Multidimensional Visualization Research Articles

MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization.

With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

ELViM: Exploring Biomolecular Energy Landscapes through Multidimensional Visualization.

Molecular dynamics (MD) simulations provide a powerful means of exploring the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast data sets generated by MD simulations poses significant challenges. This article discusses the energy landscape visualization method (ELViM), a multidimensional reduction technique inspired by the energy landscape theory. ELViM transcends one-dimensional representations, offering a comprehensive analysis of the effective conformational phase space without the need for predefined reaction coordinates. We apply the ELViM to study the folding landscape of the antimicrobial peptide Polybia-MP1, showcasing its versatility in capturing complex biomolecular dynamics. Using dissimilarity matrices and a force-scheme approach, the ELViM provides intuitive visualizations, revealing structural correlations and local conformational signatures. The method is demonstrated to be adaptable, robust, and applicable to various biomolecular systems.

Multidimensional visualization analysis based on large-scale GNSS data

Abstract With the deployment of global navigation systems such as GPS, GLONASS, GALILEO, and BeiDou, and the construction of the global observation network, global navigation satellite system (GNSS) data have greatly expanded. Traditional relational databases and file systems can no longer efficiently store and process GNSS data. In addition, multidimensional, in-depth visual analysis methods for such data are lacking. In this paper, we provide a solution and implement a GNSS data visualization system based on MapV and cesium running on the Hadoop platform, which can effectively solve the problems of the lack of storage and computing resources for these massive multisource heterogeneous data. The proposed system provides multidimensional presentations to show how the space and ground segments of the entire system are organized by analyzing GNSS data.

Multivariate Data Explanation by Jumping Emerging Patterns Visualization.

Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.

Unraveling educational networks: Data-driven exploration through multivariate regression, geographical clustering, and multidimensional scaling

Enhancing rates of school participation holds significant importance for a nation’s educational achievements. This research employs a comprehensive approach that combines various methodologies, including multivariate regression analysis, geographic categorization, and multidimentional visualization, to examine the factors influencing school enrollment in Indonesia. Through the integration of diverse data sources, we investigate the connections among variables such as economic status, school accessibility, educational quality, and societal considerations concerning enrollment rates. This discrete impact of each factor on enrollment variations is analyzed through multivariate regression. Geospatial clustering analysis reveals enrollment trends in different regions, while multidimensional visualization untangles the intricate interplay of influencing factors. This holistic approach facilitates a nuanced comprehension of these dynamics within Indonesia’s varied geographical and society offering guidance in the formulation of more efficient strategies to improve school attendance, tackle enrollment disparities, and advocate for inclusive education based on fundamental determinants.

Multidimensional visualization and analysis of chronic pain variables of patients who attended a chronic pain program.

Psychologically-based chronic pain variables measure multiple domains of the pain experience such as anxiety, depression, catastrophizing, acceptance and stages of change. These variables measure specific areas such as emotional and cognitive states towards chronic pain and its management, acceptance towards the chronic pain condition, and an individual's readiness to move towards self-management methods. Conceptually, these variables appear to be interrelated to each other, and also form groupings of similar underlying themes. Groupings that have been previously discussed for these variables include positive and negative affect, and improved and poor adjustment. Psychological experience of chronic pain as a whole is mostly understood through conceptually consolidating individual scores across different measures covering multiple domains. A map of these variables in relation to each other can offer an overview for further understanding and exploration. We hereby visualize highlights of relationships among 11 psychosocial chronic pain variables including measures examining physical and somatic aspects, using three-dimensional biplots. Variables roughly form two groupings, with one grouping consisting of items of negative affect, cognition, and physical state ratings, and the other grouping consisting of items of acceptance and the later three stages of change (contemplation, action, maintenance). Also, we follow up with canonical correlation as a complement to further identify key relationships between bimodal groupings. Key variables linking bimodal relationships consist of catastrophizing, depression and anxiety in one grouping and activity engagement in the other. Results are discussed in the context of existing literature.

