Scope
Places with the well-being of participants and the prosperity of the region may be regarded as a lively, active, and bustling atmosphere associated with crowds - where a lot of people gather close to each other. However, not only liveliness or activeness, but the mutual interaction of participants enhances the expected prosperity of the local society which can grow to make a region preferable to live and work in.
It is an open problem to discover such a place because the criteria for evaluating the value of things, events, or even that information to be available about a place have not yet been established. For example, just a large number of people, such as a marching army in which participants walk in the same direction, is not expected to create new value in the place. Even if they are shouting to fire themselves up, that will not still make the place a residential town or a market where various intellectual and commercial values emerge, which is often required to make a location valuable in terms of sustainable prosperity. To enjoy such prosperity with creativity, a crowd, a workplace, a venue, or marketplace should embrace the diversity of participants' interests and knowledge, which often cannot be evaluated in a few dimensions of value criteria but should be combine with other sources of dimensions via physical, mental, and intellectual interactions.
Here, we stand on the belief that such a valued place is the basis of the sustainable prosperity of human society, where a lively society with active markets is created via the synergetic interaction of individuals, which are observed as activities involving movements, communication, and exchange of values and information. Through such activities, the place can provide social, financial, physical, and community well-being to young, working, and elderly people to enjoy wellness and careers by which they are working to develop the values evaluated in the created dimensions.
In this special session, we would like to have papers and presentations about methods or theories for creating, collecting, combining, or utilizing data on the activities of humans or relevant events so that the values or potential values of places can be discovered. We will have the keynote presentation by Renate Fruchter, Founding Director of the Project Based Learning Laboratory (PBL Lab) at Stanford University, a designer of physical and virtual interactive learning and workspaces. Focusing on the relationship between technology, people, place, and process. As you will find in her talk, although the target is "places" where a space turns into a valued environment for human(s) to live in, the information useful for adding value to the place and fostering social interactions of individuals can be beyond what we believe "mobility data" are. We should involve data on human words, thoughts, health, weather etc., for mining values that may have been invisible or undetected so far. The authors are welcome to show approaches for creating and using novel data as well as novel values in the places. Hence, we communicate studies using mobility data or their extensions for value discovery and creation.
Relevant areas
We call for presentations relevant to, but not restricted
(as far as it is relevant to our interest above) to the three scopes below.
01
[Design with Big Data from/for Places] The topics below closely align with our focus on designing valuable places and creating meaningful physical environments for people to inhabit, experience, and enjoy. Each area offers rich opportunities for research and innovation in this field.:
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Place-Making Strategies with Big data:
Methods and approaches to create meaningful and engaging environments that foster social interaction, community cohesion, and a sense of belonging, on the data on daily human experiences and activities.
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Participatory Design:
Approaches to involve stakeholders, including residents, businesses, and community groups, in the design process to ensure that their needs, values, and aspirations are reflected in the final outcomes.
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Environmental Psychology:
Relationship between the built environment and human behavior, emotions, and well-being, and explore design interventions to create supportive and restorative places from behavioral and emotional data.
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Smart Cities and Technologies:
Integration of digital technologies, data analytics, and Internet of Things (IoT) solutions to enhance the functionality, efficiency, and sustainability of urban spaces and infrastructure.
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Placemaking for Health and Well-being:
The role of design in promoting physical activity, mental health, and social well-being through the creation of accessible, inclusive, and health-promoting environments, involving data on human health and social/natural phenomena, for example, weather data.
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Sustainable Design Practices:
Research strategies for integrating principles of sustainability, resilience, and ecological stewardship into the design of buildings, landscapes, and urban infrastructure to minimize environmental impact and resource consumption --- the proposal of data for this and the following topics-are expected.
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Long and short-term interventions:
Interventions, such as revitalizing historic sites and landmarks, street festivals, and community gardens, to activate underutilized spaces, foster community engagement, and catalyze long-term urban transformation, on the data on visitor and habitant communication.
