GEOVISUALIZATION OF AERIAL PHOTOGRAMMETRIC FLIGHTS FOR DATA QUALITY ASSESSMENT

Spatiotemporal data visualization plays an important role for simulating the changes over time and representing dynamic geospatial phenomena. In aerial photogrammetry, image acquisition is the most important stage for obtaining high-quality products; and can be affected by various factors such as the weather and illumination conditions, imaging geometry, etc. 3D simulation of the aircraft trajectories at the planning stage helps the flight planners to make better decisions especially for unmanned aerial vehicle (UAV) missions in areas with mixed land use land cover, such as rugged topography, water bodies, restricted areas, etc.; since images with poor texture or large differences in scale may deteriorate the quality of the final products. In this study, a geovisualization approach for photogrammetric flights carried out with UAVs or airplane platforms was implemented using CesiumJS Virtual Globe. The measured flight trajectory parameters, such as image perspective centre coordinates and the camera rotations, the time of acquisition, and the interior orientation parameters (IOPs) of the camera were used for spatiotemporal visualization. In the developed approach, the EOPs and IOPs of the images were utilized to reconstruct the flight paths, the camera position, the footprints of the acquired images on the ground, and the rotation of the aircraft; and to present them on a 3D web environment precisely. The approach was demonstrated by using two case studies, one from a UAV flight mission and the other one from an airplane carried out with a large-format aerial camera.

Spatiotemporal Visualization of Insecticides and Fungicides within Fruits and Vegetables Using Gold Nanoparticle-Immersed Paper Imprinting Mass Spectrometry Imaging

Food safety issues caused by pesticide residue have exerted far-reaching impacts on human daily life, yet the available detection methods normally focus on surface residue rather than pesticide penetration to the internal area of foods. Herein, we demonstrated gold nanoparticle (AuNP)-immersed paper imprinting mass spectrometry imaging (MSI) for monitoring pesticide migration behaviors in various fruits and vegetables (i.e., apple, cucumber, pepper, plum, carrot, and strawberry). By manually stamping food tissues onto AuNP-immersed paper, this method affords the spatiotemporal visualization of insecticides and fungicides within fruits and vegetables, avoiding tedious and time-consuming sample preparation. Using the established MSI platform, we can track the migration of insecticides and fungicides into the inner region of foods. The results revealed that both the octanol-water partition coefficient of pesticides and water content of garden stuffs could influence the discrepancy in the migration speed of pesticides into food kernels. Taken together, this nanopaper imprinting MSI is poised to be a powerful tool because of its simplicity, rapidity, and easy operation, offering the potential to facilitate further applications in food analysis. Moreover, new perspectives are given to provide guidelines for the rational design of novel pesticide candidates, reducing the risk of food safety issues caused by pesticide residue.

Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart

Background Considering the widespread of coronavirus disease 2019 (COVID-19) pandemic in the world, it is important to understand the spatiotemporal development of the pandemic. In this study, we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries. Methods Using COVID-19 case and death data from February to December 2020 offered by Johns Hopkins University, we generated time-associated balloon charts with multiple epidemiological indicators including crude case fatality rate (CFR), morbidity, mortality and the total number of cases, to compare the progression of the pandemic within a specific period across regions and countries, integrating seven related dimensions together. The area chart is used to supplement the display of the balloon chart in daily new COVID-19 case changes in UN geographic regions over time. Javascript and Vega-Lite were chosen for programming and mapping COVID-19 data in browsers for visualization. Results From February 1st to December 20th 2020, the COVID-19 pandemic spread across UN subregions in the chronological order. It was first reported in East Asia, and then became noticeable in Europe (South, West and North), North America, East Europe and West Asia, Central and South America, Southern Africa, Caribbean, South Asia, North Africa, Southeast Asia and Oceania, causing several waves of epidemics in different regions. Since October, the balloons of Europe, North America and West Asia have been rising rapidly, reaching a dramatically high morbidity level ranging from 200 to 500/10 000 by December, suggesting an emerging winter wave of COVID-19 which was much bigger than the previous ones. By late December 2020, some European and American countries displayed a leading mortality as high as or over 100/100 000, represented by Belgium, Czechia, Spain, France, Italy, UK, Hungary, Bulgaria, Peru, USA, Argentina, Brazil, Chile and Mexico. The mortality of Iran was the highest in Asia (over 60/100 000), and that of South Africa topped in Africa (40/100 000). In the last 15 days, the CFRs of most countries were at low levels of less than 5%, while Mexico had exceptional high CFR close to 10%. Conclusions We creatively used visualization integrating 7-dimensional epidemiologic and spatiotemporal indicators to assess the progression of COVID-19 pandemic in terms of transmissibility and severity. Such methodology allows public health workers and policy makers to understand the epidemics comparatively and flexibly.

Dynamic Electrode-to-Image (DETI) Mapping Reveals the Human Brain’s Spatiotemporal Code of Visual Information

A chief goal of systems neuroscience is to understand how the brain encodes information in our visual environments. Understanding that neural code is crucial to explaining how visual content is transformed via subsequent semantic representations to enable intelligent behavior. Although the visual code is not static, this reality is often obscured in voxel-wise encoding models of BOLD signals due to fMRI’s poor temporal resolution. We leveraged the high temporal resolution of EEG to develop an encoding technique based in state-space theory. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. This technique offers a spatiotemporal visualization of the evolution of the neural code of visual information thought impossible to obtain from EEG and promises to provide insight into how visual meaning is developed through dynamic feedforward and recurrent processes.

Visualization of epithelial-mesenchymal transition in an inflammatory microenvironment–colorectal cancer network

Epithelial-mesenchymal transition (EMT) is a biological process by which epithelial cells acquire mesenchymal characteristics. In malignant tumors, EMT is crucial for acquisition of a mesenchymal phenotype with invasive and metastatic properties, leading to tumor progression. An inflammatory microenvironment is thought to be responsible for the development and progression of colorectal cancer (CRC); however, the precise role of inflammatory microenvironments in EMT-related CRC progression remains unclear. Here, we show the spatiotemporal visualization of CRC cells undergoing EMT using a fluorescence-guided EMT imaging system in which the mesenchymal vimentin promoter drives red fluorescent protein (RFP) expression. An inflammatory microenvironment including TNF-α, IL-1β, and cytokine-secreting inflammatory macrophages induced RFP expression in association with the EMT phenotype in CRC cells. In vivo experiments further demonstrated the distribution of RFP-positive CRC cells in rectal and metastatic tumors. Our data suggest that the EMT imaging system described here is a powerful tool for monitoring EMT in inflammatory microenvironment–CRC networks.

A Surveillance and Spatiotemporal Visualization Model for Infectious Diseases Using Social Network

In this paper, the authors propose a surveillance and spatiotemporal visualization system to simulate the infectious diseases spread which enables users to make decisions during a simulated pandemic. This system is based on compartment Susceptible, Exposed, Infected, and Removed (SEIR) model within a Small World network and Geographic Information System. The main advantage of this system is that it allows not only to understand how epidemic spreads in the human population and which risk factors promote this transmission but also to visualize epidemic outbreaks on the region's map. Experiments results reflect significantly the dynamical behavior of the influenza epidemic and the system can provide significant guidelines for decision makers when coping with epidemic diffusion controlling problems.