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)
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
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)
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)
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
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-58 - Content standards