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  Subjects -> GEOGRAPHY (Total: 493 journals)
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Journal of Spatial Information Science
Journal Prestige (SJR): 0.551
Citation Impact (citeScore): 2
Number of Followers: 4  

  This is an Open Access Journal Open Access journal
ISSN (Online) 1948-660X
Published by U of Maine Homepage  [2 journals]
  • Surface network extraction from high resolution digital terrain models

    • Authors: Eric Guilbert
      Abstract: A surface network is a topological data structure formed by a set of thalwegs and ridges on a digital terrain model. Its computation relies on the detection of saddles on the terrain. Hence, computation methods must guarantee enough saddles are detected but also that no improper conflicts between ridges and thalwegs are created, leading to an inconsistent network. This paper presents a new approach that maximizes the number of saddles and ensures this topological consistency for high-resolution terrain models represented by a raster grid. The grid is triangulated in order to preserve saddles and to facilitate thalweg and ridge computation. It does not require any user parameter and lines remain aligned with triangulation edges, avoiding numerical errors. The method also includes a coherent partitioning of the terrain into hills and dales. A case study shows that the surface network computation can be achieved in reasonable time and hence can be applied to the analysis of large terrain models.
      PubDate: Thu, 12 Aug 2021 08:34:41 PDT
       
  • Examining satellite images market stability using the Records theory:
           Evidence from French spatial data infrastructures

    • Authors: Chadi Jabbour et al.
      Abstract: The spatial data infrastructures (SDIs) which constitute a direct link between spatial data users and the large Earth observation industry, have a leading role in establishing market opportunities in the space sector. The spatial information supplied through various forms of SDI platforms exhibits large increases in demand volatility. The users' demand is unpredictable and the market is vulnerable to high evolution shifts. We study the effect of extreme demands for a particular type of spatial information, the satellite images. Drawing on two French SDIs, GEOSUD and PEPS, we examine the shifts occurring on their platforms and assess the probability of witnessing a spike/drop in the short term of different satellite imagery schemes: the high resolution images through GEOSUD; the Landsat (U.S.), Sentinel (Europe) and SPOT (France) images through PEPS. We analyze the market stability through the two SDIs and evaluate the probability of future records by using the Records theory. The results show that the high resolution images demand through GEOSUD, for which the classical i.i.d. model fits the most, is stable. Moreover, the Yang-Nevzorov model fits to the Landsat data, due to more records concentrated beyond the first observations. The Landsat demand is the less stable out of the other three satellite images series, and the probability of having a record in the coming years is the highest. While the use of Records theory drops mathematical constraints, it offers an alternative solution to the non-applicability of the machine learning techniques and long-term memory models.
      PubDate: Thu, 12 Aug 2021 08:34:40 PDT
       
  • Towards detecting, characterizing, and rating of road class errors in
           crowd-sourced road network databases

    • Authors: Johanna Guth et al.
      Abstract: OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifications to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub.
      PubDate: Thu, 12 Aug 2021 08:34:40 PDT
       
  • Don't forget about geography

    • Authors: Micah L. Brachman
      Abstract: Maps are a fundamental form of human communication, and for millennia geographers have created maps that measure and describe features and phenomena on the Earth's surface. Yet since the "quantitative revolution" of the 1960s, the ancient scientific discipline of geography has become increasingly devalued within the academe and misunderstood by the general public. A review of the academic affiliations and job titles of the esteemed authors from the JOSIS 10th anniversary edition is indicative of how constant rebranding and renaming of geography has resulted in fragmentation of the discipline. While terms such as "Spatial Data Science‚" have a cross-disciplinary appeal, other terms such as "geoinformation", "geoinformatics‚", "geographic data science", and "geographical information science‚" primarily conflate geography and computer science. Geographers have been valued for our ability to addressed complex problems and create maps that cross scientific boundaries since antiquity. To reclaim a position of centrality within the academe and the minds of public we must be unequivocal that the central value proposition for geography is the fundamental form of human communication that geographers can truly claim as their own: the map.
      PubDate: Tue, 13 Jul 2021 19:00:18 PDT
       
