Enhancing Spatial Thinking Awareness of World-Scale Geography with Excel Dynamic Map Charts and Virtual Globes

Spatial thinking is a unique thinking skill that geographers use to reason. Every individual is believed to have this thinking skill, but not all are aware of it. This condition causes differences in the development of each person. A person’s spatial thinking can be improved by training. Geospatial technology is a representation tool that many people believe can be used to train spatial thinking skills. However, not many people encounter obstacles when using this technology. The complexity of the command to run is an obstacle that is often found. In line with the development of geospatial technology, many applications integrate this technology as part of visualization tools. This teaching and learning were conducted with Action Research Classroom (three cycles) in the form of Project-Based Learning with Science, Engineering, Technology, and Mathematic (STEM) approach. This article discusses the findings of the research on the use of Excel dynamic map chart and virtual globe to improve spatial thinking in research subjects with the case study of Indonesian Geography and World Regional Geography. With the Excel application project, for the context of upper-level education, the findings show an increase in spatial thinking skills and mastery of the use of mapping platforms without the need for prior experience of coding, software, or cartography, although it needs to be corroborated by other studies

Quantitative data graphics in 3D desktop-based virtual environments – an evaluation

3D desktop-based virtual environments provide a means for displaying quantitative data in context. Data that is inherently spatial in three-dimensions may benefit from visual exploration and analysis in relation to the environment in which they were collected and to which they relate. We empirically evaluate how effectively and efficiently such data can be visually analyzed in relation to location and landform in 3D versus 2D visualizations. In two experiments, participants performed visual analysis tasks in 2D and 3D visualizations and reported insights and their confidence in them. The results showed only small differences between the 2D and 3D visualizations in the performance measures that we evaluated: task completion time, confidence, complexity, and insight plausibility. However, we found differences for different data sets and settings suggesting that 3D visualizations, or 2D representations respectively, may be more or less useful for particular data sets and contexts

GeoVisual analytics: interactivity, dynamics, and scale

3D desktop-based virtual environments provide a means for displaying quantitative data in context. Data that is inherently spatial in three-dimensions may benefit from visual exploration and analysis in relation to the environment in which they were collected and to which they relate. We empirically evaluate how effectively and efficiently such data can be visually analyzed in relation to location and landform in 3D versus 2D visualizations. In two experiments, participants performed visual analysis tasks in 2D and 3D visualizations and reported insights and their confidence in them. The results showed only small differences between the 2D and 3D visualizations in the performance measures that we evaluated: task completion time, confidence, complexity, and insight plausibility. However, we found differences for different data sets and settings suggesting that 3D visualizations, or 2D representations respectively, may be more or less useful for particular data sets and contexts

Visual exploration of eye movement data using the Space-Time-Cube

Eye movement recordings produce large quantities of spatio- temporal data, and are more and more frequently used as an aid to gain further insight into human thinking in usability studies in GIScience domain among others. After reviewing some common visualization methods for eye movement data, the limitations of these methods are discussed. This paper proposes an approach that enables the use of the Space-Time-Cube (STC) for representation of eye movement recordings. Via interactive functions in the STC, spatiotemporal patterns in eye movement data could be analyzed. A case study is presented according to proposed solutions for eye movement data analysis. Finally, the advantages and limitations of using the STC to visually analyze eye movement recordings are summarized and discussed