Donut visualizations for network-level and regional-level overview of Spatial Social Networks

TL;DR

This paper introduces a novel donut visualization technique for Spatial Social Networks that effectively combines spatial and social data to provide clear network overviews at both regional and network levels.

Contribution

The paper presents a new donut visualization method specifically designed for SSNs, addressing the limitations of traditional topological algorithms in spatially embedded networks.

Findings

Effective summarization of SSNs using donut visualization

Demonstrated on two standard SSNs from literature

Enhances understanding of spatial and social connection orientations

Abstract

Spatial Social Networks (SSN) build on the node and edge structure used in Social Network Analysis (SNA) by incorporating spatial information. Thus, SSNs include both topological and spatial data. The geographic embedding of the nodes makes it impossible to move the nodes freely, rendering standard topological algorithms (e.g. force layout algorithms) used in SNA ineffective to visualize SSN sociograms. We propose a new visualization technique for SSNs that utilize the spatial and social information to provide information about the orientation and scale of connections. The donut visualization can be used to summarize the entire network or can be used on a part of the network. We demonstrate the effectiveness of the donut visualization on two standard SSNs used in literature.