2  Data Acquisition

2.1 Online databases


Several data sets are referenced by the ESoR (Environnement, Societies and Health Risk) research group here

2.1.1 On global scale (access with R package)

Since the appearance of the sf package, which has greatly contributed to the popularization of spatial data manipulation with R, many packages for making geographic data (geometries and/or attributes) available have been developed. Most of them are API packages that allow to query data made available on the Web, directly with R. This chapter presents a non-exhaustive list of them.

2.1.2 On regional scale

Several open data portals are particularly useful in the region, including Humanitarian Data Exchange and Open Development.

2.1.3 On national scale (Cambodia)

Cambodia administrative boundaries, divided in 4 levels:

  • level 0: country,
  • level 1: province / khaet and capital / reach thani,
  • level 2: municipality / district
  • level 3: commune / khum quarter / sangkat.

The contours maps (shapefiles) are made available from the Humanitarian Data Exchange (HDX), provided by OCHA (United Nations Offices for the Coordination of Humanitarian Affairs). These maps were originally produced by the Departement of geography of the Ministry of Land Management, Urbanization and Construction in 2008 and unofficially updated in 2014 according to sub-decrees on administrative modifications. They were provided by WFP - VAM unit Cambodia.

You can download these administrative boundaries, as zip folders, here:

Population data:

Population data is available at these different levels from the Humanitarian Data Exchange (HDX) repository. It comes from the Commune database (CDB), provided by the Cambodia Ministry of Planning.


Health Facility data:

The Humanitarian Data Exchange (HDX) repository provides a dataset on the location of health facilities (Referral Hospitals, Health Centers, Health Posts). These maps were originally produced by the Cambodia Ministry of Health (MoH).


Transportation data:

The roads network is available from Humanitarian Data Exchange (HDX) repository. These maps were originally produced by the Cambodia Department of Geography of the Ministry of Land Management, Urbanization and Construction. They include: National road primary and secondary, Provincial road primary, Provincial and rural roads, Foot path, Cart track, Bridge line.


Hydrology data:

The hydrological network is available from Humanitarian Data Exchange (HDX) repository. These maps were originally produced by the Cambodia Department of Geography of the Ministry of Land Management, Urbanization and Construction. They include: rivers (“Non-Perenial/Intermittent/Fluctuating” and “Perennial/Permanent”), lakes


Digital Elevation Model (DEM):

The SRTM (Shuttle Radar Topography Mission) is a free DEM provided by NASA and NGA (formerly NIMA). Space Shuttle Endeavour (STS-99) collected these altimetry data during an 11-day mission in February 2000 at an altitude of 233 km using radar interferometry. The SRTM covers nearly 80% of the land area from 56° South latitude to 60° North latitude. Spatial resolution is approximately 30 meters on the line of the Equator.

The SRTM data can be downloaded here: http://srtm.csi.cgiar.org

2.2 OpenStreetMap

OpenStreetMap (OSM) is a participatory mapping project that aims to built 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. (…)


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

2.2.1 Display and interactive map

The two main packages that allow to display as interactive map based on OSM are leaflet (Cheng, Karambelkar, and Xie 2022) and mapview (Appelhans et al. 2022). leaflet

leaflet uses the javascript library Leaflet (Agafonkin 2015) to create interactive maps.


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)

Website of leaflet
Leaflet for R mapview

mapview relies on leaflet to create interactive maps, its use is easier and its documentation is a bit dense.

mapview(banan) + mapview(health_banan)

Website of mapview

2.2.2 Import basemaps

The package maptiles (Giraud 2021) 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).

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(st_geometry(district), border = "grey20", lwd = .7, add = TRUE)
mtext(side = 1, line = -2, text = get_credit("OpenStreetMap"), col="tomato")

2.2.3 Import OSM data osmdata

The package osmdata (Padgham et al. 2017a) allows extracting vector data from OSM using the Overpass turbo API.


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 hospitals. To obtain a coherent point layer we can use the centroids of the polygons.

Spherical geometry (s2) switched off
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

mapview(country) + mapview(hospitals)