GISc framework

• Define basic terms of query processing (e.g., SQL, primary and foreign keys, table join)
• Explain the basic logic of SQL syntax
• Demonstrate the basic syntactic structure of SQL
• Create an SQL query to retrieve elements from a GIS

• Define “direction” and its measurement in different angular measures
• Compare and contrast how direction is determined and stated in raster and vector data
• Describe operations that can be performed on qualitative representations of direction
• Explain any differences in the measured direction between two places when the data arepresented in a GIS in different projections
• Compute the mean of directional data

• Identify situations in which shape affects geometric operations
• Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data
• Explain why the shape of an object might be important in analysis
• Exemplify situations in which the centroid of a polygon falls outside its boundary
• Compare and contrast different shape indices, include examples of applications to which each could be applied
• Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull
• Develop an algorithm to determine the skeleton of polygons
• Find centroids of polygons under different definitions of a centroid and different polygon shapes
• Calculate several different shape indices for a polygon dataset

• List reasons why the area of a polygon calculated in a GIS might not be the same as the realworld object it describes• Explain how variations in the calculation of area may have real world implications, such ascalculating density• Demonstrate how the area of a region calculated from a raster data set will vary by resolution andorientation• Outline an algorithm to find the area of a polygon using the coordinates of its vertices

• Describe real world applications where distance decay is an appropriate representation of thestrength of spatial relationships (e.g., shopping behavior, property values)
• Describe real world applications where distance decay would NOT be an appropriaterepresentation of the strength of spatial relationships (e.g., distance education, commuting,telecommunications)
• Explain the rationale for using different forms of distance decay functions
• Explain how a semi-variogram describes the distance decay in dependence between data values
• Outline the geometry implicit in classical “gravity” models of distance decay
• Plot typical forms for distance decay functions
• Write typical forms for distance decay functions
• Write a program to create a matrix of pair-wise distances among a set of points

• Explain why the process “dissolve and merge” often follows vector overlay operations
• Explain what is meant by the term “planar enforcement”
• Outline the possible sources of error in overlay operations
• Exemplify applications in which overlay is useful, such as site suitability analysis
• Compare and contrast the concept of overlay as it is implemented in raster and vector domains
• Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS
• Demonstrate why the registration of datasets is critical to the success of any map overlayOperation
• Formalize the operation called map overlay using Boolean logic

• Discuss the role of Voronoi polygons as the dual graph of the Delaunay triangulation
• Explain how the range of map algebra operations (local, focal, zonal and global) relate to theconcept of neighbourhoods
• Explain how Voronoi polygons can be used to define neighborhoods around a set of points
• Outline methods that can be used to establish non-overlapping neighborhoods of similarity in raster datasets
• Create proximity polygons (Thiessen/Voronoi polygons) in point datasets
• Write algorithms to calculate neighborhood statistics (minimum, maximum, focal flow) using a moving window in raster datasets

• Describe the relationships between kernels and classical spatial interaction approaches, such assurfaces of potential
• Differentiate between kernel density estimation and spatial interpolation
• Outline the likely effects on analysis results of variations in the kernel function used and thebandwidth 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 usingstandard software

• Identify several cluster detection techniques and discuss their limitations
• Discuss the characteristics of the various cluster detection techniques
• Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests
• Perform a cluster detection analysis to detect “hot spots” in a point pattern

• State the classic formalization of the interaction model
• Differentiate between the gravity model and spatial interaction models
• Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial interaction model
• Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena makespatial interaction models more appropriate than gravity models
• Explain the concept of competing destinations, describing how traditional spatial interactionmodel forms are modified to account for it
• Create a matrix that shows spatial interaction

• Relate plots of multidimensional attribute data to geography by equating similarity in data spacewith proximity in geographical space
• Assemble a data matrix of attributes
• Produce plots in several data dimensions using a data matrix of attributes
• Conduct a simple hierarchical cluster analysis to classify area objects into statistically similarregions
• Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce thenumber of co-ordinates, or dimensionality, of a problem

• Describe the difference between prescriptive and descriptive cartographic models
• Discuss the origins of cartographic modeling with reference to the work of Ian McHarg
• Develop a flowchart of a cartographic model for a site suitability problem

• Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation
• Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis
• Differentiate between contributing factors and constraints in a multi-criteria application
• Explain the legacy of multi-criteria evaluation in relation to cartographic modelling
• Determine which method to use to combine criteria (e.g., linear, multiplication)
• Create initial weights using the analytical hierarchy process (AHP)
• Calibrate a linear combination model by adjusting weights using a test data set

• Identify the spatial concepts that are assumed in different interpolation algorithms
• Describe how surfaces can be interpolated using splines
• Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting andKriging
• Differentiate between trend surface analysis and deterministic spatial interpolation
• Explain why different interpolation algorithms produce different results and suggest ways bywhich these can be evaluated in the context of a specific problem
• Design an algorithm which 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 regressionfunction from any standard statistical package

• Describe how a network of stream channels and ridges can be estimated from a Digital ElevationModel (DEM)
• Explain how ridgelines and streamlines can be used to improve the result of an interpolationProcess

• Define intervisibility
• Explain the sources and impact of errors that affect intervisibility analyses
• Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by digital elevation models (DEM)
• Perform siting analyses using specified visibility, slope and other surface related constraints

• List the two basic assumptions of the purely random process
• Justify the stochastic process approach to spatial statistical analysis
• Exemplify deterministic and spatial stochastic processes
• Exemplify non-stationarity involving first and second order effects
• Differentiate between isotropic and anisotropic processes
• Discuss the theory leading to the assumption of intrinsic stationarity
• Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts

• Explain how different types of spatial weights matrices are defined and calculated
• Explain the rationale used for each type of spatial weights matrix
• Discuss the appropriateness of different types of spatial weights matrices for various problems
• Construct a spatial weights matrix for lattice, point, and area patterns

• Describe the effect of the assumption of stationarity on global measures of spatial association
• Explain how a statistic that is based on combining all the spatial data and returning a singlesummary value or two can be useful in understanding broad spatial trends
• Explain how the K function provides a scale-dependent measure of dispersion
• Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
• Compute measures of overall dispersion and clustering of point datasets using nearest neighbour distance statistics
• Compute the K function
• Justify, compute, and test the significance of the join count statistic for a pattern of objects

• Describe the effect of non-stationarity on local indices of spatial association
• Compare and contrast global and local statistics and their uses
• Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association
• Explain how geographically weighted regression provides a local measure of spatial association
• Decompose Moran’s I and Geary’s c into local measures of spatial association
• Compute the Gi and Gi* statistics

• Explain how outliers affect the results of analyses
• Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics

• Identify and define the parameters of a semi-variogram (range, sill, nugget)
• Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association
• Demonstrate how semi-variograms react to spatial nonstationarity
• Construct a semi-variogram and illustrate with a semi-variogram cloud

• List the possible sources of error in a selected and fitted model of an experimental semivariogram
• Describe some commonly used semi-variogram models
• Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
• Explain the necessity of defining a semi-variogram model for geographic data
• Apply the method of weighted least squares and maximum likelihood to fit semi-variogrammodels to datasets

• Describe the relationship between the semi-variogram and kriging
• Explain why kriging is more suitable as an interpolation method in some applications than others
• 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 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 Anselin’s typology of spatial autoregressive models
• Conduct a spatial econometric analysis to test for spatial dependence in the residuals from leastsquares models and spatial autoregressive models
• Demonstrate how the parameters of spatial auto-regressive models can be estimated usingunivariate and bivariate optimization algorithms for maximizing the likelihood function
• Find a “best” model
• Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters
• Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model

• Identify modeling situations where spatial filtering might not be appropriate
• Describe the relationship between factorial kriging and spatial filtering
• Explain how spatial correlation can result as a side effect of the spatial aggregation in a givendataset
• Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem
• Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatialcomponent variables into OLS regression analysis in order to remedy misspecification and theproblem of spatially auto-correlated residuals
• Demonstrate how spatial autocorrelation can be “removed” by resampling

• Describe how data mining can be used for geospatial intelligence
• Differentiate between data mining approaches used for spatial and non-spatial applications
• Compare and contrast the primary types of data mining: summarization/characterization,clustering/categorization, feature extraction, and rule/relationships extraction
• Explain how spatial statistics techniques are used in spatial data mining
• Explain how the analytical reasoning techniques, visual representations, and interactiontechniques 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 AnalysisMachine (GAM) demonstration of disease incidence diffusion

• Explain how spatial data mining techniques can be used for knowledge discovery
• Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets
• Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve allrelevant datasets given an initial user query

