Metrics for characterizing network structure and node importance in Spatial Social Networks

ABSTRACT

Social Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales.

KEYWORDS:

• Spatial Social Network
• metrics
• network structure
• node importance
• social relationships

Acknowledgments

This research was supported by Rathlyn Fieldwork Award, Rathlyn GIS Award and the Graduate Mobility Award from McGill University. Colin Chapman was supported by the Humboldt Foundation, the Robert Koch Institute, Office for Academician Northwest University and an IDRC Grant while writing this paper. We would also like to thank Makerere University Biological Field Station and Uganda Wildlife Authority for their support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The metrics and visualization discussed in this article are available as an R library downloadable from https://github.com/diptosarkar/SpatNet/.

Additional information

Notes on contributors

Dipto Sarkar

Dipto Sarkar is a faculty in the Department of Geography at National University of Singapore. His research focuses on geographic information science, social networks, and computational social sciences. He is particularly interested in applying his methods to understand the human dimensions of biodiversity conservation.

Clio Andris

Clio Andris is an assistant professor of geography at The Pennsylvania State University where she serves as director of the Friendly Cities Lab. Her research interests are geographic information science, social networks, interpersonal relationships, urban planning and spatial data mining.

Colin A. Chapman

Dr. Colin A. Chapman’s research focuses on how the environment influences animal abundance and social organization and given their plight, he has applied his research to primate conservation. He has published 450+ articles, been cited 31000+ times, has a H factor of 97 and has received ~ $11 million in research funding and ~10 million in training grants. He has received a number of prestigious awards (Fellow of the Royal Society of Canada, Killam Fellow, Konrad Adenauer Research Award from the Alexander von Humboldt Foundation, Anderson Teacher Scholar), was appointed as a Conservation Fellow to the Wildlife Conservation Society and as an advisor to National Geographic, and received the Velan Award for Humanitarian Service. He has conducted research in Kibale National Park in Uganda for 30 years and is interested in the roles of food abundance, disease, nutrition, and stress in determining primate abundance and how to best to conserve the world's biodiversity, where he focuses on primates and recently elephants because of their plight. During this time, he has not just been an academic, but has devoted great effort to promoting conservation by help the rural communities in the area he works.

Raja Sengupta

Prof. Raja Sengupta is an Associate Professor in the Department of Geography and School of Environment at McGill University, Montreal, Quebec, Canada.  His research interests in GIScience include Agent-Based Models (ABMs) of both human and primates, with his recent research focusing on behaviour and movement of red colobus monkeys in Kibale National Park, Uganda.  He is also interested to understand how resource sites can be locations for transmissions of infectious diseases (as verified using ABMs), and is using network analysis to understand both spread of diseases and landscape-based factors that affect movement patterns of primates and other animals.  He has published 35 journal papers, 10 book chapters, and one edited volume on related topics.