2016 QUARTER 02

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

Differentiate between exploratory and confirmatory geospatial data analysis
Explain the origins of the term “Quantitative Revolution” in geography and other disciplines
Contrast the analytical approaches taken in various academic disciplines in which geospatial analysis has evolved
Explain how the “Quantitative Revolution” was important in the development of GIS&T
Differentiate geospatial data analysis from non-spatial data analysis

Identify the key academic disciplines that contributed to the development of GIS&T
Evaluate the role that the Quantitative Revolution in geography played in the development of GIS
Describe the major research foci in GIS and related fields in the 1970s, 1980s, 1990s, and 2000s
Evaluate the importance of the NCGIA and UCGIS in coalescing GIScience as a sub-field of GIS&T
Discuss the contributions of early academic centers of GIS&T research and development (e.g., Harvard Laboratory for Computer Graphics, UK Experimental Cartography Unit)

Describe alternate definitions of accessibility on a network
Describe methods for measuring different kinds of accessibility on a network
Contrast accessibility modeling at the individual level versus at an aggregated level
Compare current accessibility models with early models of market potential

Describe different approaches to represent the effects of agent adaptation in the context of a specific agent-based model
Explain the effects of agent adaptation in the context of a specific agent-based model

List different ways connectivity can be determined in a raster and in a polygon dataset
Explain the nine-intersection model for spatial relationships
Demonstrate how adjacency and connectivity can be recorded in matrices
Calculate various measures of adjacency in a polygon dataset
Create a matrix describing the pattern of adjacency in a set of planar enforced polygons
Describe real world applications where adjacency and connectivity are a critical component of analysis

Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs
Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards
Identify organizations that focus on developing standards related to GIS&T
Identify standards that are used in GIS&T
Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS

Use photo interpretation keys to interpret features on aerial photographs
Calculate the nominal scale of a vertical aerial image
Calculate heights and areas of objects and distances between objects shown in a vertical aerial image
Produce a map of land use/land cover classes using a vertical aerial image
Describe the elements of image interpretation

Describe perspectives on the nature and scope of system benefits among agency officials, organizational personnel, and citizens
Discuss implications of unequal economic power on the kinds of organizations that use, and benefit from, GIS&T

Demonstrate the relationship between district size (resolution/support) and patterns in aggregate data
Demonstrate how changing the geometry of regions changes the data values (e.g., voting patterns before and after redistricting)
Discuss the potential pitfalls of using regions to aggregate geographic information (e.g., census data)
Explain the nature and causes of the Modifiable Areal Unit Problem (MAUP)
Attempt to design aggregation regions that overcome MAUP
Discuss the conditions that require individual spatial entities to be aggregated (e.g., privacy, security, proprietary interests, data simplification)
Summarize the attributes of individuals within regions using spatial joins

Differentiate supervised classification from unsupervised classification
Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
Compare pixel-based image classification methods with segmentation techniques
Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
Describe an application of hyperspectral image data
Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
Describe a situation in which filtered data are more useful than the original unfiltered data
Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories

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