Spatio-temporal Visualization Research Articles

Enhancing the Understanding of the EU Gender Equality Index through Spatiotemporal Visualizations

The Gender Equality Index allows analyzing and measuring the progress of gender equality in the EU and, therefore, the relation between men and women in different domains, such as Health, Work or Money. Even though the European Institute for Gender Equality has created some visualizations that are useful to look at the data, this website does not manage to make graphs that allow for observing the spatiotemporal variable. This article enhances the understanding of the index with spatiotemporal visualizations, such as cartograms, heatmaps and choropleth maps, and some strategies focusing on analyzing the evolution of the countries over the years in an open-access environment. The results show how the application created may be used as an addition to the EIGE website.

Alkaline vents recreated in two dimensions to study pH gradients, precipitation morphology, and molecule accumulation.

Alkaline vents (AVs) are hypothesized to have been a setting for the emergence of life, by creating strong gradients across inorganic membranes within chimney structures. In the past, three-dimensional chimney structures were formed under laboratory conditions; however, no in situ visualization or testing of the gradients was possible. We develop a quasi-two-dimensional microfluidic model of AVs that allows spatiotemporal visualization of mineral precipitation in low-volume experiments. Upon injection of an alkaline fluid into an acidic, iron-rich solution, we observe a diverse set of precipitation morphologies, mainly controlled by flow rate and ion concentration. Using microscope imaging and pH-dependent dyes, we show that finger-like precipitates can facilitate formation and maintenance of microscale pH gradients and accumulation of dispersed particles in confined geometries. Our findings establish a model to investigate the potential of gradients across a semipermeable boundary for early compartmentalization, accumulation, and chemical reactions at the origins of life.

Visualizing the Spatio-Temporal Evolution of Gameplay using Storyline Visualization: A Study with League of Legends

Players increasingly adopt a data-driven approach to review and improve their gaming skills. In the wake of this, spatio-temporal visualizations gained popularity but remain challenging to design. Storyline visualizations are unique in the way they integrate time and location information into a single view to show how entity relationships develop over time. We adopt the storyline visualization technique to summarize gameplay for the purpose of post-play review. We demonstrate the method by applying it to League of Legends matches and evaluated it with 39 players of the game in a task-based online study using the triad framework for spatio-temporal queries by Peuquet. Results indicate that players responded positively to the approach and could, by and large, solve tasks well but that time-based tasks proved most challenging and least efficient to solve. Based on our findings, we reflect on possibilities for enhancing the design of storyline visualizations for game-related data analysis.

The New Quantum Image by Dynamic Nuclear Polarized MRI for the Assessment of Cardiac Radioablation to the Cavotricuspid Isthmus

Cardiac arrhythmias are usually treated with invasive, time consuming catheter ablation techniques. While recently stereotactic body radiotherapy (SBRT) is an emerging non-invasive treatment in the management of cardiac arrhythmias. To identify and assess the cardiac radioablation by MR examination, including diffusion-weighted MRI, dynamic Gd-enhanced MRI, MR spectroscopy, and T2-weighted MRI early after SBRT is very difficult. We have been developing the free radical imaging methods using Dynamic Nuclear Polarization (DNP)-MRI with nitroxyl radicals as a redox probe (e.g., 4-Methacryloyloxy-2,2,6,6-tetramethylpiperidine-1-oxyl (Tempo methacrylate; TempoMC)). In this study, we examined the possibility of in vivo spatiotemporal visualization of SBRT for cardiac radioablation to the cavotricuspid isthmus (CTI) based on redox reaction by in vivo DNP-MRI. All animal procedures were approved by institutional animal care and use committee and performed in full compliance with its guidelines. This study was conducted with two approaches. First, four mini pigs underwent electrophysiology assessment using electroanatomic mapping (EAM) before and 3 months after SBRT with single-fraction doses of 25 Gy. The target of CTI was defined by cardiologist. We defined the planning target volume (PTV): the internal margin (IM) + set up margin (SM) = SI 15 mm, AP 10mm, LR 10 mm were added to the target. Radiotherapy plans were created by the software used in daily clinical practice. Second, free radical imaging by low filed type of DNP-MRI was performed on the four mice before and after 25 Gy and 10 Gy irradiation to whole heart. ESR signal measurements were also performed. A total dose of 25 Gy was successfully delivered to PTV in a single procedure in all mini pigs. EAM visualized the irradiated site and confirmed clockwise conduction block across the CTI. Although routine MRI could not detect the cardiac radiation injury clearly, the four mice of heart were well delineated on MRI and clearly visualized by DNP-MRI. DNP-MRI signal of TempoMC were decreased depending on prescribed irradiation dose. These data demonstrated the safety and feasibility of SBRT for creating conduction block across the CTI in mini pigs. Although to identify and assess the irradiated site by routine MR examination was impossible, the free radical imaging methods using Dynamic Nuclear Polarization (DNP)-MRI with TempoMC could be a promising successful method for the assessment of cardiac radioablation. This new quantum image by DNP-MRI will open the possibility of treating cardiac arrhythmias by SBRT safety and noninvasively.

