Wayfinding in complex indoor spaces is challenging, particularly when route instructions are incomplete or ambiguous. While humans often successfully navigate under such conditions by leveraging past experiences, computationally modeling this adaptation remains an open research area. This paper addresses the problem of assessing the usability of incomplete route instructions in unknown indoor environments. The objective is to determine if Reinforcement Learning (RL) agents, trained in diverse settings, can acquire transferable wayfinding policies to navigate effectively despite missing information. We present an approach using RL to computationally model the acquisition and transfer of wayfinding policies. Agents are trained via curriculum learning in simulated text-based indoor environments of varying complexity and with different turn-based instruction grammars. Their ability to navigate with both complete and incomplete instructions is then evaluated in seen and unseen environments. Our results show that RL agents learn to navigate with incomplete instructions and, in our experiments, outperform random agents. Agents trained on diverse environments demonstrate generalization to the novel settings we examined. This research offers a probabilistic framework for quantifying route instruction usability re-framing route evaluation from deterministic error checking to probabilistic risk assessment. It lets designers gauge when incomplete instructions remain usable, and optimize grammar.

As a key support for people’s daily wayfinding, satellite navigation systems generate a considerable amount of by-product tracking data. Such human mobility data offer opportunities for quantitative studies of the link between human mobility and spatial information needs. Here, we propose a methodological framework for causal inference on observational individual-level spatiotemporal mobility data. We demonstrate how this framework enables to isolate and quantify the causal strength of environmental familiarity on reduced supporting navigation information needs. The results reported here show an approximately 9% average treatment effect of familiarity on reduced information needs. We explore the sensitivity of the results to variants of the realization of the causal model in the data analysis, as well as to the impact of environmental and trip-related confounders. This research introduces concepts of causal analysis on observational data to the spatial community, and mobility analytics community in particular. It is one of the first attempts to link causally spatial information needs during wayfinding across familiar and unfamiliar environments and thus advances the methodological toolkit for individual-level spatial causal inference.

Integrating landmarks into the communication of wayfinding services is an established strategy that enhances wayfinding efficiency and user confidence. In this context, we present a strategy for selecting landmarks for route descriptions that keeps the number of selected landmarks small. We argue that limiting the number of landmarks to be referred to in a route description can help to reduce the length and complexity of the description. Instead of selecting a different landmark located at each decision point, we reduce the selection to landmarks that can be seen from multiple decision points. We preferably choose those landmarks that are already clearly visible when approaching a decision point, thus optimizing advance visibility. We formalize an optimization problem that requires that at least one selected landmark is visible from each decision point. While minimizing the number of selected landmarks, we aim to maximize their advance visibility along the route. We show that our problem is NP-hard and present both an exact approach that uses integer linear programming and a greedy heuristic. In our experiments, we demonstrate that our approach can substantially reduce the number of selected landmarks compared to a baseline strategy. We determine a compromise between the optimization criteria and show that the heuristic generates high-quality solutions within a short running time.

Many place-related questions can only be answered by complex spatial reasoning, a task poorly supported by factoid question retrieval. Such reasoning using combinations of spatial and non-spatial criteria pertinent to place-related questions is increasingly possible on linked data knowledge bases. Yet, to enable question answering based on linked knowledge bases, natural language questions must first be re-formulated as formal queries. Here, we first present an enhanced version of YAGO2geo, the geospatially-enabled variant of the YAGO2 knowledge base, by linking and adding more than one million places from OpenStreetMap data to YAGO2. We then propose a novel approach to translate the place-related questions into logical representations, theoretically grounded in the core concepts of spatial information. Next, we use a dynamic template-based approach to generate fully executable GeoSPARQL queries from the logical representations. We test our approach using the Geospatial Gold Standard dataset and report substantial improvements over existing methods.

In everyday communication, where-questions are answered by place descriptions. To answer where-questions automatically, computers should be able to generate relevant place descriptions that satisfy inquirers’ information needs. Human-generated answers to where-questions constructed based on a few anchor places that characterize the location of inquired places. The challenge for automatically generating such relevant responses stems from selecting relevant anchor places. In this paper, we present templates that allow to characterize the human-generated answers and to imitate their structure. These templates are patterns of generic geographic information derived and encoded from the largest available machine comprehension dataset, MS MARCO v2.1. In our approach, the toponyms in the questions and answers of the dataset are encoded into sequences of generic information. Next, sequence prediction methods are used to model the relation between the generic information in the questions and their answers. Finally, we evaluate the performance of predicting templates for answers to where-questions.

