Spatio-temporal Visualization Research Articles

Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization.

We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM (Jupyter notebook), https://github.com/EESI/ncov_ism (command line tool) and via an interactive website at https://covid19-ism.coe.drexel.edu/.

A NEW CLOUD-EDGE-TERMINAL RESOURCES COLLABORATIVE SCHEDULING FRAMEWORK FOR MULTI-LEVEL VISUALIZATION TASKS OF LARGE-SCALE SPATIO-TEMPORAL DATA

Abstract. To address the multi-modal spatio-temporal data efficient scheduling problem of the diverse and highly concurrent visualization applications in cloud-edge-terminal environment, this paper systematically studies the cloud-edge-terminal integrated scheduling model of multi-level visualization tasks of multi-modal spatio-temporal data. By accurately defining the hierarchical semantic mapping relationship between the diverse visual application requirements of different terminals and scheduling tasks, we propose a multi-level task-driven cloud-edge-terminal multi-granularity storage-computing-rendering resource collaborative scheduling method. Based on the workflow, the flexible allocation strategy of cloud-edge-terminal scheduling service chain that consider the characteristics of spatio-temporal task is constructed. Finally, we established a cloud-edge-terminal scheduling adaptive optimization mechanism based on the service quality evaluation model, and developed a prototype system. Experiments are conducted with the urban construction and construction management, the results show that the new method breaks through the bottleneck of traditional spatio-temporal data visualization scheduling, and it can provide theoretical and methodological support for the visualization and scheduling of spatio-temporal big data.

Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading

With the acceleration of the urbanization process, the problems caused by extreme weather such as heavy rainstorm events have become more and more serious. During such events, the road and its auxiliary facilities may be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a space–time dimension, and as rainfall data are obtained through discrete monitoring stations, the acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate users in understanding the impact of urban waterlogging on traffic, the visualization of waterlogging information needs to be displayed under different spatial and temporal granularity. Therefore, the appropriateness of the visualization granularity directly affects the user’s cognition of the road waterlogging map. To solve this problem, this paper established a spatial granularity and temporal granularity computing quantitative model for spatio-temporal visualization of road waterlogging and the evaluation method of the model was based on the cognition experiment. The minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading method and two color schemes for road waterlogging visualization based on the user’s cognition. To verify the feasibility of the method, we developed a prototype system and implemented a dynamic spatio-temporal visualization of the waterlogging process in the main urban area of Nanjing, China. The user cognition experiment showed that most participants thought that the segmentation of road was helpful to the local visual expression of waterlogging, and the color schemes of waterlogging depth were also helpful to display the road waterlogging information more effectively.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research.

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

CD24-targeted fluorescence imaging in patient-derived xenograft models of high-grade serous ovarian carcinoma.

BackgroundThe survival rate of patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains disappointing. Clinically translatable orthotopic cell line xenograft models and patient-derived xenografts (PDXs) may aid the implementation of more personalised treatment approaches. Although orthotopic PDX reflecting heterogeneous molecular subtypes are considered the most relevant preclinical models, their use in therapeutic development is limited by lack of appropriate imaging modalities.MethodsWe developed novel orthotopic xenograft and PDX models for HGSOC, and applied a near-infrared fluorescently labelled monoclonal antibody targeting the cell surface antigen CD24 for non-invasive molecular imaging of epithelial ovarian cancer. CD24-Alexa Fluor 680 fluorescence imaging was compared to bioluminescence imaging in three orthotopic cell line xenograft models of ovarian cancer (OV-90luc+, Skov-3luc+ and Caov-3luc+, n = 3 per model). The application of fluorescence imaging to assess treatment efficacy was performed in carboplatin-paclitaxel treated orthotopic OV-90 xenografts (n = 10), before the probe was evaluated to detect disease progression in heterogenous PDX models (n = 7).FindingsApplication of the near-infrared probe, CD24-AF680, enabled both spatio-temporal visualisation of tumour development, and longitudinal therapy monitoring of orthotopic xenografts. Notably, CD24-AF680 facilitated imaging of multiple PDX models representing different histological subtypes of the disease.InterpretationThe combined implementation of CD24-AF680 and orthotopic PDX models creates a state-of-the-art preclinical platform which will impact the identification and validation of new targeted therapies, fluorescence image-guided surgery, and ultimately the outcome for HGSOC patients.FundingThis study was supported by the H2020 program MSCA-ITN [675743], Helse Vest RHF, and Helse Bergen HF [911809, 911852, 912171, 240222, HV1269], as well as by The Norwegian Cancer Society [182735], and The Research Council of Norway through its Centers of excellence funding scheme [223250, 262652].

