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Publications (43)
The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controllin...
Due to its near-real-time crowdsourcing nature, social media demonstrates great potential of rapidly reflecting public opinion during emergency events. However, systematic approaches are still desired to perceive public opinion in a rapid and reliable manner through social media. This research proposes two quantitative metrics-the fraction of event...
Accurate weather prediction, particularly accurate temperature prediction, is critical in decision-making for energy consumption, health risks, and economics. Regional numerical weather prediction (NWP) models produce operational-level temperature forecasts based on local atmospheric circulation conditions. They suffer from data- and computational...
Previous research has noted that many factors greatly influence the spread of COVID‐19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unob...
The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for econ...
The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate yet becam...
In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countr...
Smart cities evolve rapidly along with the technical advances in wireless and sensor networks, information science, and human–computer interactions. Urban computing provides the processing power to enable the integration of such technologies to improve the living quality of urban citizens, including health care, urban planning, energy, and other as...
Objectives
The US and rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate ye...
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 effectivenes...
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 pop...
Under the global health crisis of COVID-19, timely, and accurate epidemic data are important for observation, monitoring, analyzing, modeling, predicting, and mitigating impacts. Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic. Current COVID-19 case data are scattered across a va...
Under the global health crisis of COVID-19, timely, and accurate epidemic data are important for observation, monitoring, analyzing, modeling, predicting, and mitigating impacts. Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic. Current COVID-19 case data are scattered across a va...
The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatali...
17 The global covid-19 pandemic puts great pressure on medical resources worldwide and leads 18 healthcare professionals to question which individuals are in imminent need of care. With 19 appropriate data of each patient, hospitals can heuristically predict whether or not a patient 20 requires immediate care. We adopted a deep learning model to pr...
The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center...
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 shari...
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, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by th...
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, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the...
Finding geospatial data has been a big challenge regarding the data size and heterogeneity across various domains. Previous work has explored using machine learning to improve geospatial data search ranking, but it usually relies on training data labelled by subject matter experts, which makes it laborious and costly to apply to scenarios in which...
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 shari...
This research is a collaborative and innovative effort in building such an archive, including the collection of a variety of data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socio-economics, and global news. Due to the heterogeneity between data sources, our effort als...
Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data so...
One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospat...
Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing eme...
Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (A...
In the research field of spatiotemporal data discovery, how to utilize the semantic characteristics of spatiotemporal datasets is an important topic. This paper presented a content-based recommendation method, and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery. The source data o...
An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources to enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA tec...
Big Earth data are produced from satellite observations, Internet-of-Things, model simulations, and other sources. The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding, responding, and addressing challenges of Earth sciences and applications. In the past years, new technologies...
Planetary Defense (PD) has become a critical effort of protecting our home planet by discovering potentially hazardous objects (PHOs), simulating the potential impact, and mitigating the threats. Due to the lack of structured architecture and framework, pertinent information about detecting and mitigating near earth object (NEO) threats are still d...
The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web us...
Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently...
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 r...
Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1) the system ena...
Spatial characteristics reveal the concentration of vaccine-preventable disease in Africa and the Near East and that disease dispersion is variable depending on disease. The exception is whooping cough, which has a highly variable center of concentration from year to year. Measles exhibited the only statistically significant spatial autocorrelation...
Tropical cyclones (TCs) usually cause severe damages and destructions. TC intensity forecasting helps people prepare for the extreme
weather and could save lives and properties. Rapid Intensifications (RI) of TCs are the major error sources of TC intensity forecasting.
A large number of factors, such as sea surface temperature and wind shear, affec...
Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering...
It is challenging to find relevant data for research and development purposes in the geospatial big data era. One long-standing problem in data discovery is locating, assimilating and utilizing the semantic context for a given query. Most research in the geospatial domain has approached this problem in one of two ways: building a domain-specific on...
Big Data has emerged with new opportunities for research, development, innovation, and business. It is characterized by the so-called four Vs: volume, velocity, veracity, and variety, and it may bring significant value through the processing of a large amount of data. The transformation of Big Data's four Vs into the fifth V (value) is a grand chal...
It is very challenging for scientists to find the right oceanographic data in a fast manner. A novel approach was proposed to analyze user access logs to explore the implicit relationship between oceanographic datasets. This paper reports a cloud-based data analytics framework to speed up the process for dealing with problems, such as (1) user acce...
Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upo...
Projects
Projects (5)
This project aims to automatically detect severe weather events from big geospatial datasets, including observations, simulations, and user-generated datasets. These events include dust storms, tropical cyclones, floods, wildfires, etc. The ultimate goal of this project is to facilitate better mitigation of these severe weather events and improve the better understanding of their physical process.