  • Service quality monitoring in confined spaces through mining Twitter data

    • Authors: Mohammad Masoud Rahimi et al.
      Abstract: Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel fine-tuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches.
      PubDate: Tue, 13 Jul 2021 19:00:17 PDT
       
  • The impact of urban road network morphology on pedestrian wayfinding
           behaviour

    • Authors: Debjit Bhowmick et al.
      Abstract: During wayfinding pedestrians do not always choose the shortest available route. Instead, route choices are guided by several well-known wayfinding strategies or heuristics. These heuristics minimize cognitive effort and usually lead to satisfactory route choices. Our previous study evaluated the costs of four well-known pedestrian wayfinding heuristics and their variation across nine network morphologies. It was observed that the variation in the cost of these wayfinding heuristics increased with an increase in the irregularity of the network, indicating that people may opt for more diverse heuristics while walking through relatively regular networks, and may prefer specific heuristics in the relatively irregular ones. The study presented here aims to investigate this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum. We found that the values of mean route length and mean Network Hausdorff Distance for walking trips made in Melbourne were consistently lesser than the corresponding values obtained in Beijing. Also, across both the cities, we found that while there was minimal variation in the popularity of heuristics overall, in cases where different heuristics produced dissimilar routes, the shortest leg first strategy and the least angle strategy were more popular.
      PubDate: Tue, 13 Jul 2021 19:00:17 PDT
       
  • How does socio-economic and demographic dissimilarity determine physical
           and virtual segregation'

    • Authors: Michael Dorman et al.
      Abstract: It is established that socio-economic and demographic dissimilarities between populations are determinants of spatial segregation. However, the understanding of how such dissimilarities translate into actual segregation is limited. We propose a novel network-analysis approach to comprehensively study the determinants of communicative and mobility-related spatial segregation, using geo-tagged Twitter data. We constructed weighted spatial networks representing tie strength between geographical areas, then modeled tie formation as a function of socio-economic and demographic dissimilarity between areas. Physical and virtual tie formation were affected by income, age, and race differences, although these effects were smaller by an order of magnitude than the geographical distance effect. Tie formation was more frequent when destination" area had higher median income and lower median age. We hypothesize that physical tie formation is more "costly" than a virtual one resulting in stronger segregation in the physical world. Economic and cultural motives may result in stronger segregation of relatively rich and young populations from their surroundings. Our methodology can help identify types of states that lead to spatial segregation and thus guide planning decisions for reducing its adverse effects."
      PubDate: Tue, 13 Jul 2021 19:00:16 PDT
       
  • Modelling Orebody Structures: Block Merging Algorithms and Block Model
           Spatial Restructuring Strategies Given Mesh Surfaces of Geological
           Boundaries

    • Authors: Raymond Leung
      Abstract: This paper describes a framework for capturing geological structures in a 3D block model and improving its spatial fidelity, including the correction of stratigraphic, mineralisation and other types of boundaries, given new mesh surfaces. Using surfaces that represent geological boundaries, the objectives are to identify areas where refinement is needed, increase spatial resolution to minimise surface approximation error, reduce redundancy to increase the compactness of the model and identify the geological domain on a block-by-block basis. These objectives are fulfilled by four system components which perform block-surface overlap detection, spatial structure decomposition, sub-blocks consolidation and block tagging, respectively. The main contributions are a coordinate-ascent merging algorithm and a flexible architecture for updating the spatial structure of a block model when given multiple surfaces, which emphasises the ability to selectively retain or modify previously assigned block labels. The techniques employed include block-surface intersection analysis based on the separable axis theorem and ray-tracing for establishing the location of blocks relative to surfaces. To demonstrate the robustness and applicability of the proposed block merging strategy in a more narrow setting, it is used to reduce block fragmentation in an existing model where surfaces are not given and the minimum block size is fixed. To obtain further insight, a systematic comparison with octree subblocking subsequently illustrates the inherent constraints of dyadic hierarchical decomposition and the importance of inter-scale merging. The results show the proposed method produces merged blocks with less extreme aspect ratios and is highly amenable to parallel processing. The overall framework is applicable to orebody modelling given geological boundaries, and 3D segmentation more generally, where there is a need to delineate spatial regions using mesh surfaces within a block model.
      PubDate: Tue, 13 Jul 2021 19:00:15 PDT
       