[ Scope of data science] Overall, data scientists bring a diverse set of perspectives to the analysis and design of scenarios involving activities in various places and their synergetic effects, with a focus on ensuring data quality, creating and applying analysis techniques, leveraging advanced technologies, and addressing ethical considerations. Potential interests of the presentations include:.
02
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Data Integration:
Integration of data which may emanate from several different sources and are represented in several different formats, resolving entities within and across data for deriving utility from data. Acquisition of knowledge for decision-making, which may be beyond the reach of a single dataset, involving the interaction with data marketplaces and the cyber/real world. New aspects of computation, cognition, or communication for learning from integrated and/or visualized data to reinforce predictive performance and interpretability of knowledge.
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Data Quality Assurance:
The focus is on ensuring the utility, accuracy, reliability, and completeness of the data collected from places. Scientists may challenge the analysis by scrutinizing the data collection/analysis methods, identifying potential biases, or verifying the integrity of the data.
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Statistical Analysis:
Application of statistical techniques to analyze the activities of individuals and their communication and mobility with or without vehicles in the target places. Authors of papers may challenge the analysis by exploring different statistical models, testing hypotheses, or conducting inferential analyses to extract meaningful insights from the data.
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Machine Learning and Predictive Modeling:
Utilization or development of machine learning algorithms to predict the behavior of a crowd or its individuals or to obtain patterns based on historical data. This is also related to challenging the design of a synergetic community, marketplace, or public place by experimenting with analysis models, feature engineering techniques, or tuning the model parameters to simulate the quality of lives of diverse people in a local region.
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Data Visualization and Interpretation:
Data visualization techniques to foster insights into the values in the place. The author may challenge the analysis by creating interactive visualizations, exploring different visualization tools, or designing intuitive dashboards to effectively communicate insights.
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Ethical and Privacy Considerations:
Ethical implications and privacy issues related to the analysis of the above or other categories. The author may challenge the design by implementing privacy-preserving techniques, ensuring data anonymization, or adhering to ethical guidelines to protect individuals' rights and confidentiality.
03
[Scope of the data society] Overall, data providers involved in the analysis or design of data on activities in the real space are motivated by a combination of revenue generation, value creation, product innovation, customer engagement, partnerships, and risk management, all aimed at maximizing the value derived from the data collected and analyzed. Thus, the following interests in the data society fit this special session. In addition, scientific analysis and the design of the data market with synergetic interactions among participants are related.
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Value creation:
Creation of value-added services, such as analytics, insights, or consulting, for clients or customers in the market. Proprietary algorithms or models to extract actionable insights from the data and methods to provide these insights as part of service offerings.
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Data Monetization:
Methods and technologies to monetize the data collected from crowds or their activities by selling them to businesses, researchers, or government agencies interested in analyzing the behavior of crowds. Methods of generating revenue through data licensing agreements or subscription-based models.
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Product/Service Development:
Insights gained from analysis of live individuals. Communities or crowds to inform the development of new products or features. For example, crowd management solutions, event planning tools, and location-based services are tailored to the needs of businesses or event organizers.
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Customer Engagement:
Methods for making personalized experiences or recommendations based on the analysis of human individual/community/crowd/social behaviors based on data-driven insights to improve customer satisfaction. In addition, there are methods for driving user engagement on these platforms.
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Partnerships and Collaborations:
Methods for exploring partnerships or collaborations with other organizations, such as other individuals, companies, or government agencies, to leverage complementary expertise and resources to activate the interactions of those acting in a place.
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Regulatory Compliance and Risk Management:
Ensuring regulatory compliance and mitigating the risks associated with privacy, security, and ethical concerns. Robust governance frameworks for personal data, security measures, and compliance processes to address these issues.
Information for Authors.
