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1.
A spatially explicit reinforcement learning model for geographic knowledge graph summarization 总被引：1，自引：0，他引：1
Web‐scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non‐spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned. 相似文献
2.
This article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases. 相似文献
3.
Geospatial Ontology Development and Semantic Analytics 总被引：3，自引：0，他引：3
I Budak Arpinar Amit Sheth Cartic Ramakrishnan E Lynn Usery Molly Azami Mei-Po Kwan 《Transactions in GIS》2006,10(4):551-575
Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP. 相似文献
4.
A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local Density 
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下载免费PDF全文 Spatial anomalies may be single points or small regions whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a S patial A nomaly P oints and R egions D etection method using multi‐constrained graphs and local density ( SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non‐spatial attributes of adjacent spatial entities, two levels of non‐spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub‐graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real‐life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm. 相似文献
5.
介绍了空间数据三维可视化基本原理及运用IMAGIS进行三维可视化设计与开发的过程。同时,以鞍钢厂区为例,在其基础地理数据库的基础上,应用VB和IMAGIS进行二次开发,实现厂区景观的三维可视化。 相似文献
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Stefan Lang Stefan Kienberger Dirk Tiede Michael Hagenlocher Lena Pernkopf 《制图学和地理信息科学》2014,41(3):214-226
The design of methods and tools to build adequate representations of complex geographical phenomena in a way that spatial patterns are emphasized is one of the core objectives of GIScience. In this paper, we build on the concept of geons as a strategy to represent and analyze latent spatial phenomena across different geographical scales (local, national, regional) incorporating domain-specific expert knowledge. Focusing on two types, we illustrate and exemplify how geons are generated and explored. So-called composite geons represent functional land-use classes, required for regional planning purposes. They are created via class modeling to translate interpretation schemes from mapping keys. Integrated geons, on the other hand, address abstract, yet policy-relevant phenomena such as societal vulnerability to hazards. They are delineated by regionalizing continuous geospatial data sets representing relevant indicators in a multidimensional variable space. Using the geon approach, we create spatially exhaustive sets of units, scalable to the level of policy intervention, homogenous in their domain-specific response, and independent from any predefined boundaries. From a GIScience perspective, we discuss either type of geons in a semantic hierarchy of geographic information constructs. Despite its validity for decision-making and its transferability across scales and application fields, the delineation of geons requires further methodological research to assess their statistical and conceptual robustness. 相似文献
9.
The dynamics of the earth and its inhabitants have become a core topic and focus in research in the spatial sciences. The spatio-temporal data avalanche challenges researchers to provide efficient and effective means to process spatio-temporal data. It is of vital importance to develop mechanisms that allow for the transition of data not only into information but also into knowledge. Knowledge representation techniques from artificial intelligence play an important role in laying the foundations for theories dealing with spatio-temporal data. Specifically, the advances in the area of qualitative spatial representation and reasoning (QSTR) have led to promising results. Categorical distinctions of spatio-temporal information identified by QSTR calculi potentially correspond to those relevant to humans. This article presents the first behavioral evaluation of qualitative calculi modeling geographic events associated with scaling deformations of entities, that is, changes in size by either expansion or contraction. Examples of such dynamics include a lake flooding its surroundings or an expanding oil spill in the ocean. We compare four experiments using four different semantic domains. Each domain consists of two spatially extended entities: one entity is undergoing scaling deformations while the other is static. We kept the formal QSTR characterization, which are paths through a topologically defined conceptual neighborhood graph, identical across all semantic domains. Our results show that for geographic events associated with scaling deformations (a) topological relations are not equally salient cognitively; (b) domain semantics has an influence on the conceptual salience of topological relations. 相似文献
10.
The Role of Knowledge Representation in Geographic Knowledge Discovery: A Case Study 总被引：1，自引：0，他引：1
With the advent of massive, heterogeneous geographic datasets, data mining and knowledge discovery in databases (KDD) have become important tools in deriving meaningful information from these data. In this paper, we discuss how knowledge representation can be employed to significantly enhance the power of the knowledge discovery process to uncover patterns and relationships. We suggest that geographic data models that support knowledge discovery must represent both observational data and derived knowledge. In addition, knowledge representation in the context of KDD must support the iterative and interactive nature of the knowledge discovery process to enable the analyst to iteratively apply, and revise the parameters of, specific analytical techniques. Our approach to knowledge representation and discovery is demonstrated through a case study that focuses on the identification and analysis of storms and other related climate phenomena embedded within a spatio‐temporal data set of meteorological observations. 相似文献
11.
