The objective of this study was to investigate and classify the roles, or distinct contribution styles, adopted by participants within the OpenStreetMap (OSM) community. Using a quantitative analysis of mapping behaviors, we devised a methodology to identify distinct features associated with specific roles. We used an unsupervised clustering approach and unveiled eight discernible roles, or types of mapper in OSM. Each role displays specific patterns of mapping behaviors related to their habits and preferences for adding or editing map objects over time. We validated our roles, in part, using known affiliations with humanitarian and corporate organizations. Using these roles, we examine community composition and contributor retention over time. Our contributions include applying existing methods on the analysis of contributor behavior in online platforms to OSM, the identification of eight roles that can guide future research and design within OSM, and further understanding into the overall trajectory of the world’s largest geospatial peer production community.

The social life and long-term trajectories of online peer production communities are shaped and animated in part by value tensions that arise when distributed, heterogeneous participants are brought together into collaboration. This study of OpenStreetMap (OSM) draws upon values-based approaches to investigate how peer production communities enact their values and navigate tensions between them. We examine how conflicts within the community over the rise of corporate participation in OSM provided a stage for the articulation and enactment of community values, shedding light on the broader dynamics and trajectory of the platform and its participants. The contributions of this work include reflections on how increasing corporate participation in OSM intersects with discourses about the emancipatory potential of emerging mapping technologies, insights into the challenges of scaling membership in peer production communities, and exploring the role of values in understanding the social life and governance of online communities.

Overconsumption of resources is a global issue. To deal with resource depletion and mitigate impending crises, the circular economy (CE) solution provides an ecosystem by reducing waste via the reuse, repair, refurbishment, and recycling the existing materials and products. However, as the complexity of supply chains is increasing an effective CE management is very crucial. We want to address this issue by performing a feasibility study with AI-enabled blockchain technology using our developed customised NFT platform, TrackGenesis NFT, along with the OpenSea.io architecture for CE management to decrease transaction costs, enhance performance and communication along the supply chain, and reduce carbon footprints. Circulogy is an e-waste management system that can respond to supply chain challenges using blockchain technologies. A supply chain can get complicated very quickly, considering that each product component has its supply chain. In our proposed solution, blockchain provides a solution to this by establishing transparency in every node of the product's lifecycle and users can exchange or sell/buy NFTs. There are multiple copies of the audit trail for every transaction using blockchain, which will provide the ability to track and reuse/recycle Waste Electric and Electronic Equipment (WEEE).

Research is an interconnected global endeavor. Networks of research collaborations are often using Social Networks Analysis. Its variant Spatial Social Networks allowing explicit embedding of spatial information in the network. Variations in incorporating spatial information results in multiple conceptualizations of networks, enabling exploration of a variety of questions regarding collaborations. To elucidate this approach the National Geographic Society grants database (1890–2016) is utilized to create three different networks that embed spatial information in distinct ways. Each network highlights a different aspect of connectivity latent in the dataset and along with the spatial information, emphasizes international and regional trends of collaborations. The networks explicate the international nature of collaborative research by virtue of people collaborating explicitly, or by working in the same places. It also highlights the multidisciplinary nature of research in various countries, and how it can be useful to ideate about new projects. Additionally, the network approach highlights the dominance of global north in conducting fieldwork-based research across the world, mostly through collaborations. The abstraction afforded by social network models requires further deliberation on the way spatial relationships can be captured differently using the node-edge structure and how these alternate networks compare to traditional networks in GIScience.

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.