Data Visualization Methods Research Articles

MEDICINE USAGE ANALYSIS OF THE CITY

Drug use is a complicated and widespread problem that has an impact on people and communities all around the world. It is essential to examine drug use patterns, spot high- risk locations, and comprehend the implications for social welfare and public health in order to successfully manage this societal dilemma. This study intends to provide a thorough examination of drug usage data in a particular city, using a variety of analytical tools to produce significant findings. The study makes use of a broad dataset that includes social indicators, criminal justice records, demographic data, and occurrences involving drugs. To guarantee data quality and consistency, the dataset is put through rigorous data preprocessing, cleansing, and integration processes. To locate a target, descriptive statistics, data visualization, and sophisticated analytics methods like clustering and predictive modeling are used. Keyword:Socialindicators,consistency,visualization,descriptiv e statistics, societal dilemma.

A Gaussian Process Decoder with Spectral Mixtures and a Locally Estimated Manifold for Data Visualization

Dimensionality reduction plays an important role in interpreting and visualizing high-dimensional data. Previous methods for data visualization overestimate the local structure and lack the consideration of global preservation. In this study, we develop a Gaussian process latent variable model (GP-LVM) for data visualization. GP-LVMs are one of the frameworks of principal component analysis and preserve the global structure effectively. The drawbacks of GP-LVMs are the absence of local structure preservation and the use of low-expressive kernel functions. Therefore, we introduce regularization for local preservation and an expressive kernel function into GP-LVMs to overcome these limitations. As a result, we reflect the global and local structures in low-dimensional representations, improving the reliability and visibility of embeddings. We conduct qualitative and quantitative experiments comparing baselines and state-of-the-art methods on image and text datasets.

Innovations in the agro-industrial complex of the Aktobe region of the Republic of Kazakhstan: an effective mechanism for solving the food issue

Purpose – the article discusses the current aspects of the development of agroindustrial complex on the example of the Aktobe region, presents recommendations that contribute to the solution of these issues. Research methods – abstract-logical, statistical, observation, scientific abstraction. Factor analysis, grouping method, tabular and graphical methods of data visualization were used as statistical tools. Results – studying the foreign practice of modernizing agricultural sector in order to ensure food security, the authors note that the main criteria should include the system of state and local self-government, legislative framework, fiscal and monetary regulation, mechanism of subsidizing and lending, investment, innovation policy. The main indi cators of agricultural production in the region for 2017-2021 are analyzed. It is proved that at present it is necessary to restore this strategically important sector of economy on a qualitatively new technical and technological basis that meets modern trends. Agrarian science of Kazakhstan has developed a fairly large number of innovations, the implementation of which in the agroindustrial complex would raise it to a high level. However, the degree of use of innovations by commodity producers remains very low. One of the problems of the low activity of innovative processes is the lack of motivation in the development of innovations by agricultural producers. Conclusions – innovative development involves the transition to resource-saving technologies that contribute not only to increasing production volumes, but also saving resources, as well as achieving the required level of food security. This complex task requires the creation of appropriate conditions: appropriate infrastructure for innovation activity, a set of material, technical, legislative means for information, marketing, financial, and personnel services for business entities.

Semi-supervised t-SNE with multi-scale neighborhood preservation

Unsupervised dimensionality reduction (DR) aims to preserve input data structure in a low-dimensional (LD) space based on neighborhood information. In contrast, supervised DR intends to improve the learning performance, i.e., classification and regression, in an LD representation. Unfortunately, obtaining the complete label outputs of a data set for real-world applications is hard. Here, we introduce a novel DR framework coupling both available class labels and input feature similarities to extend the well-known t-distributed Stochastic Neighbor Embedding (SNE) for semi-supervised scenarios. Our proposal, termed Semi-Supervised t-SNE (SS.t-SNE), properly fixes the widths of Gaussian neighborhoods to reveal the salient local and global data structures in an LD space. Indeed, our approach is presented as a generalization of unsupervised and supervised versions of t-SNE. SS.t-SNE outperforms other semi-supervised DR methods in data visualization and classification tasks in LD embeddings.

NOMspectra: An Open-Source Python Package for Processing High Resolution Mass Spectrometry Data on Natural Organic Matter.

The present study introduces NOMspectra, a Python package for processing high resolution mass spectrometry data on complex systems of natural organic matter (NOM). NOM is characterized by multicomponent composition reflected as thousands of signals producing very complex patterns in high resolution mass spectra. This complexity sets special demands on the methods of data processing used for analysis. The developed NOMspectra package offers a comprehensive workflow for processing, analyzing, and visualizing information-rich mass spectra of NOM and HS including algorithms for filtering spectra, recalibrating, and assigning elemental compositions to molecular ions. Additionally, the package includes functions for calculating various molecular descriptors and methods for data visualization. A graphical user interface (GUI) has been developed to make a user-friendly interface for the proposed package.

