The concept of validity is a cornerstone of science. Given this central role, it is somewhat surprising to find that validity remains a rather obscure concept. Unfortunately, the term is often reduced to a matter of ground truth data, seemingly because we fail to come to grips with it. In this paper, instead, we take a purpose-based approach to the validity of spatio-temporal models. We argue that a model application is valid only if the model delivers an answer to a particular spatio-temporal question specifying some experiment including spatio-temporal controls and measures. Such questions constitute the information purposes of models, forming an intermediate layer in a pragmatic knowledge pyramid with corresponding levels of validity. We introduce a corresponding question-based grammar that allows us to formally distinguish among contemporary inference, prediction, retrodiction, projection, and retrojection models. We apply the grammar to corresponding examples and discuss the possibilities for validating such models as a means to a given end.
We propose a theoretical framework for qualitative spatial representation and reasoning about curves on a two-dimensional plane. We regard a curve as a sequence of segments, each of which has its own direction and convexity, and give a symbolic expression to it. We propose a reasoning method on this symbolic expression; when only a few segments of a curve are visible, we find missing segments by connecting them to create a global smooth continuous curve. In addition, we discuss whether the shape of the created curve can represent that of a real object; if the curve forms a spiral, such a curve is sometimes not appropriate as a border of an object. We show a method that judges the appropriateness of a curve, by considering the orientations of the segments.
Demonstratives (this/that) situate objects in space with the aid of gestures and a proximal-distal contrast. However, it is unclear how these cues interact to aid the listener in referent selection. The current paper presents a referent selection task where listeners choose an object out of a group of objects based on a physical and verbal cue provided by a speaker. Results indicate that listeners are sensitive to a variety of cues, but only integrate the minimum amount of information necessary for referent selection, with physical cues being prioritized over the semantic contributions of the demonstrative.
Isolines visually characterize scalar fields by connecting all points of the same value by a closed curve at repeated intervals. They work only as a set which gives the viewer an indication of the shape of the underlying field. Hence, when simplifying isolines it is important that the correspondence - the harmony - between adjacent isolines is preserved whenever it is present. The majority of state-of-the-art simplification methods treat isolines independently; at best they avoid collisions between adjacent simplified isolines. A notable exception is the work by Van Goethem et al. (2021) who were the first to introduce the concept of harmony between adjacent isolines explicitly as an algorithmic design principle. They presented a proof-of-concept algorithm that harmoniously simplifies a sequence of polylines. However, the sets of isolines of scalar fields, most notably terrain, consist of closed curves which are nested in arbitrarily complex ways and not of an ordered sequence of polylines. In this paper we significantly extend the work by Van Goethem et al. (2021) to capture harmony in general sets of isolines. Our new simplification algorithm can handle sets of isolines describing arbitrary scalar fields and is more efficient, allowing us to harmoniously simplify terrain with hundreds of thousands of vertices. We experimentally compare our method to the results of Van Goethem et al. (2021) on bundles of isolines and to general simplification methods on isolines extracted from DEMs of Antartica. Our results indicate that our method efficiently preserves the harmony in the simplified maps, which are thereby less noisy, cartographically more meaningful, and easier to read.
Sustainable transport is becoming an increasingly pressing issue, with two major pillars being the reduction of car usage and the promotion of public transport. One way to approach both of these pillars is through the large number of daily commute trips in urban areas, and their modal split. Previous research gathered knowledge on influencing factors on the modal split mainly through travel surveys. We take a different approach by analysing the "raw" network and the time-optimised trips on a multi-modal graph. For the case study of Vienna, Austria we investigate how the option to use a private car influences the modal split of routes towards the city centre. Additionally, we compare the modal split across seven inner districts and we relate properties of the public transport network to the respective share of public transport. The results suggest that different districts have varying options of public transport connections towards the city centre, with a share of public transport between about 5% up to a share of 45%. This reveals areas where investments in public transport could reduce commute times to the city centre. Regarding network properties, our findings suggest, that it is not sufficient to analyse the joint public transport network. Instead, individual public transport modalities should be examined. We show that the network length and the direction of the lines towards the city centre influence the proportion of subway and tram in the modal split.
Despite the frequent use of sketch maps in assessing environmental knowledge, it remains unclear how and to what degree familiarity impacts sketch map content. In the present study, we assess whether different levels of familiarity relate to differences in the content and spatial accuracy of environmental knowledge depicted in sketch maps drawn for the purpose of route instructions. To this end, we conduct a real-world wayfinding study with 91 participants, all of whom have to walk along a pre-defined route of approximately 2.3 km length. Prior to the walk, we collect self-report familiarity ratings from participants for both a set of 15 landmarks and a set of areas we define as hexagons along the route. Once participants finished walking the route, they were asked to sketch a map of the route, specifically a sketch that would enable a person who had never walked the route to follow it. We found that participants unfamiliar with the areas along the route sketched fewer features than familiar people did. Contrary to our expectations, however, we found that landmarks were sketched or not regardless of participants' level of familiarity with the landmarks. We were also surprised that the level of familiarity was not correlated to the accuracy of the sketched order of features along the route, of the position of sketched features in relation to the route, nor to the metric locational accuracy of feature placement on the sketches. These results lead us to conclude that different aspects of feature salience influence whether the features are included on sketch maps, independent of familiarity. They also point to the influence of task context on the content of sketch maps, again independent of familiarity. We propose further studies to more fully explore these ideas.
