Visualization of generalized mean estimators using auxiliary information in survey sampling

Abstract

The mean estimators with ratio depend on multiple auxiliary variables and unknown parameters in a finite population setting. We propose a new generalized approach with matrices for modeling the mutivariate mean estimators with two auxiliary variables. Our approach brings naturally a graphical analysis for comparing mean estimators.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Using logic model and visualization to conduct portfolio evaluation

Portfolio evaluation is the evaluation of multiple projects with a common purpose. While logic models have been used in many ways to support evaluation, and data visualization has been used widely to present and communicate evaluation findings, adopting logic models for portfolio evaluation and using data visualization to share findings simultaneously is surprisingly limited in the literature. With the data from a sample portfolio of 209 projects which aims to improve the system of early care and education (ECE), this study illustrated how to use logic model and data visualization techniques to conduct a portfolio evaluation by answering two evaluation questions: “To what extent are the elements of a logic model (strategies, sub-strategies, activities, outcomes, and impacts) reflected in the sample portfolio?” and “Which dominant paths through the logic model were illuminated by the data visualization technique?” For the first question, the visualization technique illuminated several dominant strategies, sub-strategies, activities, and outcomes. For the second question, our visualization techniques made it convenient to identify critical paths through the logic model. Implications for both program evaluation and program planning were discussed.     

《民族学刊》2014~2018年影响因子比较分析——基于《中国学术期刊影响因子年报（人文社会科学）》数据

《民族学刊》于2010年6月创刊，相继入选“复印报刊资料重要转载来源期刊”“北大核心期刊”“人文社科核心期刊”和“CSSCI来源期刊”，表明了《民族学刊》在学界和期刊界的学术影响，其期刊的影响因子值得研究和探讨。本文基于《中国学术期刊影响因子年报（人文社会科学）》公布的影响因子、各类计量指标和人文社科类影响因子、被引频次及可被引文献量等数据，对《民族学刊》的相应数据与C95民族学类期刊在期刊影响力指数、影响因子均值及量效指数均值、各类计量指标均值和人文社科类影响因子、被引频次及可被引文献量均值等方面进行统计对比，分析《民族学刊》2014~2018年五年间的影响因子数据在民族学类期刊位置及其学术影响。     

COWORDS: a probabilistic model for multiple word clouds

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.     

Data Science: A Powerful Catalyst for Cross-Sector Collaborations to Transform the Future of Global Health—Developing a New Interactive Relational Mapping Tool (Demo)

ABSTRACT

The increasingly complex and rapidly changing global health and socioeconomic landscape requires fundamentally new ways of thinking, acting, and collaborating to solve growing systems challenges. Cross-sectoral collaborations between governments, businesses, international organizations, private investors, academia, and nonprofits are essential for lasting success in achieving the Sustainable Development Goals (SDGs), and securing a prosperous future for the health and well-being of all people (United Nations, n.d United Nations. (n.d.). SDGs: Sustainable development knowledge platform. Sustainable Development United Nations. Retrieved from https://sustainabledevelopment.un.org/ [Google Scholar].). Our aim is to use data science and innovative technologies to map diverse stakeholders and their initiatives around SDGs and specific health targets—with particular focus on SDG 3 (Good Health & Well Being) and SDG 17 (Partnerships for the Goals)—to accelerate cross-sector and multidisciplinary collaborations. Initially, the mapping tool focuses on Geneva, Switzerland as the world center of global health diplomacy with over 80 key stakeholders and influencers present. As we develop the next level pilot, we aim to build on users’ interests, with a potential focus on non-communicable diseases (NCDs) as one of the emerging and most pressing global health issues that requires new collaborative approaches. Building on this pilot, we can later expand beyond only SDG 3 to other SDGs.     

旅游研究中的认同话题——基于CiteSpaceⅢ的知识图谱及可视化分析

在旅游研究中,国际学术界对认同话题的研究肇始于20世纪80年代,从最初的族群认同发展到今天诸如目的地认同、角色认同、文化认同和自我认同等多个层面的认同,研究领域(范畴)不断拓展,研究主题趋于多样,研究视角愈加宽泛新颖,研究内容越发深入。文章以SSCI所收录国外旅游研究中的认同话题相关文献为研究对象,利用科学知识图谱分析软件CiteSpaceⅢ,对研究现状进行可视化分析,探测研究热点与演化过程。文章不仅客观反映出国外研究前沿,捕捉国外研究热点,也可以为国内相关研究提供探讨话题、关键表述、研究方法等方面的借鉴。     

Social network and content analysis of the North American Carbon Program as a scientific community of practice

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of communities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.     

The role of titles in enhancing data visualization

Many in the data visualization and evaluation communities recommend conveying the message or takeaway of the visualization in the visualization's title. This study tested that recommendation by examining how informative or generic titles impact a visualization’s visual efficiency, aesthetics, credibility, and the perceived effectiveness of the hypothetical program examined. Furthermore, this study tested how simple or complex graphs, and positive, negative, or mixed results (i.e., valence of the results) affected outcomes. Participants were randomly assigned to one of 12 conditions, representing a 2 (graph: simple or complex) x 2 (title: generic or informative) x 3 (valence: positive, negative, mixed) between-subjects study. The results indicated that informative titles required less mental effort and were viewed as more aesthetically pleasing, but otherwise did not lead to greater accuracy, credibility, or perceived effectiveness. Furthermore, titles did not interact with graph type or the valence of the findings. While the results suggest it is worthwhile to consider adding an informative title to data visualizations as they can reduce mental effort for the viewer, the intended goal of the visualization should be taken into consideration. Considering the goal of the visualization can be a deciding factor of the type of graph and title that will best serve its intended purposes. Overall, this suggests that data visualization recommendations that impact evaluation reporting practices should be scrutinized more closely through research.     

Critically assessing digital documents: materiality and the interpretative role of software

As a contribution to the ongoing tradition of critically assessing documents for research, this paper aims to highlight materiality as a key factor in the co-shaping of knowledge derived from digital documents. The paper first builds upon prior debates in document studies with work from the fields of Science and Technology Studies, and Communication Studies, to establish the role of document materiality in the interpretative process. By first establishing digital documents’ material reality as electrical signal, the paper then discusses the interpretative role of software, in both the representation of that signal for human interpretation and the production of the document through software tools. Finally, the paper considers the implications for persistence and access to digital documents posed by their material reality and the private archival contexts in which they often reside.