Three-Dimensional Visualization of Long-Range Atmospheric Transport of Crop Pathogens and Insect Pests

Some of the most devastating crop diseases and insect pests can be transmitted by wind over extremely long distances. These low-probability but high-impact events can have severe consequences for crop production and food security by causing epidemic outbreaks or devastating insect infestations in previously uninfected geographic areas. Two prominent examples that have recently caused substantial damage to agricultural production are novel strains of wheat rusts and desert locust swarm infestations. Whilst quantitative estimates of long-range atmospheric transport events can be obtained using meteorological transport simulations, the exact characteristics of three-dimensional spatiotemporal dynamics of crop pathogen transport and insect flight on extremely large spatial scales, over entire regions and continents, remain largely unknown. Here, we investigate the feasibility and usefulness of new advanced geospatial data visualization methods for studying extremely long-distance airborne transmission of crop pathogens and insect pests. We combine field surveillance data and a Lagrangian Particle Dispersion Model with novel techniques from computer graphics to obtain, for the first time, detailed three-dimensional visual insights into airborne crop pathogen and insect pest transport on regional and continental scales. Visual insights into long-distance dispersal of pests and pathogens are presented as a series of short 3D movies. We use interactive three-dimensional visual data analysis for explorative examination of long-range atmospheric transport events from a selection of outbreak and infestation sites in East Africa and South East Asia. The practical usefulness of advanced 3D visualization methods for improving risk estimates and early warning is discussed in the context of two operational crop disease and insect pest management systems (for wheat rusts and desert locusts). The tools and methods introduced here can be applied to other pathogens, pests, and geographical areas and can improve understanding of risks posed to agricultural production by crop disease and insect pest transmission caused by meteorological extreme events.

Genetic data visualization using literature text-based neural networks: Examples associated with myocardial infarction

Data visualization is critical to unraveling hidden information from complex and high-dimensional data. Interpretable visualization methods are critical, especially in the biology and medical fields, however, there are limited effective visualization methods for large genetic data. Current visualization methods are limited to lower-dimensional data and their performance suffers if there is missing data. In this study, we propose a literature-based visualization method to reduce high-dimensional data without compromising the dynamics of the single nucleotide polymorphisms (SNP) and textual interpretability. Our method is innovative because it is shown to (1) preserves both global and local structures of SNP while reducing the dimension of the data using literature text representations, and (2) enables interpretable visualizations using textual information. For performance evaluations, we examined the proposed approach to classify various classification categories including race, myocardial infarction event age groups, and sex using several machine learning models on the literature-derived SNP data. We used visualization approaches to examine clustering of data as well as quantitative performance metrics for the classification of the risk factors examined above. Our method outperformed all popular dimensionality reduction and visualization methods for both classification and visualization, and it is robust against missing and higher-dimensional data. Moreover, we found it feasible to incorporate both genetic and other risk information obtained from literature with our method.

Employment Big Data Analysis of Higher vocational colleges based on python -- A case study of civil and architectural majors

In recent years, with the increase of graduates, the impact of the novel coronavirus pandemic led to economic slowdown and other factors, the problem of difficult employment has attracted wide attention from all walks of life. This paper takes the employment data of civil engineering graduates of a vocational college as an example. Through data collection, data cleaning, data analysis, data visualization and other methods, it shows the employment rate of graduates, employment industry, matching degree between employment units and majors, salary level, geographical distribution and other information, so as to provide reference for students' career planning and employment choice. It also provides job market data support for universities and government departments. Finally, countermeasures and suggestions are put forward to help students better plan their career development.

Urban vernacular landscapes: toward a visual pedagogy of the ordinary

ABSTRACT Although many social scientists employ visual media in their research and teaching, the connection between the ‘visual’ and the ‘social science’ is often tenuous. Photographs often are reserved for illustrating textual concepts, or making standard findings more interesting to readers than the normal textual exposition in tables, pie charts, graphs, or other methods of data visualization. In my research about urban lives and cultures, I have extensively photographed what John Brinckerhoff Jackson called ‘vernacular landscapes.’ Although photojournalists, as well as documentary and street photographers, have also engaged in similar image capturing practices, as opposed to their more aesthetic and artistic accomplishments, I have tried to firmly connect my images to normal social science theories and methods. This visually enhanced essay will describe, discuss, and give examples from my own photographic studies of urban vernacular landscapes over several decades and across the globe. It is hoped that in this autobiographical process, the difference between artistic and scientific photography as to theories of class, globalization, race, and other social issues will be made clearer.

