Crazyseismic: A MATLAB GUI‐Based Software Package for Passive Seismic Data Preprocessing

We introduce an open‐source MATLAB software package, named Crazyseismic, for passive seismic data preprocessing. Built‐in core functions such as seismic phase travel‐time calculation and multichannel cross correlation significantly improve the efficiency of data processing. Compared with conventional command‐line‐style toolboxes, all functions in Crazyseismic are embedded in one single graphic user interface (GUI). The human–machine interactive nature of GUI facilitates data quality control. The simplicity of the software allows users to process Seismic Analysis Code format seismic data with great ease and also provides a means by which users can tailor the software for their specific needs. We demonstrate the power of our software through two field examples: one for P‐wave arrival‐time picking and the other for receiver function calculation. The software can essentially be used for analyzing all major body‐wave phases in seismology

Crazyseismic: A MATLAB GUI‐Based Software Package for Passive Seismic Data Preprocessing

We introduce an open‐source MATLAB software package, named Crazyseismic, for passive seismic data preprocessing. Built‐in core functions such as seismic phase travel‐time calculation and multichannel cross correlation significantly improve the efficiency of data processing. Compared with conventional command‐line‐style toolboxes, all functions in Crazyseismic are embedded in one single graphic user interface (GUI). The human–machine interactive nature of GUI facilitates data quality control. The simplicity of the software allows users to process Seismic Analysis Code format seismic data with great ease and also provides a means by which users can tailor the software for their specific needs. We demonstrate the power of our software through two field examples: one for P‐wave arrival‐time picking and the other for receiver function calculation. The software can essentially be used for analyzing all major body‐wave phases in seismology

Crustal structure of the central Tibetan plateau and geological interpretation

Based on teleseismic data obtained from 225 stations from two networks in the central Tibetan plateau, we have generated detailed crustal structure images using P-wave receiver function techniques with more accurate piercing-depth-correction and time-depth-correction than what have previously been available. Our images indicate an undulatory Moho beneath the Tibetan plateau with a steep jump beneath the northern Himalaya, and obviously different structures in proximity to the Bangong-Nujiang suture. In several sections of the Tibetan plateau, the lower crust is characterized by pervasive high-velocity regions, which are consistent with the preservation of eclogite bodies beneath the plateau, whose presence affects the dynamics of the Tibetan plateau.China Earthquake Administration (Grant 201308013)National Natural Science Foundation (China) (Grants 40974034, 41174086, 41074052 and 41021003

Revisiting the Design Patterns of Composite Visualizations

Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. These well-crafted composite visualizations have formed a valuable collection for designers and researchers to address real-world problems and inspire new research topics and designs. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this paper, we opted to revisit the composite visualizations in VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from IEEE VIS publications and decomposed them into a series of basic visualization types (e.g., bar chart, map, and matrix). With this corpus, we studied the spatial (e.g., separated or overlaying) and semantic relationships (e.g., with same types or shared axis) between visualizations and proposed a taxonomy consisting of eight different design patterns (e.g., repeated, stacked, accompanied, and nested). Furthermore, we analyzed and discussed common practices of composite visualizations, such as the distribution of different patterns and correlations between visualization types. From the analysis and examples, we obtained insights into different design patterns on the utilities, advantages, and disadvantages. Finally, we developed an interactive system to help visualization developers and researchers conveniently explore collected examples and design patterns

NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial Data

Understanding how local environments influence individual behaviors, such as voting patterns or suicidal tendencies, is crucial in social science to reveal and reduce spatial disparities and promote social well-being. With the increasing availability of large-scale individual-level census data, new analytical opportunities arise for social scientists to explore human behaviors (e.g., political engagement) among social groups at a fine-grained level. However, traditional statistical methods mostly focus on global, aggregated spatial correlations, which are limited to understanding and comparing the impact of local environments (e.g., neighborhoods) on human behaviors among social groups. In this study, we introduce a new analytical framework for analyzing multi-variate neighborhood effects between social groups. We then propose NeighVi, an interactive visual analytics system that helps social scientists explore, understand, and verify the influence of neighborhood effects on human behaviors. Finally, we use a case study to illustrate the effectiveness and usability of our system.Comment: Symposium on Visualization in Data Science (VDS) at IEEE VIS 202