• Demonstrate how networks can be measured using the number of elements in a network, thedistances along network edges, and the level of connectivity of the network
• Explain the concept of the diameter of a network
• Compute the estimated number of fundamental cycles in a graph
• Compute the alpha, beta, and gamma indices of network connectivity
• Compute the Detour Index and the measure of network density for a given network

• Describe some variants of Dijkstra’s algorithm that are even more efficient
• Explain how a leading World Wide Web-based routing system works (e.g., MapQuest, YahooMaps, Google)
• Discuss the difference of implementing Dijkstra’s algorithm in raster and vector modes
• Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network
• Compute the optimum path between two points through a network with Dijkstra’s algorithm

• Describe practical situations in which flow is conserved while splitting or joining at nodes of theNetwork
• Explain how the concept of capacity represents an upper limit on the amount of flow through theNetwork
• Demonstrate how capacity is assigned to edges in a network using the appropriate data structure
• Apply a maximum flow algorithm to calculate the largest flow from a source to a sink, using theedges of the network, subject to capacity constraints on the arcs and the conservation of flow

• Explain why, if supply equals demand, there will always be a feasible solution to the ClassicTransportation Problem
• Exemplify the Classic Transportation Problem
• Demonstrate how the Classic Transportation Problem can be structured as a linear program
• Implement the Transportation Simplex method to determine the optimal solution

• List several classic problems to which network analysis is applied (e.g., The Traveling SalesmanProblem, The Chinese Postman Problem)
• Explain why heuristic solutions are generally used to address the combinatorially complex natureof these problems and the difficulty of solving them optimally

• Define the following terms: data, information, knowledge, and wisdom
• Transform a conceptual model of information for a particular task into a data model
• Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, text, etc.

• Define the properties that make a phenomenon geographic
• Describe some insights that a spatial perspective can contribute to a given topic
• Justify or refute whether geography (as a discipline) should have a central role in GI S&T
• Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography since the quantitative revolution
• Justify a chosen position on which disciplines should have as important a role in GI S&T asGeography
• Discuss the differing denotations and connotations of the terms spatial, geographic, andGeospatial

• Explain how the concept of place is more than just location
• Define the notions of cultural landscape and physical landscape
• Select a place or landscape with personal meaning and discuss its importance
• Evaluate the differences in how various parties think or feel differently about a place beingModelled
• Describe the elements of a sense of place or landscape that are difficult or impossible toadequately represent in GIS
• Differentiate between space and place
• Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies

• Identify common-sense views of geographic phenomena that sharply contrast with establishedtheories and technologies of geographic information
• Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatialprofessionals to create, distribute, and map geographic information
• Effectively communicate the design, procedures, and results of GIS projects to non-GISaudiences (clients, managers, general public)
• Collaborate with non-GIS experts who use GIS to design applications that match common-senseunderstanding to an appropriate degree
• Differentiate applications that can make use of common-sense principles of geography fromthose that should not

• 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 andperceptions and how it influences one’s use of GIS
• Collaborate effectively with colleagues of differing social backgrounds in developing balancedGIS applications

• Differentiate between mathematical and phenomenological theories of the nature of time
• Exemplify different temporal frames of reference: linear and cyclical, absolute and relative
• Recognize the role that time plays in “static” GISystems
• Compare and contrast models of a given spatial process using continuous and discreteperspectives of time
• Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications

• Discuss common prepositions and adjectives (in any particular language) that signify eitherspatial or temporal relations but are used for both kinds, such as “after” or “longer”
• Compare and contrast the characteristics of spatial and temporal dimensions
• Identify various types of geographic interactions in space and time
• Describe different types of movement and change
• Understand the physical notions of velocity and acceleration which are fundamentally aboutmovement across space through time

• Compare and contrast the concepts of continuants (entities) and occurrents (events)
• Compare and contrast the concepts of event and process
• Describe particular events or processes in terms of identity, categories, attributes, locations, etc.
• Evaluate the assertion that “events and processes are the same thing, but viewed at differenttemporal scales”
• Apply or develop formal systems for describing continuous spatio-temporal processes
• Describe the “actor” role that entities and fields play in events and processes
• Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so

