Search and rescue (SAR) is a complex teamwork environment that requires efficient spatial communication between commanders and field teams with heterogeneous perspectives and asymmetric information. Maps are central artifacts in SAR, yet they are also a space of technological tension due to constantly changing situation at disaster sites. Sketch mapping is an effective method of externalizing and communicating spatial understanding, increasing situation awareness in spatial decision-making tasks including SAR. Current paper-based sketch mapping in SAR struggles to handle the three-dimensional nature of physical space and remote collaboration. We propose CoMap, a collaborative 3D sketch mapping system validated in a virtual reality fire-rescue game. In a within-subject study with 13 commander–field team pairs, CoMap enabled more accurate and efficient spatial communication than conventional 2D sketch mapping. Communication analysis further showed that CoMap fostered proactive descriptions. We distill three design implications for next-generation mapping tools to advance SAR training and real-world operations.

Pupil size is a key eye-based indicator of mental processing and internal states. Nonetheless, pupil size is also influenced by light, causing challenges for monitoring internal states with eye tracking in dynamic and naturalistic settings. We investigated how convolution-based modeling can capture the influence of luminance and emotional arousal on pupil size, and conversely, how pupil dynamics inform arousal prediction. In a lab study, we analyzed data collected from 19 participants who watched travel-themed videos with different arousal levels and their pixelated counterparts. We present and evaluate a convolution-based approach for pupil size modeling that integrates low-level visual features and higher-order emotional factors, as previous work has shown effectiveness of such methods in modeling pupil light reflex. Our results show that incorporating luminance and contrast indeed enhances arousal prediction from pupil data, although performance varies due to different user behavior and stimuli.

Urban land use classification and mapping are critical for urban planning, resource management, and environmental monitoring. Existing remote sensing techniques often lack precision in complex urban environments due to the absence of ground-level details. Unlike aerial perspectives, street view images provide a ground-level view that captures more human and social activities relevant to land use in complex urban scenes. Existing street view-based methods primarily rely on supervised classification, which is challenged by the scarcity of high-quality labeled data and the difficulty of generalizing across diverse urban landscapes. This study introduces an unsupervised contrastive clustering model for street view images with a built-in geographical prior, to enhance clustering performance. When combined with a simple visual assignment of the clusters, our approach offers a flexible and customizable solution to land use mapping, tailored to the specific needs of urban planners. We experimentally show that our method can generate land use maps from geotagged street view image datasets of two cities. As our methodology relies on the universal spatial coherence of geospatial data ("Tobler's law"), it can be adapted to various settings where street view images are available, to enable scalable, unsupervised land use mapping and updating. The code is available at https://github.com/lin102/CCGP.

Navigating complex environments is crucial for human life, yet understanding the cognitive processes involved in its wayfinding component remains challenging. One theoretical model that explains these processes is Downs and Stea’s four-step model. Our study builds on this model to empirically analyze its steps, focusing particularly on the monitoring step. Machine learning models were trained on gaze behavior and head/body movement data from over 300 routes walked by 56 participants in a real-world outdoor study, predicting three of these wayfinding steps: self-localization, route planning, and goal recognition. Applying this trained model to the respective monitoring segment of the same routes suggests that monitoring includes micro-versions of these three steps, indicating it operates as a recursive process rather than a distinct cognitive step. By bridging theoretical frameworks with empirical evidence, these findings enhance our understanding of spatial cognition and can inform the design of navigational tools and urban spaces.

When predicting observations across space and time, the spatial layout of errors impacts a model’s real-world utility. For instance, in bike sharing demand prediction, error patterns translate to relocation costs. However, commonly used error metrics in GeoAI evaluate predictions point-wise, neglecting effects such as spatial heterogeneity, autocorrelation, and the Modifiable Areal Unit Problem. We put forward Optimal Transport (OT) as a spatial evaluation metric and loss function. The proposed framework, called GeOT, assesses the performance of prediction models by quantifying the transport costs associated with their prediction errors. Through experiments on real and synthetic data, we demonstrate that (1) the spatial distribution of prediction errors relates to real-world costs in many applications, (2) OT captures these spatial costs more accurately than existing metrics, and (3) OT enhances comparability across spatial and temporal scales. Finally, we advocate for leveraging OT as a loss function in neural networks to improve the spatial accuracy of predictions. Experiments with bike sharing, charging station, and traffic datasets show that spatial costs are significantly reduced with only marginal changes to non-spatial error metrics. Thus, this approach not only offers a spatially explicit tool for model evaluation and selection but also integrates spatial considerations into model training.

