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In order to improve the effect of urban road congestion analysis, this paper aims to study the mechanism of traffic congestion diffusion under the condition that users have real-time traffic information, and analyze the urban road congestion situation combined with big data visualization technology. According to the characteristics of traffic flow propagation, this paper conducts multi-granularity abstraction and multi-scale modeling of node-intersection-link-network for the complex and dynamic traffic congestion process, and establishes an improved SIS virus propagation model for traffic congestion propagation. In addition, this paper uses the method of state transition probability to construct an interactive dynamic model of traffic congestion propagation and early warning information propagation in a multi-layer network. The experimental research results show that the big data visualization technology introduced in this paper can play an important role in urban road congestion. [ABSTRACT FROM AUTHOR]

This paper presents an overview of emerging research at the Manifest Data Lab a collective of artists and climate scientists who employ climate data experimentally in public settings. We discuss one of our projects The Carbon Chronicles, a reverse-engineered climate data set, projected on prominent public buildings in London during the period of COP26. Our approach examines the potential of climate data visualisations to operate as a public form that surface issues of climate justice and historic responsibilities. We report on the project's methodological framing, production and associated artistic strategies. [ABSTRACT FROM AUTHOR]

The gradual establishment of systematic, equalized, and standardized basic public services has drawn attention of the academic community to the mismatch between supply-demand, and public dissatisfaction. Big-data-driven public services innovatively attempt to solve these problems, and reflect the theoretical essence of the process by which big data can empower the responsiveness of governments. In this study, we adopted the theoretical frameworks of 'diversified needs–selective responses', 'risk shocks–forward-looking responses', and 'forward-looking predictions–creative responses'. We propose that big data-driven public services should respond not only to present needs but also to social risks and future needs. Therefore, it is imperative to review the status, problems, and future directions of big data-driven public service research in China. This study uses bibliometric visualization analysis on data from research projects, monographs, and journal publications. The results reveal that the main research topics are basic theoretical issues, service-oriented government development guided by big data strategies, practical innovation of public services in the context of smart governance, and the effective supply of big data-driven public services. Previous studies suffered from weak theoretical reflection and construction, lacked relevant institutions, had less fine-grained and fragmented technical support, and lacked foresight and guidance. Attention should be paid to normative theories and institutions in big data-driven public services to ensure that these services are more targeted and prospective; creative research should be conducted. The systematic summarization of the current state of research and reflections on prospective and creative research trends will provide new ideas regarding future research directions. [ABSTRACT FROM AUTHOR]

Road-based freight movements are a critical component of the supply chain and transportation networks. With the drive to invest in big data collection capabilities, most of the collected freight movement data remain underutilized. To improve the efficiency and resiliency of the supply chain, it is essential to enhance the visibility of goods movements on road networks. To this end, we provide in this paper a comprehensive literature review on this topic and analyze the previous research from different perspectives, such as data levels of abstraction and existing visualization techniques. In addition, we provide a taxonomy of freight transportation visualization according to the underlying analytic objective. Furthermore, we propose a decision support tool to aid freight data analysts in selecting the right visualization tools. Finally, we identify research gaps in the field of freight transportation visualization. Highlights Systematic bibliometric analysis of state of art research related to location-based telematics data visualization Applications for location-based telematics data based on different levels of abstraction A taxonomy for visualization techniques for location-based telematics data and possible application in freight transportation A proposed decision support tool for selecting possible visualization techniques based on the abstraction and availability of data Discussion of challenges and future research direction for freight transportation data visualization [ABSTRACT FROM AUTHOR]

This paper explores data analytics applied to urban education, focusing in particular on issues of representationalism, the view that representations (here, digital data) stand in mimetic relation to some external reality from which they are ontologically distinct. Based on interviews conducted over the 2016–2017 school year with a team of data professionals employed by a Charter Management Organization (CMO) that operates one dozen schools in South and East Los Angeles, this paper shows that respondents endorse a range of views about how data can stand in for things, events, and people. Data professionals charged with the creation and management of student-level data expressed views consistent with a representationalist understanding of digital data; by contrast, those charged chiefly with aggregating and analyzing heterogeneous streams of data expressed greater skepticism toward representationalist commitments. Despite this heterogeneous set of views, all of the data professionals interviewed relied on the same medium to communicate their interpretations of data to other members of the organization, including school personnel: a bespoke platform that consists of 125 dashboards. Critically, graphical means of visualization expressed objectivity, certainty, and actuarial foresight, even in cases where data professionals expressed ambivalence about the representational power of a given source of data. [ABSTRACT FROM AUTHOR]

The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. Rather than understanding the correlations, this research applies the Granger causality test to build the Granger Causality Directed Network for 33 global major stock market indices. The paper further analyzes how the markets influence one another by investigating the directed edges in the different filtered networks. The network topology that evolves in different market periods is analyzed via a sliding window approach and Finance Big Data visualization. By quantifying the influences of market indices, 33 global major stock markets from the Granger causality network are ranked in comparison with the result based on PageRank centrality algorithm. Results reveal that the ranking lists are similar in both approaches where the U.S. indices dominate the top position followed by other American, European, and Asian indices. The lead-lag analysis reveals that there is lag effects among the global indices. The result sheds new insights on the influences among global stock markets with implications for trading strategy design, global portfolio management, risk management, and markets regulation. [ABSTRACT FROM AUTHOR]

