The tutorials presented in rspatialdata have been created using the open-source R Project for Statistical Computing (RRID:SCR_001905) ⁷ and a number of R packages that allow us to download spatial data corresponding to specific geographic regions and periods of time, as well as to manipulate and visualize the data. Here, we provide a description on how to install the statistical software R and R packages. Then, we show an example on how to download and visualize one of the datasets presented in the website, namely, maximum temperature data. The complete code for all the tutorials can be found at the rspatialdata website, and a summary of the datasets and associated R packages included in the website are summarized in Table 1. The code is available from GitHub and is archived with Zenodo. ⁸⁷

R ⁷ is a free, open source, software environment for statistical computing and graphics with many useful packages for importing and manipulating data, statistical modeling, and visualization. R can be downloaded and installed from the Comprehensive R Archive Network (CRAN) (RRID:SCR_003005). R packages can be installed from CRAN with the function install.packages() passing the name of the package as first argument in quotes. Then, to use the package, the package needs to be loaded with the function library(). For example, we can install and load the visualization package ggplot2 by typing install.packages("ggplot2") and library(ggplot2).

The WorldClim (RRID:SCR_010244) ¹⁰ database contains global weather and climate data for historical and future conditions at high spatial resolution. These datasets can be easily downloaded with the R package raster, ¹¹ which implements several functions for reading, writing, manipulating, analyzing and modeling of spatial data. To use the raster package, we first need to install it and load it. Then, to download data, we can use the getData() function of the raster package by specifying several arguments about the dataset we wish to obtain. For example, to download global maximum temperature, we specify the database name ( e.g., "worldclim"), the variable we want to download ( e.g., "tmax"), and the spatial resolution in minutes of a degree as follows. install.packages("raster"); library("raster") dataset <- getData (name = "worldclim", var = "tmax", res = 10)

The downloaded object contains 12 files that correspond to the maximum temperature observed each month. We can manipulate the downloaded object to obtain temperature values for a specific month or average temperature spanning several months, and use other R packages to model and visualize the data. library(ggplot2) gain(tmax_data) <- 0.1 # Convert temperature to degrees Celsius # Converting the raster object into a dataframe tmax_data_may_df <- as.data.frame(tmax_data$tmax5, xy = TRUE, na.rm = TRUE) rownames(tmax_data_may_df) <- c() ggplot(data = tmax_data_may_df, aes(x = x, y = y)) + geom_raster(aes(fill = tmax5)) + labs( title = "Maximum temperature in May", subtitle = "For the years 1970-2000" ) + xlab("Longitude") + ylab("Latitude") + scale_fill_gradientn( name = "Temperature ( ^° C)", colours = c("#0094D1", "#68C1E6", "#FEED99", "#AF3301"), breaks = c(-20, 0, 20, 40) )

Administrative boundaries are an essential component for making maps and define the spatial extent needed for electoral, planning and statistical studies. These boundaries, which often guide the spatial scale at which data is collected, offer important context to a wide-range of issues. geoBoundaries ¹² is an open license resource database of political administrative boundaries. The R package rgeoboundaries ¹³ is an R client for the geoBoundaries application programming interface (API) that allows us to download administrative boundaries of countries at different administrative levels.

This package has been used as a visualization tool for the study of many different real-world problems, such as mapping coronavirus-19 presence in Vietnam, ¹⁴ understanding the impact of Global Environment Facility Projects in Uganda ¹⁵ and the influence of travel time to health facilities on stillbirths in Nigeria. ¹⁶

The rspatialdata tutorial includes an example of how to retrieve the administrative boundaries of single and multiple countries at different administrative boundary levels. It also covers how to download and visualize these data using the sf ¹⁷ and leaflet ¹⁸ packages.

Knowing population sizes and their spatial distributions is crucial for many critical decisions from improving access to health, transportation and energy, to planning and building more resilient and sustainable cities. WorldPop ¹⁹ aims to provide an open access archive of spatial demographic datasets with a focus on low and middle income countries (LMICs) to support development, disaster response and health applications.

Population data from WorldPop has been used extensively to map health conditions such as cancer, ²⁰ child growth failure, ²¹ HIV prevalence, ²² and the burden of cholera ²³ in Africa. It has also been used to map local variation in educational attainment in Africa, ²⁴ to evaluate the reduction of tree cover in West African Woodlands ²⁵ and to assess clean air in the context of the SDGs. ²⁶

The WorldPop Open Population Repository provides access to high-resolution population estimates for individual countries and these data can be obtained with the R package wopr. ²⁷ The rspatialdata tutorial shows examples on how to use wopr to download population data for different countries and administrative levels.

