# Chapter 7 Geographic data I/O

## Prerequisites

This chapter requires the following packages:

library(sf)
library(raster)
library(dplyr)
library(spData)

## 7.1 Introduction

This chapter is about reading and writing geographic data. Geographic data import is essential for geocomputation: real-world applications are impossible without data. For others to benefit from the results of your work, data output is also vital. Taken together, we refer to these processes as I/O, short for input/output.

Geographic data I/O is almost always part of a wider process. It depends on knowing which datasets are available, where they can be found and how to retrieve them. These topics are covered in Section 7.2, which describes various geoportals, which collectively contain many terabytes of data, and how to use them. To further ease data access, a number of packages for downloading geographic data have been developed. These are described in Section 7.3.

There are many geographic file formats, each of which has pros and cons. These are described in Section 7.5. The process of actually reading and writing such file formats efficiently is not covered until Sections 7.6 and 7.7, respectively. The final Section 7.8 demonstrates methods for saving visual outputs (maps), in preparation for Chapter 8 on visualization.

## 7.2 Retrieving open data

A vast and ever-increasing amount of geographic data is available on the internet, much of which is free to access and use (with appropriate credit given to its providers). In some ways there is now too much data, in the sense that there are often multiple places to access the same dataset. Some datasets are of poor quality. In this context, it is vital to know where to look, so the first section covers some of the most important sources. Various ‘geoportals’ (web services providing geospatial datasets such as Data.gov) are a good place to start, providing a wide range of data but often only for specific locations (as illustrated in the updated Wikipedia page on the topic).

Some global geoportals overcome this issue. The GEOSS portal and the Copernicus Open Access Hub, for example, contain many raster datasets with global coverage. A wealth of vector datasets can be accessed from the National Aeronautics and Space Administration agency (NASA), SEDAC portal and the European Union’s INSPIRE geoportal, with global and regional coverage.

Most geoportals provide a graphical interface allowing datasets to be queried based on characteristics such as spatial and temporal extent, the United States Geological Services’ EarthExplorer being a prime example. Exploring datasets interactively on a browser is an effective way of understanding available layers. Downloading data is best done with code, however, from reproducibility and efficiency perspectives. Downloads can be initiated from the command line using a variety of techniques, primarily via URLs and APIs (see the Sentinel API for example). Files hosted on static URLs can be downloaded with download.file(), as illustrated in the code chunk below which accesses US National Parks data from: catalog.data.gov/dataset/national-parks:

download.file(url = "http://nrdata.nps.gov/programs/lands/nps_boundary.zip",
destfile = "nps_boundary.zip")
unzip(zipfile = "nps_boundary.zip")
usa_parks = st_read(dsn = "nps_boundary.shp")

## 7.3 Geographic data packages

A multitude of R packages have been developed for accessing geographic data, some of which are presented in Table 7.1. These provide interfaces to one or more spatial libraries or geoportals and aim to make data access even quicker from the command line.

TABLE 7.1: Selected R packages for geographic data retrieval.
Package Description
raster getData() imports administrative, elevation, WorldClim data.
rnoaa Imports National Oceanic and Atmospheric Administration (NOAA) climate data.
rWBclimate Access World Bank climate data.

It should be emphasised that Table 7.1 represents only a small number of available geographic data packages. Other notable packages include GSODR, which provides Global Summary Daily Weather Data in R (see the package’s README for an overview of weather data sources); tidycensus and tigris, which provide socio-demographic vector data for the USA; and hddtools, which provides access to a range of hydrological datasets.

Each data package has its own syntax for accessing data. This diversity is demonstrated in the subsequent code chunks, which show how to get data using three packages from Table 7.1. Country borders are often useful and these can be accessed with the ne_countries() function from the rnaturalearth package as follows:

library(rnaturalearth)
usa = ne_countries(country = "United States of America") # United States borders
class(usa)
#> [1] "SpatialPolygonsDataFrame"
#> attr(,"package")
#> [1] "sp"
# alternative way of accessing the data, with raster::getData()
# getData("GADM", country = "USA", level = 0)

By default rnaturalearth returns objects of class Spatial. The result can be converted into an sf objects with st_as_sf() as follows:

usa_sf = st_as_sf(usa)

A second example downloads a series of rasters containing global monthly precipitation sums with spatial resolution of ten minutes. The result is a multilayer object of class RasterStack.

