Mapping spatiotemporal patterns of events using social media: a case study of influenza trends

Gao, Yizhao; Wang, Shaowen; Padmanabhan, Anand; Yin, Junjun; Cao, Guofeng

doi:10.1080/13658816.2017.1406943

Cited by 50 publications

(38 citation statements)

References 48 publications

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“…The two parameters were calculated using the GridSearchCV method [45] based on the training data subset. The validation subset data was then used to evaluate the separation accuracy using the five-fold cross validation method [46]. In this study, we obtained an F-score of 0.85, which indicates that the SVM classifier could be used to identify the trulyrainstorm-related Weibo posts.…”

Section: Extraction Of Rainstorm-related Weibo Postsmentioning

confidence: 88%

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Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Wang

Liang

et al. 2019

Applied Sciences

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Natural disasters cause significant casualties and losses in urban areas every year. Further, the frequency and intensity of natural disasters have increased significantly over the past couple of decades in the context of global climate change. Understanding how urban dwellers learn about and response to a natural hazard is of great significance as more and more people migrate to cities. Social media has become one of the most essential communication platforms in the virtual space for users to share their knowledge, information, and opinions about almost everything in the physical world. Geo-tagged posts published on different social media platforms contain a huge amount of information that can help us to better understand the dynamics of collective geo-tagged human activities. In this study, we investigated the spatiotemporal distribution patterns of the collective geo-tagged human activities in Beijing when it was afflicted by the “6-22” rainstorm. We used a variety of machine learning and statistical methods to examine the correlations between rainstorm-related microblogs and the rainstorm characteristics at a fine spatial and a fine temporal scale across Beijing. We also studied factors that could be used to explain the changes of the rainstorm-related blogging activities. Our results show that the human response to a disaster is very consistent, though with certain time lags, in the virtual and physical spaces at both the grid and city scales. Such a consistency varies significantly across our study area.

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Section: Extraction Of Rainstorm-related Weibo Postsmentioning

confidence: 88%

“…A higher HERI value would indicate city dwellers are more active in blogging the rainstorms. A very similar index was used to estimate hazard-induced damages and monitor the post-hazard recovery speed [40,46].…”

Section: Heri =mentioning

confidence: 99%

Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Wang

Liang

et al. 2019

Applied Sciences

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“…Several algorithms have been developed to compute spatial and spatiotemporal patterns and several survey can be found in the literature [20,43,50,53].…”

Section: Automated Approachesmentioning

confidence: 99%

Visual analytics for spatiotemporal events

Silva

Pires

Datia

et al. 2019

Multimed Tools Appl

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Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user' perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts' perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.

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“…Nakaji and Yanai proposed a method of visualizing real-word events by showing event-related photos selected from geo-tagged social media [33]. Gao discussed different visualization methods to present the influenza activities in the United States [34].…”

Section: Event-related Research Based On Geo-tagged Social Media Datamentioning

confidence: 99%

Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

Zhu

Lin

Jendryke

et al. 2018

IJGI

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Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

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Mapping spatiotemporal patterns of events using social media: a case study of influenza trends

Cited by 50 publications

References 48 publications

Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Visual analytics for spatiotemporal events

Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

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