Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.

Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

VRFlex: Towards the Design of a Virtual Reality Hyflex Class Model

Social distancing requirements urged by the COVID-19 pandemic along with high transportation cost reduced inperson meetings significantly in recent times. In consequence, many people are seeking for virtual reality (VR) to feel a similar experiences of visiting and enjoying places that are unaccessible. VR has immense success in domains, such as automotive industry, healthcare, tourism, entertainment, sports etc. It is observed that traditional online synchronous and asynchronous class structure is not quite effective in engaging students in class participation and discussion. Therefore, we introduce a novel VRbased class structure that will simulate the classroom environment for students participating a class virtually. We equipped the classroom with several internet of things (IoT) devices that collects information from the classroom, analyze those information, and determine some interesting information to display for the students participating the class virtually. We design a classroom prototype and validate the prototype with simulation. The result of the simulation shows that such a VR-based classroom model is feasible and can introduce in classrooms. © 2023 IEEE.

Evaluation of Prevalence and Associated Factors of Dry Eye Syndrome among Medical Students Exposed to Visual Display Terminal in Health Campus, Universiti Sains Malaysia

Introduction: Dry eye syndrome (DES) has become a public health concern, especially during the COVID-19 pandemic. Medical students are at risk due to an increase in visual display terminal (VDT) exposure given the transition to full-time online lectures. The presence of reduced blink rate and tear film instability in VDT users causes an increase in tear evaporation leading to symptoms of DES. This study helps us to learn about the associated factors of VDT use and DES among the young generation. This study aims to determine the prevalence and associated factors of DES among medical students exposed to VDT at the health campus, Universiti Sains Malaysia (USM). Methods: A cross-sectional study involving 140 undergraduate medical students aged 22 to 29 years old who were VDT users. Factors analysed are age, gender, race and duration of VDT usage. Data collection included both subjective assessment (OSDI questionnaire) and objective assessment (TBUT and Schirmer's test). Statistical analysis was conducted using Statistical Package for the Social Science (SPSS Inc Version 24). Results were analysed using descriptive analysis and multivariate logistic regression. Results: Most of the medical student cohort was female and Malay. Most of the students use VDT for less than 8 hours. A high incidence of DES was noted among medical students (92.1%). None of the factors showed significant association with positive findings DES by subjective and objective assessment and duration of VDT usage. Conclusion: DES is common among VDT users. This study showed a high prevalence of DES among medical students in USM. The factors analysed did not show a significant association between DES and duration of VDT usage. This study may help to recognize the problem and will raise awareness of their daily practice and implement preventive measures to avoid VDT-related DES. [ FROM AUTHOR] Copyright of Malaysian Journal of Medicine & Health Sciences is the property of Universiti Putra Malaysia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Effects of Information Layout, Display Mode, and Gender Difference on the User Interface Design of Mobile Shopping Applications

Online shopping has revolutionized our daily lives in the modern era. We can purchase needed goods on mobile shopping applications (apps) anytime and anywhere without leaving home. Especially during the COVID-19 pandemic, we have become increasingly dependent on various mobile shopping activities. However, the visual design of the shopping app interface often affects the user's interactive experience and the efficiency of browsing product information. In addition, gender differences are also worth being considered in the shopping interface design process. To achieve the goal, the research conducted a user study (N=40) of a 2× 2 x 2 mixed factorial design (i.e., information layout x display mode x gender difference). Each participant performed four tasks during the experiment. The authors measured the task completion time, collected the subjective responses from the SUS and the 7-point Likert scale questionnaire, and interviewed participants. The results revealed that: (1) females perform faster in lighter mode when searching for information location, while males perform faster in darker mode. (2) The information layout affects the user's visual search performance and subjective evaluation;females prefer the list style, but men prefer the matrix style. (3) Participants (both males and females) perceived matrix style as more popular than list style in dark mode;however, the result was reversed in light mode. The findings generated from the research can serve as a good reference for the development of user experience in the user interface design of mobile shopping apps. © 2013 IEEE.