library(raster)
class(worldclim_prec)
#> [1] "RasterStack"
#> attr(,"package")
#> [1] "raster"

A third example uses the osmdata package (Padgham et al. 2018) to find parks from the OpenStreetMap (OSM) database. As illustrated in the code-chunk below, queries begin with the function opq() (short for OpenStreetMap query), the first argument of which is bounding box, or text string representing a bounding box (the city of Leeds in this case). The result is passed to a function for selecting which OSM elements we’re interested in (parks in this case), represented by key-value pairs. Next, they are passed to the function osmdata_sf() which does the work of downloading the data and converting it into a list of sf objects (see vignette('osmdata') for further details):

library(osmdata)
parks = opq(bbox = "leeds uk") %>%
add_osm_feature(key = "leisure", value = "park") %>%
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 9, 12 and 13.

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. The following code chunk illustrates the latter two options using the world dataset (already loaded by attaching its parent package with library(spData)):31

world2 = spData::world
world3 = st_read(system.file("shapes/world.gpkg", package = "spData"))

## 7.4 Geographic web services

In an effort to standardize web APIs for accessing spatial data, the Open Geospatial Consortium (OGC) has created a number of specifications for web services (collectively known as OWS, which is short for OGC Web Services). These specifications include the Web Feature Service (WFS), Web Map Service (WMS), Web Map Tile Service (WMTS), the Web Coverage Service (WCS) and even a Web Processing Service (WPS). Map servers such as PostGIS have adopted these protocols, leading to standardization of queries. Like other web APIs, OWS APIs use a ‘base URL’, an ‘endpoint’ and ‘URL query arguments’ following a ? to request data (see the best-practices-api-packages vignette in the httr package).

There are many requests that can be made to a OWS service. One of the most fundamental is getCapabilities, demonstrated with httr below. The code chunk demonstrates how API queries can be constructed and dispatched, in this case to discover the capabilities of a service run by the Food and Agriculture Organization of the United Nations (FAO):

base_url = "http://www.fao.org"
endpoint = "/figis/geoserver/wfs"
q = list(request = "GetCapabilities")
res = httr::GET(url = httr::modify_url(base_url, path = endpoint), query = q)
res$url #> [1] "http://www.fao.org/figis/geoserver/wfs?request=GetCapabilities" The above code chunk demonstrates how API requests can be constructed programmatically with the GET() function, which takes a base URL and a list of query parameters which can easily be extended. The result of the request is saved in res, an object of class response defined in the httr package, which is a list containing information of the request, including the URL. As can be seen by executing browseURL(res$url), the results can also be read directly in a browser. One way of extracting the contents of the request is as follows:

txt = httr::content(res, "text")
xml = xml2::read_xml(txt)
xml
#> {xml_document} ...
#> [1] <ows:ServiceIdentification>\n  <ows:Title>GeoServer WFS...
#> [2] <ows:ServiceProvider>\n  <ows:ProviderName>Food and Agr...
#> ...

Data can be downloaded from WFS services with the GetFeature request and a specific typeName (as illustrated in the code chunk below).

Available names differ depending on the accessed web feature service. One can extract them programmatically using web technologies (Nolan and Lang 2014) or scrolling manually through the contents of the GetCapabilities output in a browser.

qf = list(request = "GetFeature", typeName = "area:FAO_AREAS")
file = tempfile(fileext = ".gml")
httr::GET(url = base_url, path = endpoint, query = qf, httr::write_disk(file))
fao_areas = sf::read_sf(file)

Note the use of write_disk() to ensure that the results are written to disk rather than loaded into memory, allowing them to be imported with sf. This example shows how to gain low-level access to web services using httr, which can be useful for understanding how web services work. For many everyday tasks, however, a higher-level interface may be more appropriate, and a number of R packages, and tutorials, have been developed precisely for this purpose.

Packages ows4R, rwfs and sos4R have been developed for working with OWS services in general, WFS and the sensor observation service (SOS) respectively. As of October 2018, only ows4R is on CRAN. The package’s basic functionality is demonstrated below, in commands that get all FAO_AREAS as we did in the previous code chunk:32

library(ows4R)
wfs = WFSClient$new("http://www.fao.org/figis/geoserver/wfs", serviceVersion = "1.0.0", logger = "INFO") fao_areas = wfs$getFeatures("area:FAO_AREAS")

There is much more to learn about web services and much potential for development of R-OWS interfaces, an active area of development. For further information on the topic, we recommend examples from European Centre for Medium-Range Weather Forecasts (ECMWF) services at github.com/OpenDataHack and reading-up on OCG Web Services at opengeospatial.org.