Synthetic vision on head-mounted displays (HMDs) has developed into an increasingly common type of assistance for pilots, both in commercial and in general aviation. As synthetic vision only augments the view on objects in the outside world, there is untapped potential to extend the use of HMDs to assisting pilots with augmented reality (AR) inside of the cockpit. We present two in-cockpit AR assistance designs for in-flight emergency assistance in a simulator study with fifteen licensed pilots. The two AR assistances were designed to be sensitive to either temporal context, or to spatial and temporal context. A The simulator study revealed that the presented AR assistances proved effective for mitigating an unexpected failure. Pilots preferred the fully context-sensitive AR assistance overall, while only the AR representation with temporal context showed significantly shorter reaction times than the conventional aid. Our findings suggest that AR can be a valuable tool for assisting pilots in critical flight phases, contributing to increased safety in aviation.

Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insufficient integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed utilizing these context-aware representations. Our experiments demonstrate that CKG’s configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model establishes a benchmark for 10-120 min predictions, achieving average MAE, MAPE, and RMSE of 3.46 $\pm$ 0.01, 14.76 $\pm$ 0.09%, and 5.08 $\pm$ 0.01, respectively. Compared to the baseline DCRNN model, integrating the spatial unit improves the MAE by 0.04 and the temporal unit by 0.13, while integrating both units further reduces it by 0.18. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model’s ability to prioritize recent time slots in prediction from the sequence-based view. Overall, this study underscores the importance of merging context-aware knowledge graphs with graph neural networks to improve traffic forecasting.

Decisions with spatial visualizations are often made under uncertainty and high time pressure. However, missing or improper representation of uncertainty can hamper the decision-making process. This paper investigates the impact of uncertainty visualization on cognitive load in the context of a safety-critical, time-sensitive decision-making task with a transportation system map. In a controlled experiment (n = 40) with a dual-task paradigm, we compared three different uncertainty visualization techniques and a baseline for different levels of time pressure. Cognitive load was measured using psycho-physiological metrics based on eye tracking and galvanic skin response, as well as self-reported. The results reveal significant differences in cognitive load among different visualization types, with line uncertainty representation techniques leading to lower cognitive load under both low and high-time pressure scenarios (α<0.05).

Unobtrusiveness has been highlighted as an important design principle in Human-Computer Interaction (HCI).However, the understanding of unobtrusiveness in the literature varies.Researchers often claim unobtrusiveness for their interaction method based on their understanding of what unobtrusiveness means.This lack of a shared definition hinders effective communication in research and impedes comparability between approaches.In this article, we approach the question "What is unobtrusive interaction?" with a systematic and extensive literature review of 335 papers and an online survey with experts.We found that not a single definition of unobtrusiveness is universally agreed upon.Instead, we identify five working definitions from the literature and experts' responses.We summarize the properties of unobtrusive interaction into a design framework with five dimensions and classify the reviewed papers with regard to these dimensions.The article aims to provide researchers with a more unified context to compare their work and identify opportunities for future research.

An intuitive way to externalize spatial memory is to sketch it. Compared to traditional paper-based sketches, virtual reality (VR) creates new opportunities to investigate the 3D aspect of spatial memory as it empowers users to express 3D information on a 3D interface directly. The goal of this study is to design a 3D sketch mapping tool for researchers and non-expert users without sketching expertise that enables externalizing memories of spatial information after some 3D-critical tasks. There exist 3D sketching tools using VR, but there are two issues with the current mid-air 3D sketching approach: (1) distortion of sketches due to depth perception errors and (2) increased cognitive and sensorimotor demands due to an increased degree of freedom and absence of physical support. To address these problems, we implemented VResin, a novel sketching interface that synergizes 3D mid-air sketching with 2D surface sketching to scaffold 3D sketching into a layer-by-layer process. An experimental study with 48 participants on multi-layer building scenarios showed that VResin supports users in creating less distorted sketches while maintaining the level of completeness and generalization compared to mid-air sketching in VR. We also demonstrate the potential applications that can benefit from 3D sketch maps and the suitability of VResin for a variety of building shapes.

