library(mapview)
mapview(banan) + mapview(health_banan)
```

::: {.callout-note appearance="minimal" icon="false"}
Website of `mapview`  
[`mapview`](https://r-spatial.github.io/mapview/)
:::

### Import basemaps

The package `maptiles` [@maptiles] allows downlaoding and displaying raster basemaps.\
The function `get_tiles()` allow you to download OSM background maps and the function `plot_tiles()` allows to display them.\
Renders are better if the input data used the same coordinate system as the tiles ([EPSG:3857](https://github.com/riatelab/maptiles#projection)).

```{r display_point, fig.width=6, fig.height=6, eval=TRUE, class.output="code-out", warning=FALSE, message=FALSE}
library(sf)
library(maptiles)
district <- st_read("data_cambodia/cambodia.gpkg", layer = "district", quiet = TRUE)
district <- st_transform(district, 3857)
osm_tiles <- get_tiles(x = district, zoom = 10, crop = TRUE)
plot_tiles(osm_tiles)
plot(st_geometry(district), border = "grey20", lwd = .7, add = TRUE)
mtext(side = 1, line = -2, text = get_credit("OpenStreetMap"), col="tomato")
```

### Import OSM data

#### `osmdata`

<img src="img/logo_osmdata.png" align="right" height="140" width="122/"/> The package `osmdata` [@osmdata] allows extracting vector data from OSM using the [Overpass turbo](https://wiki.openstreetmap.org/wiki/Overpass_turbo) API.

```{r osmdata, eval=TRUE, class.output="code-out", warning=FALSE, message=FALSE}
library(sf)
library(osmdata)
library(sf)

country <- st_read("data_cambodia/cambodia.gpkg", layer = "country", quiet = TRUE)
ext <- opq(bbox = st_bbox(st_transform(country, 4326)))                    #Define the bounding box
query <- add_osm_feature(opq = ext, key = 'amenity', value = "hospital")   #Health Center Extraction
hospital <- osmdata_sf(query)
hospital <- unique_osmdata(hospital)                                       #Result reduction (points composing polygon are detected)
```

The result contains a point layer and a polygon layer. The polygon layer contains polygons that represent fast food-food place. To obtain a coherent point layer we can use the centroids of the polygons.

```{r osmdata2, class.output="code-out", warning=FALSE, message=FALSE}
hospital_point <- hospital$osm_points
hospital_poly <- hospital$osm_polygons                                                             #Extracting centroids of polygons
hospital_poly_centroid <- st_centroid(hospital_poly)

cambodia_point <- intersect(names(hospital_point), names(hospital_poly_centroid))                  #Identify fields in Cambodia boundary
hospitals <- rbind(hospital_point[, cambodia_point], hospital_poly_centroid[, cambodia_point])     #Gather the 2 objects
```

**Result display**

```{r osmdatadisplay, cache=FALSE, class.output="code-out", warning=FALSE, message=FALSE}
library(mapview)
mapview(country) + mapview(hospitals)
```

::: {.callout-note appearance="minimal" icon="false"}
Website of `osmdata`  
[`osmdata`](https://docs.ropensci.org/osmdata/)
:::

#### `osmextract`

<img src="img/logo_osmextract.svg" align="right" width="120"/> The package `osmextract` [@osmextract] allows to extract data from an OSM database directly. This package make it possible to work on very large volumes of data.

::: {.callout-note appearance="minimal" icon="false"}
Website of `osmextract`  
[`osmextract`](https://docs.ropensci.org/osmextract/)
:::

For administrative boundaries, check [here](https://wiki.openstreetmap.org/wiki/Tag:boundary%3Dadministrative#10_admin_level_values_for_specific_countries) the administrative levels by country:

```{r, class.output="code-out", warning=FALSE, message=FALSE}
library(osmextract)
library(mapsf)
province <- oe_get(
  place = "Cambodia",
  download_directory = "data_cambodia/",
  layer = "multipolygons",
  extra_tags = c("wikidata", "ISO3166-2", "wikipedia", "name:en"),
  vectortranslate_options = c(
    "-t_srs", "EPSG:32648",
    "-nlt", "PROMOTE_TO_MULTI",
    "-where", "type = 'boundary' AND boundary = 'administrative' AND admin_level = '4'"
  ))

mf_map(x = province)
```

```{r, class.output="code-out", warning=FALSE, message=FALSE}
roads <- oe_get(
  place = "Cambodia",
  download_directory = "data_cambodia/",
  layer = "lines",
  extra_tags = c("access", "service", "maxspeed"),
  vectortranslate_options = c(
  "-t_srs", "EPSG:32648",
  "-nlt", "PROMOTE_TO_MULTI",
  "-where", "
    highway IS NOT NULL
    AND
    highway NOT IN (
    'abandonded', 'bus_guideway', 'byway', 'construction', 'corridor', 'elevator',
    'fixme', 'escalator', 'gallop', 'historic', 'no', 'planned', 'platform',
    'proposed', 'cycleway', 'pedestrian', 'bridleway', 'footway',
    'steps', 'path', 'raceway', 'road', 'service', 'track'
    )
    "
),
  boundary = subset(province, name_en == "Phnom Penh"),
  boundary_type = "clipsrc"
)
mf_map(x = roads)
```

## Geocoding

Serveral pakages alow you to geocode addresses. <img src="img/logo_tidygeocoder.png" align="right" width="120"/> The package `tidygeocoder` [@tidygeocoder] allow the use of a [large number of online geocoding sevices](https://jessecambon.github.io/tidygeocoder/articles/geocoder_services.html). The package `banR` [@banR], which is based on the [National Address Base](https://adresse.data.gouv.fr/), is the particularly suitable for geocoding addresses in France.

### `tidygeocoder`

```{r tidygeocoder, eval=TRUE, class.output="code-out", warning=FALSE, message=FALSE}
library(tidygeocoder)
test_adresses <- data.frame(
  address = c("Phnom Penh International Airport, Phnom Penh, Cambodia",
              "Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia"))
places1 <- geocode(test_adresses, address)
places1
```

::: {.callout-note appearance="minimal" icon="false"}
Website by `tidygeocoder` :   
[tidygeocoder](https://jessecambon.github.io/tidygeocoder/)
:::

### `banR` (Base Adresse Nationale)

```{r banR, eval=TRUE, class.output="code-out", warning=FALSE, message=FALSE}
# remotes::install_github("joelgombin/banR")
library(banR)
mes_adresses <- data.frame(
  address = c("19 rue Michel Bakounine, 29600 Morlaix, France",
              "2 Allee Emile Pouget, 920128 Boulogne-Billancourt")
)
places2 <- geocode_tbl(tbl = mes_adresses, adresse = address)
places2
```

::: {.callout-note appearance="minimal" icon="false"}
Website of `banR` :   
[An R client for the BAN API](http://joelgombin.github.io/banR/)
:::

## Digitization

The package `mapedit` [@mapedit] allows you to digitize base map directly in R. Although it can be practical in some cases, in package cannot replace the functionalities of a GIS for important digitization tasks.

![<small>Gif taken from [`mapedit` website](https://r-spatial.org/r/2019/03/31/mapedit_leafpm.html)</small>](img/mapedit-leafpm-1.gif)