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1دورية أكاديمية
المؤلفون: Xiankang Xu, Jian Hao, Jingwei Shen
المصدر: ISPRS International Journal of Geo-Information, Vol 13, Iss 6, p 173 (2024)
مصطلحات موضوعية: school location, knowledge graph, territorial spatial planning, topological relationship, Geography (General), G1-922
الوصف: The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning.
وصف الملف: electronic resource
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2دورية أكاديمية
المؤلفون: Lianlian He, Hao Li, Rui Zhang
المصدر: ISPRS International Journal of Geo-Information, Vol 13, Iss 4, p 106 (2024)
مصطلحات موضوعية: knowledge graph, place entity matching, location-based service, place data, conflation, Geography (General), G1-922
الوصف: Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.
وصف الملف: electronic resource
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3دورية أكاديمية
المؤلفون: Xinya Lei, Yuewei Wang, Wei Han, Weijing Song
المصدر: ISPRS International Journal of Geo-Information, Vol 13, Iss 3, p 88 (2024)
مصطلحات موضوعية: urban storm surge hazard, knowledge graph, spatio-temporal framework, hazard event ontology model, geospatial information modeling, Geography (General), G1-922
الوصف: Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency response. However, current knowledge graph representations face the challenge of integrating multi-source information with various spatial and temporal scales. To address this challenge, we propose a new information model for storm surge hazard events, involving a two-step process. First, a hazard event ontology is designed to model the components and hierarchical relationships of hazard event information. Second, we utilize multi-scale time segment integer coding and geographical coordinate subdividing grid coding to create a spatio-temporal framework, for modeling spatio-temporal features and spatio-temporal relationships. Using the 2018 typhoon Mangkhut storm surge event in Shenzhen as a case study and the hazard event information model as a schema layer, a storm surge event knowledge graph is constructed, demonstrating the integration and formal representation of heterogeneous hazard event information and enabling the fast retrieval of disasters in a given spatial or temporal range.
وصف الملف: electronic resource
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4دورية أكاديمية
المؤلفون: Ce Liang, Jun Zhu, Jinbin Zhang, Qing Zhu, Jingyi Lu, Jianbo Lai, Jianlin Wu
المصدر: ISPRS International Journal of Geo-Information, Vol 13, Iss 3, p 78 (2024)
مصطلحات موضوعية: geographic digital twins, knowledge graph, tunnel excavation profile, geometric deformation diagnosis, fusion visualization, Geography (General), G1-922
الوصف: It is essential to establish a digital twin scene, which helps to depict the dynamically changing geographical environment accurately. Digital twins could improve the refined management level of intelligent tunnel construction; however, research on geographical twin models primarily focuses on modeling and visual description, which has low analysis efficiency. This paper proposes a knowledge-guided intelligent analysis method for the geometric deformation of tunnel excavation profile twins. Firstly, a dynamic data-driven knowledge graph of tunnel excavation twin scenes was constructed to describe tunnel excavation profile twin scenes accurately. Secondly, an intelligent diagnosis algorithm for geometric deformation of tunnel excavation contour twins was designed by knowledge guidance. Thirdly, multiple visual variables were jointly used to support scene fusion visualization of tunnel excavation profile twin scenes. Finally, a case was selected to implement the experimental analysis. The experimental results demonstrate that the method in this article can achieve an accurate description of objects and their relationships in tunnel excavation twin scenes, which supports rapid geometric deformation analysis of the tunnel excavation profile twin. The speed of geometric deformation diagnosis is increased by more than 90% and the cognitive efficiency is improved by 70%. The complexity and difficulty of the deformation analysis operation are reduced, and the diagnostic analysis ability and standardization of the geographic digital twin model are effectively improved.
وصف الملف: electronic resource
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5دورية أكاديمية
المؤلفون: Mingkang Da, Teng Zhong, Jiaqi Huang
المصدر: ISPRS International Journal of Geo-Information, Vol 12, Iss 10, p 403 (2023)
مصطلحات موضوعية: indoor fire, emergency evacuation, spatio-temporal process, domain ontology, knowledge representation model, knowledge graph, Geography (General), G1-922
الوصف: Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan ‘5.25’ Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation.
وصف الملف: electronic resource
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6دورية أكاديمية
المؤلفون: Ranran Tang, Jun Zhu, Ying Ren, Yongzhe Ding, Jianlin Wu, Yukun Guo, Yakun Xie
المصدر: ISPRS International Journal of Geo-Information, Vol 12, Iss 10, p 424 (2023)
مصطلحات موضوعية: mountain highway, digital twin scene, knowledge graph, spatial semantic constraint, fusion expression, Geography (General), G1-922
الوصف: Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.
