CAREER: A Visual Analysis Approach to Space-Time Data Exploration

职业：时空数据探索的可视化分析方法

• 批准号: 1350573
• 负责人: Ross Maciejewski
• 金额: $ 43.48万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350573&HistoricalAwards=false
• 关键词: CAREER; Visual; Analysis; Approach; Space

From smart phones to fitness trackers to sensor enabled buildings, data is currently being collected at an unprecedented rate. Now, more than ever, data exists that can be used to gain insight into how policy decisions can impact our daily lives. For example, one can imagine using data to help predict where crime may occur next or inform decisions on police resource allocations or diet and activity patterns could be used to provide recommendations for improving an individual's overall health and well-being. Underlying all of this data are measurements with respect to space and time. However, finding relationships within datasets and accurately representing these relationships to inform policy changes is a challenging problem. This research addresses fundamental questions of how we can effectively explore such space-time data in order to enhance knowledge discovery and dissemination. This research both extends traditional visual representations and develops novel views for showing how correlations, clusters and other various spatial dynamics change over time. Broader impacts of the research program include: (1) enhanced infrastructure for research and education in the form of new visual analytics algorithms and open source software; (2) broad dissemination of visual analysis methods across various domains including geography, urban planning, and public health; and (3) impacts on society including the dissemination of novel tools and methods for improved public health and safety. The primary educational goals of this CAREER project are to increase students' access to crucial but highly unavailable visual analytic technologies and to broaden participation in data science and engineering. Toward those ends, the Visual Analytics Education program will engage broad student populations (undergraduate and graduate) through innovative curricula focusing on visual data analysis and the core technologies that drive the research program (visual analytics tools). By focusing on those technologies and their synergy in the research program, the education program directly integrates the proposed research with education. The programs will benefit multiple groups (researchers, patients, students, underrepresented groups) and institutions (academia, industry, healthcare, education) both locally and globally.For spatial data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. Multivariate color maps, textures, small multiples and 3D views have been employed as means of increasing the amount of information that can be conveyed when plotting spatial data to a map. However, each of these methods has their own limitations. Multivariate color maps and textures result in cognitive overload where much time is spent trying to separate data elements in the visual channel. In 3D, occlusion and clutter remain fundamental challenges for effective visual data understanding. Utilizing small multiples can help in side-by-side comparison, but their scalability is limited by the available screen space and the cognitive overhead associated with pairwise comparisons. Instead of being confined to the original spatiotemporal domain, this proposal seeks to both extend traditional visual representations and develop novel views for showing how correlations, clusters and other various spatial dynamics change over time. Underlying these novel views is also the need for visual representations in which the manipulation of the representation is directly tied to the underlying computational analytics. Specifically, this research focuses on datasets from urban planning, geography, public health and crime to address: (1) the extraction of semi-supervised templates for spatial and temporal aggregation; (2) the development of interaction techniques for visual steering and classification of spatiotemporal data; (3) the integration of multiple families of anomaly detection algorithms and information theoretic methods for semi-supervised anomaly detection, and; (4) novel algorithms for the extraction of flow fields from spatiotemporal data. Additional information can be found at the project website (http://vader.lab.asu.edu/Space-TimeVA) including open source software, course learning modules and podcasts.

从智能手机到健身追踪器，再到具有传感器功能的建筑，目前正在以前所未有的速度收集数据。现在，比以往任何时候都有更多的数据可以用来深入了解政策决定如何影响我们的日常生活。例如，可以想象使用数据来帮助预测下一次犯罪可能发生的地方，或为警察资源分配的决定提供信息，或者可以使用饮食和活动模式来提供建议，以改善个人的整体健康和福祉。所有这些数据的基础是对空间和时间的测量。然而，寻找数据集中的关系并准确地表示这些关系以通知政策变化是一个具有挑战性的问题。本研究解决了我们如何有效地探索这些时空数据以增强知识发现和传播的基本问题。这项研究既扩展了传统的视觉表现，又开发了新的观点来展示相关性、集群和其他各种空间动态如何随时间变化。该研究计划的更广泛影响包括：(1)以新的可视化分析算法和开源软件的形式增强了研究和教育的基础设施；(2)视觉分析方法在地理、城市规划和公共卫生等各个领域的广泛传播；(3)对社会的影响，包括传播改善公共卫生和安全的新工具和方法。这个CAREER项目的主要教育目标是增加学生获得关键但高度不可用的视觉分析技术的机会，并扩大对数据科学和工程的参与。为了实现这些目标，视觉分析教育项目将通过专注于视觉数据分析和驱动研究项目的核心技术（视觉分析工具）的创新课程，吸引广泛的学生群体（本科生和研究生）。通过关注这些技术及其在研究计划中的协同作用，教育计划直接将拟议的研究与教育相结合。这些项目将惠及本地和全球的多个群体（研究人员、患者、学生、代表性不足的群体）和机构（学术界、工业界、医疗保健、教育）。对于空间数据，将这些数据转换为可视形式，使用户能够快速查看模式、探索摘要并将有关潜在地理现象的领域知识联系起来，这些在表格形式中是不明显的。然而，在可视化和探索这些大型时空数据集时，出现了几个关键的挑战。虽然数据的潜在地理组成部分很适合以传统制图表示形式（例如，等值线、等线、非对称地图）进行单变量可视化，但随着数据变得多变量，制图表示变得更加复杂。多变量彩色地图、纹理、小倍数和3D视图被用作增加在将空间数据绘制到地图时可以传达的信息量的手段。然而，每种方法都有自己的局限性。多元颜色贴图和纹理会导致认知超载，因为我们需要花费大量时间去尝试分离视觉通道中的数据元素。在3D中，遮挡和杂波仍然是有效视觉数据理解的基本挑战。使用小的倍数可以帮助并行比较，但是它们的可伸缩性受到可用屏幕空间和与两两比较相关的认知开销的限制。该方案不局限于原始的时空域，而是寻求扩展传统的视觉表征，并开发新的视角来展示相关性、集群和其他各种空间动态如何随时间变化。这些新颖观点的基础是对可视化表示的需求，其中表示的操作直接与底层的计算分析联系在一起。具体而言，本研究以城市规划、地理、公共卫生和犯罪数据集为重点，解决以下问题：(1)提取时空聚合的半监督模板；(2)时空数据视觉导向与分类交互技术的发展；(3)将多类异常检测算法与信息论方法相结合，实现半监督异常检测；(4)从时空数据中提取流场的新算法。更多信息可以在项目网站（http://vader.lab.asu.edu/Space-TimeVA）上找到，包括开源软件、课程学习模块和播客。