OSM ²⁸ is a collaborative project to create a free editable map of the world. OSM is built by a community of mappers that contribute and maintain global data about roads, trails, cafés, railway stations, and more. OSM data can be used in many ways. For example, as a basemap to put other data into context, for routing or navigation, and for planning or logistics for humanitarian groups, utilities and governments. OSM data have been used in a wide range of applications including flood inundation modeling, ²⁹ air pollution exposure, ³⁰ assessment of socio-economic factors and property prices, ³¹ and for the study of crime and place. ³²

The package osmdata ³³ allows us to easily import OSM data in R. The rspatialdata tutorial includes an example of how to retrieve OSM data using the osmdata by creating a bounding box and a query and how to visualized the data with ggplot2, ggmap ³⁴ and leaflet. ¹⁸

Elevation data are important in many different applications. For instance, for environmental problems, elevation data have been used as a tool to study the land cover change over the years, in particular, the evolution of European forest cover. ³⁵ As another example, researchers also have been using elevation data as a complementary source of information in the analysis of species connectivity through genetic structure. ^{36 , 37}

For retrieving elevation data from many different regions, one may choose to work with the the elevatr package. ³⁸ elevatr provides access to elevation data from several web services including the Amazon Web Services Terrain Tiles, ³⁹ the Open Topography Global Datasets API, ⁴⁰ and the USGS Elevation Point Query Service. ⁴¹

The rspatialdata tutorial includes an example of how to retrieve and visualize point elevation data for the USA and raster elevation data from a digital elevation model (DEM) for global elevation data.

WorldClim ¹⁰ is a database that provides high spatial resolution global weather and climate data for historical and future conditions. For example, it provides monthly climate data for minimum, mean, and maximum temperature, precipitation, solar radiation, wind speed, water vapor pressure, and for total precipitation.

These data may be applicable in many different areas. For environmental problems, it has been used for the study of the global tree restoration potential, ⁴² the understanding of temperature profile in forest regions, ⁴³ and the monitoring of drought in South Asia. ⁴⁴ In ecology, to understand geographic distribution of sloths in Costa Rica. ² In health and disease-control related problems, these data have been used, for example, in the study of the levels of arsenic in groundwater, ⁴⁵ the prediction of lymphatic filariasis prevalence in sub-Saharan Africa, ⁴⁶ and the loss of biodiversity on Earth due to the amphibian chytridiomycosis panzootic disease. ⁴⁷

The package raster ¹¹ allows us to easily download the WorldClim data as well as to manipulate and analyze spatial datasets. The rspatialdata tutorial includes an example of how to retrieve maximum temperature data from the WorldClim database and visualize the monthly maximum and mean monthly temperature and other bioclimatic variables over time using ggplot2 and the sf package. ¹⁷

The NASA Prediction Of Worldwide Energy Resources (POWER) Project ⁴⁸ provides meteorology, surface solar energy and climatology data for support of renewable energy, building energy efficiency and agricultural needs. Data retrieved from the NASA POWER Project have been used in a few different applications. For example, POWER data have been used in the study of the potential utilization of wind electric pumping systems for water distribution in Cameroon, ⁴⁹ in the analysis of photovoltaic systems usage in China ⁵⁰ and in the study of Dunaliella salina (a type of green micro-algae) cultivation. ⁵¹

nasapower ⁵² aims to make it quick and easy to automate downloading NASA-POWER data in R. In rspatialdata, we show how to use this package to download rainfall and humidity.

Vegetation data are used in a wide variety of applications ranging from environmental applications, such as the rice crop monitoring in Europe, ⁵³ to health and disease-control applications, such as malaria transmission dynamics in an indigenous province in Panama. ⁵⁴

Vegetation data are captured using Moderate Resolution Imaging Spectroradiometer (MODIS), an instrument onboard the Terra and Aqua NASA scientific research satellites. MODIS captures data in 36 spectral bands in three spatial resolutions across the surface of the earth. Data products derived from these observations include features of the atmosphere, land, cryosphere, and ocean, made available at different frequencies and spatial resolutions. Each data product contains multiple product layers, including original MODIS layers, quality layers and spectral indexes, produced at different intervals and at different spatial resolutions. User guides on each of the product areas are available, which provide in-depth explanations on them.

