Contextual Influences on the Category Construction of Geographic Scale Movement Patterns

我们生活的世界在各个尺度上都是动态的。在小尺度上，想象一下穿过一个房间或开车去杂货店；在大尺度上，想象一场飓风横扫一个州。因此，研究人类如何理解空间和时间之间的关系，不仅是地理学的核心，也是认知科学和信息科学的核心。这个项目的目标有两个：开发一个研究框架，检查地理尺度上的运动模式是如何被理解的，以及评估地理信息科学中使用的形式主义如何能够捕捉到人们如何理解MPGS。将认知特征和形式特征联系起来，使模型能够与人们对大规模空间过程的思考保持一致，从而增强了人与计算机之间的界面交流。宾夕法尼亚州立大学的亚历山大·克利佩尔博士将进行一系列实验，这些实验基于一种用于获取概念性知识(即分类)的分组范例。参与者观看在不同时空阶段结束的大规模空间现象的动画。例如，参与者将观看飓风逼近海岸、登陆并最终向内陆移动的63个不同动画。然后，参与者将根据它们的相似性对这些动画进行分类。这些研究试图回答的问题是，不同领域(例如，移动的实体是飓风还是冰川)的形式化、拓扑学等价特征在认知上是否也是等价的。换言之，动态特征的语义如何影响其认知概念化？语义是否改变了个体拓扑关系的认知突显？此外，该项目将解决规模效应在地理上的关键问题，并将通过将运动模式的概念化与语言外化联系起来，为地理事件语言的正式基础作出贡献。为了实现这些目标，PI开发了软件来高效地收集和分析行为数据。分析数据的软件将在现有的地理视觉分析软件中开发，该软件由宾夕法尼亚州立大学地理系Geovista中心开发。该项目将有助于理解人类如何从地理尺度上概念化移动模式，如横跨半岛的飓风，从这些移动模式如何正式表征的角度。将时空信息的形式特征和认知特征联系起来，对于开发高效的人机界面和空间认知模型至关重要。该项目将开发设计实验的软件解决方案，并使用可视化分析方法对收集的数据进行有效分析。由于认知概念化是本体论和范畴化研究的核心，也是人类学等领域的核心，我们预计所开发的研究框架和软件解决方案都将有益于更大的研究社区。

The world we live in is dynamic on all scales. On a small scale, think of walking across a room or driving to the grocery store; on a large scale, imagine a hurricane crossing a state. Research that addresses how humans understand relationships between space and time is, therefore, central not only to geography, but also to cognitive and information sciences. The objectives of this project are two-fold: to develop a research framework for examining how movement patterns at the geographic scale (MPGS) are understood, and to evaluate how formalisms used in geographic information science are able to capture how people understand MPGS. Linking cognitive and formal characterizations enables models that align with how people think about large scale spatial processes, thereby enhancing communication at the interface between humans and computers. Dr. Alexander Klippel at the Pennsylvania State University will conduct a set of experiments that are based on a grouping paradigm that is used to elicit conceptual knowledge (i.e., categorization). Participants view animations of large-scale spatial phenomena ending at different spatio-temporal stages. For example, participants will look at 63 different animations of a hurricane approaching shore, making landfall, and finally moving far inland. Participants will then classify these animations based on their similarities. The question these studies are attempting to answer is whether formal, topologically equivalent characterizations across different domains (e.g., whether the moving entity is a hurricane or a glacier) are also equivalent cognitively. In other words, how does the semantics of dynamic features influence the cognitive conceptualizations thereof? Does semantics change the cognitive salience of individual topological relations? Additionally, the project will address the geographically critical question of scale effects and a contribution will be made to the formal underpinning of geographic event language by relating the conceptualizations of movement patterns to linguistic externalizations. To accomplish these goals, the PI has developed software to both collect and analyze behavioral data efficiently. The software to analyze the data will be developed within existing geovisual analytics software developed at the GeoVISTA Center in the Geography Department at the Pennsylvania State University.This project will contribute to the understanding of how humans conceptualize movement patterns at the geographic scale, such as hurricanes moving across a peninsula, from the perspective of how these movement patterns are characterized formally. Connecting formal and cognitive characterizations of spatio-temporal information is essential to develop efficient human-computer interfaces and models of spatial cognition. This project will develop software solutions for the design of experiments and the efficient analysis of the collected data using visual analytics approaches. As cognitive conceptualizations are core to research on ontologies and categorization, and also to fields such as anthropology, we expect both the developed research framework and the software solutions to be beneficial to a large research community.