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Update 07-read-write-plot.Rmd #656

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Update 07-read-write-plot.Rmd
Robinlovelace Oct 5, 2021
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Update 07-read-write-plot.Rmd
Robinlovelace Oct 5, 2021
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16 changes: 11 additions & 5 deletions 07-read-write-plot.Rmd
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Expand Up @@ -59,14 +59,15 @@ usa_parks = st_read(dsn = "nps_boundary.shp")
## Geographic data packages

\index{data packages}
A multitude of R packages have been developed for accessing geographic data, some of which are presented in Table \@ref(tab:datapackages).
Many R packages have been developed for accessing geographic data, some of which are presented in Table \@ref(tab:datapackages).
These provide interfaces to one or more spatial libraries or geoportals and aim to make data access even quicker from the command line.

```{r datapackages, echo=FALSE, warning=FALSE}
datapackages = tibble::tribble(
~`Package`, ~Description,
"getlandsat", "Provides access to Landsat 8 data.",
"osmdata", "Download and import of OpenStreetMap data.",
"osmdata", "Download and import small OpenStreetMap datasets.",
"osmextract", "Download and import large OpenStreetMap datasets.",
"raster", "getData() imports administrative, elevation, WorldClim data.",
"rnaturalearth", "Access to Natural Earth vector and raster data.",
"rnoaa", "Imports National Oceanic and Atmospheric Administration (NOAA) climate data.",
Expand Down Expand Up @@ -140,9 +141,14 @@ parks = opq(bbox = "leeds uk") %>%
osmdata_sf()
```

OpenStreetMap is a vast global database of crowd-sourced data and it is growing daily.
Although the quality is not as spatially consistent as many official datasets, OSM data have many advantages: they are globally available free of charge and using crowd-source data can encourage 'citizen science' and contributions back to the digital commons.
Further examples of **osmdata** in action are provided in Chapters \@ref(gis), \@ref(transport) and \@ref(location).
A limitation with the **osmdata** package is that it is *rate limited*, meaning that it cannot download large OSM datasets (e.g. all the OSM data for a large city).
To overcome this limitation, the **osmextract** package was developed, which can be used to download and import binary `.pbf` files containing compressed versions of the OSM database for pre-defined regions.
<!--todo: add proper citation-->

OpenStreetMap is a vast global database of crowd-sourced data, is growing daily, and has a wider ecosystem of tools enabling easy access to the data, from the [Overpass turbo](https://overpass-turbo.eu/) web service for rapid development and testing of OSM queries to [osm2pgsql](https://osm2pgsql.org/) for importing the data into a PostGIS database.
Although the quality of datasets derived from OSM varies, the data source and wider OSM ecosystems have many advantages: they provide datasets that are available globally, free of charge, and constantly improving thanks to an army of volunteers.
Using OSM encourages 'citizen science' and contributions back to the digital commons (you can start editing data representing a part of the world you know well at [www.openstreetmap.org](https://www.openstreetmap.org)).
Further examples of OSM data in action are provided in Chapters \@ref(gis), \@ref(transport) and \@ref(location).

Sometimes, packages come with inbuilt datasets.
These can be accessed in four ways: by attaching the package (if the package uses 'lazy loading' as **spData** does), with `data(dataset)`, by referring to the dataset with `pkg::dataset` or with `system.file()` to access raw data files.
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