The Wearables for Health Toolkit (W4H Toolkit) is an open-source platform that provides a robust, end-to-end solution for the centralized management and analysis of wearable data. With integrated tools and frameworks, the toolkit facilitates seamless data acquisition, integration, storage, analysis, and visualization of both stored and streaming data from various wearable devices. The W4H Toolkit is designed to provide medical researchers and health practitioners with a unified framework that enables the analysis of health-related data for various clinical applications. We provide an overview of the system and demonstrate how it can be used by health researchers to import and analyze a wide range of wearable data and perform data analysis, highlighting the versatility and functionality of the system across diverse healthcare domains and applications.

CrowdMap is an anonymous occupancy monitoring system developed in response to the COVID-19 pandemic. CrowdMap collects, cleans, and visualizes occupancy data derived from connection logs generated by large arrays of Wi-Fi access points. Thus, CrowdMap is a passive digital tracking tool that can be used to reopen buildings safely, as it helps actively manage occupancy limits and identify utilization trends at scale. Occupancy monitoring is possible at various levels of resolution over large spatial (e.g., from individual rooms to entire buildings) and temporal (e.g., from hours to months) extents. The CrowdMap web-based front-end implements powerful spatiotemporal querying and visualization tools to quickly and effectively explore occupancy patterns throughout large campuses. We will demonstrate CrowdMap and its spatiotemporal GUI that was deployed for an entire university campus with data continuously being collected since summer 2020.

Generating hyper-local news at scale is challenging because publicly available data is not provided at the desired spatial and temporal granularity. Besides, there is a lack of automated analytical and publishing tools. Crosstown Foundry, which is being actively developed and used by engineers and journalists, is a novel data-driven system that leverages a massive multi-modal dataset to generate personalized newsletters for Los Angeles County readers.

Social relationships between people, e.g., whether they are friends with each other, can be inferred by observing their behaviors in the real world. Thanks to the popularity of GPS-enabled mobile devices or online services, a large amount of high-resolution location data becomes available for such inference studies. However, due to the sensitivity of location data and user privacy concerns, those studies cannot be largely carried out on individually contributed data without privacy guarantees. Furthermore, we observe that the actual location may not be needed for social relationship studies, but rather the fact that two people met and some statistical properties about their meeting locations, which can be computed in a private manner. In this paper, we envision an extensible framework, dubbed Privacy-preserving Location Analytics and Computation Environment (PLACE), which enables social relationship studies by analyzing individually generated location data. PLACE utilizes an untrusted server and computes several building blocks to support various social relationship studies, without disclosing location information to the server and other untrusted parties. We present PLACE with three example social relationship studies which utilize four privacy-preserving blocks with encryption and differential privacy primitives. The successful realization of PLACE will facilitate private location data acquisition from individual devices, thanks to the strong privacy guarantees, and will enable a wide range of applications.

Aerial LiDAR (Light Detection and Ranging) point clouds are gathered by a downward scanning laser on a low-flying aircraft. Due to the imaging process, vertical surface features such as building walls, and ground areas under tree canopies are totally or partially occluded, resulting in gaps and sparsely sampled areas. These gaps produce unwanted holes and uneven point distributions that often produce artifacts when visualized using point-based rendering (PBR) techniques. We show how to extend PBR by inferring the physical nature of LiDAR points for visual realism and added comprehension. More specifically, the class of object a point is related to augments the point cloud in pre-processing and/or adapts the online rendering, to produce visualizations that are more complete and realistic. We provide examples of point cloud augmentation for building walls and ground areas under tree canopies. We show how different types of procedurally generated geometry can be used to recover building walls. These methods are generic and can be applied to any aerial LiDAR data set with buildings and trees. Our work also incorporates an out-of-core strategy for hierarchical data management and GPU-accelerated PBR with extended deferred shading. The combined system provides interactive visually-complete rendering of virtually unlimited-size LiDAR point clouds. Experimental results show that our rendering approach adds only a slight overhead to PBR and provides comparable visual cues to visualizations generated by off-line pre-computation of 3D polygonal urban models.

We present a scalable out-of-core technique for mapping colors from aerial oblique imagery to large scale aerial LiDAR (Light Detection and Ranging) point cloud. Our method does not require meshing or intensive processing of points, only fast and effective augmentation is applied to fill occluded points on building walls and under tree canopies. The presented system applies a modified visibility pass of GPU splatting to map colors, where occluded points are filtered out by projecting all points as oriented surface splats into images. A weighting scheme is utilized to accumulate colors from all contributing images while leveraging image resolution and surface orientation. The effectiveness of color mapping is demonstrated through visualizations of colored points by a GPU splatting algorithm.

Harnessing the potential of today's ever growing and dynamic geospatial data requires the development of novel visual analysis interfaces, tools and technologies. In this paper, we present GeoDec, a generic framework capable of supporting queries and visualizations of real-world spatio-temporal data sets. We show, for various locations and applications, how our innovative Query Driven Design enhances the visual analysis of geospatial data through the interactive manipulation of queries and the temporal navigation of these query results.