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.

Abstract Geometric shape classification of vector polygons remains a challenging task in spatial analysis. Previous studies have primarily focused on deep learning approaches for rasterized vector polygons, while the study of discrete polygon representations and corresponding learning methods remains underexplored. In this study, we investigate a graph-based representation of vector polygons and propose a simple graph message-passing framework, PolyMP, along with its densely self-connected variant, PolyMP-DSC, to learn more expressive and robust latent representations of polygons. This framework hierarchically captures self-looped graph information and learns geometric-invariant features for polygon shape classification. Through extensive experiments, we demonstrate that combining a permutation-invariant graph message-passing neural network with a densely self-connected mechanism achieves robust performance on benchmark datasets, including synthetic glyphs and real-world building footprints, outperforming several baseline methods. Our findings indicate that PolyMP and PolyMP-DSC effectively capture expressive geometric features that remain invariant under common transformations, such as translation, rotation, scaling, and shearing, while also being robust to trivial vertex removals. Furthermore, we highlight the strong generalization ability of the proposed approach, enabling the transfer of learned geometric features from synthetic glyph polygons to real-world building footprints.

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.

Assessing OpenStreetMap (OSM) data quality against authoritative data sources may not always be viable. This is primarily because of the multi-dimensional nature and heterogeneity of the maps, yet the activity is pivotal for targeted data cleansing and quality enhancement undertakings in these data sets. A salient facet of OSM, allowing contributors to flag potential problems encountered during the mapping process, is the FIXME tag. In this article, we examine and discuss OSM data quality through the vast expanse of issues (knowledge) documented via FIXME. We present a classification and analysis of these quality issues, exposed as topic models and grounded in the ISO-19157 standard, across USA and Australia. Regional distributions of these topics are further qualitatively analyzed to ascertain the variation of key issues in OSM. We also present a comparison of the intrinsic issue classification against those identified in an issue corpus of an authoritative map data source. Due to the considerable heterogeneity in user mapping and reporting, OSM issue detection and classification remains problematic. This research presents a flexible and intrinsic data-mining approach, linking established ISO data quality standards to OSM issue categorization. Our work, thus informs the development of automated error correction methods for VGI datasets.

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.

There has not been a time in the history of GIScience when movement analytics and mobility insights have played such an important role in policymaking as in today’s global responses to the COVID-19...

The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.

Traditionally, recommender systems modelled the physical and cyber contextual influence on people's moving, querying, and browsing behaviors in isolation. Yet, searching, querying, and moving behaviors are intricately linked, especially indoors. Here, we introduce a tripartite location-query-browse graph (LQB) for nuanced contextual recommendations. The LQB graph consists of three kinds of nodes: locations, queries, and Web domains. Directed connections only between heterogeneous nodes represent the contextual influences, while connections of homogeneous nodes are inferred from the contextual influences of the other nodes. This tripartite LQB graph is more reliable than any monopartite or bipartite graph in contextual location, query, and Web content recommendations. We validate this LQB graph in an indoor retail scenario with extensive dataset of three logs collected from over 120,000 anonymized, opt-in users over a 1-year period in a large inner-city mall in Sydney, Australia. We characterize the contextual influences that correspond to the arcs in the LQB graph, and evaluate the usefulness of the LQB graph for location, query, and Web content recommendations. The experimental results show that the LQB graph successfully captures the contextual influence and significantly outperforms the state of the art in these applications.

Map databases traditionally capture snapshot representations of the world following strict data collection and representation guidelines. The content of these map databases is often assessed using data quality metrics focusing on accuracy, completeness and consistency. The success of volunteered geographic information, supporting evolving representations of the world based on fluid guidelines, has rendered these measures insufficient. In this paper, we address the need to capture the variability in quality of a map database. We propose a new spatial data quality measure – dataset maturity – enabling assessment of the database based on temporal trends in feature definitions, specifically geometry-type definitions. The proposed measure can be (1) efficiently used to identify feature definition patterns reflecting community consensus that could be formalised in community guidelines and (2) deployed to identify regions that would benefit from increased editorial activity to achieve greater map homogeneity. We demonstrate the measure based on the content of the OpenStreetMap database in four regions of the world and show how the proposed dataset maturity measure captures a distinct quality of the datasets, distinct to data completeness and consistency.

We analyze 18‐ million rows of Wi‐Fi access logs collected over a 1‐year period from over 120,000 anonymized users at an inner city shopping mall. The anonymized data set gathered from an opt‐in system provides users' approximate physical location as well as web browsing and some search history. Such data provide a unique opportunity to analyze the interaction between people's behavior in physical retail spaces and their web behavior, serving as a proxy to their information needs. We found that (a) there is a weekly periodicity in users' visits to the mall; (b) people tend to visit similar mall locations and web content during their repeated visits to the mall; (c) around 60% of registered Wi‐Fi users actively browse the web, and around 10% of them use Wi‐Fi for accessing web search engines; (d) people are likely to spend a relatively constant amount of time browsing the web while the duration of their visit may vary; (e) the physical spatial context has a small, but significant, influence on the web content that indoor users browse; and (f) accompanying users tend to access resources from the same web domains.

We report a preliminary study of mobile Web behaviour in a large indoor retail space. By analysing a Web log collected over a 1 year period at an inner city shopping mall in Sydney, Australia, we found that 1) around 60% of registered Wi-Fi users actively browse the Internet, and the rest 40% do not, with around 10% of these users using Web search engines. Around 70% of this Web activity in the investigated mall come from frequent visitors; 2) the content that indoor users search for is different from the content they consume while browsing; 3) the popularity of future indoor search queries can be predicted with a simple theoretical model based on past queries treated as a weighted directed graph. The work described in this paper underpins applications such as the prediction of users' information needs, retail recommendation systems, and improving the mobile Web search experience.

This article presents the results of a user evaluation of automatically generated concept keywords and place names (toponyms) for geo‐referenced images. Automatically annotating images is becoming indispensable for effective information retrieval, since the number of geo‐referenced images available online is growing, yet many images are insufficiently tagged or captioned to be efficiently searchable by standard information retrieval procedures. TheTripod project developed original methods for automatically annotating geo‐referenced images by generating representations of the likely visible footprint of a geo‐referenced image, and using this footprint to query spatial databases and web resources. These queries return raw lists of potential keywords and toponyms, which are subsequently filtered and ranked. This article reports on user experiments designed to evaluate the quality of the generated annotations. The experiments combined quantitative and qualitative approaches: To retrieve a large number of responses, participants rated the annotations in standardized online questionnaires that showed an image and its corresponding keywords. In addition, several focus groups provided rich qualitative information in open discussions. The results of the evaluation show that currently the annotation method performs better on rural images than on urban ones. Further, for each image at least one suitable keyword could be generated. The integration of heterogeneous data sources resulted in some images having a high level of noise in the form of obviously wrong or spurious keywords. The article discusses the evaluation itself and methods to improve the automatic generation of annotations.

In this paper, we present the functional capabilities scoping for a novel eResearch infrastructure providing urban researchers with access to datasets and analytical tools. The AURIN portal provides a "lab in a browser" environment, leveraging a complex, loosely-coupled internal architecture and a growing number of federated data sources. Datasets can be "shopped" for, visually explored and analyzed using a growing number of analytical capabilities orchestrated in a workflow environment. While spatial analytical tasks are at the heart of most targeted research disciplines, AURIN aims to reach beyond the scope of traditional GIS and map-based portals. In this paper, we discuss how the functional requirements of AURIN can be realized to enable exploratory and confirmatory data analysis supported by high performance Web based infrastructure.