## 2018 QUARTER 03

##### KE-26 - Incorporating GIS&T into existing job classifications
• Select two effective methods of overcoming resistance to change
• Illustrate how methods for overcoming resistance to change can aid implementation of a GIS
• Explain how resistance to change and the need to standardize operations when trying to incorporate GIS&T can promote inclusion into existing job classifications
##### PD-02 - Integer programming
• Explain why integer programs are harder to solve than linear programs
• Differentiate between a linear program and an integer program
##### DM-24 - Integrated models
• Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)
• Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model
• Discuss the degree to which these models can be implemented using current technologies
• Design data models for specific applications based on these comprehensive general models
• Illustrate major integrated models of geographic information, such as Peuquet’s triad, Mennis’ pyramid, and Yuan’s three-domain
##### AM-16 - Interpolation methods
• Identify the spatial concepts that are assumed in different interpolation algorithms
• Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting, and kriging
• Differentiate between trend surface analysis and deterministic spatial interpolation
• Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem
• Design an algorithm that interpolates irregular point elevation data onto a regular grid
• Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting
• Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package
• Describe how surfaces can be interpolated using splines
• Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations
• Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)
• Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)
##### AM-17 - Intervisibility
• Define “intervisibility”
• Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by DEMs
• Perform siting analyses using specified visibility, slope, and other surface related constraints
• Explain the sources and impact of errors that affect intervisibility analyses
##### AM-08 - Kernels and density estimation
• Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential
• Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted
• Explain why and how density estimation transforms point data into a field representation
• Explain why, in some cases, an adaptive bandwidth might be employed
• Create density maps from point datasets using kernels and density estimation techniques using standard software
• Differentiate between kernel density estimation and spatial interpolation
##### AM-37 - Knowledge discovery
• Explain how spatial data mining techniques can be used for knowledge discovery
• Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query
• Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets
##### AM-29 - Kriging methods
• Describe the relationship between the semi-variogram and kriging
• Explain why it is important to have a good model of the semi-variogram in kriging
• Explain the concept of the kriging variance, and describe some of its shortcomings
• Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support)
• Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping
• Outline the basic kriging equations in their matrix formulation
• Conduct a spatial interpolation process using kriging from data description to final error map
• Explain why kriging is more suitable as an interpolation method in some applications than others
##### KE-09 - Labor and management
• Identify the positions necessary to design and implement a GIS
• Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry
• Evaluate the labor needed in past cases to build a new geospatial enterprise
• Create a budget of expected labor costs, including salaries, benefits, training, and other expenses
##### DC-02 - Land records
• Distinguish between GIS, LIS, and CAD/CAM in the context of land records management
• Evaluate the difference in accuracy requirements for deeds systems versus registration systems
• Exemplify and compare deed descriptions in terms of how accurately they convey the geometry of a parcel
• Distinguish between topological fidelity and geometric accuracy in the context of a plat map