Eye Sight - A Bull's Eye Target of COVID-19

Aim: To determine the effect of COVID-19 on eye sight due to increase screen time in undergraduate students of medical school. Study design: Cross-sectional study. Place and duration of study: This survey was carried out from October 2022 to December 2022 in Army Medical College Rawalpindi. Questionnaires were filled in person and also online-based platform was used to distribute the e-questionnaire, developed using the Google Form. The participants were asked to share the e-questionnaire with their friends using Facebook and Messenger. Method(s): Participants were selected for the study using non-probability consecutive sampling. College students of 20-25 years were included in the study. Sample size was 400 according to a study done internationally. Participants with comorbidities (cataract, glaucoma) were excluded from study. Participants having (trouble concentrating on things such as reading the newspaper, books or watching television) were included in the study. Digital eye strain was calculated using validated computer vision syndrome (CVS-Q) questionnaire to measure the symptoms such as eye fatigue, headache, blurred vision, double vision, itching eyes, dryness, tears, eye redness and pain, excessive blinking, feeling of a foreign body, burning or irritation, difficulty in focusing for near vision, feeling of sight worsening, and sensitivity to light. Qualitative data was analyzed using Chi square test. Results A total number of 470 responses were recorded, out of which 257 (54.7%) were males and 213(45.3%) were females. In our study, the most common symptom was headache, affecting 58.1% of the population before COVID 19 which has increased to 83.2% and the P value is less than 0.001.Theother symptoms which also showed P value less than 0.001 were blurred vision while using digital device, irritated or burning eyes, dry eyes and sensitivity to bright light. Conclusion The practical implication of the study is to create awareness among general population about COVID, that eye sight is Bull`s Target to be affected by it and simple preventing measures can be taken. The purpose of this study is to limelight the importance that during COVID 19 lockdown the excessive use of digital devices and their cons on the ocular health among future health care workers.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Omega-3 fatty acids supplements for dry eye - Are they effective or ineffective?

Purpose: To evaluate effectiveness of omega-3 fatty acid supplements in relieving dry eye symptoms and signs in symptomatic visual display terminal users (VDT). Methods: A randomized controlled study was done; eyes of 470 VDT users were randomized to receive four capsules twice daily for 6 months (O3FAgroup), each containing 180 mg of eicosapentaenoic acid and 120 mg docosahexaenoic acid. The O3FA group was compared with another group (n = 480) who received four capsules of a placebo (olive oil) twice daily. Patients were evaluated at baseline, 1, 3, and 6 months, respectively. The primary outcome was improvement in omega-3 index (a measure of EPA and DHA ratio in RBC membrane). Secondary outcomes were improvement dry eye symptoms, Nelson grade on conjunctival impression cytology, Schirmer test values, tear film breakup time (TBUT), and tear film osmolarity. Means of groups (pre-treatment, 1, 3, and 6-months) were compared with repeated measure analysis of variance. Results: At baseline, 81% patients had low omega-3 index. In the O3FA group, a significant increase in omega-3 index, improvement in symptoms, reduction in tear film osmolarity, and increase in Schirmer, TBUT, and goblet cell density was observed. These changes were not significant in the placebo group. Improvement in test parameters was significantly (P < 0.001) better in patients with low omega3 index (<4%) subgroup. Conclusion: Dietary omega-3 fatty acids are effective for dry eye in VDT users; omega-3 index appears to be the predictor to identify potential dry eye patients who are likely to benefit from oral omega-3 dietary intervention.

Biodegradable Bio-based Material for Wearable Air Filtering Skin

COVID-19, imagine having a temporary lip sticker that offers the protection of an n95 mask without the uncomfortable bulk. Using green electrospun nanofibers the lip sticker filters the virus and can communicate geospatial data to your phone using embedded NFC technology. Available in different designs and skins, some fiber formations can display temperature changes on your face. This paper investigates several prototypes of the described product. © 2022 Owner/Author.

