Recognizing the activities being performed on a map is crucial for adaptive map design based on user context. Despite eye tracking (ET) demonstrating potential in recognizing map activities and electroencephalography (EEG) measuring map users’ cognitive load, no studies have yet combined ET and EEG for recognition of the user’s activity on maps. Our study collected participants’ ET and EEG data during four types of map activities. After feature extraction and selection, we trained LightGBM (light Gradient-Boosting Machine) to classify these activities, and achieved 88.0% accuracy when combining ET and EEG features in the entire map usage trial, which is higher than using ET (85.9%) or EEG (53.9%) alone. Acceptable recognition accuracy could also be achieved with the early time windows (73.1% when using the first 3 seconds). Saccade features of ET were the most important for differentiating map activities, indicating selective map content for different tasks. Our findings demonstrate the feasibility and advantages of combining ET and EEG for activity recognition in map use. The results not only improve our understanding of visual patterns and cognitive processes in map use, but also enable the design of adaptive maps that can automatically adapt to the activities a map user is performing.

Every route instruction type (e.g. map, symbol, photo) induces a specific cognitive load. However, when these types are used at different decision points in a building, the building configuration of these points also influences the induced cognitive load. Therefore, the process of route guidance results in an interaction between the instruction type and the decision point, which determines the induced cognitive load. One way of reducing cognitive load during route guidance is by using adaptive systems that show specific route instruction types at specific decision points. Therefore, in this VR experiment, the usability of such an adaptive indoor route guidance system is tested by tracking the wayfinding and gaze behavior of the users. First, the difference in wayfinding and gaze behavior between all route instruction types is compared. Next, the building configuration at the decision points is quantified through the architectural theory of space syntax, and the correlation with the wayfinding and gaze behavior is determined. Our findings indicate that adapting the route instruction type does make a difference for the user.

To be able to design indoor wayfinding systems that adhere better to the needs of the users, user perception on complexity needs to be examined and linked to user characteristics and decision point characteristics. To identify how these characteristics influence perception, an online survey is executed in which participants had to indicate how complex they found a decision point, while interpreting a route instruction. The results show that complexity ratings depend both on user characteristics and on the function of the decision point. Decision points to change levels, start or end a route and to take turns each received significantly different complexity ratings. Isovist and visibility graph analysis characteristics of these decision points show that the first two actions were perceived as more complex when they took place in a narrow hallway, while the third action was perceived as more complex in a convex space. The results of this study can be used in the design of an adaptive wayfinding system that adapts the route instructions to the perceived decision point complexity. This adaptation will adhere better to the needs of the users compared to an adaptation based on solely theoretical complexity.

In this paper, we represent (moving) point configurations along a curved directed line qualitatively by means of a system of relational symbols based on two distance descriptors: one representing distance along the curved directed line and the other representing signed orthogonal distance to the curved directed line. The curved directed line represents the direction of the movement of interest. For instance, it could be straight as in the case of driving along a highway or could be curved as in the case of an intersection or a roundabout. Inspired by the Point Calculus, the order between the points on the curved directed line is described by means of a small set of binary relations (<,=,>) acting upon the distance descriptors. We call this representation the Point-Descriptor-Precedence-Static (PDPS) representation at a time point and Point-Descriptor-Precedence-Dynamic (PDPD) representation during a time interval. To illustrate how the proposed approach can be used to represent and analyse curved movements, some basic micro-analysis traffic examples are studied. Finally, we discuss some extensions of our work to highlight the practical benefits of PDP in identifying motion patterns that could be useful in GIS, autonomous vehicles, sports analytics, and gait analysis.

We present methodological advances to a recently developed framework to study sequential habitat use by animals using a visually-explicit and tree-based Sequence Analysis Method (SAM), derived from molecular biology and more recently used in time geography. Habitat use sequences are expressed as annotations obtained by intersecting GPS movement trajectories with environmental layers. Here, we develop IM-SAM, where we use the individual reference area of use as the reference spatial context. To assess IM-SAM’s applicability, we investigated the sequential use of open and closed habitats across multiple European roe deer populations ranging in landscapes with contrasting structure. Starting from simulated sequences based on a mechanistic movement model, we found that different sequential patterns of habitat use were distinguished as separate, robust clusters, with less variable cluster size when habitats were present in equal proportions within the individual reference area of use. Application on real roe deer sequences showed that our approach effectively captured variation in spatio-temporal patterns of sequential habitat use, and provided evidence for important behavioral processes, such as day-night habitat alternation. By characterizing sequential habitat use patterns of animals, we may better evaluate the temporal trade-offs in animal habitat use and how they are affected by changes in landscapes.