Open Access to Data about Silk Heritage: A Case Study in Digital Information Sustainability

This article builds on work conducted and lessons learned within SILKNOW, a research project that aimed at enhancing the preservation and digital dissemination of silk heritage. Taking the project and this heritage typology as a case study in the digital transformation of cultural heritage institutions, it illustrates specific challenges that these institutions must face and demonstrates a few innovative answers to meet those challenges. The methodology combines approaches typical of the humanities and others usual in ICT, being inductive regarding materials and methods (consisting of a detailed review of existing online repositories and research projects devoted to textile heritage) and descriptive for the results and discussion (which explain at length the development of some tools and resources that responded to the needs detected in the previous analysis). The article reports on the state of the art and recent developments in the field of textile heritage, the tools implemented to allow the semantic access and text analysis of descriptive records associated with silk fabrics, and the spatiotemporal visualization of that information. Finally, it argues that institutional policies, namely the creation and free dissemination of open data related to cultural heritage are just as important as technical developments, showing why any future effort in these areas should take data sustainability, both in its technical and in institutional aspects, into account, since it is the most responsible and reasonable approach in terms of efficient resource allocation.

Exploring year-to-year spatiotemporal changes in cycling patterns for bike-sharing system in the pre-, during and post-pandemic periods

The bike-sharing system plays an important role during the COVID-19 pandemic due to its benefit of reducing the risk of infection. Previous studies have employed bike trip datasets to analyze the impact of COVID-19 on spatiotemporal cycling mobility, which was limited to a short pandemic period. An understanding of the long-term impact of the pandemic on human travel patterns is urgently needed. This paper presents a framework for investigating the long-term spatiotemporal change in cycling patterns in the pre-, during and post-pandemic periods. A spatiotemporal clustering algorithm is combined with spatiotemporal visualization, network analysis, and important location identification to explore latent mobility patterns. The strength of the proposed framework is that it can model a unified representation of cycling networks across different years, and provide insightful spatiotemporal patterns that allow easy interpretation. The experiments are conducted on five years of data collected from the ‘Citibike’ system in New York City. The results demonstrate the efficiency of the proposed approach in exploring the evolutionary cycling patterns of how people respond to the COVID-19 pandemic and are beneficial for various stakeholders.

Human AGEs: an interactive spatio-temporal visualization and database of human archeogenomics.

Archeogenomics is a rapidly growing interdisciplinary research field driven by the development of techniques that enable the acquisition and analysis of ancient DNA (aDNA). Recent advances in aDNA studies have contributed significantly to increasing our understanding of the natural history of humans. One of the most significant challenges facing archeogenomics is the integration of highly heterogeneous genomic, archeological, and anthropological data and their comprehensive analysis, considering changes that occur in time and space. Only this complex approach can explain the relationship between past populations in the context of migration or cultural development. To address these challenges, we developed a Human AGEs web server. It focuses on creating comprehensive spatiotemporal visualizations of genomic, archeogenomic, and archeological information, which can be provided by the user or loaded from a graph database. The interactive map application at the center of Human AGEs can display multiple layers of data in various forms, such as bubble charts, pie charts, heatmaps, or tag clouds. These visualizations can be modified using various clustering, filtering, and styling options, and the map state can be exported to a high-resolution image or saved as a session file for later use. Human AGEs, along with their tutorial, are accessible at https://archeogenomics.eu/.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020

BackgroundCutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters.MethodsOn the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance.ResultsIn general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.ConclusionsOur study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.Graphical

A System Coupled GIS and CFD for Atmospheric Pollution Dispersion Simulation in Urban Blocks

