1 / 10

Understanding Types of Data Reduction Techniques in GIS

Explore various types of data forms in GIS such as points, polygons, polylines, rasters, and volumes, along with methods like clustering and gridding to reduce data size while preserving vital information. Learn about file formats, software, and tools for effective data management.

shaeleigh-fuller
Télécharger la présentation

Understanding Types of Data Reduction Techniques in GIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Types of Data • Points: Occurrences, Surveys • Polygons: Census, Soils, Refuges • Polylines: ? • Rasters: Remotely Sensed, Models • Volumes: • Marine data • 2D + Time: • Climate (PRISM) • 4D (3D + Time): • Climate, Currents marineemlab.ucsd.edu

  2. Problems • Software/methods do not all support large datasets • Performance (i.e. time to develop methods and get final results) • Need to “reduce” the size of the data while maintaining the important information • Or, get a lot of computers • (more on this later)

  3. File Formats • CSV, Txt: Points • Shapefiles • GeoDatabases • “Las” for LiDAR • HDF and NetCDF: • General hierarchical data formats • “CF” standard for NetCDF data • ArcGIS supports NetCDF

  4. Data Reduction • Point Methods: • Clusters: group related data (spatially, temporally, categorically) • Gridding: find density, mean values • Windowing: moving a “window” over the data (does not reduce processing)

  5. Polygons • Generalization/Simplification • Reduce resolution • Remove less critical polygons Soil Data for Czech Republic, eusoils.jrc.ec.europa.eu

  6. Temporal • Group by: • Month, Season, Decade • Model “trends”

  7. Software • ArcGIS will work up to a point • Then, we have to program • Python: • TXT and CSV files • Maybe for rasters, ND data • Java: • Effectively no limits • High performance

  8. Databases • The simpler the data is, the faster it is to access: • Small, simple: • Text files • Small to Medium, complicated: • SQL Databases • Large: • Text and binary files • Avoid large, complicated data

  9. BlueSpray • Java-based GIS application • Requires Java 7 • Built to be: • High-performance • Extensible • Portable • Takes advantage of RAM, processors • Easy to install and use • Owned by SchoonerTurtles, Inc. • Available at www.schoonerturtles.com • In early beta

  10. Graphics 16 15 10 12 23 27 14 19 29 30 18 22 34 32 21 25

More Related