Present-day indoor navigation systems are often not well adapted to the specific needs and requirements of its users. This research aims at improving those indoor navigation systems by providing navigation support that cognitively closer to user preferences and behaviour. More specifically, the focus is on the implementation of an accurate turn calculation method in a turn minimization algorithm, aiming to lower the complexity of routes and route instructions. This new-introduced perception-based turn calculation procedure is based on a direct door-to-door walking pattern, and, in contrast to previous algorithms, independent of the underlying indoor network type. It takes into account the effects of geometry of indoor space on human movement. To evaluate its functioning, both the traditional algorithm and the proposed perception-based algorithm are applied in the fewest turns path algorithm. It is demonstrated that the proposed algorithm accurately calculates turns in alignment with people’s perception. The implementation of the calculation algorithm in the fewest turns path algorithm also allows future applications in indoor simplest path algorithms, and overall contributes to cognitively richer indoor navigation systems.

Trajectory analysis has attracted growing attention in the research field of geography. Beyond traditional moving object trajectories, another type of trajectory exists in which the coordinates are object attributes rather than geographical coordinates. In this paper, a framework to analyse these so-called attribute trajectories is proposed that uses four techniques typically employed in the analysis of moving object trajectories: the Reeb graph, the similarity matrix, the convoy and the mega-convoy. The Reeb graph provides the ability to visualise the temporal dynamics of attribute similarities. The similarity matrix is a supplement of the Reeb graph whose purpose is to visualise the pairwise similarities among the attributes. Moreover, the similarity matrix forms a basis for clustering. The convoy highlights objects whose attributes remain similar for a sufficiently long period. The mega-convoy reduces the number of convoys and reveals their evolutionary histories by merging overlapping convoys. A small real-world meteorological dataset is used as an example to illustrate the attribute trajectory analysis framework and the techniques. This paper aims to form a starting point for applying trajectory analysis techniques in many research fields.

The accuracy of old maps can hold interesting historical information, and is therefore studied using distortion analysis methods. These methods start from a set of ground control points that are identified both on the old map and on a modern reference map or globe, and conclude with techniques that compute and visualise distortion. Such techniques have advanced over the years, but leave room for improvement, as the current ones result in approximate values and a coarse spatial resolution. We propose a more elegant and more accurate way to compute distortion of old maps by translating the technique of differential distortion analysis, used in map projection theory, to the setting where an old map and a reference map are directly compared. This enables the application of various useful distortion metrics to the study of old maps, such as the area scale factor, the maximum angular distortion and the Tissot indicatrices. As such a technique is always embedded in a full distortion analysis method we start by putting forward an optimal analysis method for a general-purpose study, which then serves as the foundation for the development of our technique. Thereto, we discuss the structure of distortion analysis methods and the various options available for every step of the process, including the different settings in which the old map can be compared to its modern counterpart, the techniques that can be used to interpolate between both, and the techniques available to compute and visualise the distortion. We conclude by applying our general-purpose method, including the differential distortion analysis technique, to an example map also used in other literature.

Spatial interactions between agents (humans, animals, or machines) carry information of high value to human or electronic observers. However, not all the information contained in a pair of continuous trajectories is important and thus the need for qualitative descriptions of interaction trajectories arises. The Qualitative Trajectory Calculus (QTC) (Van de Weghe, 2004) is a promising development towards this goal. Numerous variants of QTC have been proposed in the past and QTC has been applied towards analyzing various interaction domains. However, an inherent limitation of those QTC variations that deal with lateral movements is that they are limited to two-dimensional motion; therefore, complex three-dimensional interactions, such as those occurring between flying planes or birds, cannot be captured. Towards that purpose, in this paper QTC3D is presented: a novel qualitative trajectory calculus that can deal with full three-dimensional interactions. QTC3D is based on transformations of the Frenet–Serret frames accompanying the trajectories of the moving objects. Apart from the theoretical exposition, including definition and properties, as well as computational aspects, we also present an application of QTC3D towards modeling bird flight. Thus, the power of QTC is now extended to the full dimensionality of physical space, enabling succinct yet rich representations of spatial interactions between agents.