Visual design of green information in urban environment based on global similarity calculation and multi-dimensional visualization technology.

In recent years, the escalating prevalence of elevated consumption and carbon emissions within urban operations has reached a disconcerting extent. This surge in resource depletion and environmental pollution exerts an adverse influence on the well-being of individuals, while impeding societal progress and hindering the enhancement of overall quality of life. Within the domain of urban environmental design, the integration of visual displays emerges as a superior approach to facilitate the assimilation and analysis of green and low-carbon information. However, urban environmental data usually contains multiple dimensions, so it is a problem to realize the data representation of multiple dimensions while maintaining the correlation and interactivity between data. To surmount the challenge of visualizing such intricate information, this investigation initially employs a sophisticated memory-based clustering algorithm for information extraction, accompanied by a global similarity algorithm that meticulously computes attribute component quantities within specific dimensions of the vector. Furthermore, leveraging the inherent power of Vue's bidirectional data binding capabilities, the study adopts the esteemed MVVM (Model-View-View-Model) pattern, fostering seamless two-way interaction through the established logical relationship. As a result, the amalgamation of multidimensional visualization technology empowers comprehensive data mining through a captivating visual augmentation. Concurrently, the application of data visualization dimension control delivers tailored displays tailored to green and low-carbon scenarios within urban environmental design. Experimental results impeccably validate the effectiveness of the proposed algorithm, substantiated by a mere 1.77% false alarm rate for data stream difference detection and a clustering difference of 1.34%. The aforementioned algorithm accentuates the efficacy of visual displays, thus engendering a profound synergy between the industrial and supply chains. Moreover, it facilitates the design, production, and utilization of environmentally friendly products and energy sources. This, in turn, serves as a catalyst, propelling the widescale adoption of green and low-carbon practices throughout the entire industrial chain, fueled by the seamless integration of multimedia data.

Experimental Study on Multi-Dimensional Visualization Simulation of Gas and Gel Foam Flooding in Fractured-Vuggy Reservoirs.

Gas flooding and foam flooding are potential technologies for tertiary oil recovery in fractured-vuggy reservoirs. The development and mechanism research of fractured-vuggy reservoirs is difficult due to the complex structures and the strong heterogeneity of fractured-vuggy reservoirs. Visualization simulation is one of the effective methods to study the flow behavior of fluid in fractured-vuggy reservoirs. In this study, an upscaling method of visualization simulation from one dimension (1D) to three dimensions (3D) was established, and the physical models of fractured-vuggy reservoirs were designed and fabricated. Water flooding, gas flooding, and gel foam flooding were carried out in the models. The experimental results showed that gas flooding has a single flow channel and water flooding has multiple flow channels in fractures and vugs. Gel foam with an excellent capability of mobility control and a high microscopic displacement efficiency swept in all directions at a uniform velocity. The EOR mechanisms of gel foam in fractured-vuggy reservoirs were mainly as follows: reducing interfacial tension, increasing mobility ratio, selectively plugging high permeability channels, and discontinuous flow. In the displacement process of fractured-vuggy reservoirs, water should be injected from the well at the bottom of the reservoir, and gas should be injected from the well located in the vug at the high part of the reservoir. Gel foam with strong stability and high viscosity should be selected and injected in most kinds of injection wells in fractured-vuggy reservoirs. This study provides a complete method of visualization simulation for the study of flow behavior in fractured-vuggy reservoirs and provides theoretical support for the application of gas flooding and gel foam flooding in fractured-vuggy reservoirs.

