Spatio-temporal variations of night-time lights at early stages of the COVID-19 epidemic in the United States

The COVID-19 epidemic swept the world and continued to spread. Without effective medical treatments and vaccine during the early stage of the pandemic, local governments in various countries had to lock down cities and adopt non-pharmaceutical interventions (NPIs), such as the stay-at-home order, social distancing, and so on. NPIs against the COVID-19 epidemic have significantly changed socioeconomic activities in cities. However, characteristics and patterns of urban socio-economic activities under this influence are still unclear. Benefiting from the development of earth observation technologies, such large-scale changes in socioeconomic activities are enough to be captured by satellites through remotely sensed night-time lights (NTL). In this study, we selected 20 major cities in the United States including New York, Chicago and Los Angeles to analyze spatio-temporal variations of NTL caused by the lockdown of cities. The first round of COVID-19 epidemic occurred in the United States in mid-March 2020. Since March 2020, American cities have successively issued stay-at-home orders, but there are differences in the time and strictness of policy implementation. Large cities have a higher population density and a higher intensity of social activities, so they are more susceptible to infectious diseases. The diversity of lockdown dates and strictness of lockdowns in cities in the United States are conducive to investigating the spatio-temporal variations of NTL. We acquired monthly averaged NPP VIIRS products of February, March and April, 2020, which are from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (NPP). We further analyzed the spatial pattern, distance decay and disparities in land use types of changes in NTL. Results show that NTL generally dimmed by 5-8% in U.S. cities caused by the lockdown of cities. There are 6 cities where the luminous brightness has dropped by more than 10%: Chicago, Dallas, Denver, Detroit, Minneapolis, and St. Louis. Among them, Minneapolis has the largest decrease in luminous brightness, with a decrease of about 40% in March. The spatial change of NTL shows obvious "core-periphery" pattern that the reduction of NTL declines with the distance from the city center. This is mainly because the central area of the city is a concentrated commercial area. After the closure of the city, commercial activities have dropped significantly, resulting in an obvious reduction in NTL around city centers. The reduction of NTL varies among diverse urban land use types. In New York, NTL decreased the most on land for residence and aviation facilities by 12% and 11%, respectively. In Chicago, NTL generally decreased by 20% in all types of urban land, and NTL recovered after one month of the lockdown of cities in other urban land except sports facilities land. This study only analyzes the spatio-temporal changes of NTL. In the future, it can be combined with multi-source data to explain the driving force of NTL changes. Nighttime light remote sensing effectively reflects urban socio-economic dynamics with an important application in monitoring and assessing socio-economic impacts of emergencies. © 2022 National Remote Sensing Bulletin. All rights reserved.

Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models.

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

Spatiotemporal Variation of Nitrogen Dioxide (No2) Over the Region Of Ahmedabad City

Nitrogen Dioxide (NO2) monitoring is a necessary step towards the understanding of climate change and public health. In this study, we tried to understand the comparative analysis of variation of NO2 over the region of Ahmedabad city. We have extracted NO2 concentration data for the year 2019 and 2020. The data was collected from both ground-based measurements and satellite based measurements of NO2 concentrations values. The results highlighted complete dynamics of seasonal NO2 concentration during the year 2019 and 2020 including the lockdown effect of COVID-19 outbreak. The validation approach of satellite data, based on cross-correlation analysis with ground data, it provided value of the Pearson correlation factor of 0.613 and correlation coefficients (R2) of 0.376. The huge fall in seasonal trend of NO2 concentration because of the pandemic is also shown in this study. © 2021 IEEE.

Visualization Analysis of Spatiotemporal Data of COVID-19

Aiming at the problem of COVID-19 epidemic data visualization, this paper proposes a spatiotemporal visualization analysis method based on the technology of scraping crawler and visualization, and carries on the visualization analysis and research, intuitively shows the development and change of the epidemic situation in different countries and regions, and excavates its spatiotemporal variation rules. Firstly, we use scrapy crawler framework to collect COVID-19 epidemic data;then, the collected data were cleaned and processed to construct a spatiotemporal data set of COVID-19 epidemic;finally, pyecharts is used to analyze the dataset data visually. The results showed the changes and trends of epidemic situation in different countries and regions, and provided reference for epidemic prevention and control. © 2021 IEEE.

Variation in age of primiparity in mountain goats (Oreamnos americanus) estimated from horn growth increments

Variation in age of primiparity is important for population dynamics and wildlife management because it can affect population growth. Using a novel technique based on the trade-off between annual horn growth and reproduction, we estimated the age of primiparity for 2274 female mountain goats (Oreamnos americanus (Blainville, 1816)) harvested across British Columbia, Canada, from 1976 to 2019. We then investigated spatio-temporal variation in the probability that harvested females were primiparous when aged 3, 4, or 5 years and older using Bayesian ordinal regressions. We found that the probability of primiparity at 3 years decreased over time in nearly all mountain ranges. In the Coastal Mountain range, however, the probability of primiparity at age 3 significantly increased. These results suggest that the large coastal populations of mountain goats could be more resilient to harvest than other populations in British Columbia, which may be experiencing environmental effects promoting later primiparity. Models predicting age of primiparity from annual growth measures are a valuable tool for wildlife management and could help conservation of many species. (English) [ FROM AUTHOR] L'âge à la primiparité est une composante importante de la dynamique des populations et de la gestion de la faune, car elle peut influencer la croissance des populations. Utilisant une technique novatrice reposant sur les compromis entre la croissance annuelle des cornes et la reproduction, nous avons estimé l'âge à la primiparité de 2274 chèvres de montagne (Oreamnos americanus (Blainville, 1816)) femelles récoltées en Colombie-Britannique (Canada) de 1976 à 2019. Ensuite, en utilisant des régressions ordinales bayésiennes, nous avons examiné les tendances spatiotemporelles de la probabilité que les femelles soient primipares à 3, 4 ou 5 ans et plus. Nous avons constaté un déclin temporel de la probabilité de récolter une femelle primipare à 3 ans dans presque toutes les chaînes de montagnes de la province. Cependant, dans la chaîne de montagne côtière, cette probabilité augmente significativement au fil du temps. Ces résultats semblent indiquer que les grandes populations côtières de chèvres de montagne seraient plus résilientes aux récoltes annuelles que d'autres populations en Colombie-Britannique, chez lesquelles la primiparité pourrait être retardée par des effets environnementaux. Les modèles qui prédisent l'âge à la primiparité à partir de mesures de la croissance annuelle constituent un outil à fort potentiel pour la gestion de la faune et pourraient s'avérer utiles pour la conservation de plusieurs espèces. (French) [ FROM AUTHOR] Copyright of Canadian Journal of Zoology is the property of Canadian Science Publishing 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.)