About
232
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Introduction
Dr.Yang is a leader of GIS and computing on geospatial cyberinfrstructure, spatial cloud computing, spatiotemporal computing, and spatial computing. He is PI on over $10M research grants and participated in over $40M projects. Several of his >200 publications have been among the top five cited and read papers of IJDE and CEUS. His PNAS spatial computing definition paper is captured by Nobel Intent Blog in 2011. He placed 25+ professors in the U.S. and China, and served in 10+ leader positions.
Additional affiliations
August 2014 - present
September 2013 - present
August 2009 - May 2014
Education
August 1997 - July 2000
September 1991 - July 1997
Publications
Publications (232)
Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data p...
In recent years, our world has experienced significant disruptions due to the COVID-19 pandemic, and Russia's 2022 invasion of Ukraine, impacting human activities and the global environment. This paper explored air quality changes in Ukraine due to COVID-19, and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approa...
Geoscience is now facing the huge potential enabled by the cyberinfrastructure, sensor network, big data, cloud computing, and data science. In this new era, what skills should geoscientists know and what actions can they take to foster new research topics? Are there already successful stories of data science in geosciences and what are the experie...
The retrieval of cloud fraction in satellite hyperspectral sounder field of view (FOV) is crucial for numerical weather prediction. This study proposes an innovative cloud fraction retrieval model for the hyperspectral infrared sounder - Cross-track Infrared Sounder (CrIS). The model is trained with a deep neural network (DNN), using the CrIS radia...
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...
Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transpo...
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...
In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated p...
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...
Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in disasters for forming timely situational awareness. Yet, using social media to sense electricity infrastructure conditions has not...
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...
This paper investigates spatiotemporal changes of nitrogen dioxide (NO2) tropospheric vertical column density due to the COVID-19 pandemic using satellite observations before, during and after the lockdown (hereafter referred as the pre-, peri- and post-periods) in six different countries: China, South Africa, Brazil, India, the UK and the US, and...
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...
The outbreak of COVID-19 from late 2019 not only threatens the health and lives of humankind but impacts public policies, economic activities, and human behavior patterns significantly. To understand the impact and better prepare for future outbreaks, socioeconomic factors play significant roles in (1) determinant analysis with health care, environ...
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 death of George Floyd has brought a new wave of 2020 Black Lives Matter (BLM) protests into U.S. cities. Protests happened in a few cities accompanied by reports of violence over the first few days. The protests appear to be related to rising crime. This study uses newly collected crime data in 50 U.S. cities/counties to explore the spatiotempo...
Coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China as an infectious disease, and has quickly resulted in an ongoing pandemic. A data-driven approach was developed to estimate medical resource deficiencies due to medical burdens at county level during the COVID-19 pandemic. The study duration was mainly from Fe...
Detection of cloud contaminated field of views (FOV) from satellite hyperspectral infrared sounders is essential for numerical weather prediction. A new cloud detection model is developed for the cross-track infrared sounder (CrIS) using the artificial deep neural network (DNN) technique. The truth cloud information used is from another instrument...
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...
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...
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...
Various recent studies have shown that societal efforts to mitigate (e.g. “lockdown”) the outbreak of the 2019 coronavirus disease (COVID-19) caused non-negligible impacts on the environment, especially air quality. To examine if interventional policies due to COVID-19 have had a similar impact in the US state of California, this paper investigates...
A data-driven approach is developed to estimate medical resource deficiencies or medical burden at county level during the COVID-19 pandemic from February 15, 2020 to May 1, 2020 in the U.S. Multiple data sources were used to extract local population, hospital beds, critical care staff, COVID-19 confirmed case numbers, and hospitalization data at c...
A data-driven approach is developed to estimate medical resource deficiencies or medical burden at county level during the COVID-19 pandemic from February 15, 2020 to May 1, 2020 in the U.S. Multiple data sources were used to extract local population, hospital beds, critical care staff, COVID-19 confirmed case numbers, and hospitalization data at c...
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...
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...
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 Night Time Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. Our results show that the monthly average NTL brightness is much lower...
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...
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...
Database systems are pervasive components in the current big data era. However, efficiently managing and querying grid-based or array-based multidimensional climate data are still beyond the capabilities of most databases. The mismatch between the array data model and relational data model limited the performance to query multidimensional data in a...
The integration of space and time into a spatiotemporal framework enables us to obtain unprecedented information and knowledge to tackle many challenging scientific questions and engineering problems. The key to such enablement is our capability to analyze, fuse, mine, learn, simulate, and visualize patterns and relationships embeded in Big Spatiot...
Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to ob...
The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity...
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...
The previous 25 chapters introduced relevant technologies, applications,
and other topics related to Digital Earth. Respective challenges and future research
were also proposed by various authors. In this concluding chapter, we briefly review
Digital Earth past and present, followed by a set of challenges and future trends,
speculating on how Digit...
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...
The previous 25 chapters introduced relevant technologies, applications, and other topics related to Digital Earth. Respective challenges and future research were also proposed by various authors. In this concluding chapter, we briefly review Digital Earth past and present, followed by a set of challenges and future trends, speculating on how Digit...
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...
Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) prod...
Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amount...
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...
Currently, studies on constructing integrated modeling solutions in a web-based environment primarily focus on the chaining of different model-services, while the flow of exploring solutions for complex geo-problems – geographically distributed issues – has been less studied. To address this gap, a teamwork-oriented integrated modeling approach to...
strong>In this study, optical and microwave satellite observations are integrated to estimate soil moisture at high spatial resolution and applied for drought analysis in the continental United States. To estimate soil moisture, a new refined model is proposed to estimate soil moisture (SM) with auxiliary data like soil texture, topography, surface...
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...
Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situa...
Social media platforms have been contributing to disaster management during the past several years. Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes, such as caution and advice, to better understand the meaning and leverage useful information from the social med...
An increasingly rich array of geographic information, such as real-time sensor data, public web pages, social media data, and dynamic maps, are available over the Internet tobe integrated by multiple geographic information science (GIScience) applications that were not possible in the past. The conventional tightly coupled development approach is n...
Weather radar data, which have obvious spatial characteristics, represent an important and essential data source for weather identification and prediction, and the multi-dimensional visualization and analysis of such data in a three-dimensional (3D) environment are important strategies for meteorological assessments of potentially disastrous weathe...