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 States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter.Methods: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.Results: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis.Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors.

Tourism is one of the most economically important industries. It is, however, vulnerable to disaster events. Geotagged social media data, as one of the forms of volunteered geographic information (VGI), has been widely explored to support the prevention, preparation, and response phases of disaster management, while little effort has been put on the recovery phase. This study develops a scientific workflow and methods to monitor and assess post-disaster tourism recovery using geotagged Flickr photos, which involve a viewshed based data quality enhancement, a space-time bin based quantitative photo analysis, and a crowdsourcing based qualitative photo analysis. The developed workflow and methods have also been demonstrated in this paper through a case study conducted for the Philippines where a magnitude 7.2 earthquake (Bohol earthquake) and a super typhoon (Haiyan) occurred successively in October and November 2013. In the case study, we discovered spatiotemporal knowledge about the post-disaster tourism recovery, including the recovery statuses and trends, and the photos visually showing unfixed damages. The findings contribute to a better tourism rehabilitation of the study area.