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This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution patterns in urban areas using geo-tagged tweets (the messages containing users’ geospatial locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter’s Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans from non-human users (bots). The next step was to calculate the number of unique Twitter users every hour within census blocks. The final step was to estimate the actual population by transforming the numbers of unique Twitter users in each census block into estimated population densities with spatial and temporal factors using dasymetric maps. The temporal factor was estimated based on hourly changes of Twitter messages within San Diego County, CA. The spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Comparing to census data, our methods can provide better estimated population in airports, shopping malls, sports stadiums, zoo and parks, and business areas during the day time. 

This paper presents a series of social media analytic methods with geographical context which are useful for understanding public discourse in different cities regarding social and political issues through content analysis and social network analysis. Moreover, this study shows that geographical context should be considered in understanding social media discussion in different cities by using a case study, the 2017 tax bill issue in the US. While previous studies mainly focused on examining non-spatial aspects in online discourse, this study attempts to explain how geographical contexts play a role in shaping the discourse in cyberspace. We found out that point mutual information (PMI) analysis and retweet social network analysis are two effective methods to compare public discourse among different cities. The results of this study indicate that topics and the information diffusion networks regarding the issue reflect the characteristics of each city. 

To illuminate understanding of how social media can be leveraged to glean insights into public health issues such as e-cigarette use, we use a social media analytics and research testbed (SMART) dashboard to observe Twitter messages and follow content about e-cigarettes in different cities across the U.S. Our case studies indicate that the majority of e-cigarette tweets are positive (68%), which represents a potential problem for public health. Stigma plays the most important roles in both confirmed and rejected messages for e-cigarettes. We also noticed that some advocates of ecigarettes might be hybrid human-bot accounts (or multiple users using one account). Our key findings demonstrate the use of the SMART dashboard as a means of public healthrelated belief surveillance, and identification of campaign targets and informational needs of different communities in real-time. Future uses of this tool include monitoring social messages about e-cigarettes for combating the spread of tobacco-related misinformation and disinformation, and detecting and targeting informational needs of communities for intervention. 

Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotemporal patterns of evacuation travels by leveraging users’ location information from millions of tweets posted in the hours prior and concurrent to Hurricane Matthew. Our analysis yields several practical insights, including the following: (1) We identified trajectories of Twitter users moving out of evacuation zones once the evacuation was ordered and then returning home after the hurricane passed. (2) Evacuation zone residents produced an unusually large number of tweets outside evacuation zones during the evacuation order period. (3) It took several days for the evacuees in both South Carolina and Georgia to leave their residential areas after the mandatory evacuation was ordered, but Georgia residents typically took more time to return home. (4) Evacuees are more likely to choose larger cities farther away as their destinations for safety instead of nearby small cities. (5) Human movements during the evacuation follow a log-normal distribution. 

This article seeks to go beyond traditional GIS methods used in creating maps for disaster response that commonly look at the disaster extent. Instead, a slightly different approach is taken using social media data collected from Twitter to explore how people communicate during disaster events, how online communities form and evolve, and how communication methods can improve. This study collected the Twitter data during the 2015 Nepal earthquake disaster and applied a spatiotemporal analysis to find any patterns that show shadows or gaps in communication channels in local communities’ communication. Linkages in social media can be used to understand how people communicate, how quickly they diffuse information, and how social networks form online during disasters. These can improve communication throughout disaster phases. This study offers a deeper understanding of the kinds of spatiotemporal patterns and spatial social networks that can be observed during disaster events. The need for better communication during disaster events is imperative for better disaster management, increasing community resilience, and saving lives.