The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people’s daily lives, work, and the surrounding environment. Investigations into these changes are very important for decision makers to implement policies on economic loss assessments and stimulation packages, city reopening, resilience of the environment, and arrangement of medical resources. In order to analyze the impact of COVID-19 on people’s lives, activities, and the natural environment, this paper investigates the spatial and temporal characteristics of Nighttime Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. The monthly mean NTL radiance, and daily and monthly mean AQI are calculated over mainland China and compared before and during the pandemic. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation, and public facilities and commercial centers, with NTL radiance ranges of 5–20, 20–40 and greater than 40 (nW· ${\mathrm{cm}}^{-2}$ · ${\mathrm{sr}}^{-1}$ ), respectively. We found that the Number of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for mainland China from January to March 2020, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people’s daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of mainland China has improved with the reduction in non-essential industries and motor vehicle usage. This evidence demonstrates that the Chinese government has executed very stringent quarantine policies to deal with the pandemic. The decisive response to control the spread of COVID-19 provides a reference for other parts of the world.

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, Brazil, Russia, and the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., India, Russia, and Brazil. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, human mobility, environmental impact, socioeconomical impact; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.