• Define a field in terms of properties, space, and time
• Identify applications and phenomena that are not adequately modeled by the field view
• Identify examples of discrete and continuous change found in spatial, temporal, and spatiotemporal fields
• Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)
• Formalize the notion of field using mathematical functions and Calculus
• Relate the notion of field in GIS to the mathematical notions of scalar and vector fields
• Recognize the influences of scale on the perception and meaning of fields
• Evaluate the representation of movement as a field of location over time [e.g. = f(t) ]
• Evaluate the field view’s description of “objects” as conceptual discretizations of continuousPatterns

• Describe particular geographic phenomena in terms of their place in mereonomic hierarchies(parts and composites)
• Identify phenomena that are best understood as networks
• Explain the modeling of structural relationships in standard GIS data models, such as storedTopology
• Represent structural relationships in GIS data
• Explain the effects of spatial or temporal scale on the perception of structure
• Explain the contributions of formal mathematical methods such as Graph Theory to the study and application of geographic structures

• Describe ways in which a geographic entity can be created from one or more others
• Describe the genealogy (as identity-based change or temporal relationships) of particulargeographic phenomena
• Determine whether it is important to represent the genealogy of entities for a particular application
• Discuss the effects of temporal scale on the modeling of genealogical structures

• Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect
• Describe geographic phenomena in terms of their topological relationships (in space and time) to other phenomena
• List the possible topological relationships between entities in space (e.g., 9-intersection) and time
• Use methods that analyze topological relationships
• Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships

• Describe geographic phenomena in terms of their distances and directions (in space and time) toother phenomena
• Define spatial autocorrelation in the context of geographic proximity
• Use methods that analyze metrical relationships
• Identify situations in which Tobler’s First Law of Geography is valuable
• Identify situations in which Tobler's First Law of Geography does not apply
• Explain why Tobler’s First Law of Geography is fundamental to many operations in GIS andwhether it should be
• Define the principle of friction of distance and geographic models that are based on it (e.g.,gravity models, spatial interaction models)

• Find spatial patterns in the distribution of geographic phenomena using geographic visualizationand other techniques
• Discuss the causal relationship between spatial processes and spatial patterns, including thepossible problems in determining causality
• Hypothesize the causes of a pattern in the spatial distribution of a phenomenon
• Differentiate among distributions in space, time, and attribute
• Identify influences of scale on the appearance of distributions
• Employ techniques for visualizing, describing, and analyzing distributions in space, time, andAttribute

• Delineate regions using properties, spatial relationships, and geospatial technologies
• Exemplify regions found at different scales
• Explain the relationship between regions and categories
• Differentiate among different types of regions, including functional, cultural, physical,administrative, and others
• Identify the kinds of phenomena that are commonly found at the boundaries of regions
• Explain why general-purpose regions rarely exist
• Compare and contrast the opportunities and pitfalls of using regions to aggregate geographicinformation (e.g., census data)
• Use established analysis methods that are based on the concept of region (e.g., landscapeecology)
• Explain the nature of the Modifiable Areal Unit Problem (MAUP)

• Discuss advantages and disadvantages of various data classification methods for choroplethmapping, including equal interval, quantiles, mean-standard deviation, natural breaks, and“optimal” methods
• Discuss the limitations of current technological approaches to generalization for mappingPurposes
• Explain how generalization of one data theme can and must be reflected across multiple themes(e.g., if the river moves, the boundary, roads and towns also need to move)
• Explain how the decisions for selection and generalization are made with regard to symbolizationin mapping
• Explain why the reduction of map scale sometimes results in the need for mapped features to bereduced in size and moved
• Identify mapping tasks that require each of the following: smoothing, aggregation, simplification,and displacement
• Illustrate specific examples of feature elimination and simplification suited to mapping at smallerScales
• Demonstrate how different classification schemes produce very different maps from a single setof interval or ratio data
• Apply appropriate selection criteria to change the display of map data to a smaller scale
• Write algorithms to perform equal interval, quantiles, mean-standard deviation, natural breaks,and “optimal” classification for choropleth mapping

• List the variables used in the symbolization of map data for visual, tactile, haptic, auditory, anddynamic display
• Identify the visual variables (size, lightness, shape, hue, etc.) and graphic primitives (points, lines,areas) commonly used in maps to represent various geographic features at all attributemeasurement scales (nominal, ordinal, interval, ratio)
• Illustrate how a single geographic feature can be represented by various graphic primitives e.g.land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as ahillshaded surface)
• Select effective symbols for map features based on the dimensionality and attributes of thegeographic phenomena being mapped
• Design map symbols with sufficient contrast to be distinguishable by typical users

