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Thematic Layering in GIS01:30

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In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
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Selected Data About Geographic Locations01:25

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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...
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Levels of Use of a GIS01:29

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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...
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Introduction to GIS01:28

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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...
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GIS Software, Hardware, and Sources of GIS Data01:23

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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...
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Manipulation and Analysis01:21

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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This study introduces a solution to integrate Open Data portals with NGSI-LD standards, enhancing data discoverability and interoperability. The approach ensures comprehensive, accessible, and FAIR data, validated with IoT data on the European Data Portal.

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Area of Science:

  • Data Science
  • Information Systems
  • Internet of Things

Background:

  • Massive data generation necessitates improved discoverability, accessibility, and interoperability.
  • Open Data portals enhance discoverability but lack data standardization.
  • NGSI-LD standard promotes data modeling and accessibility harmonization.

Purpose of the Study:

  • To bridge Open Data portals and NGSI-LD standards for improved data management.
  • To aggregate data into coherent datasets with high-quality descriptions.
  • To ensure data comprehensiveness, interoperability, and accessibility.

Main Methods:

  • Developed a solution integrating Open Data portal descriptions with NGSI-LD data.
  • Aggregated disparate data into structured, coherent datasets.
  • Generated comprehensive metadata descriptions adhering to interoperability standards.

Main Results:

  • Successfully exposed Internet of Things (IoT) data in NGSI-LD format via the European Data Portal (EDP).
  • Generated dataset descriptions achieved excellent rankings in the EDP's Metadata Quality Assessment.
  • Demonstrated high performance in Findability, Accessibility, Interoperability, and Reusability (FAIR) data principles.

Conclusions:

  • The proposed solution effectively combines the benefits of Open Data portals and NGSI-LD.
  • Achieved enhanced data discoverability, accessibility, and interoperability for diverse data sources.
  • Validated the approach's efficacy through a real-world implementation and FAIR data principles assessment.