scholarly journals "What Happened Here!?" A Taxonomy for User Interaction with Spatio-Temporal Game Data Visualization

2021 ◽  
Vol 5 (CHI PLAY) ◽  
pp. 1-27
Author(s):  
Erica Kleinman ◽  
Nikitha Preetham ◽  
Zhaoqing Teng ◽  
Andy Bryant ◽  
Magy Seif El-Nasr
Keyword(s):  
Data Visualization ◽  
User Study ◽  
User Interaction ◽  
Future Research ◽  
Temporal Data ◽  
Making Meaning ◽  
Visualization System ◽  
Formal Taxonomy ◽  
Spatio Temporal ◽  
Data Visualizations

Currently, there is no formal taxonomy for the activities that users engage in when interacting with and making meaning from spatio-temporal game data visualizations. As data visualization, especially spatio-temporal visualization, becomes more popular for game data analytics, it becomes increasingly crucial that we develop a formal understanding of how users, especially players, interact with and extract meaning from game data using these systems. However, existing taxonomies developed for InfoVis are not directly applicable due to domain differences and a lack of consensus within the literature. This paper presents the beginnings of a taxonomy for user interaction with spatio-temporal data specific to the domain of games, developed from the results of a qualitative user study (n=7) in which experienced players were tasked with using a spatio-temporal visualization system to explore and understand telemetry data from Defense of the Ancients 2 (DotA 2). The taxonomy includes seven activities organized into three categories: Data Interaction, Sense Making, and Validation. We discuss the implications of these activities on design and future research.

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

scholarly journals APPLICATION OF 3D SPATIO-TEMPORAL DATA MODELING, MANAGEMENT, AND ANALYSIS IN DB4GEO

2016 ◽  
Vol IV-2/W1 ◽  
pp. 163-170
Author(s):  
P. V. Kuper ◽  
M. Breunig ◽  
M. Al-Doori ◽  
A. Thomsen
Keyword(s):  
Water Supply ◽  
United Arab Emirates ◽  
Transport Systems ◽  
Reservoir Modeling ◽  
Future Research ◽  
Temporal Data ◽  
Temporal Dimension ◽  
Geospatial Database ◽  
Spatio Temporal ◽  
Temporal Objects

Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

scholarly journals A Study on Visual Representations for Active Plant Wall Data Analysis

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 74 ◽  
Author(s):  
Kahin Akram Hassan ◽  
Yu Liu ◽  
Lonni Besançon ◽  
Jimmy Johansson ◽  
Niklas Rönnberg
Keyword(s):  
User Study ◽  
Line Graph ◽  
Well Being ◽  
Future Research ◽  
Temporal Data ◽  
Indoor Climate ◽  
Structured Interviews ◽  
Future Directions ◽  
Domain Experts ◽  
The Status

The indoor climate is closely related to human health, well-being, and comfort. Thus, an understanding of the indoor climate is vital. One way to improve the indoor climates is to place an aesthetically pleasing active plant wall in the environment. By collecting data using sensors placed in and around the plant wall both the indoor climate and the status of the plant wall can be monitored and analyzed. This manuscript presents a user study with domain experts in this field with a focus on the representation of such data. The experts explored this data with a Line graph, a Horizon graph, and a Stacked area graph to better understand the status of the active plant wall and the indoor climate. Qualitative measures were collected with Think-aloud protocol and semi-structured interviews. The study resulted in four categories of analysis tasks: Overview, Detail, Perception, and Complexity. The Line graph was found to be preferred for use in providing an overview, and the Horizon graph for detailed analysis, revealing patterns and showing discernible trends, while the Stacked area graph was generally not preferred. Based on these findings, directions for future research are discussed and formulated. The results and future directions of this research can facilitate the analysis of multivariate temporal data, both for domain users and visualization researchers.

scholarly journals Aesthetics and Clarity in Information Visualization: The Designer’s Perspective

Arts ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 72
Author(s):  
Annemarie Quispel ◽  
Alfons Maes ◽  
Joost Schilperoord
Keyword(s):  
Mass Media ◽  
Data Visualization ◽  
Information Visualization ◽  
Information Design ◽  
Future Research ◽  
Good Design ◽  
The Public ◽  
Objective Information ◽  
Data Visualizations ◽  
Important Design

Designers are increasingly involved in creating ‘popular’ data visualizations in mass media. Scientists in the field of information visualization propose collaborations between designers and scientists in popular data visualization. They assume that designers put more emphasis on aesthetics than on clarity in their representation of data, and that they aim to convey subjective, rather than objective, information. We investigated designers’ criteria for good design for a broad audience by interviewing professional designers and by reviewing information design handbooks. Additionally, we investigated what might make a visualization aesthetically pleasing (attractive) in the view of the designers. Results show that, according to the information designers, clarity and aesthetics are the main criteria, with clarity being the most important. They aim to objectively inform the public, rather than conveying personal opinions. Furthermore, although aesthetics is considered important, design literature hardly addresses the characteristics of aesthetics, and designers find it hard to define what makes a visualization attractive. The few statements found point at interesting directions for future research.

Visualization of Railroad Equipment Accident Causes Using Data Visualization Systems and GIS

2017 ◽  
Author(s):  
Trefor Williams ◽  
John Betak
Keyword(s):  
Data Visualization ◽  
The United States ◽  
Complex Interaction ◽  
Visualization System ◽  
System A ◽  
Train Speed ◽  
Grade Crossing ◽  
Visualization Systems ◽  
Using Data ◽  
Data Visualizations

The objective of this paper is to demonstrate how GIS and data visualization systems can be used to identify spatial relationships to add to our understanding of railroad accident factors. Examples are given of the spatial analysis of broken rail accidents and grade crossing accidents on GIS maps. Additionally, using the Weave data visualization system a data dashboard was constructed that shows the complex interaction between variables like track type, FRA track classification, train speed and track density with broken rail accident causes. The findings indicate that broken rail accidents occur most frequently in the Midwest. Possibly this trend is related to climate change and increased temperatures and precipitation in the United States. GIS visualizations also showed that many truck-trailer accidents at grade crossings occur in low population areas. This work indicates that GIS and data visualizations are a useful method of identifying trends in railroad accidents.

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