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 linked directly to either their original (2006) or revised entries; forthcoming, future topics are italicized. 


Database Management Systems Events and Processes Plane Coordinate Systems
Data Retrieval Strategies Fields in Space & Time Tessellated Referencing Systems
Relational DBMS Integrated Models Linear Referencing
Extensions of the Relational Model Mereology: Structural Relationships Linear Referencing Systems
Object-oriented Spatial Databases Geneaological Relationships: Lineage, Inheritance Vertical Datums
Spatio-temporal GIS Topological Relationships Horizontal Datums
Database Change Modeling Tools Map Projection Properties
Modeling Database Change Conceptual Data Models Map Projection Classes
Managing Versioned Geospatial Databases Logical Data Models Map Projection Parameters
Reconciling Database Change Physical Data Models  
Data Warehouses Fuzzy Logic Georegistration
Ongoing GIS Revision Grid Compression Methods Systematic Georefencing Systems
Database Administration   Unsystematic Georeferencing Systems
Spatial Data Models   Spatial Data Infrastructure
Basic Data Structures Spatial Data Quality Spatial Data Infrastructures
Grid Representations Spatial Data Uncertainty Content Standards
The Raster Model Error-based Uncertainty Metadata
The Hexagonal Model Modeling Uncertainty Adoption of Standards
The Triangulated Irregular Network (TIN) Model Vagueness  
Hierarchical Data Models Mathemematical Models of Vaguness: Fuzzy Sets and Rough Sets  
Classical Vector Data Models    
The Topological Model Georeferencing Systems  
The Spaghetti Model History of Understanding Earth's Shape  
The Network Model Approximating the Geoid with Spheres & Ellipsoids  
Discrete Entities Approximating the Earth's Shape with Geoids  
Modeling 3D Entities The Geographic Coordinate System  


DM-56 - Georegistration
  • Differentiate rectification and orthorectification
  • Identify and explain an equation used to perform image-to-map registration
  • Identify and explain an equation used to perform image-to-image registration
  • Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum
  • Explain the role and selection criteria for “ground control points” (GCPs) in the georegistration of aerial imagery
DM-08 - Grid compression methods
  • Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)
  • Explain the advantage of wavelet compression
  • Evaluate the relative merits of grid compression methods for storage
  • Differentiate between lossy and lossless compression methods
DM-06 - Grid representations
  • Explain how grid representations embody the field-based view
  • Differentiate among a lattice, a tessellation, and a grid
  • Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation
  • Identify the national framework datasets based on a grid model
DM-11 - Hierarchical data models
  • Illustrate the quadtree model
  • Describe the advantages and disadvantages of the quadtree model for geographic database representation and modeling
  • Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, rtrees, pyramids)
  • Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data
  • Implement a format for encoding quadtrees in a data file
DM-42 - History of understanding Earth's shape
  • Describe how scientists’ understanding of the Earth’s shape has evolved throughout history
  • Describe the contributions of key individuals (e.g., Eratosthenes, Newton, Picard, Bouguer, LaPlace, La Candamine) to scientists’ understanding of the Earth’s shape
  • Explain how technological and mathematical advances have led to more sophisticated knowledge about the Earth’s shape
  • Describe and critique early efforts to measure the Earth’s size and shape
DM-52 - Horizontal datums
  • Discuss appropriate applications of the various datum transformation options
  • Explain the difference between NAD 27 and NAD 83 in terms of ellipsoid parameters
  • Outline the historical development of horizontal datums
  • Explain the difference in coordinate specifications for the same position when referenced to NAD 27 and NAD 83
  • Explain the rationale for updating NAD 27 to NAD 83
  • Explain why all GPS data are originally referenced to the WGS 84 datum
  • Identify which datum transformation options are available and unavailable in a GIS software package
  • Define “horizontal datum” in terms of the relationship between a coordinate system and an approximation of the Earth’s surface
  • Describe the limitations of a Molodenski transformation and in what circumstances a higher parameter transformation such as Helmert may be appropriate
  • Determine the impact of a datum transformation from NAD 27 to NAD 83 for a given location using a conversion routine maintained by the U.S. National Geodetic Survey
  • Explain the methodology employed by the U.S. National Geodetic Survey to transform control points from NAD 27 to NAD 83
  • Perform a Molodenski transformation manually
  • Use GIS software to perform a datum transformation
DM-24 - Integrated models
  • Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)
  • Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model
  • Discuss the degree to which these models can be implemented using current technologies
  • Design data models for specific applications based on these comprehensive general models
  • Illustrate major integrated models of geographic information, such as Peuquet’s triad, Mennis’ pyramid, and Yuan’s three-domain
DM-16 - Linear referencing
  • Discuss dynamic segmentation as a process for transforming between linear and planar coordinate systems
  • Construct a data structure to contain point or linear geometry for database record events that are referenced by their position along a linear feature
  • Explain how linear referencing allows attributes to be displayed and analyzed that do not correspond precisely with the underlying segmentation of the network features
  • Describe how linear referencing can eliminate unnecessary segmentation of the underlying network features due to attribute value changes over time
  • Demonstrate how linear referenced locations are often much more intuitive and easy to find in the real world than geographic coordinates
DM-50 - Linear referencing systems
  • Describe an application in which a linear referencing system is particularly useful
  • Explain how the datum associated with a linear referencing system differs from a horizontal or vertical datum
  • Identify several different linear referencing methods (e.g., mileposts, reference posts, link and node) and compare them to planar grid systems
  • Identify the characteristics that all linear referencing systems have in common Unit GD4 Datums (core unit) “Horizontal” datums define the geometric relationship between a coordinate system grid and the Earth’s surface, where the Earth’s surface is approximated by an ellipsoid or other figure. “Vertical” datums are elevation reference surfaces, such as mean sea level.
  • Explain how a network can be used as the basis for reference as opposed to the more common rectangular coordinate systems
  • Discuss the magnitude and cause of error generated in the transformation from linear to planar coordinate systems
DM-35 - Logical data models

A logical data model is created for the second of three levels of abstraction, conceptual, logical, and physical. A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct  tags. However, the logical data model formed is independent of a particular database management software product. Nonetheless such a model is often constrained by a class of software language techniques for representation, making implementation with a physical data model easier.  Complex entity types of the conceptual data model must be translated into sub-type/super-type hierarchies to clarify data contexts for the entity type, while avoiding duplication of concepts and data.  Entities and records should have internal identifiers.  Relationships can be used to express the involvement of entity types with activities or associations. A logical schema is formed from the above data organization.  A schema diagram depicts the entity, attribute and relationship detail for each application.  The resulting logical data models can be synthesized using schema integration to support multi-user database environments, e.g., data warehouses for strategic applications and/or federated databases for tactical/operational business applications.