skip to main content


Title: Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level
The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Internal validation reveals over 80% accuracy when compared with users’ self-reported home location. External validation results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds the potential to introduce the dynamic component often lacking in population estimates. This study could potentially benefit various fields such as demography, tourism, emergency management, and public health and create new opportunities for large-scale mobility analyses.  more » « less
Award ID(s):
1952792 2028791
NSF-PAR ID:
10293928
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Social Sciences
Volume:
10
Issue:
6
ISSN:
2076-0760
Page Range / eLocation ID:
227
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Understanding the behavior of residents and visitors is vital in tourism studies, urban planning, and local economic development. However, most existing studies consider visitors as one group, while overlooking the difference in mobility patterns between subgroups of visitors and residents. In this research, we analyzed the mobility pattern of local Twitter users and visitor Twitter users, from the flow network and evenness distribution of user activities. The results show that short distance movement is the dominant type of activity not only for residents, but also for visitors. Moreover, intra‐county movement accounts for the primary type of movement for all groups of Twitter users. Besides, the centrality index of Twitter users reconstructs a core–peripheral structure, and there is some relationship between the centrality index and population size. Further, the spatial distribution of evenness index at different spatial scales shows a clear “T”‐shaped core–peripheral structure. However, we need to synthesize multiple open big data to improve the study and conduct the analysis in future work at finer spatial scales, such as census tracts, census blocks, or the street level.

     
    more » « less
  2. null (Ed.)
    Background Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences. 
    more » « less
  3. null (Ed.)
    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 
    more » « less
  4. Abstract Belmont County, Ohio is heavily dominated by unconventional oil and gas development that results in high levels of ambient air pollution. Residents here chose to work with a national volunteer network to develop a method of participatory science to answer questions about the association between impact on the health of their community and pollution exposure from the many industrial point sources in the county and surrounding area and river valley. After first directing their questions to the government agencies responsible for permitting and protecting public health, residents noted the lack of detailed data and understanding of the impact of these industries. These residents and environmental advocates are using the resulting science to open a dialogue with the EPA in hopes to ultimately collaboratively develop air quality standards that better protect public health. Results from comparing measurements from a citizen-led participatory low-cost, high-density air pollution sensor network of 35 particulate matter and 25 volatile organic compound sensors against regulatory monitors show low correlations (consistently R 2 < 0.55). This network analysis combined with complementary models of emission plumes are revealing the inadequacy of the sparse regulatory air pollution monitoring network in the area, and opening many avenues for public health officials to further verify people’s experiences and act in the interest of residents’ health with enforcement and informed permitting practices. Further, the collaborative best practices developed by this study serve as a launchpad for other community science efforts looking to monitor local air quality in response to industrial growth. 
    more » « less
  5. null (Ed.)
    Background: Large-scale memorial development has become a growing trend around the world. While numerous studies have tracked the effects of such development on objective measures of community welfare, far less is known about the social effects of memorial tourist attractions on communities where they are placed. This study explores one such impact: how do changes in the social and physical landscape as a result of memorial tourist development affect residents’ perceptions of the crime rate in their community? Methods: Secondary crime data was coupled with a longitudinal residential survey (n=135), measuring actual and perceived crime rates before and after the attraction’s opening. Results: While race, income, and political party affiliation predicted pre-opening beliefs, post-opening perceptions of crime change were associated with prior beliefs, residential status, media consumption, and median income. When compared against the objective crime change, residential status was the only predictor of inaccurate perceptions of both property and violent crime. Conclusions: Aspects of residents’ immediate communities bias their ability to accurately perceive crime change after the opening of a public memorial. The findings encourage researchers to take a more holistic, and yet nuanced, look at the effects of tourism on communities where they are placed. In the present case, such perceptions may have a significant impact on whether or not the objectives of the memorial developers are met. Given the current wave of memorial development worldwide, these findings may contribute to the success or failure of these efforts. 
    more » « less