Virtual geographic environments in socio-environmental modeling: a fancy distraction or a key to communication?

Modeling and simulation are recognized as effective tools for management and decision support across various disciplines; however, poor communication of results to the end users is a major obstacle for properly using and understanding model output. Visualizations can play an essential role in making modeling results accessible for management and decision-making. Virtual reality (VR) and virtual geographic environments (VGEs) are popular and potentially very rewarding ways to visualize socio-environmental models. However, there is a fundamental conflict between abstraction and realism: models are goal-driven, and created to simplify reality and to focus on certain crucial aspects of the system; VR, in the meanwhile, by definition, attempts to replicate reality as closely as possible. This elevated realism may add to the complexity curse in modeling, and the message might be diluted by too many (background) details. This is also connected to information overload and cognitive load. Moreover, modeling is always associated with the treatment of uncertainty–something difficult to present in VR. In this paper, we examine the use of VR and, specifically, VGEs in socio-environmental modeling, and discuss how VGEs and simulation modeling can be married in a mutually beneficial way that makes VGEs more effective for users, while enhancing simulation models

EVALUATING ROUTE LEARNING PERFORMANCE OF OLDER AND YOUNGER ADULTS IN DIFFERENTLY-DESIGNED VIRTUAL ENVIRONMENTS: A TASK-DIFFERENTIAL ANALYSIS

Navigating in unfamiliar environments is a complex task that requires considerable cognitive resources to memorize (and eventually learn) a route. In general, virtual environments (VEs) can be useful tools in training for route learning and improving route recall. However, the visual information presented in VEs, that is, what we choose to present in a virtual scene, can strongly affect the ability to recall a route. This is especially relevant when we consider individual differences, and people’s varying abilities to navigate effectively. Taking various cognitive processes involved in route learning into account, we designed a multi-level experiment that examines route recall effectiveness in a navigation context. We conceptualized that the participants would have to recall information related to the route that is demanding on primarily visual, spatial, or visuospatial memory systems. Furthermore, because there is a clear link between memory capacity and ageing; we conducted our experiment with two different age groups (total 81 participants: 42 young people aged 20–30 yo and 39 older people aged 65–76 yo). We also measured participants’ spatial abilities and visuospatial memory capacity for control purposes. After experiencing a pre-determined route in three different VEs (that we varied in levels of visual realism, and named as AbstractVE, MixedVE, and RealisticVE), each participant solved a list of tasks that was designed to measure visual, spatial, and visuospatial recall of the scene elements and information about the route. Participants solved these tasks immediately after experiencing the route in each VE, as well as after a week, thus we could measure ‘learning’ (delayed recall). Results from our study confirm the well-known decline in recall with age (young vs. older), provide new information regarding memorability of routes and VE scene elements over time (immediate vs. delayed), and most importantly demonstrate the crucial role the visual design decisions play in route learning and memorability of visuospatial displays

Mapping and counter-mapping COVID-19: From crisis to cartocracy

Editorial

LANDFORM PERCEPTION ACCURACY IN SHADED RELIEF MAPS: A REPLICATION STUDY CONFIRMS THAT NNW LIGHTING IS BETTER THAN NW AGAINST THE RELIEF INVERSION EFFECT

Relief inversion effect is a perceptual phenomenon that leads to an inverted perception of convex and concave shapes. This perceptual inversion occurs in scenes where the shading/shadows act as the main depth cue. In visuospatial displays, such as shaded relief maps, the positioning of the shadows in the northern slopes, thus when light source placed broadly in south, mislead the cognitive system based on the ‘light from above prior’ assumption (Mamassian and Goutcher 2001). Thus, assuming the light must come from above, our mind creates an illusion, and we perceive the landforms incorrectly. To judge the 3D spatial relationships in terrain representations correctly, the relief inversion effect must be avoided. Cartographic convention against this effect is to place the light source at northwest (NW), whereas a recent study demonstrated that north-north-west (NNW), or even north yields more precise results (Biland and Çöltekin, 2016). Since this finding goes against decades of convention, to establish its validity further, we attempted replicating the results with a different sample in South Africa. In this paper, we present our findings, which broadly confirm that the NNW (or also N) is better than NW against the relief inversion effect

Extracting and comparing places using geo-social media

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places

Exploring Large Digital Library Collections Using a Map-Based Visualisation

In this paper we describe a novel approach for exploring large document collections using a map-based visualisation. We use hierarchically structured semantic concepts that are attached to the documents to create a visualisation of the semantic space that resembles a Google Map. The approach is novel in that we exploit the hierarchical structure to enable the approach to scale to large document collections and to create a map where the higher levels of spatial abstraction have semantic meaning. An informal evaluation is carried out to gather subjective feedback from users. Overall results are positive with users finding the visualisation enticing and easy to use

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Sonifying data uncertainty with sound dimensions

The communication of data uncertainty is a crucial problem in data science, information visualization, and geographic information science (GIScience). Effective ways to communicate the uncertainty of data enables data consumers to interpret the data as intended by the producer, reducing the possibilities of misinterpretation. In this article, we report on an empirical investigation of how sound can be used to convey information about data uncertainty in an intuitive way. To answer the research question How intuitive are sound dimensions to communicate uncertainty?, we carry out a cognitive experiment, where participants were asked to interpret the certainty/uncertainty level in two sounds A and B (N=33). We produce sound stimuli by varying sound dimensions, including loudness, duration, location, pitch, register, attack, decay, rate of change, noise, timbre, clarity, order, and harmony. In the stimuli, both synthetic and natural sounds are used to allow comparison. The experiment results identify three sound dimensions (loudness, order, and clarity) as significantly more intuitive to communicate uncertainty, providing guidelines for sonification and information visualization practitioners

A comparison of methods for temporal analysis of aoristic crime

Objectives: To test the accuracy of various methods previously proposed (and one new method) to estimate offence times where the actual time of the event is not known. Methods: For 303 thefts of pedal cycles from railway stations, the actual offence time was determined from closed-circuit television and the resulting temporal distribution compared against commonly-used estimated distributions using circular statistics and analysis of residuals. Results: Aoristic analysis and allocation of a random time to each offence allow accurate estimation of peak offence times. Commonly-used deterministic methods were found to be inaccurate and to produce misleading results. Conclusions: It is important that analysts use the most accurate methods for temporal distribution approximation to ensure any resource decisions made on the basis of peak times are reliable