  • GeoComputation 2019 special feature

    • Authors: Antoni Moore et al.
      PubDate: Tue, 13 Jul 2021 19:00:15 PDT
       
  • Route schematization with landmarks

    • Authors: Marcelo de Lima Galvao et al.
      Abstract: Predominant navigation applications make use of a turn-by-turn instructions approach and are mostly supported by small screen devices. This combination does little to improve users' orientation or spatial knowledge acquisition. Considering this limitation, we propose a route schematization method aimed for small screen devices to facilitate the readability of route information and survey knowledge acquisition. Current schematization methods focus on the route path and ignore context information, specially polygonal landmarks (such as lakes, parks, and regions), which is crucial for promoting orientation. Our schematization method, in addition to the route path, takes as input: adjacent streets, point-like landmarks, and polygonal landmarks. Moreover, our schematic route map layout highlights spatial relations between route and context information, improves the readability of turns at decision points, and the visibility of survey information on small screen devices. The schematization algorithm combines geometric transformations and integer linear programming to produce the maps. The contribution of this paper is a method that produces schematic route maps with context information to support the user in wayfinding and orientation.
      PubDate: Tue, 13 Jul 2021 19:00:14 PDT
       
  • Big issues for big data: challenges for critical spatial data analytics

    • Authors: Chris Brunsdon et al.
      Abstract: In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) inference when working with usually biased big data, challenging the assumed inferential superiority of data with observations, n, approaching N, the population n -> N. We also emphasise 2) the need for analyses that answer questions of practical significance or with greater emphasis on the size of the effect, rather than the truth or falsehood of a statistical statement; 3) the need to accept messiness in your data and to document all operations undertaken on the data because of this, in support of openness and reproducibility paradigms; and 4) the need to explicitly seek to understand the causes of bias, messiness etc in the data and the inferential consequences of using such data in analyses, by adopting critical approaches to spatial data science. In particular we consider the need to place individual data science studies in a wider social and economic contexts, along with the role of inferential theory in the presence of big data, and issues relating to messiness and complexity in big data.
      PubDate: Tue, 13 Jul 2021 19:00:13 PDT
       
  • Local modelling: one size does not fit all

    • Authors: A. Stewart Fotheringham
      Abstract: This editorial piece considers what happens when we abandon the concept that models of social processes have global application in favor of a local approach in which context or the influence of 'place' has an important role. A brief history of this local approach to statistical modelling is given, followed by a consideration of its ramifications for understanding societal issues. The piece concludes with futures challenges and prospects in this area.
      PubDate: Tue, 13 Jul 2021 19:00:13 PDT
       
  • Indigeneity and spatial information science

    • Authors: Matt Duckham et al.
      Abstract: Spatial information science has given rise to a set of concepts, tools, and techniques for understanding our geographic world. In turn, the technologies built on this body of knowledge embed certain ways of knowing." This vision paper traces the roots and impacts of those embeddings and explores how they can sometimes be inherently at odds with or completely subvert Indigenous Peoples' ways of knowing. However advancements in spatial information science offer opportunities for innovation whilst working towards reconciliation. We highlight as examples four active research topics in the field to support a call to action for greater inclusion of Indigenous perspectives in spatial information science."
      PubDate: Tue, 13 Jul 2021 19:00:12 PDT
       
  • Inferring movement patterns from geometric similarity

    • Authors: Maike Buchin et al.
      Abstract: Spatial movement data nowadays is becoming ubiquitously available, including data of animals, vehicles and people. This data allows us to analyze the underlying movement. In particular, it allows us to infer movement patterns, such as recurring places and routes. Many methods to do so rely on the notion of similarity of places or routes. Here we briefly survey how research on this has developed in the past 15 years and outline challenges for future work.
      PubDate: Tue, 13 Jul 2021 19:00:11 PDT
       
  • Why are events important and how to compute them in geospatial
           research'

