Data physicalization, which encodes data in physical form, has been increasingly used to engage the public with issues of social good. While public engagement is often invoked as a motivation or expected outcome, it has not been systematically examined as a design objective. This gap raises two key challenges: what characterizes engagement in data physicalization for social good (Phys4Good), and how it can be effectively designed. In this work, we address these challenges by first curating a corpus of 45 Phys4Good projects and deriving a design space structured around a modified three-act framework comprising Stage, Encounter, and Impact. We then conducted semi-structured interviews with designers of eight projects to identify recurring challenges and strategies for fostering engagement. Finally, we demonstrated the effectiveness of our design space and strategies through a case study, which showed that they can guide designers in structuring engagement, anticipating barriers, and creating more impactful Phys4Good experiences.

Design spaces serve as conceptual frameworks that enable systematic exploration of possibilities and constraints for particular design problems. Despite growing recognition of their importance in visualization research, the community faces two main challenges: characterizing what constitute a design space, given the lack of consensus on its definition, and determining how to construct these spaces in the absence of established methodologies. To address the challenges, we first conducted a literature review of visualization design space research, identifying three distinct research threads. Focusing on the thread that views design spaces as multi-dimensional frameworks, we refined our corpus to 49 papers and developed a unified conceptualization of design spaces. Building on this foundation, we proposed a systematic approach to design space construction, synthesized from an analysis of practices spanning five phases: exploration, data collection, creation, evaluation, and communication.

Data visualizations are increasingly used by news outlets on social media to communicate insights to a broad audience. However, little is known about how readers interact with and respond to data visualizations in these quick-consumption environments. In this work, we introduce a conceptual model that categorizes visualization reading that leads to the communication effect of likes on Instagram. The model was developed through a grounded theory analysis of the statements explaining the reasoning behind the likes of visualization, which were recorded from a preliminary study. Informed by coding the statements from two dimensions including scopes and design patterns concerning visualization, our model consists of three levels: depicting the "look" of a visualization (e.g., artistic style and color scheme); interpreting the "flesh and bones" of a visualization (e.g., visualization and narrative); and elucidating the "heart and soul" of a visualization (e.g., insights and conclusion). We also conducted an online crowdsourcing user study with 200 participants to demonstrate how our model can be applied to improve the communication of visualization by comparing the three levels.

Personalizing products aesthetically or functionally can help users increase personal relevance and support self-expression. However, using non-abstract personal data such as head portraits for product personalization has been understudied. While recent advances in Artificial Intelligence have enabled generating stylized head portraits, these images also raise concerns about lack of control, artificiality, and ethics, which potentially limit their broader use. In this work, we present PicMe, a design support tool that converts user face photos into stylized head portraits as vector graphics that can be used to personalize products. To enable style transfer, PicMe leverages a deep-learning-based algorithm trained on an extended open-source illustration dataset of characters in a cartoonish and minimalistic style. We evaluated PicMe through two experiments and a user study. The results of our evaluation showed that PicMe can help create personalized head portraits that support self-expression.