Reimagining City Configuration: Automated Urban Planning via Adversarial Learning

Urban planning refers to the efforts of designing land-use configurations. Effective urban planning can help to mitigate the operational and social vulnerability of a urban system, such as high tax, crimes, traffic congestion and accidents, pollution, depression, and anxiety. Due to the high complexity of urban systems, such tasks are mostly completed by professional planners. But, human planners take longer time. The recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration, so human planners can finally adjust machine-generated plans for specific needs? To this end, we formulate the automated urban planning problem into a task of learning to configure land-uses, given the surrounding spatial contexts. To set up the task, we define a land-use configuration as a longitude-latitude-channel tensor, where each channel is a category of POIs and the value of an entry is the number of POIs. The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area. In particular, we first characterize the contexts of surrounding areas of an unplanned area by learning representations from spatial graphs using geographic and human mobility data. Second, we combine each unplanned area and its surrounding context representation as a tuple, and categorize all the tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to distinguish among positive and negative samples.Comment: Proceedings of the 28th International Conference on Advances in Geographic Information Systems (2020

reimagining stem education and training with e real 3d and holographic visualization immersive and interactive learning for an effective flipped classroom

While the 19th and the 20th centuries were, in education, mainly about standardization, the 21st century is about visualization, interaction, customization, gamification and flipped teaching. What today we know about learning from cognitive psychology is that people learn by practicing, with feedback to tell them what they're doing right and wrong and how to get better. For STEM education, that means they need to practice thinking like a scientist in the field. So e-REAL is a cornerstone: developed as workplace learning system in a number of fields (from medical simulation to soft skills development within the continuing education), it's an ideal solution to root a practical – but not simplicistic - approach for STEM education.</p

What is Interaction for Data Visualization?

International audienceInteraction is fundamental to data visualization, but what "interaction" means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community-including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them

Interactive Visualization Lab

ABSTRACT This presentation describes the philosophy and ongoing interdisciplinary research projects of the Interactive Visualization Lab at the University of Minnesota

How well do we really know the world? Uncertainty in GIScience

There are many reasons why geospatial data are not geography, but merely representations of it. Thus geospatial data will always leave their user uncertain about the true nature of the world. Over the past three decades uncertainty has become the focus of significant research in GIScience. This paper reviews the reasons for uncertainty, its various dimensions from measurement to modeling, visualization, and propagation. The later sections of the paper explore the implications of current trends, specifically data science, new data sources, and replicability, and the new questions these are posing for GIScience research in the coming years

Intersex Narratives: Shifts in the Representation of Intersex Lives in North American Literature and Popular Culture

This book explores representations of intersex - intersex persons, intersex communities, and intersex as a cultural concept and knowledge category - in contemporary North American literature and popular culture. The study turns its attention to the significant paradigm shift in the narratives on intersex that occurred within early 1990s intersex activism in response to biopolitical regulations of intersex bodies. Focusing on the emergence of recent autobiographical stories and cultural productions like novels and TV series centering around intersex, the author provides a first systematic analysis of an activism-triggered resignification of intersex

Revitalization of Social Studies Education to Build the Nation's Generation with Creative Pedagogy in the 21st Century

The purpose of this study is to examine Social Studies Education in preparing the nation's generation through 21st century creative pedagogy and to find out strategies to face the challenges of 21st&nbsp; century Social Studies Education. The research method used is the library research method by involving searching, assessing and analyzing literature relevant to the research. Data analysis techniques use a content analysis model with steps: design determination, search for basic data, and consequent knowledge search. The results found that social studies education with 21st&nbsp; century creative pedagogy requires 10 future skills of the nation's generation, namely: Complex Problem Solving, Critical Thinking, Creativity, People Management, Coordinating with other, Emotional Intelligence, Judgment and Decision Making, Service Orentation, Negotiation, Cognitive Flexbility. The strategy to face the challenges of 21st&nbsp; century social studies education is to implement the four principles of the Pancasila Student Profile, namely holistic, consectual, student-centered and exploratory. Why this research is important, because as an increase in creativity and innovation that examines how social studies education can promote student creativity in responding to social and cultural issues. This research can measure the impact of Social Studies Education in shaping students' critical thinking, and analytical skills to become competent global citizens. Thus, social studies education must transform following the changing times towards technological advances as future intelligence that cannot be defeated by artificial intelligence