From the initial phases of human–computer interaction, where the computer was unaware of the users' mental states, we are now progressing toward cognition-aware user interfaces. One crucial cognitive state considered by research on cognition-aware user interfaces is the cognitive load. Eye-tracking has been suggested as one particularly unobtrusive method for estimating cognitive load. Although the accuracy of cognitive load detection has improved in recent work, it is still insufficient for cognition-aware user interfaces, which require high accuracy for getting accepted by the user. This article introduces two new eye-tracking metrics for estimating perceived cognitive load based on benign anisocoria (BA). Unlike previous pupil-based metrics, our metrics are based on pupil size asymmetry between the left and right eye. As a case study, we illustrate the effectiveness of the proposed metrics on a recently published eye-tracking dataset recorded under laboratory conditions. The results show that our proposed features based on BA can improve the performance of classifiers for detecting the perceived mental workload associated with an$N$-back test. The best classification accuracy was 84.24% while the classification accuracy in the absence of the proposed features was 81.91% for the light gradient boosting classifier.

Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and increasing the resilience of intelligent transportation systems. However, most studies ignore the involvement of context information ubiquitously distributed over the urban environment to boost speed prediction. The diversity and complexity of context information also hinder incorporating it into traffic forecasting. Therefore, this study proposes a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, ie (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (spatial-temporal graph convolutional network, STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems.

Contemporary aircraft cockpits rely mostly on audiovisual information propagation which can overwhelm particularly novice pilots. The introduction of tactile feedback, as a less taxed modality, can improve the usability in this case. As part of a within-subject simulator study, 22 participants are asked to fly a visual-flight-rule scenario along a predefined route and identify objects in the outside world that serve as waypoints. Participants fly two similar scenarios with and without a tactile belt that indicates the route. Results show that with the belt, participants perform better in identifying objects, have higher usability and user experience ratings, and a lower perceived cognitive workload, while showing no improvement in spatial awareness. Moreover, 86% of the participants state that they prefer flying with the tactile belt. These results suggest that a tactile belt provides pilots with an unobtrusive mode of assistance for tasks that require orientation using cues from the outside world.

Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options. Here, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes, which are behaviour dimensions often overlooked in previous work. In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning. For evaluation, we apply this approach to two large-scale and long-term GPS tracking datasets involving more than 600 individuals. Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods by a large margin (8.05% and 5.60% relative increase in F1-score for the two datasets, respectively). We conduct an extensive ablation study that quantifies the influence of considering temporal features, travel mode information, and the auxiliary task on the prediction results. Moreover, we experimentally determine the performance upper bound when including the next mode prediction in our model. Finally, our analysis indicates that the performance of location prediction varies significantly with the chosen next travel mode by the individual. These results show potential for a more systematic consideration of additional dimensions of travel behaviour in human mobility prediction tasks. The source code of our model and experiments is available at https://github.com/mie-lab/location-mode-prediction.

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems.

One challenge of providing guidance for search tasks consists in guiding the user’s visual attention to certain objects in a potentially large search space. Previous work has tried to guide the user’s attention by providing visual, audio, or haptic cues. The state-of-the-art methods either provide hints pointing towards the approximate direction of the target location for a fast but less accurate search or require the user to perform a fine-grained search from the beginning for a precise yet less efficient search. To combine the advantage of both methods, we propose an interaction concept called Two-Step Gaze Guidance. The first-step guidance focuses on quick guidance toward the approximate direction, and the second-step guidance focuses on fine-grained guidance toward the exact location of the target. A between-subject study (N = 69) with five conditions was carried out to compare the two-step gaze guidance method with the single-step gaze guidance method. Results revealed that the proposed method outperformed the single-step gaze guidance method. More precisely, the introduction of Two-Step Gaze Guidance slightly improves the searching accuracy, and the use of spatial audio as the first-step guidance significantly helps in enhancing the searching efficiency. Our results also indicated several design suggestions for designing gaze guidance methods.