## 7.5 File formats

Geographic datasets are usually stored as files or in spatial databases. File formats can either store vector or raster data, while spatial databases such as PostGIS can store both (see also Section 9.6.2). Today the variety of file formats may seem bewildering but there has been much consolidation and standardization since the beginnings of GIS software in the 1960s when the first widely distributed program (SYMAP) for spatial analysis was created at Harvard University (Coppock and Rhind 1991).

GDAL (which should be pronounced “goo-dal”, with the double “o” making a reference to object-orientation), the Geospatial Data Abstraction Library, has resolved many issues associated with incompatibility between geographic file formats since its release in 2000. GDAL provides a unified and high-performance interface for reading and writing of many raster and vector data formats. Many open and proprietary GIS programs, including GRASS, ArcGIS and QGIS, use GDAL behind their GUIs for doing the legwork of ingesting and spitting out geographic data in appropriate formats.

GDAL provides access to more than 200 vector and raster data formats. Table 7.2 presents some basic information about selected and often used spatial file formats.

TABLE 7.2: Selected spatial file formats.
Name Extension Info Type Model
ESRI Shapefile .shp (the main file) Popular format consisting of at least three files. No support for: files > 2GB; mixed types; names > 10 chars; cols > 255. Vector Partially open
GeoJSON .geojson Extends the JSON exchange format by including a subset of the simple feature representation. Vector Open
KML .kml XML-based format for spatial visualization, developed for use with Google Earth. Zipped KML file forms the KMZ format. Vector Open
GPX .gpx XML schema created for exchange of GPS data. Vector Open
GeoTIFF .tif/.tiff Popular raster format. A TIFF file containing additional spatial metadata. Raster Open
Arc ASCII .asc Text format where the first six lines represent the raster header, followed by the raster cell values arranged in rows and columns. Raster Open
R-raster .gri, .grd Native raster format of the R-package raster. Raster Open
SQLite/SpatiaLite .sqlite Standalone relational database, SpatiaLite is the spatial extension of SQLite. Vector and raster Open
ESRI FileGDB .gdb Spatial and nonspatial objects created by ArcGIS. Allows: multiple feature classes; topology. Limited support from GDAL. Vector and raster Proprietary
GeoPackage .gpkg Lightweight database container based on SQLite allowing an easy and platform-independent exchange of geodata Vector and raster Open

An important development ensuring the standardization and open-sourcing of file formats was the founding of the Open Geospatial Consortium (OGC) in 1994. Beyond defining the simple features data model (see Section 2.2.1), the OGC also coordinates the development of open standards, for example as used in file formats such as KML and GeoPackage. Open file formats of the kind endorsed by the OGC have several advantages over proprietary formats: the standards are published, ensure transparency and open up the possibility for users to further develop and adjust the file formats to their specific needs.

ESRI Shapefile is the most popular vector data exchange format. However, it is not an open format (though its specification is open). It was developed in the early 1990s and has a number of limitations. First of all, it is a multi-file format, which consists of at least three files. It only supports 255 columns, column names are restricted to ten characters and the file size limit is 2 GB. Furthermore, ESRI Shapefile does not support all possible geometry types, for example, it is unable to distinguish between a polygon and a multipolygon.33 Despite these limitations, a viable alternative had been missing for a long time. In the meantime, GeoPackage emerged, and seems to be a more than suitable replacement candidate for ESRI Shapefile. Geopackage is a format for exchanging geospatial information and an OGC standard. The GeoPackage standard describes the rules on how to store geospatial information in a tiny SQLite container. Hence, GeoPackage is a lightweight spatial database container, which allows the storage of vector and raster data but also of non-spatial data and extensions. Aside from GeoPackage, there are other geospatial data exchange formats worth checking out (Table 7.2).

## 7.6 Data input (I)

Executing commands such as sf::st_read() (the main function we use for loading vector data) or raster::raster() (the main function used for loading raster data) silently sets off a chain of events that reads data from files. Moreover, there are many R packages containing a wide range of geographic data or providing simple access to different data sources. All of them load the data into R or, more precisely, assign objects to your workspace, stored in RAM accessible from the .GlobalEnv of the R session.

