1. Traditionally, geographic information used in spatial analysis and research was almost exclusively produced by a relatively small set of governmental and commercial actors, and took the form of ...

This paper explores the use of probabilistic and conventional qualitative spatial reasoning (QSR) in the context of geospatial question answering (GeoQA) systems. The paper presents a thorough empirical investigation of the performance of a probabilistic and a conventional qualitative spatial reasoner, across a range increasingly sophisticated scenarios with real data and synthetically generated questions. The results indicate the potential of probabilistic QSR to provide more detailed information about spatial configurations than conventional QSR; but at the cost of less frequent errors in estimating the relative likelihood of different reasoning conclusions. Errors in probabilistic reasoning also tend to be systematically associated with lower probability conclusions. The results have implications for reliable and flexible automated spatial reasoning systems, especially where neither conventional geographic information retrieval (GIR) techniques nor large language models (LLMs) are able to provide a satisfactory solution to GeoQA problems.

ABSTRACT Engaging young learners in coding activities is increasingly important to prepare them to participate in a contemporary computationally centred society. Engagement comes from finding appropriate motivation and the use of materials and methods that are suitable to the educational stage and learning opportunities. In this article, we present our experiences of designing and developing a Python‐based platform for children to use in our Music Making with Music and Making (MMMM) workshops, enabling school students to engage with hybrid digital music instrument design through programming synthesisers and undertaking LEGO construction. We document our understanding of the design and operation of the BrickPi3 and Raspberry Pi Build Hat systems and discuss our approach to using these as part of a simple software platform for schoolchildren to use. We present our design (and discuss alternatives considered) and the way it was successfully used by 61 11–13 year‐old children in workshop settings. We discuss the technical issues encountered and their resolution, and finally reflect on the positive feedback provided by teaching staff and potential for further development. The inherently integrated and inter‐disciplinary nature of our work aligns with and makes a positive contribution to the Science Technology Engineering Arts and Mathematics (STEAM) movement.

Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and begins, a problem carried over when such entities are catalogued in gazetteers. In this paper, we take two gazetteers, GeoNames and SwissNames3D, and perform matching – identifying records in each that are about the same entity – across a sample of natural feature records. We first perform rule-based matching, establishing competitive results, then apply machine learning using Random Forests, a method well-suited to the matching task. We report on the performance of a wider array of matching features than has been previously studied, including domain-specific ones such as feature type, land cover class, and elevation. Our results show an increase in performance using machine learning over rules, with a notable performance gain from considering feature types, but negligible gains from other specialized matching features. We argue that future work in this area should strive to be more reproducible and report results on a realistic testing pipeline including candidate selection, feature extraction, and classification.

Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter - a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

We analyse silence and tranquillity in historical and contemporary corpora to understand ways landscapes were—and are—perceived in the Lake District National Park in England. Through macro and microreading we develop a taxonomy of aural experiences, and explore how changes to categories of silence from our taxonomy—for instance, the overall decline in mentions of absolute silence—provide clues to changes in the landscape and soundscape of the Lake District. Modern authors often contrast silence with anthropogenic sounds, while historical authors adhere to a cultural construction where the Lake District is presented as a tranquil area by ignoring industrial sounds. Using sentiment analysis we show that silence and tranquil sounds in our corpora are, as a whole, more positively associated than random text from the corpora, with this difference being especially marked in contemporary descriptions. Focusing closely on individual texts allows us to illustrate how this increased positivity can be related to the emergence of silence and tranquillity as valuable components of landscape. Mapping our corpora confirmed the influence of Wordsworth’s writing on descriptions of silence; and revealed the co-location of pockets of tranquillity near to transport arteries in contemporary descriptions.

Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events.

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.

Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse.