Displaying intergenerational solidarity on TikTok during the COVID-19 pandemic: understanding the implications in the grandparent-grandchild relationship

The habits of families are affected during the COVID-19 pandemic, with limitations to socialization or visits. Grandparents and grandchildren use social media to sustain interpersonal relationships, as well as display intergenerational solidarity to others. This paper presents a qualitative content analysis of the display of different dimensions of intergenerational solidarity between grandparents and grandchildren during the COVID-19 pandemic in 2020, on TikTok. The analysis extends the understanding of intergenerational solidarity between grandparents and grandchildren on TikTok, by highlighting which characteristics or activities they find important to display to other users of the platform. The results suggest that grandparents and grandchildren value qualities of physical touch and family celebrations (i.e. affectual solidarity), and big life events (i.e. normative solidarity). Moreover, grandparents and grandchildren refrain from consensual solidarity on TikTok, but other categories of intergenerational solidarity provide clues to differences in the public display of their respective roles. [ FROM AUTHOR] Copyright of Journal of Family Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Biopanning of specific peptide for SARS-CoV-2 nucleocapsid protein and enzyme-linked immunosorbent assay-based antigen assay.

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide which triggered serious public health issues. The search for rapid and accurate diagnosis, effective prevention, and treatment is urgent. The nucleocapsid protein (NP) of SARS-CoV-2 is one of the main structural proteins expressed and most abundant in the virus, and is considered a diagnostic marker for the accurate and sensitive detection of SARS-CoV-2. Herein, we report the screening of specific peptides from the pIII phage library that bind to SARS-CoV-2 NP. The phage monoclone expressing cyclic peptide N1 (peptide sequence, ACGTKPTKFC, with C&C bridged by disulfide bonding) specifically recognizes SARS-CoV-2 NP. Molecular docking studies reveal that the identified peptide is bound to the "pocket" region on the SARS-CoV-2 NP N-terminal domain mainly by forming a hydrogen bonding network and through hydrophobic interaction. Peptide N1 with the C-terminal linker was synthesized as the capture probe for SARS-CoV-2 NP in ELISA. The peptide-based ELISA was capable of assaying SARS-CoV-2 NP at concentrations as low as 61 pg/mL (∼1.2 pM). Furthermore, the as-proposed method could detect the SARS-CoV-2 virus at limits as low as 50 TCID50 (median tissue culture infective dose)/mL. This study demonstrates that selected peptides are powerful biomolecular tools for SARS-CoV-2 detection, providing a new and inexpensive method of rapidly screening infections as well as rapidly diagnosing coronavirus disease 2019 patients.

Isolation and characterization of single domain antibodies from banded houndshark (Triakis scyllium) targeting SARS-CoV-2 spike RBD protein.

The COVID-19 pandemic has significantly impacted human health for three years. To mitigate the spread of SARS-CoV-2, the development of neutralizing antibodies has been accelerated, including the exploration of alternative antibody formats such as single-domain antibodies. In this study, we identified variable new antigen receptors (VNARs) specific for the receptor binding domain (RBD) of SARS-CoV-2 by immunizing a banded houndshark (Triakis scyllium) with recombinant wild-type RBD. Notably, the CoV2NAR-1 clone showed high binding affinities in the nanomolar range to various RBDs and demonstrated neutralizing activity against SARS-CoV-2 pseudoviruses. These results highlight the potential of the banded houndshark as an animal model for the development of VNAR-based therapeutics or diagnostics against future pandemics.

Identification of SARS-CoV-2 PLpro and 3CLpro human proteome substrates using substrate phage display coupled with protein network analysis.