Electrochemiluminescence Imaging Techniques for Analysis and Visualizing

Electrochemiluminescence (ECL)-based imaging analysis is a dominant method to inspect the electrode composition, to study electrochemical reaction kinetics at a microscopic level, and also a rapid emerging technology in bioanalysis with high spatiotemporal resolutions, high-throughput and visualization characteristics. In comparison with other imaging microscopes, an optical excitation is not involved in imaging, thus the approach is free from background noise resulting in a low detection limit. In this review work, the principle of ECL, its unique natures compared to other luminescence techniques were briefed at first. Then after, the progress and basic principles of ECL imaging were summarized. Furthermore, recent and representative advances of ECL imaging for visualizing and sensing applications were reviewed. Finally, the perspectives in ECL imaging for further perspective were discussed.

Nanoparticle-immersed paper imprinting mass spectrometry imaging reveals uptake and translocation mechanism of pesticides in plants

Design and discovery of carrier-mediated modified pesticides are vital for reducing pesticide dosage and increasing utilization, yet it remains a great challenge due to limited insights into plant translocation mechanisms. Nanostructure/nanoparticle assisted laser desorption/ionization strategy has established itself as a preferential analytical tool for biological tissue analysis, whereas potential applications in plant sciences are hindered with regard to the inability to slice plant leaves and petals. Herein, we report gold nanoparticle (AuNP)-immersed paper imprinting mass spectrometry imaging (MSI) for the spatiotemporal visualization of pesticide translocation in plant leaves. This approach plays a dual role in preserving spatial information and improving ionization efficiency for pesticides regardless of imaging artifacts due to homogenous AuNP deposition. Using this MSI platform, we proposed the elaborate plant translocation mechanism of agrochemicals for the first time, which is currently poorly understood. The dynamic processes of carrier-mediated pesticides can be clearly visualized, including crossing of plasma membranes by transporters, translocation downward in stems through the phloem, diffusion to the xylem and, conversely, accumulation at margins of the treated leaves. Moreover, this AuNP-assisted paper imprinting method could be highly compatible with laser-based MSI instruments, expediting researches across a broad range of fields, especially in nanomaterial development and life sciences.

RESEARCH ON CONSTRUCTION OF SPATIO-TEMPORAL DATA VISUALIZATION PLATFORM FOR GIS AND BIM FUSION

Abstract. The visualization model of GIS and BIM fusion can provide data bearing platform and main technical support for future urban operation centers, digital twin cities, and smart cities. Based on the analysis of the features and advantages of GIS and BIM Fusion, this paper proposes a construction method of the spatio-temporal data visualization platform for GIS and BIM Fusion. It expounds and analyzes the overall architecture design of platform, multi-dimensional and multi-spatial scales visualization, space analysis for GIS and BIM fusion, and platform applications and so on. The urban virtual simulation spatio-temporal data platform project of Teda New District in Tianjin has verified and demonstrated that the effect of application is good. This provides a feasible solution for the construction of spatio-temporal Data Visualization Platform.

Near-Infrared Fluorescent Imaging for Monitoring of Treatment Response in Endometrial Carcinoma Patient-Derived Xenograft Models.

Imaging of clinically relevant preclinical animal models is critical to the development of personalized therapeutic strategies for endometrial carcinoma. Although orthotopic patient-derived xenografts (PDXs) reflecting heterogeneous molecular subtypes are considered the most relevant preclinical models, their use in therapeutic development is limited by the lack of appropriate imaging modalities. Here, we describe molecular imaging of a near-infrared fluorescently labeled monoclonal antibody targeting epithelial cell adhesion molecule (EpCAM) as an in vivo imaging modality for visualization of orthotopic endometrial carcinoma PDX. Application of this near-infrared probe (EpCAM-AF680) enabled both spatio-temporal visualization of development and longitudinal therapy monitoring of orthotopic PDX. Notably, EpCAM-AF680 facilitated imaging of multiple PDX models representing different subtypes of the disease. Thus, the combined implementation of EpCAM-AF680 and orthotopic PDX models creates a state-of-the-art preclinical platform for identification and validation of new targeted therapies and corresponding response predicting markers for endometrial carcinoma.

