2016 QUARTER 03

A B C D E F G H I K L M N O P R S T U V W

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

Describe formal and informal arrangements that promote geospatial data sharing (e.g., FGDC, ESDI, memoranda of agreements, informal access arrangements, targeted funding support)
Describe a situation in which politics interferes with data sharing and exchange

Describe how using standards can affect implementation of a GIS
Explain how validation and verification processes can be used to maintain database integrity
Summarize how data access processes can be a factor in development of an enterprise GIS implementation
Describe effective methods to get stakeholders to create, adopt, or develop and maintain metadata for shared datasets

Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software
Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database
Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database
Discuss the implication of “long transactions” on database integrity
Modify spatial and attribute data while ensuring consistency within the database

Describe a stochastic error model for a natural phenomenon
Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects, and fields or discord and non-specificity
Explain how the familiar concepts of geographic objects and fields affect the conceptualization of uncertainty

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