Visual Analytics Algorithms for Spatiotemporal Analysis

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 program are to increase exposure 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 lab website ( http://vader.lab.asu.edu/) including open source software, course learning modules and podcasts.

Funding
This material is based upon work supported by the National Science Foundation under Grant No. 1350573. Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


Date of Last Update: July 24, 2017