Measures and Models of Mobility in Physical and Virtual Environments

人类和动物如何在太空中移动的模型为设计城市和服务的工程师、研究动物行为的生物学家以及试图构建更可信世界的电脑游戏设计师提供了信息。为这些模型收集数据是商品化的，有几家公司提供技术和服务来捕获目标人群的移动数据。这些模型几乎总是基于空间特征-轨迹属性的简明数学或算法描述。根据运动特性区分人群的统计模型直接与特征相关，这些特征构成了评估中的独立变量。合成运动模型在算法上使用特征来确定运动模式何时应该改变，或者验证算法为轨迹特征产生适当的分布。然而，我们的运动特征库是不完整的，导致不完整的模型。例如，随机步行者模型可以创建行程长度和停留时间的真实分布，但是产生在检查时错误的路径，导致蜂窝塔使用或疾病传播的不正确模型。我们将通过发现，表征和验证新的功能封装移动模式为真实的和虚拟空间，通过分析轨迹字符串和空间历史图形，使更现实的移动模型。为了促进和加速新功能的开发，我们还将编纂和部署标准数据集和测试机制，以评估功能质量。制定新的衡量标准可能是一个困难的过程，因为它需要数据分析和领域专业知识，并得到经验数据和数学严谨性的支持。我的研究小组在分析数字游戏和测量人类空间行为方面拥有丰富的经验，并在过去三年中发表和验证了几个空间特征。我的研究小组的毕业生受雇于工业和学术界的数据分析。通过协作者，我们可以访问TB级的移动数据。该提案概述了一项研究计划，该计划创建了精心策划的标准化数据集，并明确禁止了有效性和可推广性的测试，以推动空间历史和轨迹特征的创新。利用基于图形和字符串的轨迹分析的创新，我们将为研究人员和从业者提供新颖，可靠和可靠的功能。

Models of how people and animals move through space inform engineers designing cities and services, biologists studying animal behavior, and computer game designers attempting to build more believable worlds. Gathering the data for these models is commodified, with several companies offering technologies and services to capture movement data on target populations. These models are almost always based on spatial features - concise mathematical or algorithmic encapsulations of trajectory properties. Statistical models that differentiate populations based on movement properties are directly tied to features, which make up the independent variables under evaluation. Synthetic movement models use features algorithmically to determine when movement patterns should change, or to validate that the algorithms produce appropriate distributions for trajectory features. However, our library of movement features is incomplete, leading to incomplete models. For example, random walker models can create realistic distributions of trip length and dwell time, but produce paths which are erroneous upon inspection, leading to incorrect models of cell tower usage or the spread of disease. We will enable more realistic mobility models through the discovery, characterization and validation of novel features encapsulating mobility patterns for both real and virtual spaces, through the analysis of trajectories as strings and spatial histories as graphs. To facilitate and accelerate the development of new features we will also codify and deploy standard datasets and testing mechanisms to evaluate feature quality. Developing new measures can be a difficult process, as it requires both data analytic and domain expertise, supported by empirical data and mathematical rigor. My research group has extensive experience in analyzing digital games and measuring human spatial behavior, and has published and validated several spatial features over the last three years. Graduates from my research group are employed in data analytics in industry and academia. Through collaborators we have access to terabytes of mobility data. This proposal outlines a program of research which creates curated standardized datasets, and clear proscribed tests of efficacy and generalizability to drive innovation in features of spatial histories and trajectories. Leveraging innovations in graph and string-based analysis of trajectories, we will provide novel, proven, and reliable features to researchers and practitioners.