Though GPS-based human trajectory data have been commonly used in travel surveys and human mobility studies, missing data or data gaps that are intrinsically relevant to research reliability remain a critical and challenging issue. This study proposes a novel framework for imputing data gaps based on frequent-pattern mining and time geography, which allows for considering spatio-temporal travel restrictions during imputation by evaluating the spatio-temporal topology relations between the space-time prisms of gaps and corresponding frequent activities or trips. For the validation, the proposed framework is applied to raw GPS trajectories that were collected from 139 participants in Switzerland. In the case study, the temporal and spatio-temporal gaps are artificially generated by randomly choosing activities and trips from the trajectory data. Through comparing the mobility indicators (i.e. duration and distance) calculated from raw data, imputed data, and data with gaps, we quantitatively evaluate the performance of the proposed method in terms of Pearson correlation coefficients and deviation. We further compare the framework with the shortest path interpolation method based on the generated spatio-temporal gaps. The comparison results demonstrate the performance and advantage of the proposed method in imputing gaps from GPS-based human movement data.

Subtitles play a crucial role in cross-lingual distribution of multimedia content and help communicate information where auditory content is not feasible (loud environments, hearing impairments, unknown languages). Established methods utilize text at the bottom of the screen, which may distract from the video. Alternative techniques place captions closer to related content (e.g., faces) but are not applicable to arbitrary videos such as documentations. Hence, we propose to leverage live gaze as indirect input method to adapt captions to individual viewing behavior. We implemented two gaze-adaptive methods and compared them in a user study (n=54) to traditional captions and audio-only videos. The results show that viewers with less experience with captions prefer our gaze-adaptive methods as they assist them in reading. Furthermore, gaze distributions resulting from our methods are closer to natural viewing behavior compared to the traditional approach. Based on these results, we provide design implications for gaze-adaptive captions.

The inspection of feature-rich information spaces often requires supportive tools that reduce visual clutter without sacrificing details. One common approach is to use focus+context lenses that provide multiple views of the data. While these lenses present local details together with global context, they require additional manual interaction. In this paper, we discuss the design space for gaze-adaptive lenses and present an approach that automatically displays additional details with respect to visual focus. We developed a prototype for a map application capable of displaying names and star-ratings of different restaurants. In a pilot study, we compared the gaze-adaptive lens to a mouse-only system in terms of efficiency, effectiveness, and usability. Our results revealed that participants were faster in locating the restaurants and more accurate in a map drawing task when using the gaze-adaptive lens. We discuss these results in relation to observed search strategies and inspected map areas.

Exploring a city panorama from a vantage point is a popular tourist activity. Typical audio guides that support this activity are limited by their lack of responsiveness to user behavior and by the difficulty of matching audio descriptions to the panorama. These limitations can inhibit the acquisition of information and negatively affect user experience. This paper proposes Gaze-Guided Narratives as a novel interaction concept that helps tourists find specific features in the panorama (gaze guidance) while adapting the audio content to what has been previously looked at (content adaptation). Results from a controlled study in a virtual environment (n=60) revealed that a system featuring both gaze guidance and content adaptation obtained better user experience, lower cognitive load, and led to better performance in a mapping task compared to a classic audio guide. A second study with tourists situated at a vantage point (n=16) further demonstrated the feasibility of this approach in the real world.

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics.

Increasing mobility demands raise the pressure on existing transport networks. As the most used mode of transport, private cars have a particularly strong environmental impact and produce congestion. Ridesharing or carpooling, where a driver and several riders form a carpool, can help to address these issues by increasing the number of persons per car. Therefore, recent years have seen a strong interest in carpooling. However, there exists no effective method of integrating carpooling into transport trip planners as of now, mainly due to the fuzzy and flexible nature (e.g., no fixed stops, possibility of making detours) of carpooling. This hinders the acceptance of carpooling by the general public. This paper proposes a new method to merge public transport and carpooling networks for multimodal route planning, considering the fuzziness and flexibility brought by carpooling. It is based on the concept of drive-time areas and points of action. The evaluation with real-world data sets shows that, compared with the state-of-the-art method, the proposed method merges static (i.e., public transport) and dynamic/fuzzy (i.e., carpooling) networks better, while retaining the desired flexibility offered by the latter, and thus creates a higher interconnectivity between the networks. Meanwhile, the merged network enables multimodal route planning, which can provide users with trips from an origin to a destination using different combinations of modes.

