Near Data and Far Data - Best Paper Award at CSCW 2025
JSD Lab members Han Qiao, Siyi Wu, and Christoph Becker received the Best Paper Award at the 28th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW 2025) in Bergen, Norway. In this blog post, we share some key insights from the paper and we invite you to read more about the paper here:
Han Qiao, Siyi Wu, and Christoph Becker. 2025. “Near Data” and “Far Data” for Urban Sustainability: How Do Community Advocates Envision Data Intermediaries? Proc. ACM Hum.-Comput. Interact. 9, 2, Article CSCW003 (May 2025), 30 pages. https://doi.org/10.1145/3710901
“Near Data” and “Far Data” for Urban Sustainability: How Do Community Advocates Envision Data Intermediaries?, examines how advocates use data in their work and how they envision data intermediaries tools for pursuing more sustainable urban futures. Based on interviews with 17 advocates from 23 community groups, the study introduces the concepts of “near data” and “far data,” identifying pathways for data intermediaries to align data exploration with storytelling, communicate context and uncertainty, and decenter artifacts for relationship-building. The quality of “near” and “far” describes how one relates to the data that they work with. This relationship is fluid, dynamic and relational. Near data and far data identifies two extremes on a spectrum of near and far. When one views, makes sense of, or works with data, the distance between the person and the dataset can sit anywhere on the spectrum. Thus this is not a binary categorical distinction. A dataset that is close to someone, might also appear to be far to others. For example, someone living in a neighborhood will likely have a very different relationship to that neighborhood’s active living score than someone encountering the same data from elsewhere. Similarly, a person living with diabetes may feel far closer to data generated by their glucose-monitoring device than someone without that lived experience. When data feels “near,” it is often accompanied by stronger emotional and visceral connections, and can afford a greater sense of agency to act. However, far data can also be valuable and necessary in many situations. Data that is abstracted, aggregated, or distanced can protect privacy, could reduce harm, and support the emotional labour of frontline workers.
We are currently working to further articulate this concept by examining what aspects of data shape how people relate to it. We also explore how reflecting on these relationships can make visible the politics and power embedded in data work, open up new design spaces, and support data practices grounded in data justice and data feminism. We hope to contribute to ongoing efforts to reimagine and pursue urban futures that are more just, sustainable and inclusive.