Important Dates
Full/short paper submission: Sept 27 (Fri), 2024
Notification of paper acceptance: Oct 27, 2024
Camera-ready of accepted papers: Nov 17, 2024
Conference: Dec 15-18, 2024
Instructions
Papers should be submitted as PDF in a 2-column IEEE format. Detailed instructions for the authors can be found on the paper submission page provided by the conference.
https://wi-lab.com/cyberchair/2024/bigdata24/scripts/submit.php?subarea=SP05&undisplay_detail=1&wh=/cyberchair/2024/bigdata24/scripts/ws_submit.php.
Accepted papers will be published in conference proceedings. All accepted papers must be presented by one of the authors to include the article in the proceedings.
If you have any questions about this special session, please do not hesitate to contact us:
info -at- panda.sys.t.u-tokyo.ac.jp (replacing "-at-" with "@")
Authors are responsible for ensuring that the sources of data used and ethical considerations are acknowledged in the paper. For example, the authors can ensure that any data used adheres to the Institutional Review Board or the Ethics Committee of the respective institutions of submitting authors.
Open Data
The following are a part of open mobility data the participants can download for free. Authors can, but are not restricted to, use these datasets for submission. In addition, we encourage authors to combine mobility data with other datasets from SNS, markets, news, voices, human relations, etc --- this is about synergization!
Geolife GPS trajectory dataset - User Guide - Microsoft Research
https://www.microsoft.com/en-us/download/details.aspx?id=52367
US Irvine ML Repository
https://archive.ics.uci.edu/dataset/354/gps+trajectories
GPS Trajectory Dataset of the Region of Hannover, Germany
YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories
Yabe, T., et al. Sci Data 11, 397 (2024). https://doi.org/10.1038/s41597-024-03237-9
Organizing Committe Members
Ohsawa, Yukio
chair: session originator
Professor, School of Engineering in The University of Tokyo, Nigiwai Lab., Japan
Kondo, Sae
co-chair: session originator
Assoc. Professor, School of Engingeering in Mie University, and RCAST in The University of Tokyo, Japan
Koshizuka, Noboru
co-chair: connection to the special session on DFFT
Professor, Interfaculty Initiative in Information Studies, The University of Tokyo, Japan
The committee members are sorted alphabetically
Auernhammer, Jan
Research Engineer, Stanford University, United States of America
Agrawal, Jitendra
Senior Lecturer, School of Civil, Aerospace and Design Engineering, Bristol University, UK
Bandini, Stefania
Professor, Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Italy
Bewong, Michael
Senior Lecturer in Computing, Charles Sturt University, Australia
Chen, Lieu-Hen
Professor, Department of Computer Science and Information Engineering, National Chi Nan University, Taiwan
Correa da Silva, Flavio
Associate Professor, Department of Computer Engineering and Digital Systems, Universidade de Sao Paulo, Brazil
Navez, Didier
Senior Vice President, Data Policy & Governance, Dawex, France
Fruchter, Renate
Funding Director, Project Based Learning Laboratory (PBL Lab), Stanford University, USA
Jugulum, Rajesh
Affiliate Professor, Dr., Northeastern University, USA
Matsushita, Mitsunori
Professor, Kansai University, Japan
Milella, Frida
Assistant Professor, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Italy
Nishinari, Katsuhiro
Professor, School of Engineering in The University of Tokyo, Japan
Sekiguchi, Kaira
Project Researcher, School of Engineering in The University of Tokyo, Japan
Takama, Yasufumi
Professor, Tokyo Metropolitan University, Japan
Van den Poel, Dirk
Professor of Data Analytics/Big Data, Ghent University, Belgium
Wang, Hao (Henry)
Vice President, Industry Large Model Team, Alibaba Cloud Group, China
Related Events and Activities
We exchange thoughts on society and marketplace of data with:
Special Session on Data Free Flow with Trust (DFFT) in IEEE Bigdata 2024 as well as we did in 2023
We will exchange ideas and news on social controbution of bigdata with:
We will exchange topics on data-federative innovation with:
Data Federative Innovation Social Cooperative Programm, The University of Tokyo