A geospatial approach to identify patterns of antibiotic susceptibility at a neighborhood level in Wisconsin, United States

The global threat of antimicrobial resistance (AMR) varies regionally. This study explores whether geospatial analysis and data visualization methods detect both clinically and statistically significant variations in antibiotic susceptibility rates at a neighborhood level. This observational multicenter geospatial study collected 10 years of patient-level antibiotic susceptibility data and patient addresses from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). We included the initial Escherichia coli isolate per patient per year per sample source with a patient address in Wisconsin (N = 100,176). Isolates from U.S. Census Block Groups with less than 30 isolates were excluded (n = 13,709), resulting in 86,467 E. coli isolates. The primary study outcomes were the results of Moran’s I spatial autocorrelation analyses to quantify antibiotic susceptibility as spatially dispersed, randomly distributed, or clustered by a range of − 1 to + 1, and the detection of statistically significant local hot (high susceptibility) and cold spots (low susceptibility) for variations in antibiotic susceptibility by U.S. Census Block Group. UW Health isolates collected represented greater isolate geographic density (n = 36,279 E. coli, 389 = blocks, 2009–2018), compared to Fort HealthCare (n = 5110 isolates, 48 = blocks, 2012–2018) and MCHS (45,078 isolates, 480 blocks, 2009–2018). Choropleth maps enabled a spatial AMR data visualization. A positive spatially-clustered pattern was identified from the UW Health data for ciprofloxacin (Moran’s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole susceptibility (Moran’s I = 0.180, p < 0.001). Fort HealthCare and MCHS distributions were likely random. At the local level, we identified hot and cold spots at all three health systems (90%, 95%, and 99% CIs). AMR spatial clustering was observed in urban areas but not rural areas. Unique identification of AMR hot spots at the Block Group level provides a foundation for future analyses and hypotheses. Clinically meaningful differences in AMR could inform clinical decision support tools and warrants further investigation for informing therapy options.

The immune system contributes to the effectiveness of vaccine therapy in patients with metastatic melanoma

The aim of the study was to identify differences in the immune system parameters between metastatic melanoma patients who responded and did not respond to dendritic cell vaccination.Material and Methods. The study group included 20 patients with stage III–IV metastatic melanoma, who received vaccine therapy with dendritic cells (DC) in a prophylactic mode. The control groups included 13 patients who had symptoms of disease progression at the time of starting vaccine therapy, and 5 healthy donors. The DC-vaccine was prepared in the form of a suspension of the patient’s autologous dendritic cells loaded with tumor antigens in vitro. A single dose had 2 million dendritic cells in 1 ml of phosphate buffer solution, which was administered intradermally in the nearest site to the regional lymphatic collectors. The immune system status was assessed before starting vaccination. The immune system status was evaluated according to the indices of 25 peripheral blood cell populations using multicolor flow cytometry and integral characteristic in the form of the visual image generated by the visualization method of multidimensional data (NovoSpark, Canada).Results. The immune status in patients with metastatic melanoma at the start of DC-vaccination differed and was associated with the effectiveness of subsequent vaccine therapy. The response to vaccination was observed in patients whose immune system status was similar to that of healthy individuals. Low efficacy of DC-vaccine therapy was shown in patients whose immune system status corresponded to that of patients with disease progression. Alterations of the immune system in patients with metastatic melanoma were registered both at the level of individual immunological parameters and at the level of visualized integral characteristics. The integral characteristics of the immune system associated with the patient’s immunocompromised status can be considered as a criterion for stratification of patients with metastatic melanoma for the effective DC-vaccine therapy.Conclusion. The effectiveness of vaccine therapy with dendritic cells in patients with metastatic melanoma is associated with the immune system state before starting this therapy.

