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KE-32 - Competence in GIS&T Knowledge Work

“Competence” is a word that rolls off the tongues of instructional designers, education administrators, and HR people. Others find it hard to swallow. For some GIS&T educators, competence connotes an emphasis on vocational instruction that’s unworthy of the academy. This entry challenges skeptical educators to rethink competence not just as readiness for an occupation, but first and foremost as the readiness to live life to the fullest, and to contribute to a sustainable future. The entry considers the OECD’s “Key Competencies for a Successful Life and Well-Functioning Society,” as well as the specialized GIS&T competencies specified in the U.S. Department of Labor’s Geospatial Technology Competency Model. It presents findings of a survey in which 226 self-selected members of Esri’s Young Professionals Network observe that competencies related to the GTCM’s Software and App Development Segment were under-developed in their university studies. Looking ahead, in the context of an uncertain future in which, some say, many workers are at risk of “technological unemployment,” the entry considers which GIS&T competencies are likely to be of lasting value.

AM-90 - Computational Movement Analysis

Figure 1. Group movement patterns as illustrated in this coordinated escape behavior of a group of mountain goat (Rubicapra rubicapra) evading approaching hikers on the Fuorcla Trupchun near the Italian/Swiss border are at the core of computational movement analysis. Once the trajectories of moving objects are collected and made accessible for computational processing, CMA aims at a better understanding of the characteristics of movement processes of animals, people or things in geographic space.

 

Computational Movement Analysis (CMA) develops and applies analytical computational tools aiming at a better understanding of movement data. CMA copes with the rapidly growing data streams capturing the mobility of people, animals, and things roaming geographic spaces. CMA studies how movement can be represented, modeled, and analyzed in GIS&T. The CMA toolbox includes a wide variety of approaches, ranging from database research, over computational geometry to data mining and visual analytics.

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. 

FC-24 - Conceptual Models of Error and Uncertainty

Uncertainty and error are integral parts of science and technology, including GIS&T, as they are of most human endeavors. They are important characteristics of knowledge, which is very seldom perfect. Error and uncertainty both affect our understanding of the present and the past, and our expectations from the future. ‘Uncertainty’ is sometimes used as the umbrella term for a number of related concepts, of which ‘error’ is the most important in GIS and in most other data-intensive fields. Very often, uncertainty is the result of error (or suspected error).  As concepts, both uncertainty and error are complex, each having several different versions, interpretations, and kinds of impacts on the quality of GIS products, and on the uses and decisions that users may make on their basis. This section provides an overview of the kinds of uncertainty and common sources of error in GIS&T, the role of a number of additional related concepts in refining our understanding of different forms of imperfect knowledge, the problems of uncertainty and error in the context of decision-making, especially regarding actions with important future consequences, and some standard as well as more exploratory approaches to handling uncertainties about the future. While uncertainty and error are in general undesirable, they may also point to unsuspected aspects of an issue and thus help generate new insights.

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
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
CP-13 - Cyberinfrastructure

Cyberinfrastructure (sometimes referred to as e-infrastructure and e-science) integrates cutting-edge digital environments to support collaborative research and education for computation- and/or data-intensive problem solving and decision making (Wang 2010).

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)

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