How do people perceive landscapes? What elements of the landscape do they identify as characteristic of a landscape? And how can we arrive at descriptions, and ultimately representations that better reflect people’s notions of landscapes? In this study, we collected landscape descriptions from five landscape types at 10 study sites in Switzerland. For each site, we collected data from three sources: free lists with participants, hiking blogs, and Flickr tags. Free lists were obtained through on-site interviews with visitors, hiking blogs were gathered by focused crawling of web content, and Flickr tags were selected based on spatial footprints obtained from the hiking blogs. We quantitatively compared landscape descriptions between data sources and landscape types using cosine similarity. We found that descriptions from the same data source were significantly more similar, irrespective of landscape type. Descriptions from the same landscape type were more similar, but only within the same data source. Through a qualitative analysis of different aspects of landscape in our content, we found that each data source offered a different distribution of landscape aspects. For example, while Flickr tags contained high proportions of toponyms, they contained little content relating to sense of place. In contrast, hiking blogs contained more information about sense of place. Our approach combining these varied textual sources thus offers a more holistic view on landscapes. This study constitutes a step toward extracting semantically rich descriptions of landscapes from a variety of sources and using this information to distinguish different landscapes, with potential applications for landscape monitoring and management.

The advent of new sources of spatial data and associated information (e.g. Volunteered Geographic Information (VGI)) allows us to explore non-expert conceptualisations of space, where the number of participants and spatial extent coverage encompassed can be much greater than is available through traditional empirical approaches. In this paper we explore such data through the prism of landscape preference or scenicness. VGI in the form of photographs is particularly suited to this task, and the volume of images has been suggested as a simple proxy for landscape preference. We propose another approach, which models landscape aesthetics based on the descriptions of some 220000 images collected in a large VGI project in the UK, and more than 1.5 million votes related to the perceived scenicness of these images collected in a crowdsourcing project. We use image descriptions to build features for a supervised machine learning algorithm. Features include the most frequent uni- and bigrams, adjectives, presence of verbs of perception and adjectives from the "Landscape Adjective Checklist". Our results include not only qualitative information relating terms to scenicness in the UK, but a model based on our features which can predict some 52% of the variation in scenicness, comparable to typical models using more traditional approaches. The most useful features are the 800 most frequent unigrams, presence of adjectives from the "Landscape Adjective Checklist" and a spatial weighting term.

The selection and retrieval of relevant information from the information universe on the web is becoming increasingly important in addressing information overload. It has also been recognized that geography is an important criterion of relevance, leading to the research area of geographic information retrieval. As users increasingly retrieve information in mobile situations, relevance is often related to geographic features in the real world as well as their representation in web documents. We present 2 methods for assessing geographic relevance (GR) of geographic entities in a mobile use context that include the 5 criteria topicality, spatiotemporal proximity, directionality, cluster, and colocation. To determine the effectiveness and validity of these methods, we evaluate them through a user study conducted on the Amazon Mechanical Turk crowdsourcing platform. An analysis of relevance ranks for geographic entities in 3 scenarios produced by two GR methods, 2 baseline methods, and human judgments collected in the experiment reveal that one of the GR methods produces similar ranks as human assessors.

In this paper, we demonstrate how a large corpus, consisting of about 10 000 articles describing Swiss alpine landscapes and activities and dating back to 1864, can be used to explore the use of language in space. In a first step, we link landscape descriptions to geospatial footprints, which requires new methods to disambiguating toponyms referring to natural features. Secondly, we identify natural features used to describe landscapes, which are compared and discussed in the light of previous work based on controlled participant experiments in laboratory settings and more exploratory ethnographic studies. Finally, we use natural features in combination with geospatial footprints to investigate variations in landscape descriptions across space.Our contributions are threefold. Firstly, we show how a corpus composed of detailed descriptions of natural landscapes can be georeferenced and mapped using density surfaces and an adaptive grid linking footprints to articles. Secondly, 95 natural features are identified in the corpus, forming a vocabulary of terms reflecting known basic levels and their relationships to other more specific landscape features. Thirdly, we can explore the use of natural features in broader spatial and temporal contexts than is possible in typical ethnographic work, by exploring when and where particular terms are used within Switzerland with respect to our corpus. On the one hand, this enables us to characterize individual regions and, on the other hand, to measure similarity between regions, on the basis of associated natural features.Our methods could be adapted to different types of corpus, for instance, referring to fine granularity entities in urban landscapes. Our results are potential building blocks for attaching place-related descriptions to automatically generated sensor data such as photographs or satellite images.