A picture is worth a thousand words: advancing the use of visualization tools in implementation science through process mapping and matrix heat mapping

BackgroundIdentifying key determinants is crucial for improving program implementation and achieving long-term sustainment within healthcare organizations. Organizational-level complexity and heterogeneity across multiple stakeholders can complicate our understanding of program implementation. We describe two data visualization methods used to operationalize implementation success and to consolidate and select implementation factors for further analysis.MethodsWe used a combination of process mapping and matrix heat mapping to systematically synthesize and visualize qualitative data from 66 stakeholder interviews across nine healthcare organizations, to characterize universal tumor screening programs of all newly diagnosed colorectal and endometrial cancers and understand the influence of contextual factors on implementation. We constructed visual representations of protocols to compare processes and score process optimization components. We also used color-coded matrices to systematically code, summarize, and consolidate contextual data using factors from the Consolidated Framework for Implementation Research (CFIR). Combined scores were visualized in a final data matrix heat map.ResultsNineteen process maps were created to visually represent each protocol. Process maps identified the following gaps and inefficiencies: inconsistent execution of the protocol, no routine reflex testing, inconsistent referrals after a positive screen, no evidence of data tracking, and a lack of quality assurance measures. These barriers in patient care helped us define five process optimization components and used these to quantify program optimization on a scale from 0 (no program) to 5 (optimized), representing the degree to which a program is implemented and optimally maintained. Combined scores within the final data matrix heat map revealed patterns of contextual factors across optimized programs, non-optimized programs, and organizations with no program.ConclusionsProcess mapping provided an efficient method to visually compare processes including patient flow, provider interactions, and process gaps and inefficiencies across sites, thereby measuring implementation success via optimization scores. Matrix heat mapping proved useful for data visualization and consolidation, resulting in a summary matrix for cross-site comparisons and selection of relevant CFIR factors. Combining these tools enabled a systematic and transparent approach to understanding complex organizational heterogeneity prior to formal coincidence analysis, introducing a stepwise approach to data consolidation and factor selection.

Abstract 5372: Supervised manifold learning and classification; application to expression data visualization and cancer prediction

Abstract Background: Manifold Learning (ML) has become essential in recent years for expression data visualization and dimension reduction. ML operates under the assumption that the data points lie on a manifold embedded in Euclidean space. The goal of ML is to learn an inverse embedding function to decipher the manifold structure. Cancer classification is a widely investigated application of expression profile data (e.g., miRNA) currently in the literature. The non-linear ML techniques commonly applied to expression data, such as t-distributed Stochastic Neighborhood Embedding (TSNE), Isomap, and Locally Linear Embedding (LLE), are intended for visualization, and cannot easily be applied to classification or unseen data, more generally. Such methods are also unsupervised, and do not factor in class information. Conventional linear ML methods, such as Principal Component Analysis (PCA), while applicable to unseen data and easy to use, are limited and can learn only linear manifolds. Results: We introduce a novel, non-linear ML technique, which incorporates class memberships, and is readily applicable to unseen data and classification. Specifically, we construct a series of neighborhood graphs which describe the manifold structure locally within each cluster and among the cluster centroids. Our objective function uses the neighborhood graph information to preserve the class separations and manifold structure in reduced dimension space, and we train a neural network to learn an explicit, inverse embedding function. This allows for fast visualization and classification of unseen data. The technique is compared against similar methods from the literature which use neural networks for ML, and is shown to offer improved cancer classification performance on expression data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA). Conclusion: We present a new, supervised ML technique designed specifically for classification, which can be efficiently applied to unseen data. The results show promise on multiple, large-scale expression data sets, and thus warrant further research into supervised ML methods for cancer classification and expression data visualization. Citation Format: James Webber, Kevin Elias. Supervised manifold learning and classification; application to expression data visualization and cancer prediction. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5372.

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.

Economic effects of changes in VAT rates in Russia

The subject of the study is the effects of reforming the value added tax. In particular, the authors analyze the economic consequences of an increase in the VAT rate from 18 to 20% in 2020. The following effects are assessed: inflationary, consumer, investment and budgetary. The results are compared with the conclusions obtained earlier by domestic and foreign scientists on the example of developed and developing countries of the world. As research methods, analysis and synthesis, tabular and graphical methods of data visualization, analysis of time series, comparison method, structural and trend analyzes are used. The study contains a number of methodological limitations, which are also consecrated in the work. A special contribution of the authors to the study of the topic is the expansion of the zones of influence of indirect taxation on business and the population. In particular, the authors conclude that despite the presence of a number of negative effects of the increase in the VAT rate for business, nevertheless, such negative effects are most pronounced in relation to the population. They consist in accelerating inflation and reducing consumption in the short term. At the same time, the hypothesis of a significant impact of the VAT rate on investment processes is refuted, which is substantiated by an analysis of the indicators of the investment activity of economic entities in Russia.

A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff.

Artificial intelligence applied in pulmonary hypertension: a bibliometric analysis.

