Social and Behavioral Sciences Commons^™

Revealing Public Opinion Towards Covid-19 Vaccines With Twitter Data In The United States: Spatiotemporal Perspective, Tao Hu, Siqin Wang, Wei Luo, Mengxi Zhang, Xiao Huang, Yingwei Yan, Regina Liu, Kelly Ly, Viraj Kacker, Bing She, Zhenlong Li

Faculty Publications

Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance.

Objective:The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United …

Odt Flow: Extracting, Analyzing, And Sharing Multi-Source Multi-Scale Human Mobility, Zhenlong Li, Xiao Huang, Tao Hu, Huan Ning, Xinyue Ye, Binghu Huang, Xiaoming Li

Faculty Publications

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users …

Measuring Global Multi-Scale Place Connectivity Using Geotagged Social Media Data, Zhenlong Li, Xiao Huang, Xinyue Ye, Yuqin Jiang, Yago Martin, Huan Ning, Michael E. Hodgson, Xiaoming Li

Faculty Publications

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 issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records …

Spatiotemporal Patterns Of Human Mobility And Its Association With Land Use Types During Covid-19 In New York City, Yuqin Jiang, Xiao Huang, Zhenlong Li

Faculty Publications

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 tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by …