‘We are managing!’ Uncertain paths to respectable adulthoods in Accra, Ghana

Studies have shown that the life experiences and future prospects of young people in many parts of the world today are more complex and less predictable than those of past generations. This has led to a recognition of the need to reconsider the conceptual frameworks applied in youth studies. Thus far, research within geography has mainly focused on young people’s paths to adulthood in the Global North or on particularly vulnerable children’s lives in the Global South. In this paper an approach based on the ‘social navigation’ of ‘vital conjunctures’ is adopted to explore the complex ways in which young people in Ghana navigate their way through life in a city marked by economic hardship and neo-liberal reform. The stories of three young people are presented to show the variety of life conditions, the complex social positions they occupy and their various and shifting future imaginations. The actions of these young people are steered by prevailing cultural ideals and social expectations of what a desirable and respectable future entails and by the actual space of possibility in the urban space they move through. It is argued that achieving respectable adulthoods in present-day Accra is a complicated and convoluted journey involving the careful management of social relationships.     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.     

Uncertainty in predictions of oil spill trajectories in open sea

A method is introduced to calculate and to account for the uncertainties in the predictions of oil spill trajectories using a classic oil spill model. The method considers the output of the oil spill model as a function of random variables, which are the input parameters, and calculates the standard deviation of the output results which gives a measure of the uncertainty of the model given the uncertainties of the input parameters.Instead of a single trajectory that is calculated by the oil spill model using the mean values of the parameters, a band of trajectories can be defined when various simulations are done taking into account the uncertainties of the input parameters. This band of trajectories defines envelopes of the trajectories that are likely to be followed by the spill given the uncertainties of the input.The method is applied to an oil spill that occurred in open sea near Madeira Islands, in the Atlantic Ocean, in December 1989. The simulations allow the understanding of how a change in the wind direction drove the spill towards the Islands.The envelope of likely trajectories that is obtained with the uncertainty modelling shows a band of trajectories that is in better agreement with the observations than the single trajectory simulated by the oil spill model, based on mean parameters.     

Uncertainty as the overlap of alternate conditional distributions

An important task in modern geostatistics is the assessment and quantification of resource and reserve uncertainty. This uncertainty is valuable support information for many management decisions. Uncertainty at specific locations and uncertainty in the global resource is of interest. There are many different methods to build models of uncertainty, including Kriging, Cokriging, and Inverse Distance. Each method leads to different results. A method is proposed to combine local uncertainties predicted by different models to obtain a combined measure of uncertainty that combines good features of each alternative. The new estimator is the overlap of alternate conditional distributions.     

Towards models of strategic spatial choice behaviour: theory and application issues

Models of spatial choice behaviour have been around in geography urban planning for decades to assess the feasibility of planning actions or to predict external (competition) effects on existing destinations. Although these models differ in terms of complexity and key concepts, they all have in common that spatial choice behaviour is predicted as a function of the attributes of the choice alternatives and distance or travel time separation only. None of these models do take into account that the attributes of choice alternatives and travel time may be highly non-stationary and that often the utility that people derive from visiting a particular location also depends on the choice behaviour of other individuals. Under these circumstances, individuals may exhibit strategic choice behaviour. That is, they will choose particular choice options taking into account their expected behaviour of others such as to maximize their own utility. The purpose of the proposed paper is to discuss possible models of strategic choice behaviour for these urban planning problems. Theory will be outlined and some critical issues in the application of such models to problems of spatial choice will be discussed.     

地震地质灾害空间预警评估定量化方法研究初探

建立“发育度”、“潜势度”、“危险度”、“危害度”等概念来描述地震地质灾害各影响因子、要素及可能的灾害危害程度,进而建立构成地震地质灾害层次分析的概念体系及地震地质灾害空间预警评估模型,将定性评估转化为定量评估进行初步研究。     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.