Spatio-Temporal Spread of Covid-19 in Children Aged 0-11 Years at Central Java Province Indonesia Using Geographical Weight Regression

During the COVID-19 pandemic, children under the age of 12 are the most vulnerable age group to health concerns. The goal of this study was to conduct a spatiotemporal analysis of the distribution of COVID-19 cases in Central Java children using the GWR (Geographically Weighted Regression) approach. The data source is the Central Java Provincial Health Office, and the study objects are 35 cities and districts in Central Java province. The data obtained are the number of COVID-19 cases in children aged 0-11 years, the total number of Covid-19 cases, the number of PCR tests per day, the number of vaccinations and the number of health care facilities per city and district per month from March 2020 to November 2021. Hotspot analysis and the GWR approach were used to examine data in semesters 1–4 (S1–S4). From S1 to S4, the number of COVID-19 cases in children increased. Areas that became hotspots for more than two semesters were Semarang City, Semarang Regency, Banyumas, Cilacap, Kendal, and Demak. According to the GWR analysis in S1-S4, the total number of COVID-19 cases, PCR tests per day, vaccinations, and health care facilities all affect the number of COVID-19 patients in children by more than 75%. The total number of COVID-19 cases has a significant impact on the number of COVID-19 cases in children but the number of health care facilities has no effect. The results of the GWR prediction of COVID-19 cases in children show that the cities of Semarang and Banyumas became areas with a larger number of COVID-19 cases in two semesters. According to the hotspot and GWR analysis, the cities of Semarang and Banyumas are regions to be on the lookout for in the spread of COVID-19 cases in S1-S4. © 2022 Praise Worthy Prize S.r.l.-All rights reserved.

Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

Key frame extraction method for lecture videos based on spatio-temporal subtitles.

Affected by the Corona Virus Disease 2019 (COVID-19), online lecture videos have witnessed an explosive growth. In the face of massive videos, this paper proposes a method for extracting key frames of lecture videos based on spatio-temporal subtitles, which can efficiently and quickly obtain effective information. Firstly, the spatio-temporal slices of subtitle area of the video sequence are extracted and spliced along the time axis to construct the video spatio-temporal subtitle. Then, the video spatio-temporal subtitle is processed in binarization, and the projection method is used to construct the SSPA curve of the video spatio-temporal subtitle. Finally, a selection method for steady-state key frame is designed, that is, the key frame extraction is realized by combining curve edge detection and subtitle existence threshold, which ensures the robustness of the proposed method. The test results of 8 videos show that the average value of the comprehensive index F1-score of the key frame extracted by the algorithm can reach 0.97, the average precision is 0.97, and the average recall rate is 0.98. It can effectively extract the key frames in lecture videos, and compared with other algorithms, the average running time is reduced to 0.072 of the original, which is helpful to extract video information quickly and accurately.

Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda.

INTRODUCTION: Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions. RESULTS: The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda. CONCLUSION: The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.

Spatio-temporal patterns of Chinese urban recovery and system resilience under the pandemic new normal

Enhancing urban resilience is an important measure to improve preparedness to public health challenges;therefore, understanding the patterns and determinants of urban recovery is of great significance for sustainable urban development under the pandemic new normal. We first propose an analytical framework of urban recovery capacity, and then apply the geographical detector model and geographic weighted regression model to investigate the dynamic characteristics of urban resilience and urban recovery capacity under the impact of COVID-19 in China. The results show that the overall pattern of vitality recovery follows the U-curve;however, the impact of COVID-19 on each region is significantly different, with the highest degree of recovery in the Northwest and East, and the lowest in the Central and West. The geographical detector model reveals that urban resilience indicators can predominantly explain the variations of urban recovery across cities. The geographically weighted regression model shows that environmental resilience, infrastructure resilience, and social resilience are positively correlated with urban recovery capacity, while economic resilience cannot improve urban recovery capacity in the short term. We suggest promoting urban system diversity and redundancy across different dimensions to enhance urban resilience, but caution that linearly promoting systemic redundancy might harm the long-term sustainability of resource allocations.

Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

In December 2019 an outbreak of a new disease happened, in Wuhan city, China, in which the symptoms were very similar to pneumonia. The disease was attributed to SARS-CoV-2 as the infectious agent and it was called the new coronavirus or Covid-19. In March 2020, the World Health Organization declared a worldwide pandemic of the new coronavirus. We have already counted more than 110 million cases and almost 2.5 million deaths worldwide. In order to assist in decision-making to contain the disease, several scientists around the world have engaged in various efforts, and they have proposed a lot of systems and solutions for tracking, monitoring, and predicting confirmed cases and deaths from Covid-19. Mathematical models help to analyze and understand the evolution of the disease, but understanding the disease was not enough, it was necessary to understand the problem in a quantitative way to lead the decision-making during the pandemic. Several initiatives have made use of Artificial Intelligence, and models were designed using machine learning algorithms with features for temporal and spatio-temporal investigation and prediction of cases of Covid-19. Among the algorithms used are Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Graph Neural Networks (GNNs), Ecological Niche Models (ENMs), Long-Short Term Memory Networks (LSTM), linear regression, and others. And these had good results, and to analyze them, the Root Mean Squared Error (RMSE), Log Root Mean Squared Error (RMSLE), correlation coefficient, and others were used as metrics. Covid-19 presents a huge problem to public health worldwide, so it is of utmost importance to investigate it, and with these two approaches it is possible to track not only how the disease evolves but also to know which areas are at risk. And these solutions can help in supporting decision-making by health managers to make the best decisions for the disease that is in the outbreak. This chapter aims to present a literature review and a brief contribution to the use of machine learning methods for temporal and spatio-temporal prediction of Covid-19, using Brazil and its federative units as a case study. From canonical methods to deep networks and hybrid committee-based, approaches will be investigated. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Uncovering the spatio-temporal impact of the COVID-19 pandemic on shared e-scooter usage: A spatial panel model.

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.

Changing spatiotemporal patterns for hepatitis of unspecified aetiology in China, 2004-2021: a population-based surveillance study.

Objectives: As global efforts continue toward the target of eliminating viral hepatitis by 2030, the emergence of acute hepatitis of unspecified aetiology (HUA) remains a concern. This study assesses the overall trends and changes in spatiotemporal patterns in HUA in China from 2004 to 2021. Methods: We extracted the incidence and mortality rates of HUA from the Public Health Data Center, the official website of the National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System from 2004 to 2021. We used R software, ArcGIS, Moran's statistical analysis, and joinpoint regression to examine the spatiotemporal patterns and annual percentage change in incidence and mortality of the HUA across China. Results: From 2004 to 2021, a total of 707,559 cases of HUA have been diagnosed, including 636 deaths. The proportion of HUA in viral hepatitis gradually decreased from 7.55% in 2004 to 0.72% in 2021. The annual incidence of HUA decreased sharply from 6.6957 per 100,000 population in 2004 to 0.6302 per 100,000 population in 2021, with an average annual percentage change (APC) reduction of -13.1% (p < 0.001). The same result was seen in the mortality (APC, -22.14%, from 0.0089/100,000 in 2004 to 0.0002/100,000 in 2021, p < 0.001). All Chinese provinces saw a decline in incidence and mortality. Longitudinal analysis identified the age distribution in the incidence and mortality of HUA did not change and was highest in persons aged 15-59 years, accounting for 70% of all reported cases. During the COVID-19 pandemic, no significant increase was seen in pediatric HUA cases in China. Conclusion: China is experiencing an unprecedented decline in HUA, with the lowest incidence and mortality for 18 years. However, it is still important to sensitively monitor the overall trends of HUA and further improve HUA public health policy and practice in China.