A novel eye-tracked measure of the frequency of pupil diameter oscillation is proposed for capturing what is thought to be an indicator of cognitive load. The proposed metric, termed the Index of Pupillary Activity, is shown to discriminate task difficulty vis-a-vis cognitive load (if the implied causality can be assumed) in an experiment where participants performed easy and difficult mental arithmetic tasks while fixating a central target (a requirement for replication of prior work). The paper's contribution is twofold: full documentation is provided for the calculation of the proposed measurement which can be considered as an alternative to the existing proprietary Index of Cognitive Activity (ICA). Thus, it is possible for researchers to replicate the experiment and build their own software which implements this measurement. Second, several aspects of the ICA are approached in a more data-sensitive way with the goal of improving the measurement's performance.

Complex information visualizations, such as thematic maps, encode information using a particular symbology that often requires the use of a legend to explain its meaning. Traditional legends are placed at the edge of a visualization, which can be difficult to maintain visually while switching attention between content and legend.

Although severe aircraft accidents have been reduced in the last decades, the number of injuries and fatalities caused by turbulence is still rising. Current aviation weather products are unable to provide a holistic and intuitive view of the overall weather situation, especially in terms of turbulence forecasts. This work introduces an interactive 3D prototype developed with a user-centered design approach. The prototype focuses on the visualization of significant weather charts, which are utilized during flight preparation. An online user study is conducted to compare the prototype with today's 2D paper maps. A total of 64 pilots from an internationally operating airline participated in the study. Among the major findings of the study is that the prototype significantly decreased the cognitive load, and enhanced spatial awareness and usability. To determine the spatial awareness, a novel similarity measure for spatial configurations of aviation weather data is introduced.

Andrew Frank, who retired in September 2016 from the chair of geoinformation position at the Vienna University of Technology that he had held for a quarter century, has had anextraordinary influenc...

During pedestrian navigation in outdoor urban environments we often utilize assistance systems to support decision-making. These systems help wayfinders by providing relevant information withing the context of their surroundings, e.g., landmark-based instructions of the type "turn left at the church". Next to the instruction type and content, also the timing of the instruction must be considered in order to facilitate the wayfinding process. In this work we present our findings concerning the user and environmental factors that have an impact on the timing of instructions. We applied a survival analysis on data collected through an experiment in a realistic virtual environment in order to analyze the expected distance to the decision point until instructions are needed. The presented results can be used by navigation systems for instruction timing based on the characteristics of the current wayfinder and environment.

Currently, systems that let people search for opportunities to fulfill their spatio-temporal needs are built according to the conceptual model of service provider and consumer: After the providers make their needs publicly available, consumers use a specifically tailored query engine to find fitting offers. E.g., in carpooling, someone wants to fill an empty seat and to share costs (and publishes this offer), while another person wants to travel the same route. This model prevents the consuming side from making their needs available to the service providers and makes it hard to generalize, as query engines require rigid (often domain-specific) properties. Addressing this problem, we propose a generic model for publishing and processing complementary spatio-temporal needs. Our model uses a simulator to assess how well the collaboration between different entities would approximate their goals. To reuse existing concepts and embed the model into the emerging Semantic Web, everything is modeled in accordance with Linked Data principles.

Location-based services (LBS) provide more useful, intelligent assistance to users by adapting to their geographic context. For some services that context goes beyond a location and includes further spatial parameters, such as the user’s orientation or field of view. Here, we introduce Gaze-Informed LBS (GAIN-LBS), a novel type of LBS that takes into account the user’s viewing direction. Such a system could, for instance, provide audio information about the specific building a tourist is looking at from a vantage point. To determine the viewing direction relative to the environment, we record the gaze direction relative to the user’s head with a mobile eye tracker. Image data from the tracker’s forward-looking camera serve as input to determine the orientation of the head w.r.t. the surrounding scene, using computer vision methods that allow one to estimate the relative transformation between the camera and a known view of the scene in real-time and without the need for artificial markers or additional sensors. We focus on how to map the point of regard of a user to a reference system, for which the objects of interest are known in advance. In an experimental validation on three real city panoramas, we confirm that the approach can cope with head movements of varying speed, including fast rotations up to to 63 degrees per second. We further demonstrate the feasibility of GAIN-LBS for tourist assistance with a proof-of-concept experiment in which a tourist explores a city panorama, where the approach achieved a recall that reaches over 99%. Finally, a GAIN-LBS can provide objective and qualitative ways of examining the gaze of a user based on what the user is currently looking at.

