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Competency Model
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Listed in this tier are 43 examples of "Critical Work Functions" that many geospatial professionals will be expected to perform during their careers. Following the Work Functions are "Technical Content Areas" – the background knowledge upon which skills and abilities are based. These lists are exemplary, not exhaustive; geospatial professionals are called upon to demonstrate other abilities and knowledge depending on their particular roles and positions. Furthermore, few if any workers are responsible for every Critical Work Function in any one job. Thus, the examples cited represent both the core competencies of the geospatial field and the diversity of professional practice within it.

Core Geospatial Abilities and Knowledge
Critical Work Functions
  • Earth Geometry and Geodesy
    • Discuss the roles of several geometric approximations of the earth's shape, such as geoids, ellipsoids, and spheres
    • Describe characteristics and appropriate uses of common geospatial coordinate systems, such as geographic (latitude and longitude), UTM and State Plane Coordinates
    • Explain the relationship of horizontal datums, such as North America Datum of 1983 (NAD 83) or the World Geodetic System of 1984 (WGS 84), to coordinate system grids and geometric approximations of the earth's shape
    • Describe characteristics and appropriate uses of common map projections, such as Transverse Mercator, Lambert Conformal Conic, Albers Conic Equal Area, Azimuthal Equidistant, and Polar Stereographic
  • Data Quality
    • Discuss the elements of geospatial data quality, including geometric accuracy, thematic accuracy, resolution, precision, and fitness for use
    • In the context of a given geospatial project, explain the difference between quality control and quality assurance
    • Identify data quality and integration problems likely to be associated with geospatial and attribute data acquired with legacy systems and processes
    • Calculate and interpret statistical measures of the accuracy of a digital data set, such as Root Mean Square Error (RMSE)
  • Satellite Positioning and Other Measurement Systems
    • Describe the basic components and operations of the Global Navigation Satellite System (GNSS), including the Global Positioning System and similar systems
    • Explain the distinction between GNSS data post-processing (such as U.S. National Geodetic Survey's Online Positioning User Service) and real time processing (such as Real-Time Kinematic)
    • Collect and integrate GNSS/GPS positions and associated attribute data with other geospatial data sets
    • Compare differential GNSS and autonomous GNSS
    • Plan a GNSS data acquisition mission that optimizes efficiency and data quality
    • Identify and describe characteristics of inertial measurement systems and other geospatial measurement systems
  • Remote Sensing and Photogrammetry
    • Use the concept of the "electromagnetic spectrum" to explain the difference between optical sensors, microwave sensors, multispectral and hyperspectral sensors
    • Differentiate the several types of resolution that characterize remotely-sensed imagery, including spatial, spectral, radiometric, temporal, and extent
    • Explain the difference between active and passive remote sensing, citing examples of each
    • Acquire information needed to compare the capabilities and limitations of various sensor types in the context of project requirements
    • Explain the use of sampling ground truth data for quality assurance in remote sensing
    • Define "orthoimagery" in terms of terrain correction and georeferencing
  • Cartography
    • Employ cartographic design principles to create and edit visual representations of geospatial data, including maps, graphs, and diagrams
    • Demonstrate how the selection of data classification and/or symbolization techniques affects the message of the thematic map
    • Critique the design of a given map in light of its intended audience and purpose
  • Geographic Information Systems
    • Demonstrate understanding of the conceptual foundations on which geographic information systems (GIS) are based, including the problem of representing change over time and the imprecision and uncertainty that characterizes all geographic information
    • Use geospatial hardware and software tools to digitize and georeference a paper map or plat
    • Acquire and integrate a variety of field data, image data, vector data, and attribute data to create, update, and maintain GIS databases
    • Specify uses of standard non-spatial data models, specifically the relational data model and its extensions
    • Compare advantages and disadvantages of standard spatial data models, including the nature of vector, raster, and object-oriented models, in the context of spatial data used in the workplace
