The study of genealogy is an increasingly popular activity pursued by millions of people, ranging from hobbyists to professional researchers. Such genealogical datasets provide a great opportunity for social science analysts, historians, and the public to study a wide variety of topics in demography, family and household, kinship, stratification, and health. Nevertheless, the large scale and characteristics of the data such as hierarchical, spatiotemporal, and multidimensional also pose special challenges for effective data analysis. In this paper, we introduce GenealogyVis, a visual analytic system to analyze family history and evolution by using the China Multigenerational Panel Dataset-Liaoning, which has more than 1.5 million observations and provides socioeconomic, demographic, and other information for more than 260 000 residents, and further enable users to explore the correlation between the development of families and the social context of environments, economics, policies, and so on. This system includes five main linked views: the Scatter-plot View to provide an overview of the data and further explore the correlation analysis, the Tree View to show the family structure and details for individuals, the Migration View to present the genealogical migratory behaviors, the Matrix View to analyze the reproduction pattern between two generations, and the Stream View to show various statistical information such as demographic information and temporal information. A design study was conducted with a research group led by a domain expert of humanities and social sciences in an iterative manner over half a year. Several in-depth case studies, involving the research group, are described to demonstrate the usefulness of GenealogyVis and discuss new findings.

Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating from user requirements and impede personalized geospatial analysis in specific application scenarios. In this article, we propose a user-driven sampling model for the visual abstraction of the large-scale geographical point data based on convolutional neural networks (CNN). First, a blue noise sampling model is applied to partition the geographical space into local areas, and a set of visual interfaces are designed to present the data features of those points in the local areas, enabling users to visually select representative points according to their requirements. Then, user preferences are quantified with a CNN model based on the eigenvectors of the representative points, which are further utilized to guide the sampling courses of the other local areas. Thus, all the sampled points will retain the spatial distribution of original data points and fulfill the user preference as far as possible. In addition, we implement a visualization framework to integrate manual point selection, CNN training, automatic point sampling, and visual comparison, allowing users to easily obtain and evaluate the sampled points from the perspectives of data analysis and user requirements. Quantitative comparisons and case studies based on real-world datasets are conducted to demonstrate the effectiveness of our sampling model in the preservation of user preferences and visual exploration of large-scale geospatial point data.

Stratigraphic correlation based on well-logging data is able to help geological interpreters analogize and deduce underground sedimentary morphology. A great deal of traditional methods has been studied to automatically or manually achieve stratigraphic correlation, the courses of which are usually time-consuming and not intuitive. Many uncertainties are easily generated to reduce the effectiveness of stratigraphic correlation and the accuracy of geological interpretation. To address this issue, this paper introduces an interactive visual analytics system for identifying correlation patterns and improving correlation accuracies using large-scale well-logging data. First, we propose a novel stratigraphic correlation model with the composition of multi-log curve integration, layer identification, and layer matching. Then, a visualization framework is designed by working closely with domain experts in an iterative manner to get deeper insights into the course of stratigraphic correlation based on a few visual interfaces such as map view, correlation view, matrix view and attribute view. Also, a rich set of interactions is provided allowing interpreters to refine the results of stratigraphic correlation according to domain knowledge and user requirements. Furthermore, case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for the stratigraphic correlation and geological interpretation.