Spatial analysis of the spread of Covid-19 and its demographic consequences in the regions of the South of European Russia

The article presents results of the multi-scale analysis of the processes of coronavirus infection spread and its impact on the demographic situation in the world, Russia and regions of the South of the European part of Russia. The methodological basis of the study was the principles of geoinformation monitoring, making it possible to process and visualize large volumes of diverse materials. The information base was statistical data from the Russian and foreign sources reflecting the spread of coronavirus infection at various spatial levels from global to regional-local. The characteristic features of changes in the parameters of the disease during its active expansion are described. The article also deals with dynamics in demographic indicators and identifies trends in their widespread deterioration. The contribution of the South of European Russia macro-region to the all-Russian Covid-19 situation is determined. Development of the coronavirus pandemic at the level of municipal districts is analyzed using individual regions as an example. The study identifies main factors of the Covid-19 pandemic development and demonstrates some of its features and consequences in the largest urban agglomerations.

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Spatio-temporal relative risks and priority areas for visceral leishmaniasis control in Brazil, between 2001 and 2020.

Visceral leishmaniasis (VL) is a pressing public health problem in Brazil. The proper implementation of disease control programs in priority areas is a challenge for healthcare managers. The present study aimed to analyze the spatio-temporal distribution and identify high risk areas of VL occurrence in the Brazilian territory. We analyzed data regarding new cases with confirmed diagnosis of VL in Brazilian municipalities, from 2001 to 2020, extracted from the Brazilian Information System for Notifiable Diseases. The Local Index of Spatial Autocorrelation (LISA) was used to identify contiguous areas with high incidence rates in different periods of the temporal series. Clusters of high spatio-temporal relative risks were identified using the scan statistics. The accumulated incidence rate in the analyzed period was 33.53 cases per 100,000 inhabitants. The number of municipalities that reported cases showed an upward trend from 2001 onward, although there was a decrease in 2019 and 2020. According to LISA, the number of municipalities considered a priority increased in Brazil and in most states. Priority municipalities were predominantly concentrated in the states of Tocantins, Maranhão, Piauí, and Mato Grosso do Sul, in addition to more specific areas of Pará, Ceará, Piauí, Alagoas, Pernambuco, Bahia, São Paulo, Minas Gerais, and Roraima. The spatio-temporal clusters of high-risk areas varied throughout the time series and were relatively higher in the North and Northeast regions. Recent high-risk areas were found in Roraima and municipalities in northeastern states. VL expanded territorially in Brazil in the 21st century. However, there is still a considerable spatial concentration of cases. The areas identified in the present study should be prioritized for disease control actions.

Beta-negative binomial nonlinear spatio-temporal random effects modeling of COVID-19 case counts in Japan.

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance.

Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data

Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this paper, we analyze a high-resolution COVID-19 dataset in Cali, Colombia, that records the precise time and location of every confirmed case. We develop a non-stationary spatio-temporal point process equipped with a neural network-based kernel to capture the heterogeneous correlations among COVID-19 cases. The kernel is carefully crafted to enhance expressiveness while maintaining model interpretability. We also incorporate some exogenous influences imposed by city landmarks. Our approach outperforms the state-of-the-art in forecasting new COVID-19 cases with the capability to offer vital insights into the spatio-temporal interaction between individuals concerning the disease spread in a metropolis.

COVID-19 in Bangladesh: A Spatio-temporal and Gender Analysis

This chapter explores the uneven transmission of coronavirus (COVID-19) and its mitigation efforts in Bangladesh through investigating the spatio-temporal and gender dimensions of this pandemic. By analysing data collected from various secondary sources, the study has revealed that the virus initially spread rapidly in Dhaka, the capital city, and its surrounding districts but later reached remote parts of the country. Among many factors, population density, distance from the epicentre, and discriminatory gender norms and practices remain crucial in determining the rates of infections, deaths, and vaccine accessibility among people. The analysis also sheds some light on the COVID-19 situation in Bangladesh with the scenario of other South Asian countries. Based on the findings, this study emphasises the need for accurate and disaggregated data to ensure more effective responses to arrest further transmission of the deadly virus and prepare for future potential pandemic situations © TheEditor(s) (ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, 2022.

