Training Workshop in Spatial Filter Modeling for Environmental and Social Scientists and Applied Statisticians

环境和社会科学家及应用统计学家空间过滤建模培训研讨会

• 批准号: 0724964
• 负责人: Michael Tiefelsdorf
• 金额: $ 6.68万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0724964&HistoricalAwards=false
• 关键词: Training; Workshop; Spatial; Filter; Modeling

The workshop introduces a group of emanating academics and established professionals, who are engaged in spatial analysis, demography, epidemiology, ecology or econometrics, to the novel methodology of spatial filtering. Non-parametric spatial filtering and semi-parametric spatial filtering belong to a developing family of techniques that is geared toward analyzing geo-referenced data in an exploratory and confirmatory framework. The dissemination of this newer methodology via the summer workshop is important for several reasons: [1] the conceptional simplicity of spatial filtering makes spatial statistics more accessible to a very wide user group, one that is familiar with conventional statistics but not necessarily with spatial statistics; [2] the increasing appearance of spatial filtering approaches in methodological and applied literatures from a broad disciplinary scope indexes both its growing popularity and a need for more researchers to be trained in its uses; [3] spatial filtering dramatically simplifies the implementation of linear, nonlinear, generalized linear as well as mixed models for spatially autocorrelated data; [4] in order to evaluate and critique the spatial filtering approaches, the workshop will juxtapose them with established modes of analyzing spatial data; and, finally, [5] the workshop participants will be introduced to software tools that prototype spatial filtering algorithms within the widely available R computing-platform. The workshop is designed to allow participants to practice their acquired skills on a wide range of provided or personally owned geo-referenced datasets. Depending on their specific backgrounds, the participants will be trained during the workshop to become well-versed users, informed instructors, or potential developers of the emerging spatial filtering methodology. With the rapidly increasing volume of geo-referenced data and the constitution of geographic information systems to support virtually every facet of our daily lives, skills to analyze geo-referenced data in an informed manner are becoming increasingly relevant. This workshop contributes toward providing academic and professional specialist these analytical skills. Potential participants will follow a formal application procedure in order to obtain one of the limited 25 seats of this one-week summer workshop in 2008. A selection committee will ensure the pluralism and qualifications of the selected group of participants in such a way that a diverse mix of scholars will attend the workshop.

该研讨会介绍了一组散发的学者和专业人士，谁是从事空间分析，人口，流行病学，生态学或计量经济学，空间过滤的新方法。 非参数空间滤波和半参数空间滤波属于一个发展中的技术家族，其面向在探索性和验证性框架中分析地理参考数据。 通过夏季讲习班传播这一较新的方法具有重要意义，原因如下：[1]空间过滤概念简单，使空间统计更容易为非常广泛的用户群体所利用，这些用户群体熟悉常规统计，但不一定熟悉空间统计;[二]《中国日报》空间滤波方法越来越多地出现在方法学和应用文献中，从广泛的学科范围来看，这既表明了它的增长，[3]空间过滤大大简化了空间自相关数据的线性、非线性、广义线性以及混合模型的实施; [4]为了评价和评论空间过滤方法，讲习班将把它们与分析空间数据的既定模式并列;最后，[5]将向研讨会参与者介绍在广泛使用的R计算平台中原型空间滤波算法的软件工具。 该研讨会旨在让参与者在广泛的提供或个人拥有的地理参考数据集上练习他们所获得的技能。将根据学员的具体背景，在讲习班期间对他们进行培训，使他们成为新出现的空间过滤方法的熟练用户、知情教员或潜在开发人员。 随着地理参考数据量的迅速增加和地理信息系统的构建，以支持我们日常生活的几乎每一个方面，以知情的方式分析地理参考数据的技能变得越来越重要。 本次研讨会有助于提供学术和专业专家这些分析技能。 潜在的参与者将遵循正式的申请程序，以获得2008年为期一周的夏季讲习班的有限的25个席位之一。 遴选委员会将确保选定参与者群体的多元化和资格，以便多元化的学者将参加研讨会。