Atmospheric pollution is a critical issue in public health systems. The simulation of atmospheric pollution dispersion in urban blocks, using CFD, faces several challenges, including the complexity and inefficiency of existing CFD software, time-consuming construction of CFD urban block geometry, and limited visualization and analysis capabilities of simulation outputs. To address these challenges, we have developed a prototype system that couples 3DGIS and CFD for simulating, visualizing, and analyzing atmospheric pollution dispersion. Specifically, a parallel algorithm for coordinate transformation was designed, and the relevant commands were encapsulated to automate the construction of geometry and meshing required for CFD simulations of urban blocks. Additionally, the Fluent-based command flow was parameterized and encapsulated, enabling the automatic generation of model calculation command flow files to simulate atmospheric pollution dispersion. Moreover, multi-angle spatial partitioning and spatiotemporal multidimensional visualization analysis were introduced to achieve an intuitive expression and analysis of CFD simulation results. The result shows that the constructed geometry is correct, and the mesh quality meets requirements with all values above 0.45. CPU and GPU parallel algorithms are 13.3× and 25× faster than serial. Furthermore, our case study demonstrates the developed system’s effectiveness in simulating, visualizing, and analyzing atmospheric pollution dispersion in urban blocks.

PO-01-072 INTER-PROCEDURAL REPRODUCIBILITY OF ELECTROGRAPHIC FLOW MAPPING BETWEEN PROCEDURES SEPARATED BY 3 MONTHS OR LONGER

Over the last decade, various algorithms for localizing extra-pulmonary vein (PV) AF drivers/sources (focal or rotational activity) have been studied. Validating these mapping systems is confounded by lack of ground truth, typically manifesting as poor map reproducibility. Short of optical mapping studies, it remains difficult to prove that identified drivers are true drivers, so other criteria are needed to buttress the specificity of identified sources. Electrographic flow (EGF) mapping uses a novel technique to create full, near real-time spatiotemporal visualizations of atrial electrical wavefront propagation to identify putative atrial fibrillation (AF) sources. To evaluate whether AF source activity identified by EGF mapping is reproducible over procedures separated by ≥ 3 months. As part of the FLOW-AF trial (NCT04473963), 18 pts underwent de novo balloon-based PVI and post-PVI EGF mapping with a 64-pole basket catheter. At ∼3 months, a remap procedure included PVI check + EGF mapping. Confirmation with ≥20 min wait to confirm isolation was required for post-PVI EGF mapping in both procedures. One pt was excluded as the post-PVI basket position was not stored with the EGF map such that anatomic location could not be verified, and 5 pts were excluded since AF was non-inducible at remapping. Of the 12 pts who were fully mapped and re-mapped (at 99.7 ± 20.9 days post-PVI), after de novo PVI, 18 sources with activity > 26% (the threshold to qualify a source as legitimate) were detected (10 RA, 8 LA, 7 both atria). The remap procedures revealed that 16 of 18 (89.0%) sources were at the same anatomic location as in the initial procedure; the remaining 2 sources were detected in the SVC after the initial procedure post-PVI and were again seen in the SVC on remap, but in a different aspect of the SVC. One patient did not have any sources detected post-PVI in the de novo procedure, but was found to have an RA source after the PVs were re-isolated during the remap procedure. EGF mapping produces reproducible results from one procedure to the next, such that if a clinically relevant source was detected post-PVI during the first procedure, but was not ablated, the source was again detectable at remapping performed at 3-mo after de novo PVI. This inter-procedural reproducibility may enable longitudinal follow-up of individual patients’ AF pathophysiology.

High-dimensional spatiotemporal visual analysis of the air quality in China

Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth’s long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interactive analysis method, and we studied and analyzed the relationship between the air quality and several influencing factors, including meteorology, population, and economics. Six visualization methods were integrated in this study, each specifically designed and improved for visualization analysis purposes. To reveal the spatiotemporal distribution and potential impact of the air quality, we designed a comprehensive coupled visual interactive analysis approach visually express both high-dimensional and spatiotemporal attributes, reveal the overall situation and explain the relationship between attributes. We clarified the current spatiotemporal distribution, development trends, and influencing factors of the air quality in China through interactive visual analysis of a 25-dimensional dataset involving 31 Chinese provinces. We also verified the correctness and effectiveness of relevant policies and demonstrated the advantages of our method.

