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A group of Dutch teachers, as part of their Master’s programme, developed a game that allows teachers to break free from their day-to-day affairs and reflect on futures by designing scenarios about the future of their school. In this game-based approach the journey of scenario exploration is composed of seven steps: (1) choice of a theme and timeframe, (2) selection of key dilemmas on which two scenario axes will be based, (3) understanding the content and context of a “matrix” provided for the game, (4) setting up scenario groups, (5) developing four scenarios, (6) sharing scenarios, and (7) reflection on the scenarios.
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By use of a literature review and an environmental scan four plausible future scenarios will be created, based on the research question: How could the future of backpack tourism look like in 2030, and how could tourism businesses anticipate on the changing demand. The scenarios, which allow one to ‘think out of the box’, will eventually be translated into recommendations towards the tourism sector and therefore can create a future proof company strategy.
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This paper presents four Destination Stewardship scenarios based on different levels of engagement from the public and private sector. The scenarios serve to support destination stakeholders in assessing their current context and the pathway towards greater stewardship. A Destination Stewardship Governance Diagnostic framework is built on the scenarios to support its stakeholders in considering how to move along that pathway, identifying the key aspects of governance that are either facilitating or frustrating a destination stewardship approach, and the required actions and resources to achieve an improved scenario. Moreover, the scenarios and diagnostic framework support stakeholders to come together to debate and scrutinise how tourism is managed in a way that meets the needs of the destination, casting new light on the barriers and opportunities for greater destination stewardship.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Many policy documents addressing the future of teacher education do not take into account the fundamental unpredictability of the future, nor the opposing forces that will try to influence that future. Through the analysis of 48 scenario documents on the future of education or teacher education, we identified a set of unpredictable key factors that have to be taken into account when addressing the future of teacher education. We also identified four main futures that may lie ahead for teacher education. We analyzed these four scenarios using the concepts of activity systems, boundary objects, and boundary crossing. This revealed that the extent to which activity systems are open to boundary crossing and are willing to remove institutional boundaries, will largely define the future that lies ahead for teacher education. Future scenarios in themselves can play a role as boundary objects that facilitate the dialogue and boundary crossing between these activity systems
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Introduction (author supplied) : In this paper we propose future mapping, an alternative approach to futures research. With future mapping we intend to overcome some of the main problems that we encountered when applying scenario thinking in the area of product design and innovation. Future mapping attempts to develop multi-layered maps of possible futures, which can be used by pro-active companies and innovation teams as an instrument to ‘navigate’ the future (Munnecke & Van der Lugt, 2006). The approach invites designers to apply their analytical, creative and emphatical skills in a dialogue about future opportunities that lay ahead. In the past few years we have taught and applied the future mapping approach with various groups of Master’s level engineering students, both in The Netherlands and Denmark. We have altered and adjusted the approach as we learned from these experiences. In this paper we will describe the current state of the approach. The paper is not meant to provide a deep theoretical overview or a thorough empirical study. Rather it is meant to provide a hands-on process description to inform about the method and to enable anyone to apply future mapping. After describing why we think future mapping is a promising direction for futures research, we will provide a concise overview of the process steps involved. Then we will describe one student project as a case example. We will discuss the various types of future maps produced by the students. We will conclude by making some general observations about using future mapping as a method for futures research, and by proposing some directions for future work.
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 147-154, 2014www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/147/2014/doi:10.5194/isprsarchives-XL-8-147-2014Integrated flood disaster management and spatial information: Case studies ofNetherlands and IndiaS. Zlatanova1, T. Ghawana2, A. Kaur2, and J. M. M. Neuvel31Faculty of Architecture, Jullianalaan, TU Delft, 134, 2628BL Delft, the Netherlands2Centre for Disaster Management Studies, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, P.O. Box-110078, India3Saxion University of Applied Sciences, Risk management, Handelskade 75, 7417 DH Deventer, the NetherlandsKeywords: Floods, Spatial Information Infrastructure, GIS, Risk Management, Emergency Management Abstract. Spatial Information is an integral part of flood management practices which include risk management &emergency response processes. Although risk & emergency management activities have their own characteristics, forexample, related to the time scales, time pressure, activities & actors involved, it is still possible to identify at least onecommon challenge that constrains the ability of risk & emergency management to plan for & manage emergencieseffectively and efficiently i.e. the need for better information. Considering this aspect, this paper explores flood managementin Netherlands& India with an emphasis on spatial information requirements of each system. The paper examines theactivities, actors & information needs related to flood management. Changing perspectives on flood management inNetherlands are studied where additional attention is being paid to the organization and preparation of flood emergencymanagement. Role of different key actors involved in risk management is explored. Indian Flood management guidelines, byNational Disaster Management Authority, are analyzed in context of their history, institutional framework, achievements andgaps. Flood Forecasting System of Central Water Commission of India is also analyzed in context of spatial dimensions.Further, information overlap between risk & emergency management from the perspectives of spatial planners & emergencyresponders and role of GIS based modelling / simulation is analyzed. Finally, the need for an integrated spatial informationstructure is explained & discussed in detail. This examination of flood management practices in the Netherlands and Indiawith an emphasis on the required spatial information in these practices has revealed an increased recognition of the stronginterdependence between risk management and emergency response processes. Consequently, the importance of anintegrated spatial information infrastructure that facilitates the process of both risk and emergency management isaddressed.Conference Paper (PDF, 1063 KB) Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 147-154, 2014www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/147/2014/doi:10.5194/isprsarchives-XL-8-147-2014Integrated flood disaster management and spatial information: Case studies ofNetherlands and IndiaS. Zlatanova1, T. Ghawana2, A. Kaur2, and J. M. M. Neuvel31Faculty of Architecture, Jullianalaan, TU Delft, 134, 2628BL Delft, the Netherlands2Centre for Disaster Management Studies, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, P.O. Box-110078, India3Saxion University of Applied Sciences, Risk management, Handelskade 75, 7417 DH Deventer, the NetherlandsKeywords: Floods, Spatial Information Infrastructure, GIS, Risk Management, Emergency ManagementAbstract. Spatial Information is an integral part of flood management practices which include risk management &emergency response processes. Although risk & emergency management activities have their own characteristics, forexample, related to the time scales, time pressure, activities & actors involved, it is still possible to identify at least onecommon&
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