Yuqin Jiang

Yuqin Jiang
University of South Carolina | USC · Department of Geography

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18
Publications
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346
Citations

Publications

Publications (18)
Article
Full-text available
Accessibility is a topic of interest to multiple disciplines for a long time. In the last decade, the increasing availability of data may have exceeded the development of accessibility modeling approaches, resulting in a modeling gap. In part, this modeling gap may have resulted from the differences needed for single versus multimodal opportunities...
Article
Full-text available
Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy i...
Article
Full-text available
The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged...
Article
Full-text available
Evacuation is an effective and commonly taken strategy to minimize death and injuries from an incoming hurricane. For decades, interdisciplinary research has contributed to a better understanding of evacuation behavior. Evacuation destination choice modeling is an essential step for hurricane evacuation transportation planning. Multiple factors are...
Article
Full-text available
Objective: Analysis of geolocation-based social media Big Data provides unprecedented opportunities for a broad range of domains including health as health is intrinsically linked to the geographic characteristics of places. HIV infection is largely driven by HIV risk behaviors, such as unsafe sexual behavior and drug abuse/addiction. This study e...
Preprint
Full-text available
Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy i...
Article
Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy i...
Article
Full-text available
This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four different sources. We further design a Responsive Ind...
Article
Full-text available
Background: Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources...
Article
Full-text available
The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission....
Preprint
BACKGROUND Human movement is among the essential forces that drive spatial spread of infectious diseases. To date, reducing and tracking human movement during the pandemic have proven effective in limiting the spread of COVID-19. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as p...
Preprint
Full-text available
This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google c...
Preprint
Full-text available
The outbreak of COVID-19 highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has been proved to be associated with the viral transmission. In this study, we analyzed 587 million tweets worldwide to see how global collaborative efforts in reducing human mobility are ref...
Article
Full-text available
Big climate data offers great opportunities for scientific discovery but demands efficient and effective analytics to investigate unknown and complex patterns. Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These systems are often not scalable to handle massive...
Article
Full-text available
Hurricanes are one of the most common natural hazards in the United States. To reduce fatalities and economic losses, coastal states and counties take protective actions including sheltering in place and evacuation away from the coast. Not everyone adheres to hurricane evacuation warnings or orders. In reality evacuation rates are far less than 100...
Conference Paper
Full-text available
City connectivity is an important measurement in characterizing human dynamics from regional to international scales. World City Network has been built based on companies' communication. The interactions between spatial and social dimensions of cities have both conceptual and practical significance. To further expand the studies of inter-city netwo...
Article
Full-text available
Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes a...

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Projects

Projects (4)
Project
In this project, we aim to highlight the impact of the long-standing inequity issue in the U.S. on the implementation of COVID-19 mitigation measures, providing fundamental knowledge that benefits policy-making for better mitigation strategies of the future pandemics. Fine-grained mobility records from SafeGraph, Descartes Labs, Google, Apple, and Twitter, are the main data sources we use. This project will be updated monthly.
Project
Human movement is among the essential forces that drive the spatial spread of COVID-19. During such a global pandemic, monitoring and analyzing human movement patterns or population flows are critical for us to gain a better understanding into current and future infectious risk at the population level. Funded by NSF and NIH, this project utilizes big movement data (e.g., social media, mobile phone data), artificial intelligence (AI), and spatiotemporal analysis to monitor the spatial spread of COVID-19, quantify the effectiveness of the control measures, and predict the current and future infectious risk at various geospatial scales. Results of this study will not only provide enhanced situation awareness for the government at all levels, but also offer valuable contributions to building collective public awareness of the role people play in the evolution of the COVID-19 pandemic.
Project
Leveraging social sensing, remote sensing and big data computing for supporting disaster management (e.g., flooding) in different disaster phases (mitigation, preparedness, response, and recovery). More information here: http://gis.cas.sc.edu/gibd/mining-twitter-data-to-enhance-disaster-situational-awareness