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Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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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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Manipulation and Analysis01:21

Manipulation and Analysis

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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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Introduction to GIS01:28

Introduction to GIS

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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...
417
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

602
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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Levels of Use of a GIS01:29

Levels of Use of a GIS

264
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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Thematic Layering in GIS01:30

Thematic Layering in GIS

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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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Biospytial: spatial graph-based computing for ecological Big Data.

Juan M Escamilla Molgora1,2, Luigi Sedda3, Peter M Atkinson4

  • 1Lancaster Environment Centre, Lancaster University, Library Avenue, Lancaster, LA1 4YQ, UK.

Gigascience
|May 12, 2020
PubMed
Summary
This summary is machine-generated.

Biospytial is an open-source engine that uses graph theory to integrate and analyze large ecological datasets. It simplifies complex data joining for biodiversity research and species distribution modeling.

Keywords:
big ecological databiodiversity informaticsecological knowledge engineopen sciencespatial data infrastructure

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Area of Science:

  • Ecology
  • Data Science
  • Bioinformatics

Background:

  • Exponential growth in environmental and ecological data presents integration challenges.
  • Open data initiatives offer opportunities for knowledge synthesis amidst environmental crises.

Purpose of the Study:

  • To introduce Biospytial, a modular open-source knowledge engine.
  • To enable import, organization, analysis, and visualization of big spatial ecological datasets.

Main Methods:

  • Utilizes a hybrid graph-relational approach for data storage and access.
  • Employs graph theory and a graph database for semantic structures.
  • Integrates tabular and geospatial data in a spatial relational database.

Main Results:

  • Demonstrates Biospytial's capability with species occurrence, taxonomic, and climate data.
  • Constructed a knowledge graph of the Tree of Life within an environmental grid.
  • Analyzed co-occurrence of threatened species with jaguars (Panthera onca).

Conclusions:

  • Biospytial simplifies complex data joining and merging from diverse sources.
  • Its scalable and modular design facilitates efficient handling of large ecological datasets.
  • Enables novel ecological analyses, biodiversity syntheses, and species distribution models.