Multi-dimensional visualization of ingestion, biological effects and interactions of microplastics and a representative POP in edible jellyfish

Due to their ubiquity and potential risks, microplastics (MPs) and nanoplastics (NPs) are concerning environmental issues. Yet there are still significant knowledge gaps in understanding the tissue-specific accumulation and dynamic change of MPs and NPs in the aquatic organism and how these micro/nano-scale emerging contaminants interact with other environmental pollutants such as persistent organic pollutants (POPs). Here, in vivo imaging systems (IVIS), radioisotope tracing, and histological staining were innovatively used to reveal the fate and toxicity of fluorescently-labeled MPs/NPs and 14C-labeled 2,4,4′-trichlorobiphenyl (PCB28) in edible jellyfish Rhopilema esculentum. These contaminants' ingestion, biological effects, and interactions were visualized at cellular, tissue, and whole-body multidimensional levels. Both MPs and NPs were shown to be preferentially accumulated in the mouthlets of oral arms, and most ingested MPs/NPs were present in the extracellular environment instead of being internalized into the mesoglea. Moreover, the presence of MPs or NPs in the seawater significantly inhibited the bioaccumulation of PCB28 in the jellyfish tissue, thus alleviating physiological alteration, gastric damage, and apoptosis caused by PCB28. This study provides a multi-dimensional visualization strategy to display the distribution and biological effects of typical pollutants in marine organisms and offers new insights for understanding the impacts of MPs/NPs and POPs on marine ecosystems.

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.

A Challenging Dialogue in the Gegenwart: The Current Meaning Generation Reflected by Emotional Awareness.

From a cultural and psychological perspective, everyday life's meaning generation is a treasure box of highly important and complex mechanisms. The potential use of the cultural thinking stream, inhabiting the individual's perception of their reality, is increasingly connected and connects to a multitude of psychological and scientific streams. By generating a new model, the concepts of irreversible time will be connected with you- (from self and other constructed concepts) and I-positioning generations, allowing us to dive deeper into the complexity of the Gegenwarts-experienced phenomena. Unfortunately, the direct application of such a theoretical elaboration to the individual phenomena is rare, wherefore the redefined model will be discussed using a wide range of daily occurrences. In this paper, the question of "how multi-dimensional visualizations are used to define the concept of now and how they impact the self in form of self-dialogues between inner voices?" is generated. The potential for therapeutical measures and theoretical implications will be derived from the findings and subsequently presented. Meanwhile, the theoretical baseline will be used to link the concepts of how beings perceive their reality and how art and science can function as interactive tools for a more I-centered expression of inner distress.

A visual analytics approach for national intangible cultural heritage in Heilongjiang province

The work of traditional intangible cultural heritage analyzes the intangible cultural works in restricted scales way. So, it is difficult to multi-scale understand the closely related to intangible cultural heritage. In this paper, visual analytics approaches are proposed for the preserve and protection those historic cultural heritages, which include multi-dimensional visualization, temporal visualization, and geospatial visualization. We take national intangible cultural heritage in Heilongjiang province as an example. Through visual analysis of intangible cultural heritage in Heilongjiang province, experts can form multi-dimensional understanding the culture of Heilongjiang province, including the categories, numbers and distributions. Furthermore, those methods we proposed can effective help experts make an in-depth analysis of those intangible cultural heritage, and provide them a new insight in a comparative way.

Optimal design of a rotor sleeve for a surface-mounted high-speed permanent-magnet motor with a multi-dimensional visualization strategy

Optimal design of a rotor sleeve for a surface-mounted high-speed permanent-magnet motor with a multi-dimensional visualization strategy

Computational Flow Cytometry Provides Accurate and Sensitive Measurable Residual Disease Assessment in Chronic Lymphocytic Leukemia