Viral proteases play key roles in viral replication, and they also facilitate immune escape by proteolyzing diverse target proteins. Deep profiling of viral protease substrates in host cells is beneficial for understanding viral pathogenesis and for antiviral drug discovery. Here, we utilized substrate phage display coupled with protein network analysis to identify human proteome substrates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteases, including papain-like protease (PLpro) and 3C-like protease (3CLpro). We first performed peptide substrates selection of PLpro and 3CLpro, and we then used the top 24 preferred substrate sequences to identify a total of 290 putative protein substrates. Protein network analysis revealed that the top clusters of PLpro and 3CLpro substrate proteins contain ubiquitin-related proteins and cadherin-related proteins, respectively. We verified that cadherin-6 and cadherin-12 are novel substrates of 3CLpro, and CD177 is a novel substrate of PLpro using in vitro cleavage assays. We thus demonstrated that substrate phage display coupled with protein network analysis is a simple and high throughput method to identify human proteome substrates of SARS-CoV-2 viral proteases for further understanding of virus-host interactions.

Can You Smell Me Now: Towards a Remote Olfactory Study

The sense of smell-olfaction involves the natural processing of ambient information in real-time. This process allows humans to detect danger, identify familiarities, and form lasting memories. During the COVID-19 pandemic, researchers were presented with challenges related to conducting in-person olfactory-based user studies. In this paper, we explore user experience and perception during olfactory-based interactions (OBI). Based upon previous literature, we propose an approach to offer future researchers a methodology for conducting olfactory-based user studies remotely. In particular, we explored a paper prototyping medium as an olfactory display. This experiment demonstrates the remote investigation of a complex sensory functionality during high mental work-load levels while participants (N=12) engage in an online memory game. Furthermore, this work seeks to inspire further discussion of olfactory-based user studies that explore functions related to human moods, memory, and behavior.

CNN approach for predicting survival outcome of patients with COVID-19

Coronavirus disease 2019 (COVID-19) has been challenged specifically with the new variant. The number of patients seeking treatment has increased significantly, putting tremendous pressure on hospitals and healthcare systems. With the potential of artificial intelligence (AI) to leverage clinicians to improve personalized medicine for COVID-19, we propose a deep learning model based on 1D and 3D convolutional neural networks (CNNs) to predict the survival outcome of COVID-19 patients. Our model consists of two CNN channels that operate with CT scans and the corresponding clinical variables. Specifically, each patient data set consists of CT images and the corresponding 44 clinical variables used in the 3D CNN and 1D CNN input, respectively. This model aims to combine imaging and clinical features to predict short-term from long-term survival. Our models demonstrate higher performance metrics compared to state-of-the-art models with AUC-ROC of 91.44 –91.60% versus 84.36 –88.10% and Accuracy of 83.39 –84.47% versus 79.06 –81.94% in predicting the survival groups of patients with COVID-19. Based on the findings, the combined clinical and imaging features in the deep CNN model can be used as a prognostic tool and help to distinguish censored and uncensored cases of COVID-19. IEEE

