Using topological analysis to support event-guided exploration in urban data

The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatio-temporal urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies

The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics

While cities around the world are looking for smart ways to use new advances in data collection, management, and analysis to address their problems, the complex nature of urban issues and the overwhelming amount of available data have posed significant challenges in translating these efforts into actionable insights. In the past few years, urban visual analytics tools have significantly helped tackle these challenges. When analyzing a feature of interest, an urban expert must transform, integrate, and visualize different thematic (e.g., sunlight access, demographic) and physical (e.g., buildings, street networks) data layers, oftentimes across multiple spatial and temporal scales. However, integrating and analyzing these layers require expertise in different fields, increasing development time and effort. This makes the entire visual data exploration and system implementation difficult for programmers and also sets a high entry barrier for urban experts outside of computer science. With this in mind, in this paper, we present the Urban Toolkit (UTK), a flexible and extensible visualization framework that enables the easy authoring of web-based visualizations through a new high-level grammar specifically built with common urban use cases in mind. In order to facilitate the integration and visualization of different urban data, we also propose the concept of knots to merge thematic and physical urban layers. We evaluate our approach through use cases and a series of interviews with experts and practitioners from different domains, including urban accessibility, urban planning, architecture, and climate science. UTK is available at urbantk.org.Comment: Accepted at IEEE VIS 2023. UTK is available at http://urbantk.or

Learning Process Visualization for Distance Learning Teachers: Design Requirements and Visual Encodings.

In distance learning, receiving feedback is critical not only for students but also for teachers — from students. However, there is a lack of empirically validated recommendations for designing visualizations of process-oriented feedback for distance learning teachers. In this work, we propose design requirements and visual encodings for process-oriented feedback, obtained through an iterative design-based method involving intense participation of teachers from online vocational courses. Our results show that i) the prototypes built according to the proposed requirements were perceived as useful by teachers and ii) granularity level control, context, data pre-processing transparency, and correlation of process data to outcome data are essential for a successful visual learning analytics system in the studied domain

COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201

Visual analytics techniques for exploration of spatiotemporal data

Spatial and temporal interactions are central and fundamental in pretty much all activities in our world and society. Every day, people and goods travel around the world at different speeds and scales; migratory animals engage in long-distance travels that demonstrate the biological integration around the globe; weather phenomena, like typhoons and hurricanes, form and move around the Earth and may have large social-economic impact. In all these examples, proper understanding of the underlying phenomena can produce insights with the potential to shape the future development in those domains. The rapid development of acquisition technology and the popularization of GPS enabled mobile devices as resulted in spatiotemporal data being produced at massive rates. These create opportunities for data-driven analysis that can highly influence decision making in a diverse set of domains. In order to take advantage of all these data and realize their potential, it is crucial to be able to extract knowledge from them. Interactive visualization systems are acknowledged to be important tools in this scenario: it leverages the human cognitive system and the power of interactive graphic tools to enable quick hypothesis testing and exploration. However, the volume and inherent complexity of spatiotemporal data makes designing such systems a difficult problem. In fact, such complex data collections pose challenges in both managing the data for interactive exploration as well as in designing visual metaphors that enable effective for data exploration. Also, such visual metaphors are limited by constraints imposed by the display and data dimensions, often resulting in extremely cluttered visualizations that are hard to interpret. While, filtering and aggregation strategies are often applied to eliminate clutter, they might hide interesting patterns. Therefore, purely visual/interaction methods need to be complemented with techniques that help in the process of pattern discovery. This dissertation presents novel visual analytics contributions for the analysis of spatiotemporal data to attack the challenges aforementioned. Visual analytics combine interactive visualization with efficient pattern mining techniques to enable analysts to explore large amounts of complex data. The first contribution is the design of the TaxiVis visual exploration system. This system couples together a novel visual query model with an efficient custom-built data layer. These two components enable easy query composition via visual methods as well as interactive query response times. TaxiVis also makes use of coordinated views and rendering strategies to generate informative visual summaries for query results even when those are large. The remaining of the contributions in this thesis consists of two pattern mining techniques that help in the navigation through the data via pattern discovery. These two techniques have the goal of enhancing the analytical power of tools such as TaxiVis. Furthermore, these techniques have in common the use of concepts and techniques widely applied in scientific visualization and computer graphics. This approach allows us to have novel perspectives on the problems of finding patterns in spatiotemporal data that, to the best of our knowledge, have not been considered in the machine learning and data mining fields. The first technique consists of a topology-based technique whose main objective is to help users to find the ``needle in the hay stack'', i.e., guide users towards interesting slices (spatiotemporal regions) of the data. We call this process event guided exploration. The overall idea behind this technique is to treat topological features of time-varying scalar functions derived from spatiotemporal data as treated as events. Via visual exploration of the collection of extreme points extracted over time, important events of the data can be found with relatively a small amount of work by the user. The second pattern mining technique consists of a novel model based clustering technique designed for trajectory datasets. This technique, called Vector Field K-Means, models trajectories as streamlines of vector fields. One important feature of this modeling strategy is that it tries to avoid overlapping trajectories to have discrepant directions at their intersections. Clustering is achieved by using the spatial component of trajectories to fit a collection of vector fields to the given trajectories. This technique achieves richness and expressivity of features, simplicity of implementation and analysis, and computational efficiency. Furthermore, the obtained vector fields serve as a visual summary of the movement patterns in each cluster. Finally, Vector Field K-Means can be naturally generalized to also consider trajectories with attributes. This is achieved by using a different modeling strategy based on scalar fields, which we call Attribute Field K-Means

JamVis: exploration and visualization of traffic jams

Traffic jams are a significant problem in urban cities that cause pollution and waste fuel, money, and time. Therefore, there is an urgent need to build tools that enable authorities to monitor and understand traffic dynamics and their causes. However, exploring these large complex data presents a challenge to domain experts. This paper proposes JamVis, a web-based visual analytics framework that leverages Waze’s multi-modal spatio-temporal data to this end. JamVis comprises two main components designed based on requirements elicited from domain experts. The first one supports the exploration of Waze’s traffic jam information through multiple linked views. The second component allows identifying events through alerts reported by Waze users about different problems (e.g., potholes, floods, or heavy traffic). A new algorithm called TST-clustering is introduced to perform event detection, which is an adaptation of the DB-Scan algorithm that allows clustering alerts by space, time, and type. Furthermore, to provide an overview of this algorithm’s spatio-temporal results, we introduce a novel visualization called ST-Heatmap. JamVis is validated through three usage scenarios analyzing different events in Rio de Janeiro

A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data

Probability distributions are omnipresent in data analysis. They are often used to model the natural uncertainty present in real phenomena or to describe the properties of a data set. Designing efficient visual metaphors to convey probability distributions is, however, a difficult problem. This fact is especially true for geographical data, where conveying the spatial context constrains the design space. While many different alternatives have been proposed to solve this problem, they focus on representing data variability. However, they are not designed to support spatial analytical tasks involving probability quantification. The present work aims to adapt recent non-spatial approaches to the geographical context, in order to support probability quantification tasks. We also present a user study that compares the efficiency of these approaches in terms of both accuracy and usability