• List the range of factors that should be considered in selecting colors
• Describe color decisions made for various production workflows
• Describe how cultural differences with respect to color associations impact map design
• Describe the common color models used in mapping
• Determine the CMYK (cyan, magenta, yellow, and black) primary amounts in a selection of colors
• Discuss the role of “gamut“ in choosing colors that can be reproduced on various devices andMedia
• Explain how real-world connotations (e.g. blue=water, white=snow) can be used to determine color selections on maps
• Exemplify colors for different forms of harmony, concordance, and balance
• Estimate RGB (red, green, blue) primary amounts in a selection of colors
• Plan color proofing suited for checking a map publication job
• Select a color scheme (e.g., qualitative, sequential, diverging, spectral) that is appropriate for a given map purpose and variable
• Select colors appropriate for map readers with color limitations
• Specify a set of colors in device-independent Commision Internationale de L'Eclairage (CIE)Specifications

• Differentiate the interpretation of a series of three maps and a single multivariate map, eachrepresenting the same three related variables
• Explain the relationship among several variables in a parallel coordinate plot
• Detect a multivariate outlier using a combination of maps and graphs
• Design a map series to show the change in a geographic pattern over time
• Design a single map symbol that can be used to symbolize a set of related variables
• Create a map that displays related variables using different mapping methods (e.g., choroplethand proportional symbol, choropleth and cartogram)
• Create a map that displays related variables using the same mapping method (e.g., bivariatechoropleth map, bivariate dot map)

• Explain how interactivity influences map use in animated displays
• Describe a mapping goal in which the use of each of the following would be appropriate:brushing, linking, multiple displays
• Describe the uses of the map as a user interface element in interactive presentations ofgeographic information
• Critique the interactive elements of an online map
• Develop a useful interactive interface and legend for an animated map
• Create an animated map for a specified purpose
• Create an interactive map suitable for a given audience

• Describe situations in which methods of terrain representation (e.g., shaded relief, contours,hypsometric tints, block diagrams, profiles) are well suited
• Describe situations in which methods of terrain representation are poorly suited
• Differentiate 3D representations from 2 ½ D representations
• Explain how maps that show the landscape in profile can be used to represent terrain
• Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring method

• Describe considerations for using maps on the Web as a method for downloading data
• Discuss the influence of the user interface on maps and visualizations on the Web
• Critique the user interface for existing Internet mapping services
• Construct a Web page that includes an interactive map
• Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web

• Discuss the nature and use of virtual environments such as Google Earth
• Explain how the virtual and immersive environments become increasingly more complex as we move from the relatively non-immersive VRML desktop environment to a stereoscopic display (e.g., a GeoWall) to a more fully immersive CAVE
• Explain how various data formats and software and hardware environments support immersive visualization
• Compare and contrast the relative advantages of different immersive display systems used for cartographic visualization (e.g., CAVEs, GeoWalls)
• Evaluate the extent to which a GeoWall or CAVE does or does not enhance understanding ofspatial data

• Explain how spatial metaphors can be used to illustrate the relationship among ideas
• Explain how spatialization is a core component of visual analytics
• Evaluate graphic techniques used to portray spatializations
• Create a pseudo-topographic surface to portray the relationships in a collection of documents
• Create a concept map that represents the contents and topology of a physical or social process

• Describe how the adding time-series data reveals or does not reveal patterns not evident in across-sectional data
• Describe how an animated map reveals patterns not evident without animation
• Demonstrate how Bertin’s “graphic variables” can be extended to include animation effects
• Create a temporal sequence representing a dynamic geospatial process

• Differentiate among the various raster map outputs (JPG, GIF, TIF) and various vector formats(PDF, Adobe Illustrator Postscript)
• Discuss the purpose of advanced production methods (e.g., stochastic screening, hexachromecolor, color management and device profiles, trapping, overprinting)
• Explain how color fastness and color consistency are ensured in map production
• Compare and contrast the file formats suited to presentation of maps on the Web (e.g., PDF andJPEG) to those suited to publication in high resolution contexts (e.g., TIFF, PDF, Adobe IllustratorPostscript)
• Compare and contrast the issues that arise for map production using black-and-white and fourcolor process specifications
• Outline the process for the digital production of offset press printed maps, including reference tofeature and color separates, feature and map composites, and resolution
• Compare outputs of the same map at various low and high resolutions
• Critique typographic integrity in export formats (e.g., some file export processes break type intoletters degrading searchability, font processing, and reliability of Raster Image Processing)
• Prepare a map file for CMYK publication in a book
• Prepare a map file for RGB presentation on a Web site