The term between is frequently used to describe spatial arrangements of objects where one described core object is positioned in the space bounded by two or more peripheral objects. As such, the relation between involves spatial configurations of at least three spatial objects. However, most of the existing qualitative spatial reasoning models focus only on binary spatial relations, and there is currently no single model that enables adequate reasoning about this ternary spatial relation. This paper proposes a novel model for expressing nuanced spatial relationships between three spatial objects, called the Ray Intersection Model (RIM). RIM evaluates rays cast between two peripheral spatial objects, and their topological relations with the core object to determine its position relative to the peripheral objects. RIM leaves the binary classification of the core object as between/not between to the user and application context. Although RIM supports all types of 2D spatial objects (i.e. points, lines, and polygons), its expressiveness is demonstrated in this paper by analyzing the total of 28 distinct configurations of triplets of polygon objects in a 2D plane. RIM has been computationally implemented and we demonstrate how RIM can be applied to analyze the arrangements of buildings at a university campus.

Human spatial concepts, such as the concept of place, are not immediately translatable to the geometric foundations of spatial databases and information systems developed over the past 50 years. These systems typically rest on the concepts of objects and fields, both bound to coordinates, as two general paradigms of geographic representation. The match between notions of place occurring in everyday where questions and the data available to answer such questions is unclear and hinders progress in place‐based information systems. This is particularly true in novel application areas such as the Digital Humanities or speech‐based human–computer interaction, but also for location‐based services. Although this shortcoming has been observed before, we approach the challenges of relating places to information system representations with a fresh view, based on a set of core concepts of spatial information. These concepts have been proposed in information science with the intent of serving human–machine spatial question asking and answering. Clarifying the relationship of the notion of place to these concepts is a significant step toward geographically intelligent systems. The main result of the article is a demonstration that the notion of place fits existing concepts of spatial information, when these are adequately exploited and combined.

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...

Probabilistic time geography considers the encounter of moving agents to be random; therefore, a quantitative time geography analysis must consider the actual encounter probability. The existing algorithm of encounter probability is oriented over a discrete space and is sensitive to the unit definition of a virtual grid; thus, it is not suitable for continuous space. For this reason, a new method is presented in this paper for the encounter of two moving agents in continuous space. When the encounters are less than a specified distance threshold apart, an encounter event occurs based on the probability of the product, which is calculated by their respective probability density functions over their respective potential location areas. This probability provides a quantitative basis for predicting the likelihood of two agents meeting, as well as the location of this meeting point. Finally, the validity of the proposed model is verified by an experiment, which uses tracking data to calculate the encounter probabilities of three zebras and analyse the distribution characteristics of these probabilities over time and space.

Place descriptions are used in everyday communication as a common way to convey spatial information. Processing the information from place descriptions poses multiple significant challenges because these descriptions are written in natural language. In particular, corpora of place descriptions provide a plethora of human spatial knowledge beyond geographical information system, even if these descriptions refer to the same places in various ways. This article focuses on resolving ambiguous or synonymous place names from place descriptions by exploring the given relationships with other spatial features. It matches place names from multiple descriptions by developing a novel labelled graph matching process that relies solely on the comparison of string, linguistic and spatial similarities between identified places. This process uses unstructured place descriptions as an input, and produces a composite place graph with qualitative spatial relations from the descriptions. The performance of this novel process exceeds current toponym resolution by coping with non-gazetteered places.

Since in many cities transport infrastructure is operating at or beyond capacity, novel approaches to organize urban mobility are gaining attraction. However, assessing the benefits of a measure that has disruptive capacity in a complex system requires a carefully designed research. This paper takes a recent idea for urban mobility - flexible road trains - and illustrates the computational and research challenges of realizing its full potential and describing its social, ecological and economical impact.

Ridesharing is an emerging travel mode that reduces the total amount of traffic on the road by combining people's travels together. While present ridesharing algorithms are trip-based, this paper aims to achieve significantly higher matching chances by a novel, activity-based algorithm. The algorithm expands the potential destination choice set by considering alternative destinations that are within given space-time budgets and would provide a similar activity function as the originals. In order to address the increased combinatorial complexity of trip chains, the paper introduces an efficient space-time filter on the foundations of time geography to search for accessible resources. Globally optimal matching is achieved by binary linear programming. The ridesharing algorithm is tested with a series of realistic scenarios of different population sizes. The encouraging results demonstrate that the matching rate by activity-based ridesharing is significantly increased from the baseline scenario of traditional trip-based ridesharing.

Existing ride sharing systems for commuting in urban environments are rigid. They rely on the communication of discrete, spatio-temporal constraints from both vehicle and client to perform ride-matching. From a client user perspective these approaches are problematic, leading to location-privacy issues and the use of additional communication channels for ad-hoc negotiation which cannot be immediately quantified. To account for these aspects, we develop a dynamic, intuitive interface technique called launch pads and a centralized system architecture, which together simplify the ride-matching process whilst preserving location-privacy. The results of two experiments reveal the latent potential existing within ride sharing systems if vehicle flexibility is quantified and incorporated into a representation of accessibility. The communication via launch pads provides a client with means to fully exploit this potential.