Measurable residual disease (MRD) is a highly sensitive reflection of disease burden that correlates with time-to-event outcomes in chronic lymphocytic leukemia (CLL). Undetectable MRD at the end of fixed-duration treatment is an independent prognostic marker and associated with favourable progression-free and overall survival. Muti-colour flow cytometry (FC) is the most common method of MRD assessment and often performed using a harmonized protocol established by the European Research Initiative on CLL. Conventional FC analysis involves sequential manual gating strategies performed on 2-dimensional scatter plots with varying axis parameters. While straightforward with a small number of parameters, this process becomes too complex and time-consuming with state-of-the-art mass flow cytometers where up to 40 markers are simultaneously measured and large multidimensional datasets are generated. Over the past decade, a variety of computational FC algorithms have been developed to automate and assist in tasks such as population identification, enumeration, and classification. These mathematical approaches are based on dimension reduction and clustering methods that require up to several hours and have not been compatible with routine diagnostic workflows until recently. Flow-Self Organizing Maps (FlowSOM) (Van Gassen et al. Cytometry A 2015) is an unsupervised clustering algorithm (R package from Bioconductor) with fast analysis times and multidimensional visualization. Implementation of FlowSOM in a diagnostic laboratory offers several advantages including lower technical variability, reduced bias, and improved efficiency. We developed a computational pipeline to assess CLL MRD using FlowSOM. CLL flow cytometry standard (FCS) files were identified from the Peter MacCallum Cancer Centre and included cases with typical and atypical immunophenotypes from peripheral blood (PB) or bone marrow (BM). Atypical CLL was defined as cases with different expression of one or more antigens from the typical immunophenotype. The diagnostic workflow was developed using a training set (n=25) containing representative samples from MRD negative (n=5), typical CLL (n=5), atypical CLL (n=10), and healthy bone marrow (n=5) cases. Computational accuracy was evaluated against expert human analysis in a validation cohort (n=293) consisting of PB typical (n=139), PB atypical (n=91), BM typical (n=30), and BM atypical CLL (n=33) samples. FCS files were preprocessed (PeacoQC, FlowCore R packages) to remove low-quality events, apply compensation, and transform the data prior to FlowSOM analysis. Two-level hierarchical clustering was performed to achieve maximum separation between normal and MRD events (level 1: lymphocytes, level 2: B cells). The training set was used to generate a diagnostic map of 225 clusters containing the full breadth of immature and mature B cells along with disease immunophenotypes (Figure 1). Clusters were verified by manual inspection in Kaluza software. Computational MRD was then determined by applying the FlowSOM map to validation samples and counting events within the disease clusters. Computational MRD demonstrated excellent correlation with expert human analysis. Linear regression R2 was 0.96, 0.79, 0.96, and 0.91 for PB typical, PB atypical, BM typical, and BM atypical CLL cohorts, respectively (Figure 2). Positive or negative MRD was highly accurate using a threshold of 0.01% for MRD. Computational analysis demonstrated high sensitivity (99%, 99%, 100%, 90%), specificity (92%, 92%, 100%, 85%), and concordance (95%, 98%, 100%, 88%) across all four validation groups (PB typical, PB atypical, BM typical and BM atypical CLL, respectively). The kappa coefficient was 0.92 across the entire validation cohort. CLL MRD assessment in BM samples can be difficult due to overlapping immunophenotypes particularly between disease and transitional B cells. We subsequently included an additional BM cohort consisting of only MRD negative samples to further assess detectable/undetectable accuracy. Samples were highly concordant (94%) except for two cases with low disease signal (0.010% and 0.012%). In summary, FlowSOM computational CLL MRD is highly accurate and offers the potential for an automated workflow. This method can be adapted to other haematological malignancies including acute leukemia and lymphoproliferative disorders. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

An evolutionary system of mineralogy, Part VII: The evolution of the igneous minerals (>2500 Ma)

