Building a Visual Analytics Tool for Location-Based Services

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

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Building a Visual Analytics Tool for Location-Based Services

Geo-Intelligence and Visualization through Big Data Trends - Advances in Geospatial Technologies ◽

10.4018/978-1-4666-8465-2.ch006 ◽

2015 ◽

pp. 150-180

Author(s):

Erdem Kaya ◽

Mustafa Tolga Eren ◽

Candemir Doger ◽

Selim Saffet Balcisoy

Keyword(s):

Visual Analytics ◽

3D Visualization ◽

Location Based Services ◽

Task Completion ◽

Temporal Data ◽

Density Map ◽

Spatio Temporal ◽

Visualization Techniques ◽

Analytical Tools ◽

2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

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Combining 2D and 3D Visualization with Visual Analytics in the Environmental Domain

Information ◽

10.3390/info13010007 ◽

2021 ◽

Vol 13 (1) ◽

pp. 7

Author(s):

Milena Vuckovic ◽

Johanna Schmidt ◽

Thomas Ortner ◽

Daniel Cornel

Keyword(s):

Data Visualization ◽

Visual Analytics ◽

Simulation Analysis ◽

3D Visualization ◽

Heavy Rain ◽

Daily Practice ◽

Environmental Domain ◽

2D Data ◽

Visualization Techniques ◽

2D And 3D

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. This is facilitated by a unique synergy of modules for simulation, analysis, and visualization, offering instantaneous visual feedback of transformative changes in the underlying data. However, even if the resulting knowledge holds great potential for supporting decision-making in the environmental domain, the consideration of such techniques still have to find their way to daily practice. To advance these developments, we demonstrate four case studies that portray different opportunities in data visualization and VA in the context of climate research and natural disaster management. Firstly, we focus on 2D data visualization and explorative analysis for climate change detection and urban microclimate development through a comprehensive time series analysis. Secondly, we focus on the combination of 2D and 3D representations and investigations for flood and storm water management through comprehensive flood and heavy rain simulations. These examples are by no means exhaustive, but serve to demonstrate how a VA framework may apply to practical research.

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A Survey on Spatio-temporal Data Analytics Systems

ACM Computing Surveys ◽

10.1145/3507904 ◽

2022 ◽

Author(s):

Md Mahbub Alam ◽

Luis Torgo ◽

Albert Bifet

Keyword(s):

Programming Languages ◽

Spatial Data ◽

Data Analytics ◽

Spatial Databases ◽

Location Based Services ◽

Temporal Data ◽

Wide Range ◽

Data Volume ◽

Spatio Temporal ◽

Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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Data Analysis through Visualization

10.1093/oso/9780192897879.003.0005 ◽

2021 ◽

pp. 107-132

Author(s):

Magy Seif El-Nasr ◽

Truong Huy Nguyen Dinh ◽

Alessandro Canossa ◽

Anders Drachen

Keyword(s):

Data Analysis ◽

Data Science ◽

University Of California ◽

Science Class ◽

Temporal Data ◽

Santa Cruz ◽

State Action ◽

Spatio Temporal ◽

Visualization Systems ◽

Visualization Techniques

This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.

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