Kent State University

About: Kent State University is a education organization based out in Kent, Ohio, United States. It is known for research contribution in the topics: Liquid crystal & Population. The organization has 10897 authors who have published 24607 publications receiving 720309 citations. The organization is also known as: Kent State & KSU.

Social media analytics for natural disaster management

Abstract: Social media analytics has become prominent in natural disaster management. In spite of a large variety of metadata fields in social media data, four dimensions i.e. space, time, content and network have been given particular attention for mining useful information to gain situational awareness and improve disaster response. In this article, we review how existing studies analyze these four dimensions, summarize common techniques for mining these dimensions, and then suggest some methods accordingly. We then propose a schema to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks. We find that 1 a large part of studies involve multiple dimensions of social media data in their analyses, 2 there are both separate analyses for each dimension and simultaneous analyses for multiple dimensions and 3 there are fewer simultaneous analyses as dimensions increase. Finally, we suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.

The self-organizing map in synoptic climatological research:

Abstract: Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar to most other clustering methods. However, as the results from a SOM are generally represented by a two-dimensional array of cluster types that ‘self-organize’, the synoptic categories in the array effectively represent a continuum of synoptic categorizations, compared with discrete realizations produced through most traditional methods. Thus, a larger number of patterns can be more readily understood, and patterns, as well as transitional nodes between patterns, can be discerned. Perhaps the most intriguing development with SOMs has been the new avenues of visualization; the resultant spatial patterns of any variable can be more readily understood when displayed in a SOM. T...