AbstractPart VII of the evolutionary system of mineralogy catalogs, analyzes, and visualizes relationships among 919 natural kinds of primary igneous minerals, corresponding to 1665 mineral species approved by the International Mineralogical Association—minerals that are associated with the wide range of igneous rock types through 4.566 billion years of Earth history. A systematic survey of the mineral modes of 1850 varied igneous rocks from around the world reveals that 115 of these mineral kinds are frequent major and/or accessory phases. Of these most common primary igneous minerals, 69 are silicates, 19 are oxides, 13 are carbonates, and 6 are sulfides. Collectively, these 115 minerals incorporate at least 33 different essential chemical elements.Patterns of coexistence among these minerals, revealed by network, Louvain community detection, and agglomerative hierarchical clustering analyses, point to four major communities of igneous primary phases, corresponding in large part to different compositional regimes: (1) silica-saturated, quartz- and/or alkali feldspar-dominant rocks, including rare-element granite pegmatites; (2) mafic/ultramafic rock series with major calcic plagioclase and/or mafic minerals; (3) silica-undersaturated rocks with major feldspathoids and/or analcime, including agpaitic rocks and their distinctive rare-element pegmatites; and (4) carbonatites and related carbonate-bearing rocks.Igneous rocks display characteristics of an evolving chemical system, with significant increases in their minerals’ diversity and chemical complexity over the first two billion years of Earth history. Earth’s earliest igneous rocks (>4.56 Ga) were ultramafic in composition with 122 different minerals, followed closely by mafic rocks that were generated in large measure by decompression melting of those ultramafic lithologies (4.56 Ga). Quartz-normative granitic rocks and their extrusive equivalents (>4.4 Ga), formed primarily by partial melting of wet basalt, were added to the mineral inventory, which reached 246 different mineral kinds. Subsequently, four groups of igneous rocks with diagnostic concentrations of rare element minerals—layered igneous intrusions, complex granite pegmatites, alkaline igneous complexes, and carbonatites—all first appeared ~3 billion years ago. These more recent varied kinds of igneous rocks hold more than 700 different minerals, 500 of which are unique to these lithologies.Network representations and heatmaps of primary igneous minerals illustrate Bowen’s reaction series of igneous mineral evolution, as well as his concepts of mineral associations and antipathies. Furthermore, phase relationships and reaction series associated with the minerals of a dozen major elements (H, Na, K, Mg, Ca, Fe, Al, Si, Ti, C, O, and S), as well as minor elements (notably Li, Be, Sr, Ba, Mn, B, Cr, Y, REE, Ti, Zr, Nb, Ta, P, and F), are embedded in these multi-dimensional visualizations.

Design and Application of Multi-Dimensional Visualization System for Large-Scale Ocean Data

With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of the reality of ocean data visualization results and the real-time interaction in the visualization process. Aiming at the challenges of 3D interactive key technology and GPU-based visualization algorithm technology, we developed a visualization system for large-scale 3D marine environmental data. The system realizes submarine terrain rendering, contour line visualization, isosurface visualization, section visualization, volume visualization and flow field visualization. In order to manage and express the data in the system, we developed a data management module, which can effectively integrate a large number of marine environmental data and manage them effectively. We developed a series of data analysis functions for the system, such as point query and line query, local analysis and multi-screen collaboration, etc. These functions can effectively improve the data analysis efficiency of users and meet the data analysis needs in multiple scenarios. The marine environmental data visualization system developed in this paper can efficiently and intuitively simulate and display the nature and changing process of marine water environmental factors.

Analysis of cavitation artifacts in Magnetic Resonance Imaging Thermometry during laser ablation monitoring.

Magnetic Resonance Thermometry Imaging (MRTI) holds great potential in laser ablation (LA) monitoring. It provides the real-time multidimensional visualization of the treatment effect inside the body, thus enabling accurate intraoperative prediction of the thermal damage induced. Despite its great potential., thermal maps obtained with MRTI may be affected by numerous artifacts. Among the sources of error producing artifacts in the images., the cavitation phenomena which could occur in the tissue during LA induces dipole-structured artifacts. In this work., an analysis of the cavitation artifacts occurring during LA in a gelatin phantom in terms of symmetry in space and symmetry of temperature values was performed. Results of 2 Wand 4 W laser power were compared finding higher symmetry for the 2 W case in terms of both dimensions of artifact-lobes and difference in temperature values extracted in specular pixels in the image. This preliminary investigation of artifact features may provide a step forward in the identification of the best strategy to correct and avoid artifact occurrence during thermal therapy monitoring. Clinical Relevance- This work presents an analysis of cavitation artifacts in MRTI from LA which must be corrected to avoid error in the prediction of thermal damage during LA monitoring.

