Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.

Physical distancing has been argued as one of the effective means to combat the spread of COVID-19 before a vaccine or therapeutic drug becomes available. How far people can be spatially separated is partly behavioral but partly constrained by population density. Most models developed to predict the spread of COVID-19 in the U.S. do not include population density explicitly. This study shows that population density is an effective predictor of cumulative infection cases in the U.S. at the county level. Daily cumulative cases by counties are converted into 7-day moving averages. Treating the weekly averages as the dependent variable and the county population density levels as the explanatory variable, both in logarithmic scale, this study assesses how population density has shaped the distributions of infection cases across the U.S. from early March to late May, 2020. Additional variables reflecting the percentages of African Americans, Hispanic-Latina, and older adults in logarithmic scale are also included. Spatial regression models with a spatial error specification are also used to account for the spatial spillover effect. Population density alone accounts for 57% of the variation (R-squared) in the aspatial models and up to 76% in the spatial models. Adding the three population subgroup percentage variables raised the R-squared of the aspatial models to 72% and the spatial model to 84%. The influences of the three population subgroups were substantial, but changed over time, while the contributions of population density have been quite stable after the first several weeks, ascertaining the importance of population density in shaping the spread of infection in individual counties, and in their neighboring counties. Thus, population density and sizes of vulnerable population subgroups should be explicitly included in transmission models that predict the impacts of COVID-19, particularly at the sub-county level.

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to making large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to summarize better and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area.

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.

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.