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This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest.

The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19.

The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety.

Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations.

Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society.

Visual expositions provide an effective media for communicating complex information to a networked society.

This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Therapeutic landscapes can be beneficial for patients recovering from cognitive and behavioural impairments. The therapeutic garden in the Royal Talbot rehabilitation centre (RTRC) located in Kew, Melbourne is a precedent for its nature design outdoor spaces and horticultural therapy (HT) program for patients with cognitive impairments due to brain and spinal cord injuries. There have been no studies into the physical design of the RTRC therapeutic garden.

This case study unveils the critical design themes, and dominant physical elements of the RTRC therapeutic garden through site observation and analysis over the course of two months, a semi-structured interview with a key staff, and graphical representations using integrated photography and collaborative digital tools of AutoCAD and Adobe Photoshop.

Seven critical design themes are identified for the RTRC garden: (1) space layout and organization, (2) physical and visual access, (3) pathway and facility, (4) multisensory vegetation, (5) amenity, wayfinding and seasonal interest, (6) cognitive stimulation, (7) utility and HT. Qualitative and quantitative data analysed in visual and descriptive format reveal multisensory vegetation, amenity, wayfinding and seasonal interest, and utility and HT are the top three most pivotal critical design themes in the RTRC therapeutic garden.

The findings of this case study can be used to impart knowledge about the design of therapeutic landscape settings for cognitive impairments to design professionals and the public.

There is no case study has been conducted for this precedent of therapeutic garden in the Royal Talbot rehabilitation centre that targets for cognitive impairment patients. With the increasing awareness in medical and healthcare environments, this case study would help to inject mutual understanding, generate knowledge and design awareness among design professionals and public, and to cultivate more good quality healthcare settings.

This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

This study aims to explore how a postmodern tribe enacts and re-interprets ideologies as a part of consumers’ collective experience, to enhance our understanding of consumer communities in conjunction with ideological capacities.

The community of “presenteers” is conceptualized as a self-organized tribe with heterogeneous components that generate capacities to act. Netnographic observation was conducted on 18 presenteer accounts and lasted around six months. Real-time data were collected by taking screenshots of the posts and stories that these users created and publicly shared. Data were analysed by adopting assemblage theory, combining inductive and deductive approaches. Firstly, a qualitative visual-textual content analysis of the tribe’s defining components was conducted. Then, the process continued with the thematic analysis of the ideological underpinnings of the tribe’s enactments.

Findings shed light on the ways in which consumer communities interpret the entanglement of religious, political, and cultural ideologies in shaping their experiences. In the case of the presenteers tribe, findings reflect a novel ideological interplay between neo-Ottomanism, post-feminism and consumerism.

The study offers a deep dive into a unique tribe that is being organized around the consumer-created practice of “presenteering” and investigates consumer communalization in alignment with the ideological turn in culture-oriented interpretative research on consumers, consumption, and markets. This exploration helps to bridge the research on the communalization of consumers with the recent discussions of ideology in the postmodern market.

The study offers a deep dive into a unique tribe that is being organized around the consumer-created practice of “presenteering” and investigates consumer communalization in alignment with the ideological turn in culture-oriented interpretative research on consumers, consumption, and markets. This exploration helps to bridge the research on the communalization of consumers with the recent discussions of ideology in the postmodern market.

This study aims to explore the intelligibility of communication with stakeholders as a result of accrual accounting adoption. It focuses on changes in the use of visual forms and the readability of text that occurred immediately after the adoption of accrual accounting in performance reports of Italian public universities.

The authors collect the stakeholder section of performance reports published before and after accrual accounting adoption. Then, the authors use manual and computer-assisted textual analysis. Finally, the authors explore the data using principal component analysis and qualitative comparative analysis.

This study demonstrates that switching from cash to accrual accounting provokes immediate changes in communication patterns. It confirms the significant reduction of readability and increase in visual forms after accruals accounting adoption. The results indicate that smaller universities especially put effort into increasing intelligibility while implementing a more complex accounting system. This study also finds a relation between the change in readability and the change in visual forms that are complementary, with the exception of several very large universities.

The findings underline the possibility of neutralising the adverse effects of accounting reform associated with its complexity and difficulties in understanding by the use of visual forms and attention to the document’s readability.

This paper adds a new dimension to the study of public sector accounting from the external stakeholder perspective. It provides further insight into the link between accrual accounting adoption and readability, together with the use of visual forms by universities.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

This chapter explores the development of social science visualizations as cultural objects within art worlds. The research examines artworks as social science visualizations to show the importance of conducting analysis within distinctive social, institutional, and cultural environments. To make these arguments, the chapter outlines some of the key features of art worlds as they have been analyzed by cultural sociologists and anthropologists. We point out how cultures of reception and institutional intermediaries, such as museums, have historically shaped the construction of artworks, which are never produced or interpreted in a vacuum. The chapter closes with a call to expand both the application of social science visualizations and our understanding of such visualizations as subject to similar art world dynamics. Such visualizations, it is argued, constitute key components of social research practice increasingly oriented toward a digitally connected public hungry for visual interpretations of contemporary social developments.