《The Cartographic journal》2013,50(3):230-241
Map data at smaller scales than their source can result in spatial conflict, whereby map symbols become too close, or overlaid. Server map generalisation operators may be applied to solve this problem, including displacement. In this paper, we show how an optimisation algorithm, the snake algorithm, was used to displace multiple objects in order to resolve spatial conflicts and maintain important spatial relationships between objects during displacement. Two principles based on the snake algorithm are proposed in this paper. First, the truss structure mirroring spatial proximity relationships between buildings and between building and road is formed based on the weighted proximity graph derived from constrained Delaunay triangulations (CDT) in each map partition. In the weighted proximity graph, each connecting line is determined as a snake and as an element unit to assemble the global stiffness matrix in snake algorithm. Second, a buffer method that calculates force between a building and a road (or other linear features) or between pair of buildings is adopted in the snake algorithm. This avoids the imbalance phenomenon caused by different force calculation methods during the displacement. The feasibility of the approach is demonstrated in obtaining real geographic data. Finally, the results are cartographically usable and in particular, the spatial relationships between objects are preserved. 相似文献
12.
Gengchen Mai Krzysztof Janowicz Ling Cai Rui Zhu Blake Regalia Bo Yan Meilin Shi Ni Lao 《Transactions in GIS》2020,24(3):623-655
Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. Most models that do consider space primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location‐aware KG embedding model called SE‐KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query–answer pairs called DBGeo to evaluate the performance of SE‐KGE in comparison to multiple baselines. Evaluation results show that SE‐KGE outperforms these baselines on the DBGeo data set for the geographic logic query answering task. This demonstrates the effectiveness of our spatially‐explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin. 相似文献
13.
Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non‐overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph‐partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. 相似文献
14.
为了能快速计算室内导航路径,必须使用简单的数据结构表达室内复杂的路径导航信息,室内三维连通图就是一种较好的手段。但是传统的室内精细建模重在几何模型的构建和纹理数据采集,缺乏室内三维连通图的构建。针对广泛存在室内几何模型提出一种基于体素的室内三维连通图自动生成算法,对建筑物内部进行分割和填充,将室内空间划分为离散的导航空间,通过自动语义关联提取连通关系,最终生成室内空间三维连通图。 相似文献
15.
地勘工程3维空间数据模型及其数据结构设计 总被引：44，自引：1，他引：44
3维现象随着研究领域的不同，描述空间实体的方法存在较大差异，不可能设计一种数据模型来适合所有的应用领域，应根据研究领域空间实体分布特征，设计出专用的3维空间数据模型。本文对传统的3维数据模型进行了分析，以地质勘探工程的各种3维空间对象为研究背景，提出了适合地质勘探工程的矢量与栅格集成的面向对象混合数据模型，以复杂体－体－面－线－点对象之间的逻辑关系来建立对象之间的拓扑关系，设计了部分对象的数据结构，并以一玢岩型铁矿床为例，对数据结构进行了描述。 相似文献
16.
Understanding the structure and evolution of family networks embedded in space and time is crucial for various fields such as disaster evacuation planning and provision of care to the elderly. Computation and visualization can potentially play a key role in analyzing and understanding such networks. Graph visualization methods are effective in discovering network patterns; however, they have inadequate capability in discovering spatial and temporal patterns of connections in a network especially when the network exists and changes across space and time. We introduce a measure of family connectedness that summarizes the dynamic relationships in a family network by taking into account the distance (how far individuals live apart), time (the duration of individuals’ coexistence within a neighborhood), and the relationship (kinship or kin proximity) between each pair of individuals. By mapping the family connectedness over a series of time intervals, the method facilitates the discovery of hot spots (hubs) where family connectedness is strong and the changing patterns of such spots across space and time. We demonstrate our approach using a data set of nine families from the US North. Our results highlight that family connectedness reflects changing demographic processes such as migration and population growth. 相似文献
17.
