Scalable analysis of movement data for extracting and exploring significant places

Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations

A systems design approach to appropriate, smart technology in a youth agriculture initiative

Abstract: A transformative research paradigm is rooted in knowledge mobilization processes involving close collaboration between researchers and the community. The research presents the development of an integrated, connected food ecosystem that, because of its fundamental design and use of appropriate, smart technology, which tends to naturally create inclusion and prosperity opportunities for many and not simply for the few. The research relies on multistakeholder participation to develop appropriate technologies to enhance economic activity amongst unemployed youths in Johannesburg, South Africa. A humancentered, systems engineering approach to develop a pilot project that promotes integrated, online, technologically supported food system is presented. The research is also concerned with how to measure the impact of the intervention the on food resilience as a result of urban farming. This paper presents the systems analysis of the current local food network and the proposed integrated solutions for a pilot project to establish a minimal viable project that can be tested. The research describes the planning and implementation of a pilot project as a minimal viable product to test in the market

Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off

Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges ("local bundles"), or also between the weakly bundled edges occurring globally over the graph ("global bundles"); we allow a user-set global-local tradeoff. We call the technique "peacock bundles". Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Bundled Crossings Revisited

An effective way to reduce clutter in a graph drawing that has (many) crossings is to group edges that travel in parallel into \emph{bundles}. Each edge can participate in many such bundles. Any crossing in this bundled graph occurs between two bundles, i.e., as a \emph{bundled crossing}. We consider the problem of bundled crossing minimization: A graph is given and the goal is to find a bundled drawing with at most $k$ bundled crossings. We show that the problem is NP-hard when we require a simple drawing. Our main result is an FPT algorithm (in $k$) when we require a simple circular layout. These results make use of the connection between bundled crossings and graph genus.Comment: Appears in the Proceedings of the 27th International Symposium on Graph Drawing and Network Visualization (GD 2019

Application opportunities of geographic information systems analysis to support achievement of the UNAIDS 90-90-90 targets in South Africa

In an effort to achieve control of the HIV epidemic, 90-90-90 targets have been proposed whereby 90% of the HIV-infected population should know their status, 90% of those diagnosed should be receiving antiretroviral therapy, and 90% of those on treatment should be virologically suppressed. In this article we present approaches for using relatively simple geographic information systems (GIS) analyses of routinely available data to support HIV programme management towards achieving the 90-90-90 targets, with a focus on South Africa (SA) and other high-prevalence settings in low- and middle-income countries. We present programme-level GIS applications to map aggregated health data and individual-level applications to track distinct patients. We illustrate these applications using data from City of Johannesburg Region D, demonstrating that GIS has great potential to guide HIV programme operations and assist in achieving the 90-90-90 targets in SA

Visualization of Frequent Itemsets with Nested Circular Layout and Bundling Algorithm

International audienceFrequent itemset mining is one of the major data mining issues. Once generated by algorithms, the itemsets can be automatically processed, for instance to extract association rules. They can also be explored with visual tools, in order to analyze the emerging patterns. Graphical itemsets representation is a convenient way to obtain an overview of the global interaction structure. However, when the complexity of the database increases, the network may become unreadable. In this paper, we propose to display itemsets on concentric circles, each one being organized to lower the intricacy of the graph through an optimization process. Thanks to a graph bundling algorithm, we finally obtain a compact representation of a large set of itemsets that is easier to exploit. Colors accumulation and interaction operators facilitate the exploration of the new bundle graph and to illustrate how much an itemset is supported by the data