This paper presents the Spatial Nudging framework - a theory-based framework that maps out nudging strategies in the mobility domain and refines its existing definitions. We link these strategies by highlighting the role of perceived affordances across physical and digital interventions based on the Nudge Theory and the Theory of Affordances. Furthermore, we propose to use graph representation techniques as a supportive methodology to better align perceived and actual environments, thereby enhancing the intervention strategies' effectiveness. We illustrate the applicability of the Spatial Nudging framework and the supportive methodology in the context of an E-bike City vision. This paper lays the foundation for future research on theoretically integrating physical and digital interventions to promote sustainable mobility.
Extracting spatial details from historical texts can be difficult, hindering our understanding of past landscapes. The study addresses this challenge by analyzing the Corpus of the Lake District Writing, focusing on the English Lake District region. We systematically link the theoretical notions from the core concepts of spatial information to provide basis for the problem domain. The conceptual foundation is further complemented with a spatial ontology and a custom gazetteer, allowing a formal and insightful semantic exploration of the massive unstructured corpus. The other contrasting side of the framework is the usage of LLMs for spatial relation extraction. We formulate prompts leveraging understanding of the LLMs of the intended task, curate a list of spatial relations representing the most recurring proximity or vicinity relations terms and extract semantic triples for the top five place names appearing in the corpus. We compare the extraction capabilities of three benchmark LLMs for a scholarly significant historical archive, representing their potential in a challenging and interdisciplinary research problem. Finally, the network comprising the semantic triples is enhanced by incorporating a gazetteer-based classification of the objects involved thus improving their spatial profiling.
Twenty years after Mark and Smith's seminal paper, a Science of Topography, we revisit some of their fundamental questions about how landforms are recognised by people and how they can be automatically extracted or delimited from representations of topographic surfaces. Many approaches and tools, essentially based on GeoOBIA, can extract objects associated with landforms from image data. But, they cannot relate these objects to the topology and topography of the terrain. Yet, geo-scientists can easily recognise landforms, considering terrain characteristics and other factors composing the context of appearance of those landforms. Revisiting Gestalt Theory, we propose a salience-based approach fostering a holistic view of the terrain which fits with the geoscientists' ability to recognise landforms using the topographic and hydrologic contexts. The terrain is represented as an extended surface network (ESN), a graph composed of elementary saliences (peaks, pits, saddles, thalweg and ridge networks) and obtained from raster data. The ESN combines both the surface and the drainage networks in a sound topological representation of the terrain. A skeletonisation technique of the ESN's thalweg and ridge networks is proposed to geometrically and topologically characterise landforms, as well as ensembles of landforms. On this basis and to represent the context of appearance of landforms, geo/topo-contexts are introduced as structures grounded in the properties of the ESN and using the skeletonisation technique. We give an illustration of how a geomorphologist can apply our approach and tools, using the depressions and drainage basins as examples of useful geo/topo-contexts.
Understanding the cognitive processes involved in wayfinding is crucial for both theoretical advances and practical applications in navigation systems development. This study explores how gaze behavior and visual attention contribute to our understanding of cognitive states during wayfinding. Based on the model proposed by Downs and Stea, which segments wayfinding into four distinct stages: self-localization, route planning, monitoring, and goal recognition, we conducted an outdoor wayfinding experiment with 56 participants. Given the significant role of spatial familiarity in wayfinding behavior, each participant navigated six different routes in both familiar and unfamiliar environments, with their eye movements being recorded. We provide a detailed examination of participants' gaze behavior and the actual objects of focus. Our findings reveal distinct gaze behavior patterns and visual attention, differentiating wayfinding stages while emphasizing the impact of spatial familiarity. This examination of visual engagement during wayfinding explains adaptive cognitive processes, demonstrating how familiarity influences navigation strategies. The results enhance our theoretical understanding of wayfinding and offer practical insights for developing navigation aids capable of predicting different wayfinding stages.
Geographic network visualizations often require assigning nodes to geographic coordinates, but this can be challenging when precise node locations are undefined. We explore this problem using U.S. senators as a case study. Each state has two senators, and thus it is difficult to assign clear individual locations. We devise eight different node placement strategies ranging from geometric approaches such as state centroids and longest axis midpoints to data-driven methods using population centers and home office locations. Through expert evaluation, we found that specific coordinates such as senators' office locations and state centroids are preferred strategies, while random placements and the longest axis method are least favored. The findings also highlight the importance of aligning node placement with research goals and avoiding potentially misleading encodings. This paper contributes to future advancements in geospatial network visualization software development and aims to facilitate more effective exploratory spatial data analysis.
