sfnetworks is an R package for analysis of geospatial networks. It connects the functionalities of the tidygraph package for network analysis and the sf package for spatial data science.

## Background

Geospatial networks are graphs embedded in geographical space. That means that both the nodes and edges in the graph can be represented as geographic features: the nodes most commonly as points, and the edges as linestrings. They play an important role in many different domains, ranging from transportation planning and logistics to ecology and epidemiology. The structure and characteristics of geospatial networks go beyond standard graph topology, and therefore it is crucial to explicitly take space into account when analyzing them.

We created sfnetworks to facilitate such an integrated workflow. It combines the forces of two popular R packages: sf for spatial data science and tidygraph for standard graph analysis. The core of the package is a dedicated data structure for geospatial networks, that can be provided as input to both the graph analytical functions of tidygraph as well as the spatial analytical functions of sf, without the need for conversion. Additionally, we implemented a set of geospatial network specific functions, such as routines for shortest path calculation, network cleaning and topology modification. sfnetworks is designed as a general-purpose package suitable for usage across different application domains, and can be seamlessly integrated in tidyverse workflows.

## Installation

You can install the latest stable version of sfnetworks from CRAN with:

install.packages("sfnetworks")

You can install the development version from GitHub with:

remotes::install_github("luukvdmeer/sfnetworks")

Note: Two important dependencies of sfnetworks, the sf package for spatial data science and the igraph package for network analysis (which is the main “analysis backend” of tidygraph), require some low-level software libraries to be installed on your system. Depending on which operating system you use, this can mean that you have to install these system requirements first, before you can install sfnetworks. See the installation guides of sf and igraph for details.

## Usage

The main goal of sfnetworks is to connect the tidygraph package for network analysis and the sf package for spatial data science. To make the most out of it, it is recommended to make yourself familiar with these two ‘parent packages’ if you don’t know them yet.

There are currently five vignettes that guide you through the functionalities of sfnetworks:

(GIF (c) by Lore Abad)

## Contribution

We look very much forward to contributions to the package. See the contributing guide for further details.

This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

## Acknowledgment

This project gratefully acknowledges financial support from the