Infrastructure Engineering - Research Publications
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ItemIndoor view graph: A model to capture route and configurational information(SAGE Publications, 2024-01-01)This paper presents a graph model that simultaneously stores route and configurational information about indoor spaces. Existing indoor information models either capture route information to compute shortest paths and to generate route descriptions (i.e., answering how-to-get-to questions), or they store configurational information about objects and places and their spatial relationships to enable spatial querying and inference (i.e., answering where-questions). Consequently, multiple representations of an indoor environment must be stored in information systems to address the various information needs of their users. In this paper, we propose a graph that can capture both configurational and route information in a unified manner. The graph is the dual representation of connected lines of sight, or views. Views can represent continuous movement in an indoor environment, and at the same time, the visible configurational information of each view can be explicitly captured. In this paper, we discuss the conceptual design of the model and an automatic approach to derive the view graph from floorplans. Finally, we demonstrate the capabilities of our model in performing different tasks such as calculating shortest paths, generating route descriptions, and deriving place graphs.
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ItemNo Preview AvailableGranularity of origins and clustering destinations in indoor wayfinding(ELSEVIER SCI LTD, 2023-01)
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ItemNo Preview AvailableNeural factoid geospatial question answering(UNIV MAINE, 2021)Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.
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ItemNo Preview AvailableThe semantics of place-related questions(UNIV MAINE, 2021)The trend to equip information systems with question-answering capabilities raises the design problem of deciding which questions a system should be able to answer. Typical solutions build on mining human conversations or logs from similar systems for question patterns. For the case of questions about geographic places, we present a complementary approach, showing how to derive possible questions from an ontology of spatial information and a classification of place facets. We argue that such an approach reduces the inherent and substantial data bias of current solutions. At a more general level, we provide a novel understanding of spatial questions and their role in designing and using spatial information systems.
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ItemSpatial Concepts in the Conversation With a Computer(ASSOC COMPUTING MACHINERY, 2021-07)Conversing about places with a computer poses a range of challenges to current AI.
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ItemMapping home internet activity during COVID-19 lockdown to identify occupation related inequalities(NATURE PORTFOLIO, 2021-10-26)During the COVID-19 pandemic, evidence has accumulated that movement restrictions enacted to combat virus spread produce disparate consequences along socioeconomic lines. We investigate the hypothesis that people engaged in financially secure employment are better able to adhere to mobility restrictions, due to occupational factors that link the capacity for flexible work arrangements to income security. We use high-resolution spatial data on household internet traffic as a surrogate for adaptation to home-based work, together with the geographical clustering of occupation types, to investigate the relationship between occupational factors and increased internet traffic during work hours under lockdown in two Australian cities. By testing our hypothesis based on the observed trends, and exploring demographic factors associated with divergences from our hypothesis, we are left with a picture of unequal impact dominated by two major influences: the types of occupations in which people are engaged, and the composition of households and families. During lockdown, increased internet traffic was correlated with income security and, when school activity was conducted remotely, to the proportion of families with children. Our findings suggest that response planning and provision of social and economic support for residents within lockdown areas should explicitly account for income security and household structure. Overall, the results we present contribute to the emerging picture of the impacts of COVID-19 on human behaviour, and will help policy makers to understand the balance between public health and social impact in making decisions about mitigation policies.
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ItemSpatial Causality: A Systematic Review on Spatial Causal Inference(WILEY, 2023-01-01)The growing interest in causal inference in recent years has led to new causal inference methodologies and their applications across disciplines and research domains. Yet, studies on spatial causal inference are still rare. Causal inference on spatial processes is faced with additional challenges, such as spatial dependency, spatial heterogeneity, and spatial effects. These challenges can lead to spurious results and subsequently, incorrect interpretations of the outcomes of causal analyses. Recognizing the growing importance of causal inference in the spatial domain, we conduct a systematic literature review on spatial causal inference based on a formal concept mapping. To identify how to assess and control for the adverse effects of spatial influences, we assess publications relevant to spatial causal inference based on criteria relating to application discipline, methods used, and techniques applied for managing issues related to spatial processes. We thus present a snapshot of state of the art in spatial causal inference and identify methodological gaps, weaknesses and challenges of current spatial inference studies, along with opportunities for future research.
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ItemIdentification of parking spaces from multi-modal trajectory data(WILEY, 2021-12)Abstract Mapping the parking spaces in cities is desirable for reducing cruising time and congestion in the city. But map information regarding parking spaces is often missing or incomplete, due to the variety of their nature: marked or unmarked, on‐street or off‐street, or public, domestic or commercial. Hence, we develop a new method for mapping parking spaces, and deliberately focus on a crowd‐sourcing solution because of its global applicability. We will use smartphone trajectory data, as collected by person‐bound navigation apps. A person‐bound navigation app collects multi‐modal trajectory data where the transitions from drive to walk or from walk to drive contain valuable information about parking spaces. Hence, mode detection is required with sufficient accuracy to be able to map parking spaces. We develop a novel mode detection focusing just on this problem and outperforming existing, generic mode detection algorithms. Further, we provide a methodology to identify the geographic locations of parking spaces from these collected trajectory data. The article presents the methodologies, their implementations, and a critical evaluation to achieve mapping of parking spaces.
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ItemPaths to social licence for tracking-data analytics in university research and services(PUBLIC LIBRARY SCIENCE, 2021-05-21)While tracking-data analytics can be a goldmine for institutions and companies, the inherent privacy concerns also form a legal, ethical and social minefield. We present a study that seeks to understand the extent and circumstances under which tracking-data analytics is undertaken with social licence-that is, with broad community acceptance beyond formal compliance with legal requirements. Taking a University campus environment as a case, we enquire about the social licence for Wi-Fi-based tracking-data analytics. Staff and student participants answered a questionnaire presenting hypothetical scenarios involving Wi-Fi tracking for university research and services. Our results present a Bayesian logistic mixed-effects regression of acceptability judgements as a function of participant ratings on 11 privacy dimensions. Results show widespread acceptance of tracking-data analytics on campus and suggest that trust, individual benefit, data sensitivity, risk of harm and institutional respect for privacy are the most predictive factors determining this acceptance judgement.
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ItemThere is no way! Ternary qualitative spatial reasoning for error detection in map data(WILEY, 2021-08)Abstract Detection and correction of errors in map data based on spatial reasoning may be used to improve their quality. However, the majority of current spatial reasoning approaches are based on binary spatial relations and are not able to perform analyses involving more than two objects. This article proposes building accessibility analysis with the ternary ray intersection model to detect potential map errors. Where buildings are not accessible from the road network, this may indicate potential errors in map data such as roads that are not mapped. The plausibility of the proposed method was tested in a case study on OpenStreetMap data. The results have been published in an online mapping challenge where volunteering mappers have used them to correct errors in map data, and have provided feedback on the analysis. The results show that the proposed method can detect errors in map data that are caused by incorrect classification of buildings, incorrect mapping of multi‐part buildings, and missing road data.