##### PD-02 - Integer programming

- Explain why integer programs are harder to solve than linear programs
- Differentiate between a linear program and an integer program

- Explain why integer programs are harder to solve than linear programs
- Differentiate between a linear program and an integer program

- 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

- 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)

- 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

- 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

- 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

- 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

- 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

- 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

## KE-26 - Incorporating GIS&T into existing job classifications