    • Describe examples of geospatial data analysis in which spatial relationships such as distance, direction, and topologic relationships (e.g. adjacency, connectivity, and overlap) are particularly relevant
    • Use geospatial software tools to perform basic GIS analysis functions, including spatial measurement, data query and retrieval, vector overlay, and raster map algebra
    • Demonstrate a working knowledge of GIS hardware and software capabilities, including real time GPS/GIS mapping systems
  • Programming, application development, and geospatial information technology
    • Demonstrate understanding of common geospatial algorithms, such as geocoding or drive time analysis, by writing or interpreting pseudo code
    • Recognize GIS tasks that are amenable to automation, such as route generation, incident response, and land use change analysis
    • Identify alternatives for customization and automation, such as APIs, SDKs, scripting languages
    • Identify the information technology components of a GIS, such as databases, software programs, application servers, data servers, SAN Devices, workstations, switches, routers, and firewalls
    • Compare benefits and shortcomings of desktop, server, enterprise, and hosted (cloud) software applications
    • Discuss trends in geospatial technology and applications
    • Compare the capabilities and limitations of different types of geospatial software, such as CAD, GIS, image processing
    • Recognize opportunities to leverage positioning technology to create mobile end-user applications
  • Professionalism
    • Identify allied fields that rely on geospatial technology and that employ geospatial professionals
    • Participate in scientific and professional organizations and coordinating organizations
    • Demonstrate familiarity with codes of professional ethics and rules of conduct for geospatial professionals
    • Identify legal, ethical, and business considerations that affect an organization's decision to share geospatial data
Technical Content Areas: Headings correspond to select knowledge areas identified in the first edition of the GIS&T Body of Knowledge (UCGIS 2006).
  • Conceptual Foundations
    • Spatial and topological relationships
  • Geospatial Data
    • Earth geometry and its approximations, including geoids, ellipsoids, and spheres
    • Georeferencing systems, including coordinate systems and land partitioning systems
    • Datums, horizontal and vertical
    • Map projections
    • Data quality, including geometric accuracy, thematic accuracy, resolution and precision
    • Surveying, including numerical methods such as coordinate geometry, least-squares adjustment, and network adjustments
    • Global Navigation Satellite System, including GPS, GLONASS, Galileo, Beidou (a.k.a. Compass), QZSS, and navigation applications
    • Data input, including field data collection, digitizing, scanning, and data conversion
    • Terrain modeling and representation
    • Photogrammetry
    • Remote Sensing, including aerial imaging, image interpretation, image processing, multispectral and hyperspectral remote sensing, and full-motion video
    • Metadata, standards and infrastructure
    • Alternative positioning technologies, such as wifi, TV, cell, and RFID
  • Data Modeling
    • Database Management Systems, including relational, object-oriented, and extensions of the relational model
    • Data Models, including grid, raster, TIN, hierarchical, topological, vector, network, and object-oriented
    • Geospatial data compression methods
    • Data archiving and retrieval
  • Design Aspects
    • Conceptual Models
  • Analytical Methods
    • Geometric Measures
    • Overlay Analysis
    • Viewshed Analysis
    • Network Analysis
  • Cartography and Visualization
    • Principles of Map Design, including symbolization, color use, and typography
    • Graphic Representation Techniques, including thematic mapping, multivariate displays, and web mapping
    • Data Considerations for Mapping, including source materials, data abstraction (classification, selection and generalization), and map projections
    • Map Production
  • GIS&T and Society
    • Legal issues, including property rights, liability, and public access to geospatial information
    • Ethical issues, including privacy, geographic profiling, and inequities due to the "digital divide"
    • Codes of ethics for geospatial professionals
  • Organizational and Institutional Aspects
    • Professional, scientific and trade organizations, such as AAG, ACSM, ASPRS, GITA, MAPPS, NSGIC, and URISA
    • Professional certification and licensing bodies, including GISCI, ASPRS and NCEES
    • Federal agencies, such as U.S. Geological Survey, U.S. Census Bureau, National Geospatial-Intelligence Agency
    • International organizations, such as GSDI, ISPRS, and ICA
    • Publications, including scholarly journals, trade magazines, and blogs
    • State and regional coordinating bodies, such as NSGIC and state Geographic Information Offices
    • Standards organizations, such as FGDC and OGC


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