An Ontology-based approach to Knowledge-assisted Integration and Visualization of Urban Mobility Data

This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT.

MAP-Vis: A Distributed Spatio-Temporal Big Data Visualization Framework Based on a Multi-Dimensional Aggregation Pyramid Model

During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high rendering delay, and poor visual effects. Research has shown that the development of distributed computing frameworks provides a feasible solution for big spatio-temporal data management and visualization. Accordingly, to address these challenges, this paper adopts a proprietary pre-processing visualization scheme and designs and implements a highly scalable distributed visual analysis framework, especially targeted at massive point-type datasets. Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid. The proposed MAP model can support the simultaneous hierarchical aggregation of time, space, and attributes, and also later transformation of the derived aggregates into discrete key-value pairs for scalable storage and efficient retrieval. Using the generated MAP datasets, we develop an open-source distributed visualization framework (MAP-Vis). In MAP-Vis, a high-performance Spark cluster is used as a parallel preprocessing platform, while distributed HBase is used as the massive storage for the generated MAP data. The client of MAP-Vis provides a variety of correlated visualization views, including heat map, time series, and attribute histogram. Four open datasets, with record numbers ranging from the millions to the tens of billions, are chosen for system demonstration and performance evaluation. The experimental results demonstrate that MAP-Vis can achieve millisecond-level query response and support efficient interactive visualization under different queries on the space, time, and attribute dimensions.

One health data audit: a spatio-temporal approach to cultivating sustainable multi-disciplinary collaboration and communication in zoonoses surveillance, control and stepwise elimination

Auditing public health data has become an important component of zoonosis surveillance for one health inclusion and communication. The concept of one health data audit may be defined as the inspection of data to assess their origins and traits, verifying their utility based on completeness, accuracy and timeliness in human-animal disease surveillance, food safety, security and other areas of public health importance. One health data audit involves looking at key metrics to make decisions about the properties of data sets in terms of the composite part of the tripartite of one health that they belong: animal, environment or human´s health attribution. In relation to the environmental component, one health data audit includes and promotes cartographic protocols for spatial and spatio-temporal data visualization, which plays a role in disease mapping and identification of associated risk factors, using Geographic Information Systems (GIS). We present ten critical points of how one health data audit was carried out, exemplified with dog bite victims´ records in rabies surveillance in West Africa. We concluded that as Global Positioning System (GPS) technology advances, so is its practical application in the tracking and mapping of zoonosis in space and time to achieve more accurate and complete one health data sources representation.

Multimodal stratified imaging of nanovaccines in lymph nodes for improving cancer immunotherapy.

Vaccines hold enormous potential in cancer immunotherapy by stimulating the body's immune response; unfortunately, the clinical response rates of cancer vaccines are less than 30%. Nanovaccines show the potential to enhance the treatment efficacy of conventional vaccines due to their unique properties, such as efficient co-delivery of cocktail to the secondary lymphatic system, high tumor accumulation and penetration, and customizable delivery of antigens and adjuvants. Meanwhile, the non-invasive visualization of vaccines after their delivery can yield information about in vivo distribution and performance, and aid in their subsequent optimization and translational studies. In this review, we summarize the strategies for the spatiotemporal visualization of nanovaccines in lymph nodes, including whole-body in vivo imaging, intravital organ/cell imaging, and ex vivo tissue/cell imaging. The application of imaging modalities in nanovaccine development is discussed. Moreover, strategies to achieve different combinations of imaging modalities are proposed.

Microfluidic flow-through reactor and 3D Raman imaging for in situ assessment of mineral reactivity in porous and fractured porous media.