### 7.6.1 Vector data

Spatial vector data comes in a wide variety of file formats, most of which can be read-in via the sf function st_read(). Behind the scenes this calls GDAL. To find out which data formats sf supports, run st_drivers(). Here, we show only the first five drivers (see Table 7.3):

sf_drivers = st_drivers()
head(sf_drivers, n = 5)
TABLE 7.3: Sample of available drivers for reading/writing vector data (it could vary between different GDAL versions).
name long_name write copy is_raster is_vector vsi
ESRI Shapefile ESRI Shapefile ESRI Shapefile TRUE FALSE FALSE TRUE TRUE
GPX GPX GPX TRUE FALSE FALSE TRUE TRUE
KML KML Keyhole Markup Language (KML) TRUE FALSE FALSE TRUE TRUE
GeoJSON GeoJSON GeoJSON TRUE FALSE FALSE TRUE TRUE
GPKG GPKG GeoPackage TRUE TRUE TRUE TRUE TRUE

The first argument of st_read() is dsn, which should be a text string or an object containing a single text string. The content of a text string could vary between different drivers. In most cases, as with the ESRI Shapefile (.shp) or the GeoPackage format (.gpkg), the dsn would be a file name. st_read() guesses the driver based on the file extension, as illustrated for a .gpkg file below:

vector_filepath = system.file("shapes/world.gpkg", package = "spData")
#> Reading layer world' from data source .../world.gpkg' using driver GPKG'
#> Simple feature collection with 177 features and 10 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
#> epsg (SRID):    4326
#> proj4string:    +proj=longlat +datum=WGS84 +no_defs

For some drivers, dsn could be provided as a folder name, access credentials for a database, or a GeoJSON string representation (see the examples of the st_read() help page for more details).

Some vector driver formats can store multiple data layers. By default, st_read() automatically reads the first layer of the file specified in dsn; however, using the layer argument you can specify any other layer.

Naturally, some options are specific to certain drivers.34 For example, think of coordinates stored in a spreadsheet format (.csv). To read in such files as spatial objects, we naturally have to specify the names of the columns (X and Y in our example below) representing the coordinates. We can do this with the help of the options parameter. To find out about possible options, please refer to the ‘Open Options’ section of the corresponding GDAL driver description. For the comma-separated value (csv) format, visit http://www.gdal.org/drv_csv.html.

cycle_hire_txt = system.file("misc/cycle_hire_xy.csv", package = "spData")
cycle_hire_xy = st_read(cycle_hire_txt, options = c("X_POSSIBLE_NAMES=X",
"Y_POSSIBLE_NAMES=Y"))

Instead of columns describing xy-coordinates, a single column can also contain the geometry information. Well-known text (WKT), well-known binary (WKB), and the GeoJSON formats are examples of this. For instance, the world_wkt.csv file has a column named WKT representing polygons of the world’s countries. We will again use the options parameter to indicate this. Here, we will use read_sf(), the tidyverse-flavoured version of st_read(): strings are parsed as characters instead of factors and the resulting data frame is a tibble. The driver name is also not printed to the console.

world_txt = system.file("misc/world_wkt.csv", package = "spData")
world_wkt = read_sf(world_txt, options = "GEOM_POSSIBLE_NAMES=WKT")
# the same as
world_wkt = st_read(world_txt, options = "GEOM_POSSIBLE_NAMES=WKT",
quiet = TRUE, stringsAsFactors = FALSE, as_tibble = TRUE)
Not all of the supported vector file formats store information about their coordinate reference system. In these situations, it is possible to add the missing information using the st_set_crs() function. Please refer also to Section 2.4 for more information.

