| 1. |
- Huang, Haosheng, et al.
(författare)
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Analytics of location-based big data for smart cities : Opportunities, challenges, and future directions
- 2021
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Ingår i: Computers, Environment and Urban Systems. - : Elsevier. - 0198-9715 .- 1873-7587. ; 90
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Tidskriftsartikel (refereegranskat)abstract
- The growing ubiquity of location/activity sensing technologies and location-based services (LBS) has led to a large volume and variety of location-based big data (LocBigData), such as location tracking or sensing data, social media data, and crowdsourced geographic information. The increasing availability of such LocBigData has created unprecedented opportunities for research on urban systems and human environments in general. In this article, we first review the common types of LocBigData: mobile phone network data, GPS data, Location-based social media data, LBS usage/log data, smart card travel data, beacon log data (WiFi or Bluetooth), and camera imagery data. Secondly, we describe the opportunities fueled by LocBigData for the realization of smart cities, mainly via answering questions ranging from “what happened” and “why did it happen” to “what's likely to happen in the future” and “what to do next”. Thirdly, pitfalls of dealing with LocBigData are summarized, such as high volume/velocity/variety; non-random sampling; messy and not clean data; and correlations rather than causal relationships. Finally, we review the state-of-the-art research trends in this field, and conclude the article with a list of open research challenges and a research agenda for LocBigData research to help achieve the vision of smart and sustainable cities.
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| 2. |
- Thrash, Tyler, et al.
(författare)
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The Future of Geographic Information Displays from GIScience, Cartographic, and Cognitive Science Perspectives
- 2019
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Ingår i: 14th International Conference on Spatial Information Theory. - Dagstuhl, Germany. - 9783959771153 ; , s. 19:1-19:11
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Konferensbidrag (refereegranskat)abstract
- With the development of modern geovisual analytics tools, several researchers have emphasized the importance of understanding users' cognitive, perceptual, and affective tendencies for supporting spatial decisions with geographic information displays (GIDs). However, most recent technological developments have focused on support for navigation in terms of efficiency and effectiveness while neglecting the importance of spatial learning. In the present paper, we will envision the future of GIDs that also support spatial learning in the context of large-scale navigation. Specifically, we will illustrate the manner in which GIDs have been (in the past) and might be (in the future) designed to be context-responsive, personalized, and supportive for active spatial learning from three different perspectives (i.e., GIScience, cartography, and cognitive science). We will also explain why this approach is essential for preventing the technological infantilizing of society (i.e., the reduction of our capacity to make decisions without technological assistance). Although these issues are common to nearly all emerging digital technologies, we argue that these issues become especially relevant in consideration of a person's current and future locations.
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| 3. |
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| 4. |
- Zhou, Zhiyong, et al.
(författare)
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HiVG: A hierarchical indoor visibility-based graph for navigation guidance in multi-storey buildings
- 2022
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Ingår i: Computers, Environment and Urban Systems. - : Elsevier. - 0198-9715 .- 1873-7587. ; 93
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Tidskriftsartikel (refereegranskat)abstract
- A hierarchical data model is needed in mobile navigation systems to generate route instructions on multiple levels of detail (LODs), thereby adapting to users’ various information needs during navigation. In complex multi-storey indoor environments, existing hierarchical data models mainly rely on logical graphs that represent indoor cellular spaces as nodes and adjacency as edges. Due to the lack of precise geometry, however, they have limited capability to support the accurate computation of walking distance and directions, which are essential in route instructions. This article proposes a hierarchical indoor visibility-based graph (HiVG) for navigation guidance in multi-storey buildings and presents a HiVG generation algorithm. The algorithm’s input is an indoor visibility graph (iVG) in which the orientations of nodes to corridor areas are represented. In the algorithm, first the functions of edges in indoor route instructions are identified, after which an edge function-based graph partitioning iteration is performed to generate each level of the HiVG. Experiments with three buildings of different geometric configurations demonstrate the potential of our HiVG generation algorithm. Furthermore, the conducted case studies show that the proposed HiVG is appropriate for generating indoor route instructions on multiple LODs.
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| 5. |
- Zhou, Zhiyong, et al.
(författare)
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Towards a hierarchical indoor data model from a route perspective
- 2021
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Ingår i: Proceedings of the 16th International Conference on Location Based Services. - : ICA Commission on Location Based Services; University of Glasgow. ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- In mobile navigation systems, an appropriate level of detail of the route instructions provided is important for navigation users to understand, memorise, and follow routes. However, few existing indoor navigation systems are capable of providing route instructions with multiple levels of detail. To close this gap, it is critical to model indoor environments with multiple granularities for route instructions to be generated on varying levels of detail. We propose a hierarchical model for route instructions in multi-storey buildings by allowing for representing actions (i.e., turning left or right, and going straight) in conceptualising route instructions. As a proof of concept, a case study is being conducted to present the feasibility of the proposed hierarchical model.
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