The rspatialdata tutorial shows how to use the R package MODIStsp, ⁵⁵ which acts as a client for downloading time series and raster images derived from MODIS Land Product data. Specifically, it shows how to download MODIS Vegetation Index Products (NDVI and EVI) ⁵⁶ and the MODIS Land Cover Products. ⁵⁷

Air pollution data can be of interest for many different agents, from the government to the general population. In this sense, many different studies have been conducted regarding how the UK and other countries have been suffering from different types of pollutants—for instance, on how wood-burning has impacted the PM ₁₀ levels in London, ⁵⁸ or how the level of air pollution has a direct impact on the population’s health, ³ or even how people from different socioeconomic groups may be exposed to different levels of air pollution depending on their commute in London. ⁵⁹

UK Air is a UK air quality database provided by the Department for Environment Food & Rural Affairs. ⁶⁰ The database provides daily information about the level of pollution for different pollutants ( e.g., ozone, carbon monoxide, PM _2.5) across the United Kingdom and its territories. Although there are many different ways to retrieve data from this database, one convenient option is using the openair ⁶¹ R package.

The openair package provides a set of functions to import and work with these datasets, which are documented in the openair's manual. ⁶² The rspatialdata tutorial includes an example of how to retrieve and visualize data from a specific monitoring network named Automatic Urban and Rural Network (AURN).

The Demographic and Health Surveys (DHS) Program ⁶³ collects, analyzes, and disseminates country-wide subnational level data on population, health, nutrition and HIV. The objective of the DHS Program is to improve and institutionalize the collection and use of data by developing countries for program monitoring and evaluation and for policy making. The R package rdhs ⁶⁴ provides a wrapper to the DHS program API, and can be used to identify particular datasets and download them in R via the DHS API. Examples of issues that have been investigated using DHS data include household smoke-exposure risks associated with cooking fuels and cooking places in Tanzania, ⁶⁵ determinants of unmet need for family planning and implications for women’s health in Gambia & Mozambique, ⁶⁶ and household access to improved drinking water sources and toilet facilities in Ethiopia. ⁶⁷

The rspatialdata tutorial includes different examples of options on how to retrieve datasets and DHS surveys for an analysis through the DHS API and DHS website from R. And how to search for a specific DHS survey using tag words demonstrating how to extract surveys on Malaria in Rwanda and Tanzania as a case study.

The Malaria Atlas Project (MAP) ⁶⁸ aims to better understand the global landscape of malaria risk, how this is changing, and the impact of malaria interventions to support malaria intervention and eradication efforts. As part of its work, MAP assembles an extensive collection of malaria data, including parasite rate data ( Plasmodium falciparum and Plasmodium vivax), vector occurrence, and satellite images capturing conditions that influence malaria transmission. malariaAtlas ⁶⁹ is an R package to open-access malaria data hosted by MAP and can be used to download all publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from the MAP servers as well as utility functions for plotting the downloaded data. Data provided by malariaAtlas can be used to explore the spatial and spatio-temporal patterns of malaria risk as well as to feed into spatial models of the risk of malaria. Several studies have used MAP data for different purposes, including mapping the global endemicity and clinical burden malaria, ⁷⁰ understand the associated patterns of insecticide resistance in field populations of malaria vectors across Africa, ⁷¹ and assess the population coverage of artemisinin-based combination treatment and Plasmodium falciparum infection in Africa. ⁷²

The rspatialdata tutorial includes examples of how to retrieve and visualize malaria data from the malariaAtlas package including parasite rate (PR) survey data, vector occurrence data, and rasters of modelled malaria research outputs.

The information of observed species play an import role in ecological studies, which motivates the existence of different repositories containing these type of data. Examples include GBIF - Global Biodiversity Information Facility (RRID:SCR_005904), ⁷³ Biodiversity Information Serving Our Nation (BISON), ⁷⁴ eBird, ⁷⁵ and VertNet. ⁷⁶ Most of these repositories allow researchers to retrieve data using different methods. In R, the aforementioned platforms can be accessed through the rgbif, ⁷⁷ rbison, ⁷⁸ rebird, ⁷⁹ and rvertnet ⁸⁰ packages, respectively. However, in order to integrate all these datasets and interact with them using just one tool, one could choose to work with the spocc package. ⁸¹ As an example, and aiming to model sloths occurrence in Costa Rica, spocc was used to retrieve relevant data from GBIF. ⁸² Other case studies may include modeling migratory movements of birds ⁸³ or estimating population size based on species occurrence. ⁸⁴

The rspatialdata tutorial includes an example of how to retrieve and visualize species occurrence data by creating a query for a species latin name using the spocc package.

Table 2 contains the databases included in the rspatialdata website.