Travel activities are embodied as people’s needs to be physically present at certain locations. The development of Information and Communication Technologies (ICTs, such as mobile phones) has introduced new data sources for modeling human activities. Based on the scattered spatiotemporal points provided in mobile phone datasets, it is feasible to study the patterns (e.g., the scale, shape, and regularity) of human activities. In this paper, we propose methods for analyzing the distribution of human activity space from both individual and urban perspectives based on mobile phone data. The Weibull distribution is utilized to model three predefined measurements of activity space (radius, shape index, and entropy). The correlation between demographic factors (age and gender) and the usage of urban space is also tested to reveal underlying patterns. The results of this research will enhance the understanding of human activities in different urban systems and demographic groups, as well as providing novel methods to expand the important and widely applicable area of geographic knowledge discovery in the age of instant access.

Pedestrian navigation systems help us make a series of decisions that lead us to a destination. Most current pedestrian navigation systems communicate using map-based turn-by-turn instructions. This interaction mode suffers from ambiguity, its user's ability to match the instruction with the environment, and it requires a redirection of visual attention from the environment to the screen. In this paper we present GazeNav, a novel gaze-based approach for pedestrian navigation. GazeNav communicates the route to take based on the user's gaze at a decision point. We evaluate GazeNav against the map-based turn-by-turn instructions. Based on an experiment conducted in a virtual environment with 32 participants we found a significantly improved user experience of GazeNav, compared to map-based instructions, and showed the effectiveness of GazeNav as well as evidence for better local spatial learning. We provide a complete comparison of navigation efficiency and effectiveness between the two approaches.

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.

Predicting the moment when a visual explorer of a place loses interest and starts to get bored is of considerable importance to the design of touristic information services. This paper investigates factors affecting the duration of the visual exploration of a city panorama. We report on an empirical outdoor eye tracking study in the real world with tourists following a free exploration paradigm without a time limit. As main result, the number of areas of interest revisited during a short period was found to be a good predictor for the total exploration duration.

The rapid development of information and communication technologies (ICTs) has provided rich data sources for analyzing, modeling, and interpreting human mobility patterns. This paper contributes to this research area by developing the Spatio-temporal Edit Distance measure, an extended algorithm to determine the similarity between user trajectories based on call detailed records (CDRs). We improve the traditional Edit Distance algorithm by incorporating both spatial and temporal information into the cost functions. The extended algorithm can preserve both space and time information from string-formatted CDR data. The novel method is applied to a large data set from Northeast China in order to test its effectiveness. Three types of analyses are presented for scenarios with and without the effect of time: (1) Edit Distance with spatial information; (2) Edit Distance with time as a factor in the cost function; and (3) Edit Distance with time as a constraint in partitioning trajectories. The outcomes of this research contribute to both methodological and empirical perspectives. The extended algorithm performs well for measuring low-resolution tracking information in CDRs, as well as facilitating the interpretation of user mobility patterns in the age of instant access.

The spatio-temporal characteristics of eye movements vary according to the activity the user of a cartographic map is performing. In this paper, we use these eye movement characteristics to automatically detect the map user's activity, an approach with great potential in gaze-assistive map interfaces. A dataset of 587 eye movement recordings from 17 participants was used to train and cross-validate a support vector machine (SVM) classifier over 229 features. The classifier can distinguish 6 common map activities with an accuracy of approx. 78%.

This paper considers the impact of location as context in mobile eye tracking studies that extend to large-scale spaces, such as pedestrian wayfinding studies. It shows how adding a subject's location to her gaze data enhances the possibilities for data visualization and analysis. Results from an explorative pilot study on mobile map usage with a pedestrian audio guide demonstrate that the combined recording and analysis of gaze and position can help to tackle research questions on human spatial problem solving in a novel way.

Orientation on small display maps is often difficult because the visible spatial context is restricted. This paper proposes to provide the history of a user's visual attention on a map as visual clue to facilitate orientation. Visual attention on the map is recorded with eye tracking, clustered geo-spatially, and visualized when the user zooms out. This implicit gaze-interaction concept, called GeoGazemarks, has been evaluated in an experiment with 40 participants. The study demonstrates a significant increase in efficiency and an increase in effectiveness for a map search task, compared to standard panning and zooming.

Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.

Mobile intention recognition is the problem of inferring a mobile user's intentions from her behavior in geographic space. Such behavior is constrained in space and time. Current approaches, however, have difficulties to handle temporal constraints. We therefore propose using the framework of time geography to formalize and visualize both spatial and temporal constraints for the mobile intention recognition problem. A new rule language is introduced which allows for modeling intentions with spatial and temporal constraints. A location-based game application demonstrates that interpreting a user's spatio-temporal behavior sequence in terms of intentions reduces ambiguity compared to mobile intention recognition without temporal constraints.

An abundance of geospatial information is flourishing on the Internet but mining and disseminating these data is a daunting task. With anything published on the web available to the public it has become a grand repository of volunteered geographic information (VGI). Internet users often provide location information for videos, pictures, travel destinations, or other events. All of these data can be gathered by a web crawling geospatial agent that later performs geospatial data mining. The discovered geoinformation can be stored, analyzed, queried, and visualized as the agent creates a data repository of what it discovered. This paper presents the design and prototypical implementation of the GEDMWA (Geospatial Exploratory Data Mining Web Agent). It reads webpage data and follows links to acquire knowledge in order to add value to geoinformation usable in a GIS. The agent creates a database of webpage text, mines it for location information, and then converts it to proper geospatial data format. The data is quickly visualized and analyzed after GEDMWA converts it into proper GIS and virtual globe formats. This provides diverse user communities a tool that utilizes a variety of distributed sources to discover additional knowledge about their fields of interest.

Virtual globes have progressed from little-known technology to broadly popular software in a mere few years. We investigated this phenomenon through a survey and discovered that, while virtual globes are en vogue, their use is restricted to a small set of tasks so simple that they do not involve any spatial thinking. Spatial thinking requires that users ask "what is where" and "why"; the most common virtual globe tasks only include the "what". Based on the results of this survey, we have developed a multi-touch virtual globe derived from an adapted virtual globe paradigm designed to widen the potential uses of the technology by helping its users to inquire about both the "what is where" and "why" of spatial distribution. We do not seek to provide users with full GIS (geographic information system) functionality, but rather we aim to facilitate the asking and answering of simple "why" questions about general topics that appeal to a wide virtual globe user base.

A user study was conducted to compare the performance of three methods for map navigation with mobile devices. These methods are joystick navigation, the dynamic peephole method without visual context, and the magic lens paradigm using external visual context. The joystick method is the familiar scrolling and panning of a virtual map keeping the device itself static. In the dynamic peephole method the device is moved and the map is fixed with respect to an external frame of reference, but no visual information is present outside the device's display. The magic lens method augments an external content with graphical overlays, hence providing visual context outside the device display. Here too motion of the device serves to steer navigation. We compare these methods in a study measuring user performance, motion patterns, and subjective preference via questionnaires. The study demonstrates the advantage of dynamic peephole and magic lens interaction over joystick interaction in terms of search time and degree of exploration of the search space.

Ad-hoc shared-ride trip planning is a planning task on a non-deterministic transportation network. We propose to solve this task in a mobile geosensor network, which consists of transportation clients and hosts. In a mobile geosensor network the communication costs are a critical factor. Trip planning agents need communication to collect knowledge about the current network, and any way to limit this need reduces the costs of a solution. This paper introduces a theoretical model based on time geography, where clients, as trip planning agents, can actively identify relevant transportation hosts before communication starts, and hosts can identify whether their route is relevant for a specific planning task before responding to any request. This model reduces the communication costs significantly, which is at first derived theoretically, and then confirmed by an example.

Agent-based systems are used to simulate the behaviour of people in various spatial environments. Designers of such systems need to give attention to ontological and epistemological concerns during the early stages of development to assure that the agent-based system is built upon a sound foundation. Only then is it possible to model the agent's behaviour in a cognitively plausible way. This paper describes an ecological approach to model ontology and epistemology for agent-based wayfinding simulation. The ontology of the wayfinding environment is based on a subdivision into medium, substances, and surfaces. The epistemological model uses the concept of affordances, which we divide into physical, social-institutional, and mental affordances. Ontology and epistemology are both grounded in people's descriptions of their wayfinding experiences. A case study of wayfinding in airports is used to demonstrate the applicability of the method for designing a simulation with a cognizing agent.