• Define Gantt and PERT charts
• Identify the people necessary to effectively design a GIS
• Collaborate effectively with a variety of people in a design team
• Create a schedule for the design and implementation of a GIS
• Justify the funding necessary for the design process of a GIS
• Use project management tools and techniques to manage the design process

• Identify current and potential users of geospatial technology in an enterprise
• Differentiate the concepts of efficiency and effectiveness in application requirements
• Recognize geographic tasks and geographic information that already exist in an enterprise
• Classify potential users as casual or professional, early adopters or reluctant users
• Educate potential users on the value of geospatial technology
• Evaluate the potential for using geospatial technology to improve the efficiency and/oreffectiveness of existing activities
• Identify new geographic tasks or information that align with institutional missions and goals

• Describe the need for user-centered requirements analysis
• Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, or and joint application development (JAD)
• Document existing and potential tasks in terms of workflow and information flow
• Create requirements reports for individual potential applications in terms of the data, procedures, and output needed
• Assess the relative importance and immediacy of potential applications
• Coalesce the needs of individual users and tasks into enterprise-wide needs
• Differentiate between the responsibilities of the proposed system and those that remain with the user
• Illustrate how a business process analysis can be used to identify requirements during a GISystem implementation
• Describe how spatial data and GI S&T can be integrated into a work flow process
• Evaluate how external spatial data sources can be incorporated into the business process

• Describe the major geospatial software architectures available currently, including desktop GIS,server-based, Internet, and component-based custom applications
• Describe non-spatial software that can be used in geospatial applications, such as databases,Web services, and programming environments
• Compare and contrast the primary sources of geospatial software, including major and minorcommercial vendors and open-source options
• List the major functionality needed from off-the-shelf software based on a requirements report
• Identify software options that meet functionality needs for a given task or enterprise
• Evaluate software options that meet functionality needs for a given task or enterprise

• Identify potential sources of data (free or commercial) needed for a particular application orEnterprise
• Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)
• Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, orproducing and managing data in-house for a particular application or enterprise

• Identify the positions necessary to design and implement a GIS
• Discuss the advantages and disadvantages of outsourcing elements of the implementation of a GI 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

• Identify the hardware and space that will be needed for a GIS implementation
• Hypothesize the ways in which capital needs for GIS may change in the future
• Compare and contrast the relative merits of housing GI systems within IT and MIS facilitiesversus keeping them separate
• Collaborate effectively with various units in an institution to develop efficient hardware and space solutions

• Define entities and relationships as used in conceptual data models
• Describe the degree to which attributes need to be modelled in the conceptual modelling phase
• Explain the goals behind the conceptual modelling phase of design
• Deconstruct an application use case into conceptual components
• Create a conceptual model diagram of data needed in a geospatial application or enterpriseDatabase
• Design application-specific conceptual models

• Differentiate between conceptual and logical models, in terms of the level of detail, constraints,and range of information included
• Define the cardinality of relationships
• Explain the various types of cardinality found in databases
• Distinguish between the incidental and structural relationships found in a conceptual model
• Determine which relationships need to be stored explicitly in the database
• Evaluate the various general data models common in GI S&T for a given project, and select themost appropriate solutions
• Create logical models based on conceptual models and general data models using UML or otherTools

• Identify the sequence of operations and statistical/mathematical methods (a procedure)appropriate for a particular application (e.g., multi-criteria evaluation for site suitability)
• Critique the necessity of the operations used in a pre-defined procedure for a particularapplication (e.g., suitability analysis)
• Develop a planned analytical procedure to solve a new unstructured problem (e.g., long-termbusiness strategy)
• Implement a pre-defined procedure for a sample dataset

• Discuss the current state-of-the-art of the coupling of scientific models and simulations with GIS
• Design a modeling procedure to integrate a spatial arrangement constraint for a mathematical optimization model

• Design an application-level software/user interface based on user requirements
• Create user interface components in available development environments