Right heart catherisation - a virtual reality

Introduction: Right heart catheterisation (RHC) is the gold standard for assessing patients with pulmonary hypertension. Doctors require training in this procedure in a safe and friendly environment with minimal risk to patients. Due to the Covid pandemic, formal RHC teaching workshops were cancelled in our country, so we sought to develop a Virtual Reality Right Heart Catheterisation (VRRHC) training program to fulfil this area of need without the need for face to face contact. The aim was to improve training, competency and confidence in this technique with improved diagnostic skills and reduction of procedural errors. Method(s): We approached a health technology company to design a VRRHC training module based on our current RHC simulation workshops. Phase 1 required virtual insertion of RHC via the right internal jugular vein using micro-puncture, double Seldinger technique under ultrasound guidance, followed by insertion of the RHC to the right atrium, right ventricle and pulmonary artery with pulmonary artery occlusion using real time pressure tracings and fluoroscopy. Thermodilution cardiac outputs and chamber saturations were also performed. The proprietary platform technology was delivered via a laptop and VR headset. Clinicians perform the VRRHC with imaging, monitoring and haptic feedback with the collection of real time performance tracking allowing user data (e.g. failed steps and proficiency scores) to be captured and subsequently visualised in the learning management system. We collected analytics and data on user engagement, experience and retention, targeted learning outcomes and learning curve, reduction in operating costs, reduction in procedure times due to higher proficiency, early diagnosis of pulmonary hypertension, reduced complications, improved interpretation and diagnosis. Result(s): The program was launched in October 2021. Preliminary data shows a learning curve is associated with both using VR (10-15 minutes) and the RHC procedure itself. Initial time to completion of the RHC was 30-40 mins, reducing to 20-30 minutes with experience and 15 minutes in experts. Completion rates increase with experience from 40-50% to 100% and error rates reduce with frequency of completion. Conclusion(s): A Virtual Reality Right Heart Catheter training program is safe, feasible and non-invasive. Increased experience results in increased completion rates, reduced procedure time and reduced errors. Using this program will potentially have beneficial effects on doctor training, outcomes, patient safety and health economics with no risk to a real patient. VRRHC images VRRHC hardware and utilisation.

Non-contact sensing system based on GaN optoelectronic chip

As the global spread of COVID-19 becomes a rapidly evolving crisis, the development of contactless shared interactive displays is an urgent issue to reduce the risk of viral and bacterial cross contamination due to the use of touch-operated shared user terminals. Here, we experimentally demonstrate a contactless user terminal fabricated with a monolithic GaN Optoelectronic system (MGOS), which integrates the transmitter and receiver into a single chip. The inherent spectral emission-responsiveness overlap of GaN QW diodes gives the device a unique ability to detect light transmitted by diodes that share the same QW structure. When the GaN transmitter emits light to illuminate an external object, the integrated GaN receiver can detect the reflected light encoding the information and convert the optical signal into an electrical signal, so that the non-contact user terminal has the ability to use light for bidirectional data communication. Compared to traditional handwriting systems, these terminals operate as contactless information entry devices that can help reduce potential cross-contamination due to contact with handwriting terminals, provide precautions to keep the environment clean, and help prevent virus transmission. © 2023 IEEE.

Region-of-Interest Aware 3D ResNet for Classification of COVID-19 Chest Computerised Tomography Scans

Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively. © 2013 IEEE.

Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

The development of new remote technologies in disaster medicine education: A scoping review.

Background: Remote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education. Objectives: The aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years. Literature scoping reviews: Mobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education. Methods: We performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022. Results: A total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems. Discussions: Artificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.

VASA: an exploratory visualization tool for mapping spatio-temporal structure of mobility–a COVID-19 case study

Exploratory data analysis tools designed to measure global and local spatial autocorrelation (e.g. Moran's (Formula presented.) statistic) have become standard in modern GIS software. However, there has been little development in amending these tools for visualization and analysis of patterns captured in spatio-temporal data. We design and implement an exploratory mapping tool, VASA (Visual Analysis for Spatial Association), that streamlines analytical pipelines in assessing spatio-temporal structure of data and enables enhanced visual display of the patterns captured in data. Specifically, VASA applies a set of cartographic visual variables to map local measures of spatial autocorrelation and helps delineate micro and macro trends in space-time processes. Two visual displays are presented: recency and consistency map and line-scatter plots. The former combines spatial and temporal data view of local clusters, while the latter drills down on the temporal trends of the phenomena. As a case study, we demonstrate the usability of VASA for the investigation of mobility patterns in response to the COVID-19 pandemic throughout 2020 in the United States. Using daily county-level and grid-level mobility metrics obtained from three different sources (SafeGraph, Cuebiq, and Mapbox), we demonstrate cartographic functionality of VASA for a swift exploratory analysis and comparison of mobility trends at different regional scales. © 2023 Cartography and Geographic Information Society.