A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

China's Largest City-Wide Lockdown: How Extensively Did Shanghai COVID-19 Affect Intensity of Human Activities in the Yangtze River Delta?

COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect.

Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan.

This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objects in the penalty matrix and to detect multiple clusters. A generalized lasso model with two L1 penalties is proposed, which can be separated into two generalized lasso models: trend filtering of temporal effect and fused lasso of spatial effect for each time point. To select the tuning parameters, the approximate leave-one-out cross-validation (ALOCV) and generalized cross-validation (GCV) are considered. A simulation study is conducted to evaluate the proposed method compared to other approaches in different problems and structures of multiple clusters. The generalized lasso with ALOCV and GCV provided smaller MSE in estimating the temporal and spatial effect compared to unpenalized method, ridge, lasso, and generalized ridge. In temporal effects detection, the generalized lasso with ALOCV and GCV provided relatively smaller and more stable MSE than other methods, for different structure of true risk values. In spatial effects detection, the generalized lasso with ALOCV provided higher index of edges detection accuracy. The simulation also suggested using a common tuning parameter over all time points in spatial clustering. Finally, the proposed method was applied to the weekly Covid-19 data in Japan form March 21, 2020, to September 11, 2021, along with the interpretation of dynamic behavior of multiple clusters.

Spatio-temporal modeling of infectious diseases by integrating compartment and point process models.

Infectious disease modeling plays an important role in understanding disease spreading dynamics and can be used for prevention and control. The well-known SIR (Susceptible, Infected, and Recovered) compartment model and spatial and spatio-temporal statistical models are common choices for studying problems of this kind. This paper proposes a spatio-temporal modeling framework to characterize infectious disease dynamics by integrating the SIR compartment and log-Gaussian Cox process (LGCP) models. The method's performance is assessed via simulation using a combination of real and synthetic data for a region in São Paulo, Brazil. We also apply our modeling approach to analyze COVID-19 dynamics in Cali, Colombia. The results show that our modified LGCP model, which takes advantage of information obtained from the previous SIR modeling step, leads to a better forecasting performance than equivalent models that do not do that. Finally, the proposed method also allows the incorporation of age-stratified contact information, which provides valuable decision-making insights. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02354-4.

COVID-19 lockdown has indirect, non-equivalent effects on activity patterns of Reeves's Pheasant (Syrmaticus reevesii) and sympatric species.

The outbreak of the COVID-19 pandemic has brought massive shifts in human activities through a global blockade, directly affecting wildlife survival. However, the indirect impacts of changes in human activities are often easily overlooked. We conducted surveys of Reeves's Pheasant (Syrmaticus reevesii) and its sympatric species by camera traps in forest-type nature reserves in three different scenarios: pre-lockdown, lockdown and post-lockdown. An increase in livestock activities observed during the lockdown and post-lockdown period in our study area provided us an opportunity to investigate the indirect impact of the lockdown on wildlife. The pre-lockdown period was used as a baseline to compare any changes in trends of relative abundance index, activity patterns and temporal spacing of targeted species and livestock. During the lockdown period, the relative abundance index of livestock increased by 50% and there was an increase in daytime activity. Reeves's Pheasant showed avoidance responses to almost all sympatric species and livestock in three different periods, and the livestock avoidance level of Reeves's Pheasant during the lockdown period was significantly and positively correlated with the relative abundance index of livestock. Species-specific changes in activity patterns of study species were observed, with reduced daytime activities of Hog Badger and Raccoon Dog during and after the confinement periods. This study highlights the effect of the COVID-19 lockdown on the responses of wildlife by considering the changes in their temporal and spatial use before, during and after lockdown. The knowledge gained on wildlife during reduced human mobility because of the pandemic aids in understanding the effect of human disturbances and developing future conservation strategies in the shared space, to manage both wildlife and livestock.

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.