Social Spatial Network Tools in R

Introduction to Social Spatial Networks (SSNs):

Researchers in the social sciences have used social networks/sociograms to visualize the connections and relationships of people in a community since the 1930s (Andris & Sarkar, 2022). However, these networks are aspatial and do not integrate geospatial information about individuals to analyze and explain these relationships (and lack of relationships). Based on Tobler's First Law (Tobler, 1970) "everything is related to everything else, but near things are more related than distant things", it could be theorized that people who live/work near one another are more likely to have similar characteristics and to interact more frequently, but this cannot be confirmed using sociograms alone as they lack a spatial component. Networking and graph theory from the field of Computer Science is also tangentially related to social-spatial networking, but is generally focused on the abstract or theoretical connections between nodes, rather than simulations of real world phenomena (Bondy, 1982). Finally, network analysis also exists within traditional GIS fields and discussions, though this generally based on the distribution of goods and services along pre-defined road/stream networks, rather than a focus on the relationship/connection outside of pre-defined networks (Andris & Sarkar, 2022). Social-Spatial Networks are an integration of the ideas found in these different fields to analyze and document social relationships/connections between individuals situated within their geospatial locations to better understand how connections are formed and maintained.

Term Definitions

In Social-Spatial Network analysis, Nodes are specific geolocated points representing people, businesses, or other points of interest. All nodes need at minimum two datapoints, a unique name/ID to reference the node and some form of location information that can be converted into a (lat, long) pair. Nodes may also contain auxiliary information about the point being represented, such as demographic information on a participant, or classifications of a business type.

Edges represent a social connection between two nodes and are at minimum composed of a pair of names/IDs that are found in the node list, all names within the edge list must be found exactly in the node list, or the program may crash. Similarly to nodes, edges can also contain additional information about a connection, such as a strength value or a type classification that may be used to weight the algorithms.

Buidling a Dataset for a Social-Spatial Network:

For the purposes of this tutorial we will use a mock dataset created from the personal knowledge of this tutorial writer about the characters of the audio-fiction podcast, The Magnus Archives, produced by Rusty Quill under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Non-specific location information for the nodes was taken from the Magnus Archives Wikia as well as a fan-made map (found here) for some that were unclear, alongside my own knowledge of the plot/characters when ambiguous/multiple locations. The dataset is available to download from the writer’s Github account https://github.com/otter-lights/SSN-Dataset_TheMagnusArchives and is composed of four files, the initial nodes.csv file and the accompanying location.csv file, which are used to create a final geolocated nodes.csv file, as well as an edges.csv file.

SSNtools - R Library

The initial intention of this tutorial was to provide an overview of Social Spatial Networking Tools in using the R programming language and the R packages "SSNtools", "tmap" and "igraph". However, the tutorial provided by the creators of the R package is very thorough, particularly in the area of the advanced statistical tools, if this is the type of analysis you would like to complete you can find that tutorial here. Rather than repeating this work, I will instead focus on clarifying for a begineer audience the visualization capabilities and instructions from tutorial Chapters 1-4, that are not available in the SNoMaN web app as the tutorial is written for a more advanced audience.

References

Andris, C., & Sarkar, D. (2022). Social networks in space. Chapters, 400-415.

Bondy, J. A. (1982). Graph theory with applications.

Tobler, Waldo R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography (Supplement: Proceedings, International Geographical Union. Commission on Quantitative Methods), 46: 234–240. DOI:10.2307/143141.