Many algorithms for graph layout have been devised over the last 30 years spanning both the graph drawing and information visualisation communities. This article first reviews the advances made in the field of graph drawing that have then often been applied by the information visualisation community. There then follows a discussion of a range of techniques developed specifically for graph visualisations. Graph drawing algorithms are categorised into the followings approaches: forcedirected layouts, the use of dimension reduction in graph layout and computational improvements including multi-level techniques. Methods developed specifically for graph visualisation often make use of node-attributes and are categorised based on whether the attributes are used to introduce constraints to the layout, provide a clustered view or used to define an explicit representation in 2D space. The similarities and distinctions between these techniques are examined and the aim is to provide a detailed assessment of currently available graph layout techniques, specifically how they can be used by visualisation practitioners and to motivate further research in the area.

During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.

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A number of crisis situations, such as natural disasters have affected the planet over the last decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated system for the synchronization of the police and Law Enforcement Agencies (LEAs) for the prevention of criminal activities during and post a large crisis situation. The paper presents a review of the literature focusing on the necessity of using social media and crowd-sourcing data mining techniques in combination with advanced web technologies for resolving problems related to criminal activities caused during and after a crisis. The focus of the paper is the ATHENA Crisis Management system which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention. Its main strength is the combined use of a variety of data mining algorithms through a number of interfaces for the purpose of extracting useful social media information related to criminal activities during and after a large crisis. Conclusions are drawn on the significance of social media and crowd-sourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.

Abstract-Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs, which are typically depicted on the graph as node colour, shape or size. Here we demonstrate that these attributes can instead be used to layout the graph in highdimensional data space. Then using a dimension reduction technique, targeted projection pursuit, the graph layout can be optimised for displaying clustering. The technique outperforms force-directed layout methods in cluster separation when applied to a sample, artificially generated, small-world network.