818 resultados para Special events -- Australia -- Congresses


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The contemporary directions of art galleries worldwide are changing as social patterns and demands, as well as visitor expectations of their experiences at art galleries, change. New programs and strategies are being developed in galleries to make these institutions more appealing to people who would not normally visit them, and one such strategy is the staging of special events. However, because galleries are staging an increasing number of special events, the factors motivating visitors to attend these institutions are changing. Visitors hope to have different experiences and encounters in the gallery during special events. This paper presents the findings from a study in Australia about visitors’ motivations to attend special events in galleries. It highlights the different factors that motivate visitors to attend the gallery specifically for a special event in comparison to visiting the gallery's permanent collections.

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This research project set out to explore Unitary Authority (UA) involvement in festivals and special events across Wales. It considers the level and nature of UA involvement and investigates activity by event purpose; reasons for, and characteristics of, UA engagement; and, crucially, the extent and nature of event evaluation. The study’s aim was to begin the development of a baseline of information for further research into the growing use of festivals and special events as a strategy for local economic development in Wales. A quantitative survey approach facilitated a comprehensive snapshot of UA responses whilst also incorporating discursive elements. A telephone survey was designed and undertaken with representatives of all 22 UA departments responsible for festivals and events in Wales. The research reveals a significant level of festival and special event activity across Wales, supported primarily for its perceived socio-cultural value. However, evaluation would appear to be focused on improving processes and measuring economic outputs rather than assessing whether socio-cultural objectives are being achieved. Whilst overwhelmingly positive about efforts to improve approaches to evaluation, respondents held clear views about the complications most likely to hamper any such efforts. These responses focused upon the need for flexibility, cost effectiveness and comparability across festival and special event typologies.

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Title from cover.

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In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

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In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

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Each year, The Australian Centre for Philanthropy and Nonprofit Studies (CPNS) at Queensland University of Technology (QUT) collects and analyses statistics on the amount and extent of tax-deductible donations made and claimed by Australians in their individual income tax returns to deductible gift recipients (DGRs). The information presented below is based on the amount and type of tax-deductible donations made and claimed by Australian individual taxpayers to DGRs for the period 1 July 2006 to 30 June 2007. This information has been extracted mainly from the Australian Taxation Office's (ATO) publication Taxation Statistics 2006-07. The 2006-07 report is the latest report that has been made publicly available. It represents information in tax returns for the 2006-07 year processed by the ATO as at 31 October 2008. This study uses information based on published ATO material and represents only the extent of tax-deductible donations made and claimed by Australian taxpayers to DGRs at Item D9 Gifts or Donations in their individual income tax returns for the 2006-07 income year. The data does not include corporate taxpayers. Expenses such as raffles, sponsorships, fundraising purchases (e.g., sweets, tea towels, special events) or volunteering are generally not deductible as „gifts‟. The Giving Australia Report used a more liberal definition of gift to arrive at an estimated total of giving at $11 billion for 2005 (excluding Tsunami giving of $300 million). The $11 billion total comprised $5.7 billion from adult Australians, $2 billion from charity gambling or special events and $3.3 billion from business sources.

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Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

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Mode of access: Internet.