A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.     

Pass the passport! Geographies of the Rugby World Cup 2011

The Rugby World Cup, held in New Zealand in 2011, was a showcase for a rapidly globalising sport involving competition between twenty national teams, comprising over 600 players. This paper analyses the geography of the players’ team affiliations and reveals patterns of labour mobility that disrupt and render complex concepts and definitions of ‘national’ identity. We see that the dominance of professional rugby, particularly by European clubs, is concentrating player resources and revenue generation in ways that suggest an evolving core–periphery relationship. We suggest that the players themselves exercise agency by finding, manipulating and exploiting cracks and opportunities in the complex regulatory framework of the sport. We build a case for a research agenda that maps out these geographies of rugby and traces their implications for understandings of identity and the political economy of globalising sport.     

Shrinked (1???α) ensemble Kalman filter and α Gaussian mixture filter

State estimation in high dimensional systems remains a challenging part of real time analysis. The ensemble Kalman filter addresses this challenge by using Gaussian approximations constructed from a number of samples. This method has been a large success in many applications. Unfortunately, for some cases, Gaussian approximations are no longer valid, and the filter does not work so well. In this paper, we use the idea of the ensemble Kalman filter together with the more theoretically valid particle filter. We outline a Gaussian mixture approach based on shrinking the predicted samples to overcome sample degeneracy, while maintaining non-Gaussian nature. A tuning parameter determines the degree of shrinkage. The computational cost is similar to the ensemble Kalman filter. We compare several filtering methods on three different cases: a target tracking model, the Lorenz 40 model, and a reservoir simulation example conditional on seismic and electromagnetic data.     

Calibration and application of a continuum damage model on the simulation of stone masonry structures: Gondar church as a case study

The conservation and rehabilitation of monuments is a matter of important investigation, and the need for accurate structural analysis, capable of effectively predicting the structural behaviour of this type of constructions, under static and dynamic loads, is increasing. Currently there are numerous computational methods and tools, supported by different theories and strategies with different levels of complexity, computation time and cost which are available to perform such analyses. A complex analysis is not always synonym of a better result and the choice of a method over another depends mostly on the purpose of the analysis. This work aims at evaluating the capacity of a non linear continuum damage model (Faria et al. in Int J Solids Struct 35(14):1533–1558, 1998), originally developed for concrete structures, to simulate the behaviour of stone masonry structures. In particular, the seismic response of an old stone masonry construction, the Gondar church, is analysed considering different levels of geometrical and material complexity. The verification and calibration procedures use the experimental results from tests performed on stone masonry walls at the Laboratory for Earthquake and Structural Engineering of the Faculty of Engineering of Porto University and from other tests found in the bibliography (Vasconcelos in Experimental investigations on the mechanics of stone masonry: Characterization of granites and behaviour of ancient masonry shear walls. PhD Thesis, Universidade do Minho, Guimar?es, Portugal, 2005). The results are compared, assessing the differences and the importance of using complex tools, such as the continuum damage model, to better simulate and understand the global behaviour of such constructions.     

Local and Spatial Joint Frequency Uncertainty and its Application to Rock Mass Characterisation

Stability is a key issue in any mining or tunnelling activity. Joint frequency constitutes an important input into stability analyses. Three techniques are used herein to quantify the local and spatial joint frequency uncertainty, or possible joint frequencies given joint frequency data, at unsampled locations. Rock quality designation is estimated from the predicted joint frequencies. The first method is based on kriging with subsequent Poisson sampling. The second method transforms the data to near-Gaussian variables and uses the turning band method to generate a range of possible joint frequencies. The third method assumes that the data are Poisson distributed and models the log-intensity of these data with a spatially smooth Gaussian prior distribution. Intensities are obtained and Poisson variables are generated to examine the expected joint frequency and associated variability. The joint frequency data is from an iron ore in the northern part of Norway. The methods are tested at unsampled locations and validated at sampled locations. All three methods perform quite well when predicting sampled points. The probability that the joint frequency exceeds 5 joints per metre is also estimated to illustrate a more realistic utilisation. The obtained probability map highlights zones in the ore where stability problems have occurred. It is therefore concluded that the methods work and that more emphasis should have been placed on these kinds of analyses when the mine was planned. By using simulation instead of estimation, it is possible to obtain a clear picture of possible joint frequency values or ranges, i.e. the uncertainty.     

Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data

Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in petroleum reservoir evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.