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相关概念视频

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

43
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
43
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

92
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
92
Levels of Use of a GIS01:29

Levels of Use of a GIS

71
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
71
Introduction to GIS01:28

Introduction to GIS

87
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
87
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
77
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Pattern-based Search of Epigenomic Data Using GeNemo
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一个用于联合GeoSPARQL查询的地理空间源选择器.

Antonis Troumpoukis1, Stasinos Konstantopoulos1, Nefeli Prokopaki-Kostopoulou2

  • 1Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) Demokritos, Ag. Paraskevi, 15341, Greece.

Open research Europe
|August 30, 2023
PubMed
概括

本研究介绍了一种地理空间总结方法,以改进联合GeoSPARQL查询处理. 通过总结数据源范围,它提高了查询链接的地理空间数据的效率和有效性.

关键词:
联合和分布式查询处理.在 GeoSPARQL 查询处理过程中,使用 GeoSPARQL 来处理查询.链接的地理空间数据源代码选择 选择 选择

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科学领域:

  • 语义网络技术 语义网络技术
  • 地理空间链接数据
  • 联合查询处理 联合查询处理

背景情况:

  • 地缘空间链接数据将丰富的描述与地理位置集成在一起,扩大语义网络的功能.
  • 在Semantic Web技术中充分整合地理空间数据仍然存在挑战,特别是在联合查询处理中.

研究的目的:

  • 探索注释数据源与边界多边形总结空间延伸.
  • 将这些摘要作为在联合查询中选择来源的标准.
  • 为了提高联合GeoSPARQL查询处理的有效性.

主要方法:

  • 用边界多边形标注数据源,表示空间资源范围.
  • 实施使用这些多边形的源选择方法,以减少查询的源.
  • 对基线进行不同总结精度的方法评估.

主要成果:

  • 更复杂的空间总结增加了源选择时间,但提高了精度.
  • 减少的规划和执行时间部分或完全抵消了更慢的源选择.
  • 联合源受到保护免受不必要的查询,提高了整体效率.

结论:

  • 拟议的源选择方法显著提高了联合GeoSPARQL查询处理的有效性.
  • 该方法使用农业环境数据对作物类型和水资源的可用性进行验证.
  • 这种方法优化了在语义网络中对链接的地理空间数据的查询.