Data Management

Data management involves the theories and techniques for managing the entire data lifecycle, from data collection to data format conversion, from data storage to data sharing and retrieval, to data provenance, data quality control and data curation for long-term data archival and preservation.

Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized

 

Spatial Databases Genealogical Relationships, Linkage, and Inheritance Georeferencing Systems
Spatial Database Management Systems Geospatial Data Conflation Approximating the Earth's Shape with Geoids
Use of Relational DBMSs Standardization & Exchange Specifications Geographic Coordinate Systems
Object-Oriented DBMSs Spatial Access Methods Planar Coordinate Systems
Extensions of the Relational DBMS Data Retrieval Methods Tesselated Referencing Systems
Topological Relationships Spatial Indexing Linear Referencing Systems
Database Administration Space-driven Structures: Grid, linear quadtree, and z-ordering tree files Vertical Datums
Conceptual Data Models Data-driven structures: R-trees and cost models Horizontal Datums
Logical Data Models Modeling Unstructured Spatial Data Georegistration
Physical Data Models Modeling Semi-Structured Spatial Data Map Projections
NoSQL Databases Query Processing  
Problems with Large Spatial Databases Optimal I/O Algorithms Spatial Data Infrastructures
Representations of Spatial Objects Spatial Joins Metadata
Raster Data Models Complex Queries Content Standards
Vector Data Models Spatial Data Quality Data Warehouses
Topological Models Spatial Data Uncertainty Spatial Data Infrastructures
Spaghetti Models Modeling Uncertainty U.S. National Spatial Data Infrastructure
Network Models Error-based Uncertainty Common Ontologies for Spatial Data & Their Applications
Modeling 3D Entities Vagueness  
Fields in Space and Time Mathematical Models of Vagueness: Fuzzy and Rough sets   
Fuzzy Models    
Triangulated Irregular Network Models    

 

DM-07 - The Raster Data Model

The raster data model is a widely used method of storing geographic data. The model most commonly takes the form of a grid-like structure that holds values at regularly spaced intervals over the extent of the raster. Rasters are especially well suited for storing continuous data such as temperature and elevation values, but can hold discrete and categorical data such as land use as well.  The resolution of a raster is given in linear units (e.g., meters) or angular units (e.g., one arc second) and defines the extent along one side of the grid cell. High (or fine) resolution rasters have comparatively closer spacing and more grid cells than low (or coarse) resolution rasters, and require relatively more memory to store. Active research in the domain is oriented toward improving compression schemes and implementation for alternative cell shapes (such as hexagons), and better supporting multi-resolution raster storage and analysis functions.

DM-12 - The spaghetti model
  • Identify a widely-used example of the spaghetti model (e.g., AutoCAD DWF, ESRI shapefile)
  • Write a program to read and write a vector data file using a common published format
  • Explain the conditions under which the spaghetti model is useful
  • Explain how the spaghetti data model embodies an object-based view of the world
  • Describe how geometric primitives are implemented in the spaghetti model as independent objects without topology
DM-13 - The topological model
  • Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)
  • Describe the integrity constraints of integrated topological models (e.g., POLYVRT)
  • Discuss the historical roots of the Census Bureau’s creation of GBF/DIME as the foundation for the development of topological data structures
  • Explain why integrated topological models have lost favor in commercial GIS software
  • Evaluate the positive and negative impacts of the shift from integrated topological models
  • Discuss the role of graph theory in topological structures
  • Exemplify the concept of planar enforcement (e.g., TIN triangles)
  • Demonstrate how a topological structure can be represented in a relational database structure
  • Explain the advantages and disadvantages of topological data models
  • Illustrate a topological relation
DM-10 - The Triangulated Irregular Network (TIN) model
  • Describe how to generate a unique TIN solution using Delaunay triangulation
  • Describe the architecture of the TIN model
  • Construct a TIN manually from a set of spot elevations
  • Delineate a set of break lines that improve the accuracy of a TIN
  • Describe the conditions under which a TIN might be more practical than GRID
  • Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application
DM-28 - Topological relationships
  • Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect
  • List the possible topological relationships between entities in space (e.g., 9-intersection) and time
  • Use methods that analyze topological relationships
  • Recognize the contributions of topology (the branch of mathematics) to the study of geographic relationships
  • Describe geographic phenomena in terms of their topological relationships in space and time to other phenomena
DM-30 - Vagueness
  • Compare and contrast the meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous
  • Describe the cognitive processes that tend to create vagueness
  • Recognize the degree to which vagueness depends on scale
  • Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena
  • Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields, and discord and non-specificity
  • Identify the hedges used in language to convey vagueness
  • Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts
  • Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable
DM-51 - Vertical datums
  • Explain how a vertical datum is established
  • Differentiate between NAVD 29 and NAVD 88
  • Illustrate the difference between a vertical datum and a geoid
  • Illustrate the relationship among the concepts ellipsoidal (or geodetic) height, geoidal height, and orthometric elevation
  • Outline the historical development of vertical datums

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