Development and Application of a Platform for Multidimensional Urban Earthquake Impact Simulation

Earthquakes can be devastating to cities. Therefore, accurate and efficient simulation of the potential seismic damage to buildings has become an indispensable part of worldwide efforts to mitigate earthquake hazards. Precise simulation results can highlight potential seismic damage and serve as a reference in urban development and for planning post-earthquake rescue operations. In urban areas, simulation of the structural dynamic response during an earthquake is key for planning an appropriate emergency response. Multidimensional visualization of the effects of earthquakes is a popular technique in current earthquake simulation research. In the present study, a dynamic multidimensional simulation method was developed for analyzing the impact of an earthquake on buildings. This method involved conducting three-dimensional (3D) dynamic analysis of data from a seismic capacity database. Polygonal models and seismic capacity data were used in a high-performance computing process to calculate the seismic response of each building in an urban environment. On the basis of the calculations, a platform was developed for multidimensional urban earthquake impact simulation (MDUES). The developed MDUES platform enables analysis of the impact of long-period earthquakes, simulation of the 3D seismic responses of buildings, and visualization of the disaster risk and earthquake impact in a specified area. In summary, simulation of structural seismic responses in an urban setting necessitates a careful examination of the characteristics of ground and building motion, and the results of this simulation can provide a crucial reference for city planning, post-earthquake rescue operations, and seismic damage assessments.

Multiobjective optimization and Pareto front visualization techniques applied to normal conducting rf accelerating structures

There has been a renewed interest in applying multiobjective (MO) optimization methods to a number of problems in the physical sciences, including to rf structure design. The results of these optimizations generate large datasets, which makes visualizing the data and selecting individual solutions difficult. Using the generated results, Pareto fronts can be found giving the trade-off between different objectives, allowing one to utilize this key information in design decisions. Although various visualization techniques exist, it can be difficult to know which technique is appropriate and how to apply them successfully to the problem at hand. First, we present the setup and execution of MO optimizations of one standing wave and one traveling wave accelerating cavity, including constraint handling and an algorithm comparison. In order to understand the generated Pareto frontiers, we discuss several visualization techniques, applying them to the problem, and give the benefits and drawbacks of each. We found that the best techniques involve clustering the resulting data first to narrow down the possible choices and then using multidimensional visualization methods such as parallel coordinate plots and decision maps to view the clustered results and select individual solutions. Finally, we give some examples of the application of these methods and the cavities selected based on arbitrary design requirements.

Design and Implementation of a Multidimensional Visualization Reconstruction System for Old Urban Spaces Based on Neural Networks.

This article presents an in-depth study and analysis of the construction of a convolutional neural network model and multidimensional visualization system of old urban space and proposes the design of a multifaceted visualization reconstruction system of old urban space based on a neural network. It also quantitatively analyzes the essential spatial attribute characteristics of urban shadow areas as nodes of the overall urban dynamic network in three dimensions—spatial connection strength, spatial connection distance, and spatial connection direction—summarizes the characteristics of urban old spatial structure from the perspective of a dynamic network, and then proposes the model of urban old spatial design from the perspective of an active network. The shallow depth of the network structure is used to reduce the parameters in the learning process of reconfigurable convolutional neural networks using data sets so that the model learns more general features. For the situation where the number of data sets is small, data augmentation is used to expand the size of the data sets and improve the recognition accuracy of the reconfigurable convolutional neural network. A real-time update method of multifaceted data visualization for big data scenarios is proposed and implemented to reduce the network load and network latency caused by charts of multidimensional data changes, reduce the data error rate, and maintain the system stability in the old urban space concurrency scenario.