An in-depth understanding of dissolution and precipitation of minerals in porous and fractured porous media and the complex feedback on the transport of fluids is essential for various subsurface applications. In this context, we developed a novel non-destructive "lab-on-chip" approach for quantitative in situ assessments of mineralogical changes in porous media. Our experimental approach involves a microfluidic flow-through reactor of reactive homogeneous and heterogeneous (fractured) porous media coupled with high-resolution imaging. Here, the reactive medium consists of compacted celestine grains seeded in a reservoir within the microfluidic chip. This medium reacts with a barium chloride solution injected into the microreactor at a constant flow rate, leading to the dissolution of celestine and growth of barite. Various seeding processes of the mineral grains allow the creation of homogeneous reactive porous media or the introduction of large heterogeneities such as fractures. Hence, our approach enables high-resolution investigations of reactive transport in fractured porous media. The use of confocal Raman spectroscopic techniques enables the spatio-temporal visualization of the mineral transformation at the pore-scale in two- and three-dimensions. Moreover, advanced pore-scale modelling correlates the hydrological heterogeneities to the geochemical observations in the micro-reactor, which explains the observed discrepancies between homogeneous and heterogeneous reactive media. Eventually, the proposed methodology can be applied to other chemical systems to provide new insights into hydro-geochemical coupling in porous and fractured porous media as well as high-fidelity datasets to benchmark reactive transport codes that are currently under development.

Live-cell Imaging by Super-resolution Confocal Live Imaging Microscopy (SCLIM): Simultaneous Three-color and Four-dimensional Live Cell Imaging with High Space and Time Resolution.

Many questions in cell biology can be solved by state-of-the-art technology of live cell imaging. One good example is the mechanism of membrane traffic, in which small membrane carriers are rapidly moving around in the cytoplasm to deliver cargo proteins between organelles. For directly visualizing the events in membrane trafficking system, researchers have long awaited the technology that enables simultaneous multi-color and four-dimensional observation at high space and time resolution. Super-resolution microscopy methods, for example STED, PALM/STORM, and SIM, provide greater spatial resolution, however, these methods are not enough in temporal resolution. The super-resolution confocal live imaging microscopy (SCLIM) that we developed has now achieved the performance required. By using SCLIM, we have conducted high spatiotemporal visualization of secretory cargo together with early and late Golgi resident proteins tagged with three different fluorescence proteins. We have demonstrated that secretory cargo is indeed delivered within the Golgi by cisternal maturation. In addition, we have visualized details of secretory cargo trafficking in the Golgi, including formation of zones within a maturing cisterna, in which Golgi resident proteins are segregated, and movement of cargo between these zones. This protocol can be used for simultaneous three-color and four-dimensional observation of various phenomena in living cells, from yeast to higher plants and animals, at high spatiotemporal resolution.

DETERMINATION OF CIGARETTE SMOKE DISPERSION USING OPTICAL RGB CAMERA AND DIGITAL IMAGE PROCESSING

Abstract. Across ages, the rapid prevalence of cigarette smoking has consistently posed adverse threats to both human health and the environment. Moreover, smoke dispersed from a single cigarette could expose serious respiratory problems to a number of non-smokers within the area. Cigarette smoke has always been a notorious, human-induced health risk and air pollutant thus, the urgent need to develop smoke management schemes by exploring strategies on observing cigarette smoke dispersion. This research aims to incorporate the potential of optical RGB cameras in the study of smoke dispersion. Through digital image processing of experimental smoking videos, a spatiotemporal visualization of smoke dispersion for an indoor and outdoor environment were created. Smoke movement starting from the source was observed in terms of smoke pixel density and maximum horizontal extent. Quantitatively, the results showed a relative maximum extent of 1.21 meters which lasted for 2 seconds for an outdoor environment while 1.05 meters which lasted for 6 seconds for an indoor environment. The maximum relative smoke pixel density values calculated for the outdoor and indoor environment are 1.46% and 1.12% respectively. The resulting graphs were indicative of a trend that creates a normal distribution curve that can be affected by external factors and represent a function relating dispersion and distance. The results of this study prove the capability of optical RGB cameras as an alternative and cost-efficient method in studying smoke dispersion. Furthermore, this practical method of monitoring smoke dispersion could lead to comprehensive analyses of air quality management and health exposure assessments.