When studying geographical phenomena, different levels of spatial and temporal granularity often have to be considered. While various approaches have been proposed to analyse geographical data in a multi-scale perspective, they have all focused on either spatial or temporal attributes rather than on the integration of space and time over multiple scales. This study introduces the continuous spatio-temporal model (CSTM), a conceptual model that seeks to address this shortcoming. The presented model is based on (1) the continuous temporal model (CTM), a multi-scale model for temporal information, and (2) the continuous spatial model (CSM), an extension of CTM for multi-scale spatial raster data. At the core of the presented conceptual model is a spatio-temporal evolution element or, in short, stevel, which is described by four variables: (1) pixel location, (2) spatial resolution, (3) temporal interval, and (4) temporal resolution. By varying one or more of these variables, a CSTM-tree consisting of (sets of) stevel arrays is created, forming the basis of an exhaustive CSTM-typology. These arrays can then be used to systematically cluster spatio-temporal information. The value of our approach is illustrated by means of a simplified example of mean temperature evolution. Various suggestions are made for modifications to be developed in future research.

This article introduces a novel geographical information system toolkit for measuring and mapping the accessibility of individuals to services. The toolkit contributes to earlier implementations by combining aspects of both place-based and person-based accessibility measures. To this end, place-based accessibility measures are derived from a person-based framework by considering space–time prisms that are centred at service facilities rather than individual anchor points. The implementation is also innovative by explicitly accounting for the opening hours of service delivery in its accessibility measurement. In addition, the toolkit is aimed to be user-friendly and to generate insightful and comprehensible results for non-technically oriented users, which is illustrated in a brief case study about library accessibility in Ghent (Belgium).

The widespread adoption of location-aware technologies (LATs) has afforded analysts new opportunities for efficiently collecting trajectory data of moving individuals. These technologies enable measuring trajectories as a finite sample set of time-stamped locations. The uncertainty related to both finite sampling and measurement errors makes it often difficult to reconstruct and represent a trajectory followed by an individual in space–time. Time geography offers an interesting framework to deal with the potential path of an individual in between two sample locations. Although this potential path may be easily delineated for travels along networks, this will be less straightforward for more nonnetwork-constrained environments. Current models, however, have mostly concentrated on network environments on the one hand and do not account for the spatiotemporal uncertainties of input data on the other hand. This article simultaneously addresses both issues by developing a novel methodology to capture potential movement between uncertain space–time points in obstacle-constrained travel environments.

Constraint‐based models and models constructing accessibility measures mainly focus on single agents having only one available transport mode. However, numerous cases exist where multiple agents or groups of individuals with different available transport modes want to participate in a joint activity at a certain location. The aim of this paper is to provide new insights into representing and reasoning about feasible space–time opportunities for multiple agents. Relying on concepts of time geography, we propose a conceptual framework in order to determine interaction spaces for groups of individuals. Besides availability of means of transport and the locations of each individual, minimum activity duration and opening hours of opportunities are taken into account. The reasoning about space and time is visualized in three dimensions using a hybrid (CAD/GIS) system.

We present an algorithm for polyline (and polygon) similarity testing that is based on the double-cross formalism. To determine the degree of similarity between two polylines, the algorithm first computes their generalized polygons, that consist of almost equally long line segments and that approximate the length of the given polylines within an $varepsilon$-error margin. Next, the algorithm determines the double-cross matrices of the generalized polylines and the difference between these matrices is used as a measure of dissimilarity between the given polylines. We prove termination of our algorithm and show that its sequential time complexity is bounded by $Oleft((fracmax(N_1,N_2)varepsilon)^2right)$, where $N_1$ and $N_2$ are the number of vertices of the given polylines. We apply our method to query-by-sketch, indexing of polyline databases, and classification of terrain features and show experimental results for each of these applications.