Increasing volumes of trajectory data require analysis methods which go beyond the visual. Methods for computing trajectory analysis typically assume linear interpolation between quasi-regular sampling points. This assumption, however, is often not realistic, and can lead to a meaningless analysis for sparsely and/or irregularly sampled data. We propose to use the space-time prism model instead, allowing to represent the influence of speed on possible trajectories within a volume. We give definitions for the similarity of trajectories in this model and describe algorithms for its computation using the Fréchet and the equal time distance.

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 I discuss the use of user generated content as a new data source for exploring geographic questions, through two exemplars: the derivation of vernacular regions and trajectory analysis. By exploring the implications of various elements of data quality on the derivation of vernacular regions, I make some initial suggestions as to the likely importance of these in the analysis of trajectories derived from user generated content.

In this paper, we describe a methodology to estimate the geographic coverage of the web without the need for secondary knowledge or complex geo-tagging. This is achieved by randomly selecting toponyms from the Ordnance Survey 50K gazetteer to create search queries and thus gather document counts from various web sources for Great Britain. The same gazetteer is then used to geo-code the results and enable mapping. To validate our approach, and demonstrate the effects of geo/non-geo and geo/geo ambiguity, we mapped the selected toponyms to Geograph, a community project that contains user generated geo-tagged photographs of the UK. Although success varies with resolution, the proposed approach is likely sufficient to be reliably used by applications exploring the geographic coverage of the web for cases where references to settlements are likely to be common. In our case, we applied the method to produce maps of web coverage for a range of sources at a resolution of 30km.

The World Wide Web has become the world's largest networked information resource, but references to geographical locations remain unstructured and typically implicit in nature. This lack of explicit spatial knowledge within the Web makes it difficult to service user needs for location-specific information. At present, spatial knowledge is hidden in many small information fragments such as addresses on Web pages, annotated photos with GPS co-ordinates, geographic mapping applications, and geotags in user-generated content. Several emerging formats that primarily or secondarily include location metadata, like GeoRSS, KML, and microformats, aim to improve this state of affairs. However, the question remains how to extract, index, mine, find, view, mashup, and exploit Web content using its location semantics. This work-shop brings together researchers from academia and industry labs to discuss and present the latest results and trends in all facets of the relationships between location concepts and Web information.

Geographical information is recorded in a wide variety of media and document types. There are innumerable paper‐based books, reports, images and maps, and there are computer databases and digital m...

Place names are often used to describe and to enquire about geographical information. It is common for users to employ vernacular names that have vague spatial extent and which do not correspond to the official and administrative place name terminology recorded within typical gazetteers. There is a need therefore to enrich gazetteers with knowledge of such vague places and hence improve the quality of place name‐based information retrieval. Here we describe a method for modelling vague places using knowledge harvested from Web pages. It is found that vague place names are frequently accompanied in text by the names of more precise co‐located places that lie within the extent of the target vague place. Density surface modelling of the frequency of co‐occurrence of such names provides an effective method of representing the inherent uncertainty of the extent of the vague place while also enabling approximate crisp boundaries to be derived from contours if required. The method is evaluated using both precise and vague places. The use of the resulting approximate boundaries is demonstrated using an experimental geographical search engine.

Much of the information stored on the web contains geographical context, but current search engines treat such context in the same way as all other content. In this paper we describe the design, implementation and evaluation of a spatially aware search engine which is capable of handling queries in the form of the triplet of ⟨theme⟩⟨spatial relationship⟩⟨location⟩. The process of identifying geographic references in documents and assigning appropriate footprints to documents, to be stored together with document terms in an appropriate indexing structure allowing real‐time search, is described. Methods allowing users to query and explore results which have been relevance‐ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine. Normalised precision for 38 queries, containing four types of spatial relationships, is significantly higher (p<0.001) for searches exploiting spatial information than pure text search.

This paper proposes and implements a new methodology for progressive transmission of vector lines and polygons over the Internet. The methodology generates continuous vector data through constrained remove operations of vertices on the server side, maintains consistent topology via a set of constraint rules, and restores original vector data through a reconstruction operator on the client side. The prototype system was implemented to investigate the performance of the methodology in terms of the preservation of consistent topology, transmission time, and qualities of the resulting visualizations. The method is shown to be scalable to large data sets, to produce graphically acceptable results and to maintain topology.

No abstract available.