Delineating and modeling activity space using geotagged social media data

Hu, Lingqian; Li, Zhenlong; Ye, Xinyue

doi:10.1080/15230406.2019.1705187

Cited by 39 publications

(23 citation statements)

References 51 publications

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“…Geotagged Twitter data have been used in human mobility studies (Martín, Li, and Cutter 2017;Martín et al 2020;Hu, Li, and Ye 2020b). Figure 6 shows the global population flows for six selected weekends and cross-day average daily travel distance derived from geotagged tweets from 1 February 2020, to 31 July 2020 (for the calculation of cross-day distance, please refer to Huang et al 2020).…”

Section: Human Movement Patterns In the Shadow Of Covid-19mentioning

confidence: 99%

Taking the pulse of COVID-19: a spatiotemporal perspective

Yang

Sha

Liu

et al. 2020

International Journal of Digital Earth

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106

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Section: Human Movement Patterns In the Shadow Of Covid-19mentioning

confidence: 99%

Taking the pulse of COVID-19: a spatiotemporal perspective

Yang

Sha

Liu

et al. 2020

International Journal of Digital Earth

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106

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“…The advantages of social media with respect to the aforementioned sources of digital information are that they are extensive (covering large spatial areas), easily accessible, with less privacy concern, and at low cost [25][26][27][28]. Extracting useful information from social media is not new, as the valuable geospatial insights from social media have been explored in a wide range of fields, including hazard mitigation [29][30][31], evacuation monitoring [27,32,33], urban analytics [34][35][36][37], and public health [38,39], to list a few. Despite the existing applications, the potential of human mobility derived from social media data has not been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Twitter reveals human mobility dynamics during the COVID-19 pandemic

et al. 2020

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The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

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“…For the county level, our previous studies indicate that these data perform well for examining human movement between different US counties [36][37][38]. For finer resolutions than county, we have successfully conducted human mobility studies at the census tract level [21] and street/community level within a city [39]. However, we are aware that studies at a spatial resolution higher than city or county only work in highly populated areas since at this resolution we can only use tweets with exact coordinates.…”

Section: Overviewmentioning

confidence: 99%

“…With the increasing prevalence of location-enabled social media, geotagged Twitter data have been widely used in human mobility studies (eg, [19][20][21]), yet limited research has been conducted to validate the potential and limitations of these data for studying human movement at different geographic scales (eg, from global to local) in the context of global infectious disease transmission. Meanwhile, the recent development of artificial intelligence (AI) has proven useful for diagnosis, drug analysis, data collection, and outbreak prediction [22].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

Li¹,

Li²,

Porter³

et al. 2020

JMIR Res Protoc

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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

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Delineating and modeling activity space using geotagged social media data

Cited by 39 publications

References 51 publications

Taking the pulse of COVID-19: a spatiotemporal perspective

Taking the pulse of COVID-19: a spatiotemporal perspective

Twitter reveals human mobility dynamics during the COVID-19 pandemic

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

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