Search · four archives
Search · four archives
17 papers · ranked by Valyu relevance
Maged N Kamel Boulos, Teeradache Viangteeravat, Matthew N Anyanwu, Venkateswara Ra Nagisetty + 1 more
'Venkateswara Ra Nagisetty' 'Emin Kuscu'] The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable…
Lukáš Marek, Pavel Tuček, Vít Pászto
Background Visual analytics aims to connect the processing power of information technologies and the user’s ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either…
Siran Li, Suzana Dragićević, François Anton, Monika Sester + 7 more
'Stephan Winter' 'Arzu Çöltekin' 'Christopher Pettit' 'Bin Jiang' 'James Haworth' 'Alfred Stein' 'Tao Cheng'] Songnian Li *, Ryerson University, Toronto, Canada, snli@ryerson.ca Suzana Dragicevic, Simon Fraser University, Vancouver, Canada, suzanad@sfu.ca François Anton, Technical University of Denmark, Lyngby…
Dong Yu, Oppermann Ian, Liang Jie, Yuan Xiaoru + 1 more
We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of…
Andrew Moran, Vijay Gadepally, Matthew Hubbell, Jeremy Kepner
—For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates…
N. Piccolotto, M. Bögl, C. Muehlmann, K. Nordhausen + 2 more
'S. Miksch'] Title: Abstract Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods…
Alexander Yoshizumi, Megan M. Coffer, Elyssa L. Collins, Mollie D. Gaines + 8 more
Geospatial factors play a key role in many of the world's most pressing challenges – pandemics, plant pest and pathogen spread, natural disasters, urban sprawl, pollution, human trafficking, food scarcity, and transportation (to name just a few). Additionally, as technologies such as GPS-equipped mobile devices, remote…
Fabio Miranda, Thomas Ortner, Gustavo Moreira, Maryam Hosseini + 5 more
'Milena Vuckovic' 'Filip Biljecki' 'Claudio Silva' 'Marcos Lage' 'Nivan Ferreira'] > Department of Computer Science, University of Illinois Chicago, USA City Form Lab, Massachusetts Institute of Technology, USA VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Austria Department of Architecture and…
Chuan Chen, Peng Luo, Bo Zhao, Feng Yu + 1 more
Visual Analytics Authors: ['Chuan Chen' 'Peng Luo' 'Bo Zhao' 'Feng Yu' 'Liqiu Meng'] > Spatial analysis can generate both exogenous and endogenous biases, which will lead to ethics issues. Exogenous biases arise from external factors or environments and are unrelated to internal operating mechanisms, while endogenous…
Yi Du, Cuixia Ma, Chao Wu, Xiaowei Xu + 3 more
'Jianhui Li'] With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates…
Simon Warchol, Grace Guo, Johannes Knittel, Dan Freeman + 4 more
Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial…
Lambda Moses, Pétur Helgi Einarsson, Kayla Jackson, Laura Luebbert + 5 more
Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial…
Carsten Juergens, Andreas P. Redecker
Geospatial data literacy is of paramount importance in an increasingly digital business world. Especially in economic decision-making processes, the ability to judge the trustworthiness of pertinent data sets is inevitable for reliable decisions. Thus, geospatial competencies need to supplement the university’s…
Rakesh Kumar Lenka, Rabindra K. Barik, Noopur Gupta, Syed Mohd Ali + 2 more
'Amiya Kumar Rath' 'Harishchandra Dubey'] In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data…
Nicholas J. Murray, David A. Keith, Daniel Simpson, John H. Wilshire + 1 more
Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to maps they need to support their routine decision-making. This deficiency is…
Anton Stratmann, Martin Beyß, Johann F. Jadebeck, Katharina Nöh
Understanding input-output relationships within multivariate datasets is an ubiquitous task in the life and data sciences. For this, visual analysis is indispensable for providing expressive summaries and preparing decision-making. We present the visual analysis approach and software MooViE, which is designed to strike…
Jaka Kokošar, Cagatay Turkay, Luka Avsec, Miha Štajdohar + 1 more
We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis—essential in biomedicine—evaluates patients’ survival rates and the onset of…