    • Authors: May Yuan
      Abstract: Geospatial research has long centered around objects. While attention to events is growing rapidly, events remain objectified in spatial databases. This paper aims to highlight the importance of events in scientific inquiries and overview general event-based approaches to data modeling and computing. As machine learning algorithms and big data become popular in geospatial research, many studies appear to be the products of convenience with readily adaptable data and codes rather than curiosity. By asking why events are important and how to compute events in geospatial research, the author intends to provoke thinking into the rationale and conceptual basis of event-based modeling and to emphasize the epistemological role of events in geospatial information science. Events are essential to understanding the world and communicating the understanding, events provide points of entry for knowledge inquiries and the inquiry processes, and events mediate objects and scaffold causality. We compute events to improve understanding, but event computing and computability depend on event representation. The paper briefly reviews event-based data models in spatial databases and methods to compute events for site understanding and prediction, for spatial impact assessment, and for discovering events' dynamic structures. Concluding remarks summarize key arguments and comment on opportunities to extend event computability.
      PubDate: Tue, 13 Jul 2021 19:00:11 PDT
       
  • From spatial to platial - the role and future of immersive technologies in
           the spatial sciences

    • Authors: Alexander Klippel
      Abstract: Immersive technologies such as virtual and augmented reality have been part of the technology mindset in computer and geospatial sciences early on. The promise of delivering realistic experiences to the human senses that are not bound by physical reality has inspired generations of scientists and entrepreneurs alike. However, the vision for immersive experiences has been in stark contrast to the ability to deliver at the technology end; the community has battled nuisances such as cybersickness, tethers, and the uncanny valley for the last decades. With the 'final wave' of immersive technologies, we are now able to fulfill a long-held promise and freely and creatively envision how immersive technologies change spatial sciences by creating embodied experiences for geospatial applications. These experiences are not restricted by time or place, nor are they limited to the physical world. This contribution envisions the future of spatial sciences in light of place-like experiences enabled through immersive technologies and their potential to infuse research in the spatial sciences community.
      PubDate: Tue, 13 Jul 2021 19:00:10 PDT
       
  • Integrated science of movement

    • Authors: Urska Demsar et al.
      Abstract: Recent technological advances in movement data acquisition have enabled researchers in many disciplines to study movement at increasingly detailed spatial and temporal scales. Yet there is little overlap in the sharing of methods and models between disciplines, despite similar research objectives and data models. Attempts to bridge this gap are leading towards the establishment of an overarching interdisciplinary science, termed the Integrated Science of Movement. Here we present opportunities and challenges of this process and outline the crucial role that GIScience as a discipline with a focus on space, place, and time can play in the integrated science of movement.
      PubDate: Tue, 13 Jul 2021 19:00:09 PDT
       
  • Thinking spatial

    • Authors: Mohamed F. Mokbel
      Abstract: The systems community in both academia and industry has tremendous success in building widely used general purpose systems for various types of data and applications. Examples include database systems, big data systems, data streaming systems, and machine learning systems. The vast majority of these systems are ill equipped in terms of supporting spatial data. The main reason is that system builders mostly think of spatial data as just one more type of data. Any spatial support can be considered as an afterthought problem that can be supported via on-top functions or spatial cartridges that can be added to the already built systems. This article advocates that spatial data and applications need to be natively supported in special purpose systems, where spatial data is considered as a first class citizen, while spatial operations are built inside the engine rather than on-top of it. System builders should consider spatial data while building their systems. The article gives examples of five categories of systems, namely, database systems, big data systems, machine learning systems, recommender systems, and social network systems, that would benefit tremendously, in terms of both accuracy and performance, when considering spatial data as an integral part of the system engine.
      PubDate: Tue, 13 Jul 2021 19:00:08 PDT
       
  • JOSIS' 10th anniversary special feature: part two

    • Authors: Benjamin Adams et al.
      PubDate: Tue, 13 Jul 2021 19:00:07 PDT
       
  • Cartographic generalization

    • Authors: Monika Sester
      Abstract: This short paper gives a subjective view on cartographic generalization, its achievements in the past, and the challenges it faces in the future.
      PubDate: Tue, 13 Jul 2021 19:00:07 PDT
       
 
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