As a final example, we will show how st_read() also reads KML files. A KML file stores geographic information in XML format - a data format for the creation of web pages and the transfer of data in an application-independent way (Nolan and Lang 2014). Here, we access a KML file from the web. This file contains more than one layer. st_layers() lists all available layers. We choose the first layer Placemarks and say so with the help of the layer parameter in read_sf().

u = "https://developers.google.com/kml/documentation/KML_Samples.kml"
st_layers("KML_Samples.kml")
#> Driver: LIBKML
#> Available layers:
#>               layer_name geometry_type features fields
#> 1             Placemarks                      3     11
#> 2      Styles and Markup                      1     11
#> 3       Highlighted Icon                      1     11
#> 4        Ground Overlays                      1     11
#> 5        Screen Overlays                      0     11
#> 6                  Paths                      6     11
#> 7               Polygons                      0     11
#> 8          Google Campus                      4     11
#> 9       Extruded Polygon                      1     11
#> 10 Absolute and Relative                      4     11
kml = read_sf("KML_Samples.kml", layer = "Placemarks")

All the examples presented in this section so far have used the sf package for geographic data import. It is fast and flexible but it may be worth looking at other packages for specific file formats. An example is the geojsonsf package. A benchmark suggests it is around 10 times faster than the sf package for reading .geojson.

### 7.6.2 Raster data

Similar to vector data, raster data comes in many file formats with some of them supporting even multilayer files. raster’s raster() command reads in a single layer.

raster_filepath = system.file("raster/srtm.tif", package = "spDataLarge")
single_layer = raster(raster_filepath)

In case you want to read in a single band from a multilayer file, use the band parameter to indicate a specific layer.

multilayer_filepath = system.file("raster/landsat.tif", package = "spDataLarge")
band3 = raster(multilayer_filepath, band = 3)

If you want to read in all bands, use brick() or stack().

multilayer_brick = brick(multilayer_filepath)
multilayer_stack = stack(multilayer_filepath)

Please refer to Section 2.3.3 for information on the difference between raster stacks and bricks.

## 7.7 Data output (O)

Writing geographic data allows you to convert from one format to another and to save newly created objects. Depending on the data type (vector or raster), object class (e.g., multipoint or RasterLayer), and type and amount of stored information (e.g., object size, range of values), it is important to know how to store spatial files in the most efficient way. The next two sections will demonstrate how to do this.

### 7.7.1 Vector data

The counterpart of st_read() is st_write(). It allows you to write sf objects to a wide range of geographic vector file formats, including the most common such as .geojson, .shp and .gpkg. Based on the file name, st_write() decides automatically which driver to use. The speed of the writing process depends also on the driver.

st_write(obj = world, dsn = "world.gpkg")
#> Writing layer world' to data source world.gpkg' using driver GPKG'
#> Writing 177 features with 10 fields and geometry type Multi Polygon.

Note: if you try to write to the same data source again, the function will fail:

st_write(obj = world, dsn = "world.gpkg")
#> Layer world in dataset world.gpkg already exists:
#> use either append=TRUE to append to layer or append=FALSE to overwrite layer
#> Error in CPL_write_ogr(obj, dsn, layer, driver, as.character(dataset_options), : Dataset already exists.

The error message tells us why the function failed: world.gpkg already contains a layer called world. To fix this problem, use the append argument. When this argument is set to TRUE, the old layer is kept and the world object is added as a second new layer:

st_write(obj = world, dsn = "world.gpkg", append = TRUE)

Alternatively with append = FALSE, existing layers are deleted before the function attempts to write our object to a file:

st_write(obj = world, dsn = "world.gpkg", append = FALSE)

You can achieve the same with write_sf() since it is equivalent to (technically an alias for) st_write(), except that its defaults for append is FALSE and quiet is TRUE.

write_sf(obj = world, dsn = "world.gpkg")

The layer_options argument could be also used for many different purposes. One of them is to write spatial data to a text file. This can be done by specifying GEOMETRY inside of layer_options. It could be either AS_XY for simple point datasets (it creates two new columns for coordinates) or AS_WKT for more complex spatial data (one new column is created which contains the well-known text representation of spatial objects).

st_write(cycle_hire_xy, "cycle_hire_xy.csv", layer_options = "GEOMETRY=AS_XY")
st_write(world_wkt, "world_wkt.csv", layer_options = "GEOMETRY=AS_WKT")

### 7.7.2 Raster data

The writeRaster() function saves Raster* objects to files on disk. The function expects input regarding output data type and file format, but also accepts GDAL options specific to a selected file format (see ?writeRaster for more details).

