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Related Concept Videos

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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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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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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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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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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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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Enhancing discovery in spatial data infrastructures using a search engine.

Paolo Corti1, Athanasios Tom Kralidis2, Benjamin Lewis1

  • 1Center for Geographic Analysis, Harvard University, Cambridge, MA, USA.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary
This summary is machine-generated.

A search engine was integrated into a spatial data infrastructure (SDI) to improve discovery of geospatial datasets. This enhancement allows users to find relevant spatial information more efficiently using advanced search capabilities.

Keywords:
Catalogue Service for the WebData discoveryGeoNodeGeoportalMetadataSearch engineSolrSpatial Data InfrastructureWorldMappycsw

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Area of Science:

  • Geographic Information Science
  • Computer Science
  • Data Management

Background:

  • Spatial Data Infrastructures (SDI) facilitate the use of geospatial information.
  • Catalogue services, often based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard, are crucial for managing and discovering metadata within an SDI.
  • Existing catalogue services may have limitations in efficiently searching large collections of geospatial datasets.

Purpose of the Study:

  • To enhance the search capabilities of a spatial data infrastructure (SDI) by integrating a dedicated search engine.
  • To improve the discovery, querying, and management of metadata for large collections of geospatial datasets.
  • To leverage advanced search engine functionalities to provide more relevant and reliable content to SDI users.

Main Methods:

  • Integration of a search engine into the existing spatial data infrastructure (SDI) stack.
  • Re-engineering the search component of the Harvard WorldMap SDI, which is based on the GeoNode platform.
  • Utilizing standard search operations of the catalogue service in conjunction with the new search engine's abilities.

Main Results:

  • Improved search performance and relevance for geospatial datasets within the SDI.
  • Enhanced ability to discover spatial datasets through metadata using advanced search techniques.
  • Successful addition of a search engine to the SDI to augment the functionality of the CSW catalogue.

Conclusions:

  • Integrating a search engine significantly improves the discoverability of geospatial data within an SDI.
  • The re-engineered search component provides users with more efficient and effective access to spatial information.
  • This approach demonstrates a viable method for enhancing existing SDIs to better serve user needs for spatial data discovery.