Advances in Artificial Intelligence (AI) offer new Information Technology (IT) opportunities in various applications and fields (industry, health, etc.). The medical informatics scientific community expends tremendous effort on the management of diseases affecting vital organs making it a complex disease (lungs, heart, brain, kidneys, pancreas, and liver). Scientific research becomes more complex when several organs are simultaneously affected, as is the case with Pulmonary Hypertension (PH), which affects both the lungs and the heart. Therefore, early detection and diagnosis of PH are essential to monitor the disease's progression and prevent associated mortality. The issue addressed relates to knowledge of recent developments in AI approaches applied to PH. The aim is to provide a systematic review through a quantitative analysis of the scientific production concerning PH and the analysis of the networks of this production. This bibliometric approach is based on various statistical, data mining, and data visualization methods to assess research performance using scientific publications and various indicators (e.g., direct indicators of scientific production and scientific impact). The main sources used to obtain citation data are the Web of Science Core Collection and Google Scholar. The results indicate a diversity of journals (e.g., IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, Sensors) at the top of publications. The most relevant affiliations are universities from United States of America (Boston Univ, Harvard Med Sch, Univ Oxford, Stanford Univ) and United Kingdom (Imperial Coll London). The most cited keywords are "Classification", "Diagnosis", "Disease", "Prediction", and "Risk". This bibliometric study is a crucial part of the review of the scientific literature on PH. It can be viewed as a guideline or tool that helps researchers and practitioners to understand the main scientific issues and challenges of AI modeling applied to PH. On the one hand, it makes it possible to increase the visibility of the progress made or the limits observed. Consequently, it promotes their wide dissemination. Furthermore, it offers valuable assistance in understanding the evolution of scientific AI activities applied to managing the diagnosis, treatment, and prognosis of PH. Finally, ethical considerations are described in each activity of data collection, treatment, and exploitation to preserve patients' legitimate rights.

Data visualisation analysis of online novel websites

With the development of science and technology and the progress of the times, the online novel reading industry is also developing at a rapid pace, and online novel reading websites have also emerged. In order to make users better understand the current trend of popular novel genre popularity and give corresponding reference when users choose the novels to read. This study analyses novel creation data, novel creation genres, novel creation era, author writings and other data visualisation methods through bar charts, pie charts, radar charts, heat maps, word cloud charts and other data visualisation methods. Using web scraper, data on novel information is collected to ensure that the data is authentic and reliable. On the basis of cleaning the data using Python, the Echarts plugin is used to visualise the basic information of novels as well as the author's information to present the information of novels and readers in a more visual and vivid way. The study helps to obtain richer information about the novels and authors that one wishes to know, while making the readers' experience better and helping the website to operate and develop better.

The popularisation of self-care: Tracing the dissemination of Orem's Self-Care Deficiency Nursing Theory into the scientific disciplines.

Bibliometric methods may be used to examine research trends, and information visualisation techniques are useful in illustrating the diffusion of knowledge and how theories are applied. The present study aimed to illustrate how Orem's Self-Care Deficiency Nursing Theory (SCDNT) has been cited and applied in nursing science and beyond. A bibliometric analysis examined scientific publications that cited Orem's nursing theory. In addition, the diffusion of SCDNT was assessed using data visualisation methods and integration scores of SCDNT versions were calculated to define trends in its theoretical usage in other scientific domains. The information visualisation demonstrated increased usage of SCDTN in different disciplines. Integration scores demonstrated that the scientific community still recognises and uses versions of SCDTN. Studying citation patterns helps to identify which publications are still cited and relevant, as well as illustrating the dissemination of theory. Findings may be used in the collection weeding of SCDTN book versions. Findings provided nursing scientists with a better conceptual understanding of SCDNT diffusion and development. For academic library managers, the findings identify which SCDNT should be retained for historical interest and curriculum needs.

Research Progress of Tumor Big Data Visualization

Background: As the number of tumor cases significantly increases, so does the quantity of tumor data. The mining and application of large-scale data have promoted the development of tumor big data. Among them, the visualization methods of tumor big data can well show the key information in a large volume of data and facilitate the human brain to receive information. Therefore, tumor big data visualization methods are a key part of the development of tumor big data. Process: This paper first summarizes the connotation, sources, characteristics, and applications of tumor big data, and expounds the current research status of tumor big data visualization at home and abroad. Then, this paper focuses on four mainstream visualization presentation methods of tumor big data, namely the visualization of tumor spatiotemporal data, the visualization of tumor hierarchy and network data, the visualization of tumor text data, and the visualization of multidimensional tumor data, and gives specific application scenarios. After this, the paper introduces the advantages, disadvantages, and scope of the use of five data visualization websites and software that can be easily obtained by readers. Finally, this paper analyzes the problems existing in tumor big data visualization, summarizes the visualization methods, and proposes the future of tumor big data visualization.