Governments use metrics made possible by new data technologies to allocate budgets, manage pandemics, valorize ecosystems, and demonstrate how these actions are legitimate. Big data is pointed to as providing objective answers that emerge untampered from observations of the world as it is – a view from nowhere. While data is more valued than ever in environmental governance, so too are arrangements that seek stakeholders' input and otherwise address their subjective interests – a view from everywhere. Different kinds of metrics perform state actors as accountable in both registers: metrics that are responsive to dynamic conditions; that account for specific stakeholders; that can be prioritized against one another in interactive data visualization tools. Louisiana, USA's Coastal Master Plan is an attempt to stem wetlands loss through fine-scale modeling of large volumes of data and calculation of these kinds of social and environmental metrics. State actors there make accountability claims that appear contradictory: their decisions are legitimate because they are driven by the best available coastal science and technology, while their data tools 'didn't make decisions for us.' As state actors deploy environmental big data and metrics to make sense of it, we should be able to explain these apparently contradictory stances and the controversies that result. STS theory on metrics in environmental governance benefits from characterizing how 'modes of authorized seeing' are given expression by different metrical forms and what brings modes into contact and conflict. [ABSTRACT FROM AUTHOR]

Big data refer to the massive amounts and varieties of information in the structured and unstructured form, generated by social networking sites, biomedical equipment, financial companies, internet and websites, scientific sensors, agriculture engineering sources, and so on. This huge amount of data cannot be processed using traditional data processing systems and technologies. Big data analytics is a process of examining information and patterns from huge data. Hence, the process needs a system architecture for data collection, transmission, storage, processing and analysis, and visualization mechanisms. In this paper, we review the background and futuristic aspects of big data. We first introduce the history, background and related technologies of big data. We focus on big data system architecture, phases and classes of big data analytics. Then we present an open source big data framework to address some of the big data challenges. Finally, we discuss different applications of big data with some examples. [ABSTRACT FROM AUTHOR]

As the collection and dissemination of "big data" become easier, we are exposed to increasing numbers of visuals, or infographics, that convey this data. Students are likely to interact with infographics through news, advertising, and social media platforms, but they may not have the skills in quantitative reasoning or visual literacy to effectively understand how data is employed or framed to tell particular stories. Teaching students about the tools, available data, and communication styles that infographics offer early in their coursework can provide them a basis on which to better develop a variety of analytic and communication tools throughout additional courses in their major field of study. Through the process of learning to locate, evaluate, and use data in infographics, students also become better consumers of data visualization. A series of scaffolded assignments that help accomplish this are detailed here. [ABSTRACT FROM AUTHOR]

Although the use of tables, graphs, and figures to summarize information has long existed, the advent of the big data era and improved computing power has brought renewed attention to the field of data visualization. As such, it is crucial that introductory statistics courses train students to become critical authors and consumers of data visualizations. To that end, we have developed a semester-long, instructor-supported, group project that exposes students to this growing field. We have found this project to be an exciting and effective way to teach students the power of statistics and, more importantly, the critical role context plays when interpreting statistics. Among the many benefits of this project are hands-on learning, improved mathematical reasoning, and better collaboration and communication skills. In this article, we describe the project structure, project assessment, and techniques for facilitating effective group work. [ABSTRACT FROM AUTHOR]

Many scholars have demonstrated growing interest in GIScience in recent years, including use of open data portals, shared code and options for open access publication. These practices have made both research and data more transparent and accessible for a broad audience. This research may be open only in a limited sense for populations without expertise in the technology and methods undergirding these data. Based on two case studies using RStudio’s Shiny web platform, we argue that a process-based approach focusing on how analysis is opened throughout the research process provides a supplementary way to define and reflect upon public facing geographic research. Reflecting upon decisions we made at key points in each case study project, we identify four key tensions inherent to work in open GIScience: standardized vs. flexible tools, expert vs. community-led design, single vs. multiple audiences and established vs. emerging metrics. [ABSTRACT FROM AUTHOR]

Is a third passage to the past possible, beyond Elton's and Fogel's two roads of narrative history and scientific/quantitative history? One that would combine narrative history's focus on the event, on individuals and their actions, at a particular time and place, to scientific/quantitative history's emphasis on explicit behavioral models based on social-science theories? That is the question this article addresses. It illustrates a computer-assisted methodology for the study of narrative—quantitative narrative analysis (QNA)—that does just that. Based on the 5 Ws + H of narrative—Who, What, When, Where, Why, and How—QNA quantifies events without losing the event itself, without losing people behind numbers, diachronic time behind synchronic statistical coefficients. When used in conjunction with dynamic and interactive data visualization tools (and new natural language processing tools), QNA may provide a third unforeseen road to the past. [ABSTRACT FROM PUBLISHER]

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