An interactive 4D spatio-temporal visualization system for hydrometeorological data in natural disasters

ABSTRACT Dynamic visualization of multidimensional hydrometeorological data is vital for decision-makers to catch situational awareness and command an emergency response in natural disasters. Nevertheless, few software tools can comprehensively visualize hydrometeorological data in different scales, dimensions, and time. In this paper, an interactive 4D spatio-temporal visualization system based on a virtual globe is proposed. Voxel-based data model and multi-level index are adopted to organize the field data in a unified data structure. Meanwhile, it is resampled in both spatial and temporal dimensions in memory to prepare smooth data stream for rendering. Ten field models, including large-scale volume rendering and adaptive streamline rendering, are accelerated and integrated to display the field data collaboratively. The profile analysis and eddy tracking improve user experience in interactively exploring specific scenes. The system is tested against both large-scale meteorological data in the atmosphere and small-scale hydrological data at the surface, using typhoon landfall and riverine flood, respectively. The results demonstrate the applicability and efficiency of the system to dynamically visualize hydrometeorological data.

Developing the Chinese Academic Map Publishing Platform

The discipline of the humanities has long been inseparable from the exploration of space and time. With the rapid advancement of digitization, databases, and data science, humanities research is making greater use of quantitative spatiotemporal analysis and visualization. In response to this trend, our team developed the Chinese academic map publishing platform (AMAP) with the aim of supporting the digital humanities from a Chinese perspective. In compiling materials mined from China’s historical records, AMAP attempts to reconstruct the geographical distribution of entities including people, activities, and events, using places to connect these historical objects through time. This project marks the beginning of the development of a comprehensive database and visualization system to support humanities scholarship in China, and aims to facilitate the accumulation of spatiotemporal datasets, support multi-faceted queries, and provide integrated visualization tools. The software itself is built on Harvard’s WorldMap codebase, with enhancements which include improved support for Asian projections, support for Chinese encodings, the ability to handle long text attributes, feature level search, and mobile application support. The goal of AMAP is to make Chinese historical data more accessible, while cultivating collaborative opensource software development.

Spatiotemporal Data Analysis and Forecasting Model for Forestland Rehabilitation

Purpose – Deforestation is one of the Global Forests issues that concern the United Nations (UN) for several decades and it thus leads to a vision of increasing the forestland area by 2030 that is the same size as South Africa. With this concern, spatiotemporal data analysis had been an effective way to visualize and represent the area that have been damaged and affected with the integration of the use of Geographical Information System. The National Greening Program (NGP) of the Philippines is in charge of the rehabilitation of unproductive, denuded and degraded forestlands in every province. Method – Using the spatiotemporal data in the form of shapefiles, predictors that could contribute on how the forestland may be rehabilitated were analysed and foreseen. Also, with the analysis stage of Artificial Neural Network (ANN) with Back Propagation, a forecasting model was identified. Result – It has been determined that with the combination of ANN and Spatiotemporal visualization, possible additional increase in the size of the rehabilitated forestland and its representation can be done efficiently. Conclusion – Thus, the finding may be used as a helpful way for the NGP for forestland rehabilitation and reforestation strategic planning and resource management. Practical Implications – A dynamic and interactive web application may be implemented to monitor implementation of the program. Furthermore, public awareness may be initiated about the importance of forestland.

Spatiotemporal visualization of the terahertz emission during high-power laser-matter interaction.

Single-cycle pulses with multimillion volts per centimeter field strengths and spectra in the terahertz (THz) band have attracted great interest due to their ability to coherently manipulate molecular orientations and electron spins resonantly and nonresonantly. The tremendous progress made in the development of compact and powerful terahertz sources have identified intense laser-thin foil interaction as a potential candidate for high-power broadband terahertz radiation. They are micrometers in size and deliver radially polarized terahertz pulses with millijoule energy and gigawatt peak power. Although several works have been carried out to investigate the terahertz generation process, their origin and angular distribution are still debated. We present here an indisputable study on their spatiotemporal characteristics and elaborate the underlying physical processes via recording the three-dimensional beam profile along with transient dynamics. These results are substructured with the quantitative visualization of the charge particle spectra.