In this paper, we review the current literature on geographic information retrieval based on place names. We focus on the positional uncertainties and the extent of vagueness frequently associated with place names in linguistic place descriptions and on the differences between common users’ perception and the way the geographic information services interpret place names. We argue that, despite some notable efforts from the scientific community, geographic information services still cannot unambiguously recognize and sufficiently perform spatial reasoning with place names used in linguistic expressions. We focus on three interrelated research areas: (1) the use of place names in gazetteers, (2) the use of formal models to reason with spatial relations and with the spatial extent of place names in linguistic place descriptions, and (3) Web-harvesting and crowd-sourcing techniques for identifying place names and their spatial extension from public and volunteer sources, such as social networks and photo-sharing sites. We identify some opportunities for synthesizing existing approaches that would expedite the process of intelligent communication about place names between services and users. We discuss the shortcomings of the current state of affairs in locating place names from place descriptions and identify new areas of importance for future research.

This article studies probabilistic time geography for space–time prisms, that is, for situations where observers know the location of an agent at one time and then again at another time. In the intervening period, the agent would have moved freely, according to its time budget. The article demonstrates that the probability of finding the agent somewhere in the space–time prism is not equally distributed, so any attempt of a quantitative time geographic analysis must consider the actual probability distribution. This article develops, implements, and demonstrates this distribution. A preceding article introduced probabilistic time geography for space–time cones. With cones and prisms, the elementary space–time volumes of time geography are provided.

Time geography uses space-time volumes to represent the possible locations of a mobile agent over time in a x-y-t space. Space-time volumes are qualitative statements, enabling qualitative analysis. In this paper these statements will be quantified, modeling an agent's possible locations from a stochastic perspective. With such a model time geography can be improved in expressiveness as well as accuracy.

Indoor spaces pose many challenges for spatial information systems, amongst them appropriate spatial communication. Compared to typical outdoor spaces, indoor spaces are clustered, have a fragmented social structure, and diverse groups use the space in different ways, which results in different conceptualizations and communication needs. This paper presents a hierarchical representation of indoor spaces that accounts for different use roles. The hierarchization exploits structural, functional and organizational dimensions and allows for communicating different aspects of the space in a way tailored to the specific user groups.

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.

Recent developments in miniaturization of computing devices, in location‐sensing technology and in ubiquitous short‐range wireless networks enable new types of social behaviour. This paper investigates one novel application of these technologies, ad hoc inner‐urban shared‐ride trip planning: Transportation clients such as pedestrians are seeking ad hoc shared rides from transportation hosts such as private automobiles, buses, taxi cabs or trains. While centralized trip planners are challenged by assigning clients and hosts in an ad hoc manner, in particular for non‐scheduled hosts, we consider the transportation network as a mobile geosensor network of agents that interact locally by short‐range communication and heuristic wayfinding strategies. This approach is not only fully scalable; we can also demonstrate that with short‐range communication, and hence, incomplete transportation network knowledge a system still can deliver near‐to‐optimal trips.

This paper presents the results and experiences of applying a formal tool to writing specifications for software in the spatial domain. We take a specification from a standards organization to demonstrate the properties of this approach, and we compare our result with the semi-formal specification of the standards organization. The expected advantages—formal semantics of specified interfaces, total abstraction from implementation, executable prototype, and extendable algebraic structure—can be verified. However negative experiences are reported also: pure abstract code turns out to appear overloaded and difficult to read (by humans). With this work we contribute to the foundations of standardisation efforts in the spatial domain, preserving an object oriented modeling approach.

Spatial databases are repositories of representations of the real world. The represented entities have to be observed in the real world and mapped to the database. An observation is interpreted here as a two-level process, consisting of the abstraction of the observed object to a concept and a measurement of the realized concept. Due to the nature of observations, regions representing the location of objects are always imprecise, the explication of a concept succeeds only incompletely, and the measurement is limited in precision. In this paper, the uncertainty in abstraction as well as the imprecision of measurement are modelled statistically. This allows the introduction of the uncertainty of observation into qualitative spatial reasoning. The example used in this paper is the determination of topological relations. The topological relation between two regions becomes uncertain if the regions are imprecise in their location. Hence, the decision about a topological relation is made by maximum likelihood classification. The classification allows a quantitative assessment of the decision by its probability, and by the probability of the alternative relations. The method is useful in data set comparison, data matching, and modelling data quality descriptions.