A Spatio-Temporal Dynamic Visualization Method of Time-Varying Wind Fields Based on Particle System

The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the vector field visualization method based on the Euler fields, such as unclear feature expression and discontinuous temporal expression, so the method cannot effectively express the characteristics of wind field on the temporal scale. We propose a Lagrangian visualization method based on spatio-temporal interpolation to solve these problems, which realizes the fusion and expression of the particle system and the time-varying wind data based on the WebGL shader. Firstly, the linear interpolation algorithm is used to interpolate to obtain continuous and dense wind field data according to the wind field data at adjacent moments. Then, we introduce the Lagrangian analysis method to study the structure of the wind field and optimize the visualization effect of the particle system based on Runge–Kutta algorithms. Finally, we adopt the nonlinear color mapping method with double standard deviation (2SD) to improve the expression effect of wind field features. The experimental results indicate that the wind visualization achieves a comprehensive visual effect and the rendering frame rate is greater than 45. The methods can render the particles smoothly with stable and outstanding uniformity when expressing continuous spatio-temporal dynamic visualization characteristics of the wind field.

Designing of nanosensor and its applications for spatiotemporal visualization and real time quantification of biotin in biological samples

Biotin is a 244 Da water-soluble vitamin involved in vital metabolic processes by acting as an essential cofactor for several enzymes. When biotin levels are low, critical enzymes such as pyruvate carboxylase, 3-methylcrotonyl-CoA, and propionyl-CoA carboxylase become dysfunctional leading to fatal human disease. Traditional methods for measuring biotin in biological fluids yield valuable results for particular applications, but the majority of them are intrusive, expensive, and lack sensitivity and specificity. A highly effective genetically encoded FRET-based nanosensor OSenB (Optical Sensor for Biotin) is developed using FRET pair enhanced cyan fluorescent protein (ECFP) and Venus attached to the N- and C-termini of the avidin module, allowing real-time monitoring and quantification of biotin at a trace level. OSenB exhibits discernible FRET ratio shifts, measures biotin with excellent specificity and selectivity, provides a rapid detection response, is resilient to pH fluctuations, and produces a very precise FRET signal with a detection range of 12 pM to 165 nM biotin quantification. Developed mutant sensor OSenB-44 with 0.717 nM apparent affinity (Kd), was found to be the most successful nanosensor. Confocal images show that OSenB-44 was successfully expressed in vivo and dynamically measured biotin in Escherichia coli, yeast, and human embryonic kidney (HEK)− 293T in real-time.

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

AbstractOpen education materials are critical for the advancement of open science and the development of open‐source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in‐line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter, that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python‐based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi‐temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open‐source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate‐level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth.

Spatio-temporal visualization and forecasting of {text {PM}}_{10} in the Brazilian state of Minas Gerais

Air pollution due to air contamination by gases, liquids, and solid particles in suspension, is a great environmental and public health concern nowadays. An important type of air pollution is particulate matter with a diameter of 10 microns or less ({text {PM}}_{10}) because one of the determining factors that affect human health is the size of particles in the atmosphere due to the degree of permanence and penetration they have in the respiratory system. Therefore, it is extremely interesting to monitor and understand the behavior of {text {PM}}_{10} concentrations so that they do not exceed the established critical levels. In this work, we will study the {text {PM}}_{10} concentrations in all available monitoring stations in the Brazilian state of Minas Gerais. To better understand its behavior, we will provide a spatio-temporal visualization of the {text {PM}}_{10} concentrations. Besides the descriptive and visualization analysis, we consider six standard and advanced time series models that will be used to fit and forecast {text {PM}}_{10} concentrations, with application to three locations, one in Belo Horizonte, the Minas Gerais state capital, and the monitoring stations with the lowest and highest average {text {PM}}_{10} concentration levels.

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology.

Ad-Hoc Monitoring of COVID-19 Global Research Trends for Well-Informed Policy Making

The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPMs) and medical experts are at the core of responding to this continuously evolving pandemic situation and are working hard to contain the spread and severity of this relatively unknown virus. Biomedical researchers are continually discovering new information about this virus and communicating the findings through scientific articles. As such, it is crucial for HPMs and funding agencies to monitor the COVID-19 research trend globally on a regular basis. However, given the influx of biomedical research articles, monitoring COVID-19 research trends has become more challenging than ever, especially when HPMs want on-demand guided search techniques with a set of topics of interest in mind. Unfortunately, existing topic trend modeling techniques are unable to serve this purpose as (1) traditional topic models are unsupervised, and (2) HPMs in different regions may have different topics of interest that they want to track. To address this problem, we introduce a novel computational task in this article called Ad-Hoc Topic Tracking , which is essentially a combination of zero-shot topic categorization and the spatio-temporal analysis task. We then propose multiple zero-shot classification methods to solve this task by building on state-of-the-art language understanding techniques. Next, we picked the best-performing method based on its accuracy on a separate validation dataset and then applied it to a corpus of recent biomedical research articles to track COVID-19 research endeavors across the globe using a spatio-temporal analysis. A demo website has also been developed for HPMs to create custom spatio-temporal visualizations of COVID-19 research trends. The research outcomes demonstrate that the proposed zero-shot classification methods can potentially facilitate further research on this important subject matter. At the same time, the spatio-temporal visualization tool will greatly assist HPMs and funding agencies in making well-informed policy decisions for advancing scientific research efforts.

Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis.

The application of spatiotemporal functional analysis techniques in environmental pollution research remains limited. As a result, this paper suggests spatiotemporal functional data clustering and visualization tools for identifying temporal dynamic patterns and spatial dependence of multiple air pollutants. The study uses concentrations of four major pollutants, named particulate matter (PM2.5), ground-level ozone (O3), carbon monoxide (CO), and sulfur oxides (SO2), measured over 37 cities in Yemen from 1980 to 2022. The proposed tools include Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing, as well as static and dynamic visualization methods. Innovatively, a functional mixture model was used to capture/identify the underlying/hidden dynamic patterns of spatiotemporal air pollutants concentration. According to the results, CO levels increased 25% from 1990 to 1996, peaking in the cities of Taiz, Sana'a, and Ibb before decreasing. Also, PM2.5 pollution reached a peak in 2018, increasing 30% with severe concentrations in Hodeidah, Marib, and Mocha. Moreover, O3 pollution fluctuated with peaks in 2014-2015, 2% increase and pollution rate of 265 Dobson. Besides, SO2 pollution rose from 1997 to 2010, reaching a peak before stabilizing. Thus, these findings provide insights into the structure of the spatiotemporal air pollutants cycle and can assist policymakers in identifying sources and suggesting measures to reduce them. As a result, the study's findings are promising and may guide future research on predicting multivariate air pollution statistics over the analyzed area.

The potential of combining distant and close reading in the history of ideas

In this article we discuss the benefits of applying a mixed methods approach to the history of ideas. The article is based upon two cases of different character, applying methods from the field of digital textual analysis, and from the field of digital spatio-temporal map visualization.&#x0D; One case concerns all churches and prayer chapels in the Diocese of Luleå, and comprises 1200 entry points. The project originates in the question of whether a Bible belt exists in the north of Sweden; what it (if there is one) looks like, what its characteristics are, and how it has evolved over time. The spatio-temporal visualization both clarifies general patterns of religious fervor, but also reveals “white spots” and areas of conflicting interests, creating questions for further research.&#x0D; The second case stems from a larger project investigating contemporary online use of the concept “Creativity”. Here, distributional concept analysis is performed using Sketch Engine on the corpus English Web 2020 (enTenTen20), consisting of 38 billion English words gathered in 2020 from Wikipedia, blogs, online magazines and journals. Lists are assembled over which words are most often used together (co-locates) with creativity. Then, the research process is outlined, where use of such distant reading tools encourages returns to close readings of other materials (for instance speeches by American presidents) in an iterative process.&#x0D; The two cases illustrate the possibilities inherent in the process of moving between overarching, “distant”, levels, where digital methods can show large patterns, and more specific and detailed, “close” levels, with focus on particular places or points of interest. The mixed method approach we propose is not new, but we argue that by using digital (instead of analogous) methods this can be done at a much larger scale, with less effort. Furthermore, we will show how this oscillation between on the one hand distant, large-scale, computer aided methods, and on the other close readings and interpretations, generate new, interesting historical questions in an iterative process.&#x0D; Finally, we discuss our experiences of multidisciplinary approaches to digital history, describe setbacks and unexpected wins, and argue that the benefits and potential of this approach outweigh the risks.&#x0D;

The Exploration of Visualization Technology in Sichuan Opera

Visualization techniques can visually present and analyze data. This paper explores the visual field in the culture of Sichuan opera. Firstly, it summarizes the data of Sichuan opera and analyzes the characteristics of the data. Then, it introduces five kinds of visualization techniques: text visualization, hierarchical data visualization, network data visualization, spatio-temporal data visualization and multidimensional data visualization, and analyzes their application in the cultural visualization design of Sichuan Opera. Finally, the paper summarizes and puts forward the prospect.