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-64 - Adoption of standards
  • Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs
  • Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards
  • Identify organizations that focus on developing standards related to GIS&T
  • Identify standards that are used in GIS&T
  • Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS
DM-44 - Approximating the Earth's shape with geoids
  • Explain why gravity varies over the Earth’s surface
  • Explain how geoids are modeled
  • Explain the role that the U.S. National Geodetic Survey plays in maintaining and developing geoid models
  • Explain the concept of an equipotential gravity surface (i.e., a geoid)
DM-43 - Approximating the geoid with spheres and ellipsoids
  • Identify the parameters used to define an ellipsoid
  • Differentiate the Clarke 1866 and WGS 84 ellipsoids in terms of ellipsoid parameters
  • Differentiate between a bi-axial and tri-axial ellipsoid and their applications
  • Explain why spheres and ellipsoids are used to approximate geoids
  • Distinguish between a geoid, an ellipsoid, a sphere, and the terrain surface
  • Describe an application for which it is acceptable to use a sphere rather than an ellipsoid
DM-01 - Basic data structures
  • Define basic data structure terminology (e.g., records, field, parent/child, nodes, pointers)
  • Analyze the relative storage efficiency of each of the basic data structures
  • Implement algorithms that store geospatial data to a range of data structures
  • Discuss the advantages and disadvantages of different data structures (e.g., arrays, linked lists, binary trees) for storing geospatial data
  • Differentiate among data models, data structures, and file structures
DM-25 - Categories
  • Explain the human tendency to simplify the world using categories
  • Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)
  • Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures
  • Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)
  • Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories
  • Describe the contributions of category theory to understanding the internal structure of categories
  • Document the personal, social, and/or institutional meaning of categories used in GIS applications
  • Create or use GIS data structures to represent categories, including attribute columns, layers/themes, shapes, and legends
  • Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes
  • Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time
DM-14 - Classic vector data models
  • Illustrate the GBF/DIME data model
  • Describe a Freeman-Huffman chain code
  • Describe the relationship of Freeman-Huffman chain codes to the raster model
  • Discuss the impact of early prototype data models (e.g., POLYVRT and GBF/DIME) on contemporary vector formats
  • Describe the relationship between the GBF/DIME and TIGER structures, the rationale for their design, and their intended primary uses, paying particular attention to the role of graph theory in establishing the difference between GBF/DIME and TIGER files
  • Discuss the advantages and disadvantages of POLYVRT
  • Explain what makes POLYVRT a hierarchical vector data model
DM-34 - Conceptual data models

Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database. This specification often times takes the form of a formalized diagram.  The process of conceptual data modeling is meant to foster shared understanding among data modelers and stakeholders when creating the specification.  As such, a conceptual data model should be easily readable by people with little or no technical-computer-based expertise because a comprehensive view of information is more important than a detailed view. In a conceptual data model, entity classes are categories of things (person, place, thing, etc.) that have attributes for describing the characteristics of the things.  Relationships can exist between the entity classes.  Entity-relationship diagrams have been and are likely to continue to be a popular way of characterizing entity classes, attributes and relationships.  Various notations for diagrams have been used over the years. The main intent about a conceptual data model and its corresponding entity-relationship diagram is that they should highlight the content and meaning of data within stakeholder information contexts, while postponing the specification of logical structure to the second phase of database design called logical data modeling. 

DM-58 - Content standards
  • Differentiate between a controlled vocabulary and an ontology
  • Describe a domain ontology or vocabulary (i.e., land use classification systems, surveyor codes, data dictionaries, place names, or benthic habitat classification system)
  • Describe how a domain ontology or vocabulary facilitates data sharing
  • Define “thesaurus” as it pertains to geospatial metadata
  • Describe the primary focus of the following content standards: FGDC, Dublin Core Metadata Initiative, and ISO 19115
  • Differentiate between a content standard and a profile
  • Describe some of the profiles created for the Content Standard for Digital Geospatial Metadata (CSDGM)
DM-02 - Data retrieval strategies
  • Analyze the relative performance of data retrieval strategies
  • Implement algorithms that retrieve geospatial data from a range of data structures
  • Describe the particular advantages of Morton addressing relative to geographic data representation
  • Discuss the advantages and disadvantages of different data structures (e.g., arrays, linked lists, binary trees, hash tables, indexes) for retrieving geospatial data
  • Compare and contrast direct and indirect access search and retrieval methods
DM-59 - Data warehouses
  • Differentiate between a data warehouse and a database
  • Describe the functions that gazetteers support
  • Differentiate the retrieval mechanisms of data warehouses and databases
  • Discuss the appropriate use of a data warehouse versus a database