局部空间同位模式挖掘旨在揭示多类地理事件在异质环境下的共生共存规律。已有的方法一方面需要模式筛选的频繁度阈值参数,另一方面需要区域探测的划分参数或聚类参数,参数的不合理设置会导致挖掘结果不可靠甚至出现错误。因此,提出了一种显著局部空间同位模式自动探测方法。首先,基于空间统计思想,采用非参数模式重建方法对空间同位模式进行显著性判别,将全局非显著空间同位模式作为进一步局部探测的候选模式;然后,借助自适应空间聚类方法提取每个候选模式的热点区域;最后,通过不断生长并测试每个热点区域,界定显著局部空间同位模式的有效边界,即空间影响域。通过实验与比较发现,该方法能够客观且有效判别空间同位模式的显著性,并且自适应地提取局部同位模式的空间分布结构,降低了现有方法参数设置的主观性。 相似文献
18.
Analysis of human spatial behavior in a GIS environment: Recent developments and future prospects 总被引：1，自引：0，他引：1
Mei-Po Kwan 《Journal of Geographical Systems》2000,2(1):85-90
In recent years, comprehensive geographic data sets of metropolitan areas and individual-level, georeferenced data are becoming
more available to social scientists. At the same time, tools for performing spatial analysis in a GIS environment have also
become more available. These developments provide many new opportunities for the analysis and theoretical understanding of
disaggregate human spatial behavior. This paper examines how these developments may enable the researcher to represent complex
urban and cognitive environments more realistically, and to overcome the limitations of aggregate spatial data framework.
It explores their implications for the theoretical and methodological development in geography and other social science disciplines. 相似文献
19.
建筑物作为城市中的重要地物,分析其群组模式对地图综合、导航定位、市政规划等具有重要作用。建筑物群组模式分析目前主要有基于规则的方法和基于机器学习的方法两种。基于规则的方法和基于传统机器学习分类器的方法均需要大量的人工处理过程。近年来兴起的深度学习特别是图卷积神经网络前期无需人工处理,因此提高了建筑物群组模式分析的自动化程度。传统的图卷积神经网络模型在训练深层网络时易出现退化问题,提取深层特征困难。为解决此问题,本文引入了图残差神经网络模型用于建筑物群组的模式分类。首先使用道路和河流等作为约束条件,利用K-means方法对建筑物进行聚类;然后根据Bertin视觉变量计算对应的建筑物特征指标,在每个建筑物群组中以建筑物质心为节点,连接节点的最小生成树作为边,构建建筑物群组图结构;最后将得到的图结构数据输入图残差神经网络进行训练,得到规则和不规则两种建筑物群组模式。试验结果表明,该模型较好地解决了传统图卷积神经网络模型的退化问题,并取得了更高的精度。 相似文献
20.
Dogus Guler 《Transactions in GIS》2023,27(8):2252-2277
To plan future land use, zoning plans (i.e., spatial plans) are prepared to get the most out of land for both the public and the government. These plans manifest which facilities can be built and where they can be built on land based on defined requirements such as building height and road length. The Land Administration Domain Model (LADM) is well-known and widely used standard for describing Rights, Restrictions, and Responsibilities (RRRs) with respect to land and buildings. The next version of the standard will contain the Spatial Plan Information (SPI) part to enable better land-use planning. Three-dimensional (3D) land-use planning has gained attention to delineate detailed requirements inclusively and allow different spatial analysis that provides a basis for decisions in the planning. Data standards pertaining to 3D geoinformation are vital to put into practice 3D spatial planning. To this extent, CityJSON is proposed for the effortless and efficient use of 3D city models. This article thus first aims to extend the CityJSON schema based on the proposed SPI part of the LADM such that it allows modeling, storing, visualizing, and utilizing the features and attributes required for the implementation of 3D spatial planning. Then, the usability of the proposed extension schema is demonstrated by the real-world use cases that benefit from the exemplary CityJSON files that are created based on approved zoning plans in the country. The results of this study show that there is an important opportunity coming from the integration of international standards that enables semantic information along with their spatial counterparts within 3D spatial planning. 相似文献