From spatial to platial - the role and future of immersive technologies in the spatial sciences

Immersive technologies such as virtual and augmented reality have been part of the technology mindset in computer and geospatial sciences early on. The promise of delivering realistic experiences to the human senses that are not bound by physical reality has inspired generations of scientists and entrepreneurs alike. However, the vision for immersive experiences has been in stark contrast to the ability to deliver at the technology end; the community has battled nuisances such as cybersickness, tethers, and the uncanny valley for the last decades. With the \u27final wave\u27 of immersive technologies, we are now able to fulfill a long-held promise and freely and creatively envision how immersive technologies change spatial sciences by creating embodied experiences for geospatial applications. These experiences are not restricted by time or place, nor are they limited to the physical world. This contribution envisions the future of spatial sciences in light of place-like experiences enabled through immersive technologies and their potential to infuse research in the spatial sciences community

A Cognitive Perspective on Spatial Context

This paper develops a representation-theoretic notion of spatial context for cognitive agents interacting with spatial environments. We discuss the current state of the art in defining context as used in context-aware and/or location- aware systems. In contrast to existing approaches, we define context through cognitive processes. The term "invisible geography" alludes to the fact that knowledge about geographic space develops through complex cognitive interaction and is not simply "out there" to be looked at. Placing (cognitive) processes in the focus of our context definition allows for a truly user-centered perspective: conceptualizations imbue spatial structures with meaning. This allows for fixing terminological problems and relating context definitions to work in spatial information theory and cognitive science. Although we focus on spatial context, the approach is generic and can be adapted to other domains in which cognitive aspects concerning users of information systems are central

Intuitive Direction Concepts

Experiments in this article test the hypothesis that formal direction models used in artificial intelligence correspond to intuitive direction concepts of humans. Cognitively adequate formal models of spatial relations are important for information retrieval tasks, cognitive robotics, and multiple spatial reasoning applications. We detail two experiments using two objects (airplanes) systematically located in relation to each other. Participants performed a grouping task to make their intuitive direction concepts explicit. The results reveal an important, so far insufficiently discussed aspect of cognitive direction concepts: Intuitive (natural) direction concepts do not follow a one-size-fits-all strategy. The behavioral data only forms a clear picture after participants\u27 competing strategies are identified and separated into categories (groups) themselves. The results are important for researchers and designers of spatial formalisms as they demonstrate that modeling cognitive direction concepts formally requires a flexible approach to capture group differences

Quantifying space, understanding minds: A visual summary approach

This paper presents an illustrated, validated taxonomy of research that compares spatial measures to human behavior. Spatial measures quantify the spatial characteristics of environments, such as the centrality of intersections in a street network or the accessibility of a room in a building from all the other rooms. While spatial measures have been of interest to spatial sciences, they are also of importance in the behavioral sciences for use in modeling human behavior. A high correlation between values for spatial measures and specific behaviors can provide insights into an environment\u27s legibility, and contribute to a deeper understanding of human spatial cognition. Research in this area takes place in several domains, which makes a full understanding of existing literature difficult. To address this challenge, we adopt a visual summary approach. Literature is analyzed, and recurring topics are identified and validated with independent inter-rater agreement tasks in order to create a robust taxonomy for spatial measures and human behavior. The taxonomy is then illustrated with a visual representation that allows for at-a-glance visual access to the content of individual research papers in a corpus. A public web interface has been created that allows interested researchers to add to the database and create visual summaries for their research papers using our taxonomy

Human Interpretation of Trade-Off Diagrams in Multi-Objective Problems: Implications for Developing Interactive Decision Support Systems

The growing need for efficient and effective human decision-makers warrants a better understanding of how decision support systems (DSS) guide users to improved decisions. Decision support approaches utilize visual aids to assist decision-making, including trade-off diagrams. These visualizations help comprehension of key trade-offs among decision alternatives. However, little is known about the role of trade-off diagrams in human decision-making and the best way to present them. Here, we discuss an empirical study with two goals: 1) evaluating DSS interactivity and 2) identifying decision-making strategies with trade-off diagrams. We specifically investigate the value of interface interactivity and problem context as users make nine increasingly complex decisions. Our results suggest that problem context and interactivity separately influence ability to navigate trade-off diagrams

Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making

Conceptualizing Landscapes: A Comparative Study of Landscape Categories with Navajo and English-speaking Participants

Abstract. Understanding human concepts, spatial and other, is not only one of the most prominent topics in the cognitive and spatial sciences; it is also one of the most challenging. While it is possible to focus on specific aspects of our spatial environment and abstract away complexities for experimental purposes, it is important to understand how cognition in the wild or at least with complex stimuli works, too. The research presented in this paper addresses emerging topics in the area of landscape conceptualization and explicitly uses a diversity fostering approach to uncover potentials, challenges, complexities, and patterns in human landscape concepts. Based on a representation of different landscapes (images) responses from two different populations were elicited: Navajo and the (US) crowd. Our data provides support for the idea of conceptual pluralism; we can confirm that participant responses are far from random and that, also diverse, patterns exist that allow for advancing our understanding of human spatial cognition with complex stimuli

GeoCAM: A geovisual analytics workspace to contextualize and interpret statements about movement

This article focuses on integrating computational and visual methods in a system that supports analysts to identify extract map and relate linguistic accounts of movement. We address two objectives: (1) build the conceptual theoretical and empirical framework needed to represent and interpret human-generated directions; and (2) design and implement a geovisual analytics workspace for direction document analysis. We have built a set of geo-enabled computational methods to identify documents containing movement statements and a visual analytics environment that uses natural language processing methods iteratively with geographic database support to extract interpret and map geographic movement references in context. Additionally analysts can provide feedback to improve computational results. To demonstrate the value of this integrative approach we have realized a proof-of-concept implementation focusing on identifying and processing documents that contain human-generated route directions. Using our visual analytic interface an analyst can explore the results provide feedback to improve those results pose queries against a database of route directions and interactively represent the route on a map

Intuitive Direction Concepts

Abstract Experiments in this article test the hypothesis that formal direction models used in artificial intelligence correspond to intuitive direction concepts of humans. Cognitively adequate formal models of spatial relations are important for information retrieval tasks, cognitive robotics, and multiple spatial reasoning applications. We detail two experiments using two objects (airplanes) systematically located in relation to each other. Participants performed a grouping task to make their intuitive direction concepts explicit. The results reveal an important, so far insufficiently discussed aspect of cognitive direction concepts: Intuitive (natural) direction concepts do not follow a one-size-fits-all strategy. The behavioral data only forms a clear picture after participants' competing strategies are identified and separated into categories (groups) themselves. The results are important for researchers and designers of spatial formalisms as they demonstrate that modeling cognitive direction concepts formally requires a flexible approach to capture group differences