وصف الملف: electronic resource
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7دورية أكاديمية
المؤلفون: Xuhui Zeng, Shu Wang, Yunqiang Zhu, Mengfei Xu, Zhiqiang Zou
المصدر: ISPRS International Journal of Geo-Information, Vol 11, Iss 12, p 625 (2022)
مصطلحات موضوعية: recommendation system, geospatial artificial intelligence, geographical knowledge graph, knowledge graph convolutional networks, countryside ecological patterns, Geography (General), G1-922
الوصف: The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.
وصف الملف: electronic resource
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8دورية أكاديمية
المؤلفون: Wenwen Li, Sizhe Wang, Xiao Chen, Yuanyuan Tian, Zhining Gu, Anna Lopez-Carr, Andrew Schroeder, Kitty Currier, Mark Schildhauer, Rui Zhu
المصدر: ISPRS International Journal of Geo-Information, Vol 12, Iss 3, p 112 (2023)
مصطلحات موضوعية: knowledge graph, visualization, cyberinfrastructure, semantics, visual analytics, humanitarian aid, Geography (General), G1-922
الوصف: The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative features of this solution. First, a location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready. Second, expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph. Third, a scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid.
وصف الملف: electronic resource
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9دورية أكاديمية
المؤلفون: Xiaorui Yang, Weihong Li, Yebin Chen, Yunjian Guo
المصدر: ISPRS International Journal of Geo-Information, Vol 11, Iss 11, p 561 (2022)
مصطلحات موضوعية: COVID-19, spatial relationship, knowledge graph visualization, relationship extraction, Geography (General), G1-922
الوصف: The outbreak of COVID-19 (coronavirus disease 2019) has generated a large amount of spatiotemporal data. Using a knowledge graph can help to analyze the transmission relationship between cases and locate the transmission path of the pandemic, but researchers have paid little attention to the spatial relationships between geographical entities related to the pandemic. Therefore, we propose a method for constructing a pandemic situation knowledge graph of COVID-19 that considers spatial relationships. First, we created an ontology design of the pandemic data in which spatial relationships are considered. We then constructed a non-spatial relationships extraction model based on BERT and a spatial relationships extraction model based on spatial analysis theory. Second, taking the pandemic and geographic data of Guangzhou as an example, we modeled a pandemic corpus. We extracted entities and relationships based on this model, and we constructed a pandemic situation knowledge graph that considers spatial relationships. Finally, we verified the feasibility of using this method as a visualization exploratory tool in the analysis of spatial characteristics, pandemic development situation, case sources, and case relationships analysis of pandemic-related areas.
وصف الملف: electronic resource
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10دورية أكاديمية
المؤلفون: Zongcai Huang, Peiyuan Qiu, Li Yu, Feng Lu
المصدر: ISPRS International Journal of Geo-Information, Vol 11, Iss 9, p 493 (2022)
مصطلحات موضوعية: geographic knowledge graph, relation completion, representation learning, similarity network, relation prediction, Geography (General), G1-922
الوصف: Geographic relation completion contributes greatly to improving the quality of large-scale geographic knowledge graphs (GeoKGs). However, the internal features of a GeoKG used in large-scale GeoKGs embedding are often limited by the weak connectivity between geographic entities (geo-entities). If there is no proper choice in the method of external semantic enhancement, this will often interfere with the representation and learning of the KG. Therefore, we here propose a geographic relation (geo-relation) prediction model based on multi-layer similarity enhanced networks for geo-relations completion (MSEN-GRP). The MSEN-GRP comprises three parts: enhancer, encoder, and decoder. The enhancer constructs semantic, spatial, structural, and attribute-similarity networks for geo-entities, which can explicitly and effectively enhance the implicit semantic associations between existing geo-entities. The encoder can obtain the long path relation dependency characteristics of geo-entities using a mixed-path sampling strategy and can support different optimization schemes for external semantic enhancement. Geo-relations prediction experiments show that the mean reciprocal ranking of this method is significantly higher than those of the traditional TransE DisMult and methods, and Hits@10 is improved by up to 57.57%. Furthermore, the spatial-similarity network has the most significant enhancement effect on geo-relations prediction. The proposed method provides a new way to perform relation completion in sparse GeoKGs.
وصف الملف: electronic resource
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