Is climate change a driver of armed conflict?

The world is generally becoming less violent, but the debate on climate change raises the specter of a new source of instability and conflict. In this field, the policy debate is running well ahead of its academic foundation—and sometimes even contrary to the best evidence. Although comparative research on security implications of climate change is rapidly expanding, major gaps in knowledge still exist. Taken together, extant studies provide mostly inconclusive insights, with contradictory or weak demonstrated effects of climate variability and change on armed conflict. This article reviews the empirical literature on short-term climate/environmental change and intrastate conflict, with special attention to possible insecurity consequences of precipitation and temperature anomalies and weather-related natural disasters. Based on this assessment, it outlines priorities for future research in this area.     

SDI planning using the system dynamics technique within a community of practice: lessons learnt from Tanzania

There exist major challenges in accelerating the spatial data infrastructure (SDI) planning process in the developing countries as well as advocating for politicians to support the development of SDI, due to the high complexity of SDI, lack of knowledge and experience, and limited insight in the benefits. To address these challenges, a methodology for SDI planning in Tanzania, based on the system dynamics technique and the communities of practice concept, was adopted and applied within a community consisting of experts from stakeholder organizations. The groups gathered to develop an SDI plan, while they shared their knowledge and discussed their ideas that helped their understanding of SDI. By running the system dynamics model, the development of SDI over time could be simulated that gave the planning community an insight about the future effects of today’s plans and decisions. Finally, an optimum model could be developed by refinements and improvements done with the consensus of the SDI stakeholders. This model included the components and policies that are essential for a successful SDI implementation in Tanzania and can be used as a basis for SDI planning and help to gain political support. Lessons learnt from this research were promising regarding the usability of the methodology for SDI planning in comparable countries.     

Urban fieldwork in geographical education in Levanger,Norway

Despite a long tradition of fieldwork and outdoor education in Norwegian schools, teachers and pupils are not familiar with conducting systematic field studies of modern urban environments. The authors of the present article are convinced that urban fieldwork ought to be included in the teaching syllabus in Norwegian schools, and for several years they have worked on developing urban fieldwork as a part of the study programme in teacher education at Nord-Trøndelag University College (Høgskolen in Nord-Trøndelag or HiNT). The fieldwork element has gradually been expanded, and in recent years pupils from neighbouring primary schools have participated in the programme. The use of ICT has increasingly become an important part of the work in the field as well as in the follow-up phase. Special emphasis has been put on the use of digital stories. The experiences of this work are presented and discussed in the article. In the final section of the article the authors discuss further development of urban fieldwork, paying particular attention to the use of new digital technology and enquiry approaches.     

Using Multiple Grids in Markov Mesh Facies Modeling

A multigrid Markov mesh model for geological facies is formulated by defining a hierarchy of nested grids and defining a Markov mesh model for each of these grids. The facies probabilities in the Markov mesh models are formulated as generalized linear models that combine functions of the grid values in a sequential neighborhood. The parameters in the generalized linear model for each grid are estimated from the training image. During simulation, the coarse patterns are first laid out, and by simulating increasingly finer grids we are able to recreate patterns at different scales. The method is applied to several tests cases and results are compared to the training image and the results of a commercially available snesim algorithm. In each test case, simulation results are compared qualitatively by visual inspection, and quantitatively by using volume fractions, and an upscaled permeability tensor. When compared to the training image, the method produces results that only have a few percent deviation from the values of the training image. When compared with the snesim algorithm the results in general have the same quality. The largest computational cost in the multigrid Markov mesh is the estimation of model parameters from the training image. This is of comparable CPU time to that of creating one snesim realization. The simulation of one realization is typically ten times faster than the estimation.