The raster package offers nine data types when saving a raster: LOG1S, INT1S, INT1U, INT2S, INT2U, INT4S, INT4U, FLT4S, and FLT8S.35 The data type determines the bit representation of the raster object written to disk (Table 7.4). Which data type to use depends on the range of the values of your raster object. The more values a data type can represent, the larger the file will get on disk. Commonly, one would use LOG1S for bitmap (binary) rasters. Unsigned integers (INT1U, INT2U, INT4U) are suitable for categorical data, while float numbers (FLT4S and FLT8S) usually represent continuous data. writeRaster() uses FLT4S as the default. While this works in most cases, the size of the output file will be unnecessarily large if you save binary or categorical data. Therefore, we would recommend to use the data type that needs the least storage space, but is still able to represent all values (check the range of values with the summary() function).

TABLE 7.4: Data types supported by the raster package.
Data type Minimum value Maximum value
LOG1S FALSE (0) TRUE (1)
INT1S -127 127
INT1U 0 255
INT2S -32,767 32,767
INT2U 0 65,534
INT4S -2,147,483,647 2,147,483,647
INT4U 0 4,294,967,296
FLT4S -3.4e+38 3.4e+38
FLT8S -1.7e+308 1.7e+308

By default, writeRaster() saves outputs in its native format as .grd files, when a file extension is invalid or missing. Other file formats can be specified by changing the extension of the output file name. Naming a file *.tif will create a GeoTIFF file, as demonstrated below:

writeRaster(single_layer, filename = "my_raster.tif", datatype = "INT2U")

Some raster file formats have additional options, that can be set by providing GDAL parameters to the options argument of writeRaster(). GeoTIFF files, for example, can be compressed using COMPRESS:

writeRaster(x = single_layer,
filename = "my_raster.tif",
datatype = "INT2U",
options = c("COMPRESS=DEFLATE"),
overwrite = TRUE)

Note that writeFormats() returns a list with all supported file formats on your computer.

## 7.8 Visual outputs

R supports many different static and interactive graphics formats. The most general method to save a static plot is to open a graphic device, create a plot, and close it, for example:

png(filename = "lifeExp.png", width = 500, height = 350)
plot(world["lifeExp"])
dev.off()

Other available graphic devices include pdf(), bmp(), jpeg(), and tiff(). You can specify several properties of the output plot, including width, height and resolution.

Additionally, several graphic packages provide their own functions to save a graphical output. For example, the tmap package has the tmap_save() function. You can save a tmap object to different graphic formats by specifying the object name and a file path to a new graphic file.

library(tmap)
tmap_obj = tm_shape(world) + tm_polygons(col = "lifeExp")
tmap_save(tm = tmap_obj, filename = "lifeExp_tmap.png")

On the other hand, you can save interactive maps created in the mapview package as an HTML file or image using the mapshot() function:

library(mapview)
mapview_obj = mapview(world, zcol = "lifeExp", legend = TRUE)
mapshot(mapview_obj, file = "my_interactive_map.html")

## 7.9 Exercises

1. List and describe three types of vector, raster, and geodatabase formats.

2. Name at least two differences between read_sf() and the more well-known function st_read().

3. Read the cycle_hire_xy.csv file from the spData package as a spatial object (Hint: it is located in the misc\ folder). What is a geometry type of the loaded object?

4. Download the borders of Germany using rnaturalearth, and create a new object called germany_borders. Write this new object to a file of the GeoPackage format.

5. Download the global monthly minimum temperature with a spatial resolution of five minutes using the raster package. Extract the June values, and save them to a file named tmin_june.tif file (hint: use raster::subset()).

6. Create a static map of Germany’s borders, and save it to a PNG file.

7. Create an interactive map using data from the cycle_hire_xy.csv file. Export this map to a file called cycle_hire.html.

### References

Coppock, J Terry, and David W Rhind. 1991. “The History of GIS.” Geographical Information Systems: Principles and Applications, Vol. 1. 1 (1): 21–43.

Nolan, Deborah, and Duncan Temple Lang. 2014. XML and Web Technologies for Data Sciences with R. Use R! New York, NY: Springer.

Padgham, Mark, Bob Rudis, Robin Lovelace, and Maëlle Salmon. 2018. Osmdata: Import ’Openstreetmap’ Data as Simple Features or Spatial Objects. https://CRAN.R-project.org/package=osmdata.

1. For more information on data import with R packages, see Sections 5.5 and 5.6 of Gillespie and Lovelace (2016).↩︎

2. To filter features on the server before downloading them, the argument cql_filter can be used. Adding cql_filter = URLencode("F_CODE= '27'") to the command, for example, would instruct the server to only return the feature with values in the F_CODE column equal to 27.↩︎