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 and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties.

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 Index ( $RI$) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the $RI$ between either two data sources, revealing their general similarity, albeit with varying Pearson’s $r$ coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The results suggest that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in $RI$between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

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 as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective: Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods: We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results: This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions: Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID): DERR1-10.2196/24432

Understanding the relationship between spatial structures of cities and environmental hazard exposures is essential for urban health and sustainability planning. However, a critical knowledge gap exists in terms of the extent to which socio-spatial networks shaped by human mobility exacerbate or alleviate urban heat exposures of populations in cities. In this study, we utilize location-based data to construct human mobility networks in twenty metropolitan areas in the U.S. The human mobility networks are analyzed in conjunction with the urban heat characteristics of spatial areas. We identify areas with high and low urban heat exposure and evaluate visitation patterns of populations residing in high and low urban heat areas to other spatial areas with similar and dissimilar urban heat exposure. The results reveal the presence of urban heat traps in the majority of the studied metropolitan areas, wherein populations residing in high-heat exposure areas primarily visited other high-heat exposure zones. Specifically, cities such as Los Angeles, Boston, and Chicago were particularly pronounced as urban heat traps. The results also show a small percentage of human mobility to produce urban heat escalation and heat escapes. The findings from this study provide a better understanding of urban heat exposure in cities based on patterns of human mobility. These findings contribute to a broader understanding of the intersection of human network dynamics and environmental hazard exposures in cities to inform more integrated urban design and planning to promote health and sustainability.

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 percent and are estimated using posthurricane questionnaire surveys to residents in the affected area. To overcome limitations of traditional data collection methods that are costly in time and resources, an increasing number of natural hazards studies have used social media data as a data source. To better understand social media users’ evacuation behaviors, this article investigates whether activity space, social network, and long-term sentiment trends are associated with individuals’ evacuation decisions by measuring and comparing Twitter users’ evacuation decisions during Hurricane Matthew in 2016. We find that (1) evacuated people have larger long-term activity spaces than nonevacuated people, (2) people in the same social network tend to make the same evacuation decision, and (3) evacuated people have smaller long-term sentimental variances than nonevacuated people. These results are consistent with previous studies based on questionnaire and survey data and thus provide researchers a new method to study human behavior during disasters. Key Words: big data, disaster management, evacuation, hurricane, social media.

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 identified associated with evacuation destination choices, in which long-term social factors have been found essential, yet neglected, in most studies due to difficulty in data collection. This study utilized long-term human movement records retrieved from Twitter to (1) reinforce the importance of social factors in evacuation destination choices, (2) quantify individual-level familiarity measurement and its relationship with an individual’s destination choice, (3) develop a big data approach for aggregated county-level social distance measurement, and (4) demonstrate how gravity models can be improved by including both social distance and physical distance for evacuation destination choice modeling.