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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)
GS-02 - Contract law
  • Differentiate “contracts for service” from “contracts of service”
  • Discuss potential legal problems associated with licensing geospatial information
  • Identify the liability implications associated with contracts
AM-61 - Coordinate transformations
  • Cite appropriate applications of several coordinate transformation techniques (e.g., affine, similarity, Molodenski, Helmert)
  • Describe the impact of map projection transformation on raster and vector data
  • Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting
AM-18 - Cost surface
  • Define “friction surface”
  • Apply the principles of friction surfaces in the calculation of least-cost paths
  • Explain how friction surfaces are enhanced by the use of impedance and barriers
AM-54 - Coupling scientific models with GIS
  • Discuss the current state-of-the-art of the coupling of scientific models and simulations with GIS
  • Design a modeling procedure to integrate a spatial arrangement constraint for a mathematical optimization model
GS-18 - Cultural influences
  • Collaborate effectively with colleagues of differing social backgrounds in developing balanced GIS applications
  • Describe the ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information
  • Recognize the impact of one’s social background on one’s own geographic worldview and perceptions and how it influences one’s use of GIS
AM-57 - Data conversion
  • Identify the conceptual and practical difficulties associated with data model and format conversion
  • Convert a data set from the native format of one GIS product to another
  • Discuss the role of metadata in facilitating conversation of data models and data structures between systems
  • Describe a workflow for converting and implementing a data model in a GIS involving an Entity-Relationship (E-R) diagram and the Universal Modeling Language (UML)
KE-08 - Data costs
  • Identify potential sources of data (free or commercial) needed for a particular application or enterprise
  • Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, or producing and managing data in-house for a particular application or enterprise
  • Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)
AM-36 - Data mining approaches
  • Describe how data mining can be used for geospatial intelligence
  • Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component
  • Demonstrate how cluster analysis can be used as a data mining tool
  • Interpret patterns in space and time using Dorling and Openshaw’s geographical analysis machine (GAM) demonstration of disease incidence diffusion
  • Differentiate between data mining approaches used for spatial and non-spatial applications
  • Explain how spatial statistics techniques are used in spatial data mining
  • Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction

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