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Data Acquisition and Management�Unit 1 – GIS Data Representation�� � �

Empowering Colleges:

Growing the Workforce

Based upon work supported by the National Science Foundation under Grants DUE 1304591, DUE 164409, DUE 1700496, DUE 1937177, Due 1938717 DUE 1937237, 2030206 and 2015927. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

“Domestic Wells in the US” | Source: USGS

Author: Wing Cheung

Title: Professor, Palomar College

Assistant Director, GeoTech Center

Email: wcheung@palomar.edu

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What is spatial data?

  • Locational information
    • Coordinates
    • Address database

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Source: Global Shark Attack Files

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What is attribute data?

  • Nonspatial/descriptive information
    • Price of a home
    • Size of a home

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Source: Redfin.com

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Software

  • Database management system (DBMS)
    • Software used to manipulate and manage spatial and attribute data

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Source: bmcblogs

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Hardware and methods

  • Tangible equipment
    • Drones, GPS, Server
  • Theory-driven procedures
  • Replicable results

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Source: Sven Teschke, CC BY-SA 3.0 de

Source: Alexander P Kapp, CC BY-SA 2.0

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Vector data model

  • Vector data
    • Points, lines, polygons
    • Discrete objects
    • Feature dataset or standalone

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Source: EpitomeGeo

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Where is my data?

  • Geodatabase
    • Feature dataset*
      • Line feature class
      • Relationship class
      • Polygon feature class
    • Standalone point feature class
    • Standalone line feature class
    • Standalone table

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Quick note on the feature dataset

  • Same projection and coordinate system
  • Different levels of complexity
    • Simple features
    • Networks, relationships, topology

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Source: What-When-How

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Raster data model

  • Raster data
    • Cells/pixels
    • Images, continuous phenomenon
    • Raster dataset

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Source: EpitomeGeo

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Where is my data?

  • Geodatabase
    • Feature dataset*
      • Line feature class
      • Relationship class
      • Polygon feature class
    • Raster Dataset
    • Standalone point feature class
    • Standalone line feature class
    • Standalone table

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Raster dataset

  • Cell value
    • Class/category
    • Quantity: light reflectance, elevation, rainfall

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Source: NREL

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Other data models

  • Triangulated irregular network (TIN)
    • Non-overlapping triangles
    • Continuous surfaces

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Conclusion

  • Choosing between data model
    • Availability of data
    • Precision needed
    • Types of analysis

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Source: NREL

Source: Solar Power Rocks

Raster (top): each cell shows the amount of solar radiation received in a given location

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Vector (bottom): each polygon is assigned a grade based on the state’s solar power potential

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See GeoTech Center website (https://geotechcenter.org) �for additional Model Courses and other curriculum resources. �This Model Course Is Licensed Under Creative Commons CC BY-SA ����� By: https://creativecommons.org/licenses/by-sa/4.0/ ��Note: some content is a derivative of other CC authors���

Author: Wing Cheung

Title: Professor, Palomar College

Assistant Director, GeoTech Center

Email: wcheung@palomar.edu

​