Talks

The COVID-19 virus has caused and continues to cause unprecedented impacts on the life trajectories of millions of people globally. Recently, to combat the transmission of the virus, vaccination campaigns around the world have become prevalent. However, while many see such campaigns as positive (e.g., protecting lives), others see them as negative (e.g., the side effects that are not fully understood scientifically), resulting in diverse sentiments towards vaccination campaigns. In addition, the diverse sentiments have seldom been systematically quantified let alone their dynamic changes over space and time. To shed light on this issue, we propose an approach to analyze vaccine sentiments in space and time by using supervised machine learning combined with word embedding techniques. Taking the United States as a test case, we utilize a Twitter dataset (approximately 11.7 million tweets) from January 2015 to July 2021 and measure and map vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the nation. In doing so, we can capture the heterogeneous public opinions within social media discussions regarding vaccination among states. Results show how positive sentiment in social media has a strong correlation with the actual vaccinated population. Furthermore, we introduce a simple ratio between Anti and Pro-vaccine as a proxy to quantify vaccine hesitancy and show how our results align with other traditional survey approaches. The proposed approach illustrates the potential to monitor the dynamics of vaccine opinion distribution online, which we hope, can be helpful to explain vaccination rates for the ongoing COVID-19 pandemic.

With progressively increased people living in cities, and lately the global COVID-19 outbreak, human mobility within cities has changed. Coinciding with this change, is the recent uptake of the ‘15-Minute City’ idea in urban planning around the world. One of the hallmarks of this idea is to create a high quality of life within a city via an acceptable travel distance (i.e., 15 minutes). However, a definitive benchmark for defining a ‘15- Minute City’ has yet to be agreed upon due to the heterogeneous character of urban morphologies worldwide. To shed light on this issue, we develop an agent-based model named ‘D-FMCities’ utilizing realistic street networks and points-of-interest, in this instance the borough of Queens in New York City as a test case. Through our modeling we grow diverse communities from the bottom up and estimate the size of such local communities to delineate 15-minute cities. Our findings suggest that the model could be helpful to detect the flexibility of defining the extent of a ‘15-minute city’ and consequently support uncovering the underlying factors that may affect its various definitions and diverse sizes throughout the world.

The global COVID-19 outbreak has deeply affected everyone’s daily life and constrained human mobility behaviors by the preventive measures put in place to mitigate the transmission. Different mass media channels and live location data streaming techniques have contributed to tracking the spread and predicting the trajectory of the pandemic developments. Moreover, the human mobility patterns hidden in these digital datasets – if properly interpreted- can document and reflect how people cope with the ‘paradoxical’ daily activities when public life and social encounter are prohibited. In this research, we utilize the mobile location data collected from January 2019 to date within Auckland city, New Zealand, to explore the social impacts on urban ‘hotspots’ concerning the transformation of social behaviors. The urban ‘hotspots’ here refer to user-defined urban spaces with crowded human mobility and intensive social and economic activities regardless of their designated urban programs. We first construct the human-geographical networks and compare the pre- and post-COVID-19 pandemic human mobility patterns with respect to visitors' density and diversity. By mapping the density and diversity patterns, we can capture and derive insights from urban ‘hotspots’ spatial distribution changes to identify the adoptable urban space typologies that are quintessential places during the disruptive crisis. The research findings provoke rethinking on our urban spaces design and planning strategies and contribute to better-informed decisions towards resilient urban life environment while countries worldwide are slowly resuming normalcy.

This conference presentation discusses an ensemble approach for meaningful location identification from geotagged social media content through a developed R package.