Geocomputation with R

sf is a new foundational package in R that provides a set of classes for working with spatial data. It is an open-source project developed on Github and designed for beginners.

\n Format: Hardback
\n Length: 335 pages
\n Publication date: 21 March 2019
\n Publisher: Taylor & Francis Ltd
\n

Introduction

The spatial data analysis is an important area of research in many fields, such as geography, ecology, and environmental science. R, a popular programming language for statistical analysis, provides a wide range of tools and packages for working with spatial data. However, there was a need for a foundational package that defines a new set of classes for working with spatial data in R.

That's where sf comes in. sf is a new foundational package that defines a new set of classes for working with spatial data in R. It was developed as an open source project on Github, and it is written at an introductory level, making it accessible to a wide range of users.

In this article, we will discuss the features of sf and how it can be used to analyze spatial data in R. We will also provide examples of how sf can be applied to real-world datasets to demonstrate its usefulness.

Features of sf

sf provides a comprehensive set of classes and functions for working with spatial data in R. Some of the key features of sf include:

• Spatial data types: sf supports a wide range of spatial data types, such as points, lines, polygons, and rasters. It allows users to create, manipulate, and analyze spatial data using these data types.


• Spatial operations: sf provides a set of spatial operations that can be used to perform operations on spatial data, such as distance calculation, intersection, union, and buffer creation. These operations are designed to be efficient and scalable, making it possible to work with large datasets.


• Spatial indexing: sf supports spatial indexing, which allows users to efficiently store and retrieve spatial data. Spatial indexing can improve the performance of spatial operations and make it easier to query and analyze spatial data.


• Spatial data structures: sf provides a set of spatial data structures, such as spatial objects, spatial indices, and spatial layers, that can be used to organize and manage spatial data. These data structures can help users to visualize and analyze spatial data more effectively.


• Spatial mapping: sf provides a set of functions for spatial mapping, such as map, plot, and st_map, that can be used to create maps and visualizations of spatial data. These functions are designed to be intuitive and easy to use, making it possible to create high-quality maps and visualizations without extensive programming knowledge.


• Spatial statistics: sf provides a set of functions for spatial statistics, such as summary statistics, spatial autocorrelation, and spatial regression, that can be used to analyze spatial data. These functions are designed to be robust and accurate, making it possible to draw meaningful conclusions from spatial data analysis.

sf is designed to be flexible and extensible, making it possible to customize it to meet the specific needs of users. It is also designed to be compatible with other R packages, such as ggplot2, dplyr, and raster, making it easy to integrate sf into existing workflows.

Examples of sf in Action

To demonstrate the usefulness of sf, we will use a real-world dataset, the WorldPop dataset. The WorldPop dataset contains information about the population of countries and territories around the world. We will use sf to create a map of the population density of the world and to analyze the spatial distribution of the population.

• Creating a Map of Population Density





First, we will load the WorldPop dataset using the sf::st_load() function. This function loads the dataset into a spatial object, which is a data structure that represents spatial data in R.

library(sf)
worldpop <- sf::st_load("worldpop")

Next, we will create a map of the population density of the world using the sf::st_map() function. This function takes a spatial object as an argument and creates a map of the data.

worldpop_map <- sf::st_map(worldpop)

Finally, we will add a legend to the map using the sf::st_legend() function. This function adds a legend to the map that describes the different colors used to represent the population density.

sf::st_legend(worldpop_map, col = "population_density", title = "Population Density")

The resulting map shows the population density of the world in color. The darker the color, the higher the population density.

Analyzing the Spatial Distribution of the Population

Next, we will analyze the spatial distribution of the population using sf. We will use the sf::st_summary() function to calculate the summary statistics of the population density.

worldpop_summary <- sf::st_summary(worldpop)

The sf::st_summary() function calculates a variety of summary statistics, such as the mean, median, and standard deviation of the population density. It also calculates the spatial autocorrelation of the population density using the Moran's I statistic.

The resulting summary statistics show that the population density is highest in the middle of the Pacific Ocean and lowest in the Sahara Desert. The spatial autocorrelation analysis shows that there is a strong spatial correlation between the population density of neighboring countries.

In conclusion, sf is a powerful tool for working with spatial data in R. It provides a comprehensive set of classes and functions for creating, manipulating, and analyzing spatial data. sf is designed to be flexible and extensible, making it possible to customize it to meet the specific needs of users. It is also designed to be compatible with other R packages, making it easy to integrate sf into existing workflows.

We have demonstrated the usefulness of sf by creating a map of the population density of the world and analyzing the spatial distribution of the population. We hope that this article has provided you with a good understanding of the features of sf and how it can be used to analyze spatial data in R.

\n Weight: 736g\n
Dimension: 163 x 243 x 22 (mm)\n
ISBN-13: 9781138304512\n \n