Twitter Early Tsunami Warning System: A Case Study in Indonesia's Natural Disaster Management

Chatfield, Akemi Takeoka; Brajawidagda, Uuf

doi:10.1109/hicss.2013.579

Cited by 82 publications

(64 citation statements)

References 9 publications

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“…Especially after Weller et al [44] collected and organized fundamental concepts of the digital platform for social research purposes, many research Sustainability 2020, 12, 1858 5 of 18 fields have experimented with the use of Twitter data (see [45][46][47]). Understanding how actors use the online space to connect and communicate in combination with a specific topic, such as disaster risk [48], tourism [49] or supply chain practice [50], has been the central drive behind this move. From an abstract point of view, this comes as no surprise as Twitter activity creates a social network between a variety of actors and institutions.…”

Section: Methodsmentioning

confidence: 99%

Influence in Technological Innovation Spaces: A Network Science Approach to Understand Innovation for Sustainability in the Global South

Schmitt

Muyoya

2020

Sustainability

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The number of scholars working on transition concepts in the Global South is rapidly increasing. In this context, a substantial amount of research output particularly focusses on niches and how they affect transition towards sustainability in a wider framework of the multi-level-perspective. At the same time, there is a growing interest in digital technology and its effect on sustainability challenges. In this article, we combine the two fields, and by utilizing social media data, we create an innovative network science approach to analyze the production environment of digital innovations in Africa. We focus on three innovation hubs that we conceptualize as niches and innovation intermediaries that not only create communities to develop, test and implement new technology but also function as networks to discuss and form new ideas around innovations. Our key findings show how local communities are embedded in larger innovation structures. The connections between local stakeholders and global actors are predominantly created through bridge actors, who hold key positions in their communities. With tools from network science, we demonstrate that these linking elements can regulate and steer discussions and therefore, strongly influence digital niche environments. Utilizing geographical location data, we can also see that the online space of technological innovations in Africa is heavily cantered in urban areas.

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Section: Methodsmentioning

confidence: 99%

Influence in Technological Innovation Spaces: A Network Science Approach to Understand Innovation for Sustainability in the Global South

Schmitt

Muyoya

2020

Sustainability

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“…On 13 th day, the number of bodies found reached 48 [33] and remained the same until the end of the observation period on January 14, 2014. All of the bodies-found announcements were carried out by Basarnas, whose main mission is to search, locate and evacuate all of the victims of an emergency or disaster event [12]. The location of the bodies was distributed across the sea in the surrounding area in which the debris of the airplane was found (red rectangle in Figure 3).…”

Section: Extreme Event Timelinementioning

confidence: 99%

The imperative of government transparency in crisis communication

Brajawidagda

Chatfield

Reddick

2015

Proceedings of the 16th Annual International Conference on Digital Government Research

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One of the crucial research issues in an IoT-based system is how to manage the huge amount of data transmitted by the potentially large number of sensors that form the system. Prior research has focused on centralized cloud-based 'Big Data' architectures for collecting, collating and analyzing the data. However, most of these scenarios accumulate thousands of petabytes in a short period of time, increasing the demand for more storage, and also slowing down speed of data analysis. Hence for real-time scenarios, e.g., agricultural crop tracking, traffic management, etc., such an approach would be impractical. Moreover, depending on the context in which the data is generated and is to be used, only a fraction of the data would be needed for analysis. Therefore, the challenges are to determine which data to keep and which to discard for both short term and long term usage, and define the contextual parameters along which this filtering is to be done. Hence one key problem addressed in this paper is how to define what data the user needs so that filtering algorithms can be defined to extract the data needed. To that end, in this paper, we present a goal driven, context-aware data filtering, transforming and integration approach for IoT-based systems. We propose a data warehouse-based data model for specifying the data needed at particular levels of granularity and frequency, that drive data storage and representation (aligned with the Semantic Sensor Network ontology). Throughout our paper, we illustrate our ideas via a realistic running example in the smart city domain, with emphasis on traffic management, and also present a proof of concept prototype. Disciplines

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“…It was compared against traditional sources and proved its value as a complementary source. [33], [35] Text processing techniques Twitter Indonesia, USA…”

Section: Disaster Managementmentioning

confidence: 99%

A review on application of data mining techniques to combat natural disasters

Goswami

Chakraborty

Ghosh

et al. 2018

Ain Shams Engineering Journal

133

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Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life etc.due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techniques designed so far for i) prediction ii) detection and iii) development of appropriate disaster management strategy based on the collected data from disasters. A detailed description of availability of data from geological observatories (seismological, hydrological), satellites, remote sensing and newer sources like social networking sites as twitter is presented. An extensive and in depth literature study on current techniques for disaster prediction, detection and management has been done and the results are summarized according to various types of disasters. Finally a framework for building a disaster management database for India hosted on open source Big Data platform like Hadoop in a phased manner has been proposed.not only the immediate effect as observed in [61], exposure to a natural disaster in the past months increases the likelihood of acute illnesses such as diarrhea, fever, and acute respiratory illness in children under 5 year by 9-18%.. The socioeconomic status of the households has a direct bearing on the magnitude and nature of these effects. The disasters have pronounced effects on business houses as well. As stated in [50] 40% of the companies, which were closed for consecutive 3 days, failed or closed down within a period of 36 months. The disasters are not infrequent as well.Only for earthquake [7], there are as many as 20 earthquakes every year which has a Richter scale reading greater than 7.0. The effects of the disasters are much more pronounced in developing countries like India. Meteorologist,Geologists, Environmental Scientists, Computer Scientistsand scientists from various other disciplines have put a lot of concerted efforts to predict the time, place and severity of the disasters. Apart from advanced weather forecasting models, data mining models also have been used for the same purpose. Another line of research, has concentrated on disaster management, appropriate flow of information, channelizing the relief work and analysis of needs or concerns of the victims. The sources of the underlying data for such tasks have often been social media and other internet media.Diverse data are also collected on regular basis by satellites, wireless and remote sensors, national meteorological and geological departments, NGOs, various other international, government and private bodies, before, during and after the disaster. The data thus collected qualifies to be called "Big Data" because of the volume, variety and the velocity in which the data are generated. A brief technical description of some of the major natural disasters:-

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Twitter Early Tsunami Warning System: A Case Study in Indonesia's Natural Disaster Management

Cited by 82 publications

References 9 publications

Influence in Technological Innovation Spaces: A Network Science Approach to Understand Innovation for Sustainability in the Global South

Influence in Technological Innovation Spaces: A Network Science Approach to Understand Innovation for Sustainability in the Global South

The imperative of government transparency in crisis communication

A review on application of data mining techniques to combat natural disasters

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