• Compare and contrast the relative merits of available environments for geospatial applications,including desktop software scripting (e.g., VBA), graphical modeling tools, geospatial componentsin standard environments, and “from-scratch” development in standard environments
• Develop a geospatial application using the most appropriate environment

• Explain the advantage of the relational model over earlier database structures includingSpreadsheets
• Demonstrate how search and relational join operations provide results for a typical GIS query and other simple operations using the relational DBMS within a GIS software application
• Define the basic terms used in relational database management systems (e.g., tuple, relation,foreign key, SQL, relational join)
• Discuss the efficiency and costs of normalization
• Describe the entity-relationship diagram approach to data modelling
• Explain how entity-relationship diagrams are translated into relational tables
• Describe the problems associated with failure to follow the first and second normal forms(including data confusion, redundancy, and retrieval difficulties)
• Create an SQL query that extracts data from related tables

• Describe the basic elements of the object-oriented paradigm, such as inheritance, encapsulation,methods, and composition
• Differentiate between object-oriented programming and object-oriented databases
• Evaluate the degree to which the object oriented paradigm does or does not approximatecognitive structures
• Explain how the principle of inheritance can be implemented using an object-orientedprogramming approach
• Defend or refute the notion that the Extensible Markup Language (XML) is a form of objectoriented database
• Explain how the properties of object orientation allows for combining and generalizing objects
• Evaluate the advantages and disadvantages of object-oriented databases compared to relationaldatabases, focusing on representational power, data entry, storage efficiency, and queryperformance
• Implement a GIS database design in an off-the-shelf object-oriented database

• Define basic terms used in the raster data model (e.g., cell, row, column, value)
• Explain how the raster data model instantiates a grid representation
• Interpret the header of a standard raster data file
• Compare and contrast the raster with other types of regular tessellations for geographic dataStorage
• Compare and contrast the raster with other types of regular tessellations for geographic dataAnalysis
• Write a program to read and write a raster data file

• Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)
• Differentiate between lossy and lossless compression methods
• Evaluate the relative merits of grid compression methods for storage
• Explain the advantage of wavelet compression

• Illustrate the hexagonal model
• Exemplify the uses (past and potential) of the hexagonal model
• Explain the limitations of the grid model compared to the hexagonal model

• Describe the architecture of the TIN model
• Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application
• Describe how to generate a unique TIN solution using Delaunay triangulation
• Construct a TIN manually from a set of spot elevations
• Delineate a set of break lines that improve the accuracy of a TIN
• Describe the conditions under which a TIN might be more practical than GRID

• Relate the concept of grid cell resolution to the more general concept of “support” and granularity
• Illustrate the impact of grid cell resolution on the information that can be portrayed
• Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software
• Evaluate the ease of measuring resolution in different types of tessellations

• Identify a widely-used example of the spaghetti model (e.g., AutoCAD DWF, ESRI shapefile)
• Describe how geometric primitives are implemented in the spaghetti model as independentobjects without topology
• Explain how the spaghetti data model embodies an object-based view of the world
• Explain the conditions under which the spaghetti model is useful
• Write a program to read and write a vector data file using a common published format

• Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logicalconsistency)
• Illustrate a topological relation
• Explain the advantages and disadvantages of topological data models
• Demonstrate how a topological structure can be represented in a relational database structure
• Exemplify the concept of planar enforcement (e.g., TIN triangles)
• Discuss the role of graph theory in topological structures
• Describe the integrity constraints of integrated topological models (e.g., POLYVRT)
• Discuss the historical roots of the Census Bureau’s creation of GBF/DIME as the foundation forthe development of topological data structures
• Explain why integrated topological models have lost favor in commercial GIS software, andevaluate the positive and negative impacts of this shift

• Illustrate the GBF/DIME data model
• Explain what makes POLYVRT a hierarchical vector data model
• Discuss the advantages and disadvantages of POLYVRT
• Describe the relationship between the GBF/DIME and TIGER structures, the rationale for their design, and their intended primary uses, paying particular attention to the role of graph theory in establishing the difference between GBF/DIME and TIGER files
• Describe a Freeman-Huffman chain code
• Describe the relationship of Freeman-Huffman chain codes to the raster model
• Discuss the impact of early prototype data models (e.g., POLYVRT and GBF/DIME) oncontemporary vector formats

• Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle
• Demonstrate how a network is a connected set of edges and vertices
• List definitions of networks that apply to specific applications or industries
• Create an adjacency table from a sample network
• Explain how a graph can be written as an adjacency matrix and how this can be used to calculatetopological shortest paths in the graph
• Create an incidence matrix from a sample network
• Explain how a graph (network) may be directed or undirected
• Demonstrate how attributes of networks can be used to represent cost, time, distance, or manyother measures
• Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases
• Discuss some of the difficulties of applying the standard process-pattern concept to lines andNetworks

• Construct a data structure to contain point or linear geometry for database record events that are referenced by their position along a linear feature
• Demonstrate how linear referenced locations are often much more intuitive and easy to find in the real world than geographic coordinates
• Explain how linear referencing allows attributes to be displayed and analyzed that do notcorrespond precisely with the underlying segmentation of the network features
• Discuss dynamic segmentation as a process for transforming between linear and planarcoordinate systems
• Describe how linear referencing can eliminate unnecessary segmentation of the underlyingnetwork features due to attribute value changes over time

• Describe the basic forms of generalization used in applications in addition to cartography (e.g.,selection, simplification)
• Discuss the possible effects of generalizing data sets on topological integrity
• Explain why areal generalization is more difficult than line simplification
• Explain the logic of the Douglas-Poiker line simplification algorithm
• Explain the pitfalls of using data generalized for small scale display in a large scale application
• Design an experiment that allows one to evaluate the effect of traditional approaches ofcartographic generalization on the quality of digital data sets created from analog originals
• Evaluate various line simplification algorithms by their usefulness in different applications

• Identify a variety of likely measurement level transformations (e.g., the classification of ratio datayields ordinal data)
• Discuss the relationship of attribute measurement levels to database query operations
• Describe the pitfalls, in terms of information loss and analytical options, of transforming attributemeasurement levels
• Reclassify (group) a nominal attribute domain to fewer, broader classes

• Describe computational intelligence methods that may apply to GI S&T
• Describe a hypothesis space that includes searches for optimality of solutions within that space
• Exemplify the potential for machine learning to expand performance of specialized geospatial analysis functions
• Identify artificial intelligence tools that may be useful for GI S&T

• Define non-linear and non-Gaussian distributions in a geospatial data environment
• Exemplify non-linear and non-Gaussian distributions in a geospatial data environment
• Understand how some machine learning methods might be more adept at modelling orrepresenting such distributions

• Describe the use of pattern recognition based on temporal relationships of objects and space(Crime or disease analyses are examples)

• Compare and contrast the assumptions and performance of parametric and non-parametricapproaches to multivariate data classification
• Compare and contrast the results of the neural approach to those obtained using more traditional
• Gaussian maximum likelihood classification (available in most remote sensing systems)
• Describe three algorithms that are commonly used to conduct geospatial data classification
• Explain the effect of including geospatial contiguity as an explicit neighborhood classificationCriterion

• Analyze the stability of the network using multiple runs with the same training data andArchitecture
• Describe the architecture and components of a feed-forward neural network
• Differentiate between feed-forward and recurrent architectures
• Compare and contrast classification results when the architecture of the network and initialparameters are changed

• Describe how space-scale algorithms can, or should, be used

• Describe how a neural network may use training rules to learn from input data

• Describe classic CA transition rules
• Describe how local and global transitional rules are handled in CA
• Describe how the rules of the Game of Life typically result in a continuously evolving pattern
• Explain two geographical processes that could be effectively represented using CA
• Explain two geographical processes that could not be effectively represented using CA

• Describe the challenges of calibrating CA models
• Describe error sources of CA models
• Explain how temporal concepts are implemented in CA models

• Appraise the possible improvement of integrating GeoAlgebra, Graph-Based Cellular Automata,or agent-based models to overcome the fixed-grid limitations of CA models
• Compare and contrast the analysis of a process using a CA with the analysis of the sameprocess in a GIS using map algebra and similar raster operations
• Explain the potential contribution of integrating data mining into CA models

• Define alternatives to the Tietz and Bart heuristic
• Describe the process whereby an element within a random solution is exchanged, and if itimproves the solution, it is accepted, and if not, it is rejected and another element is tried until no improvement occurs in the objective function value
• Outline the Tietz and Bart interchange heuristic

• Explain how the process to break out local optima can be based on a probability function
• Outline the TABU heuristic

• Outline the rationale for and usefulness of simulated annealing

• Describe how multiple, different types of agents in a given system behave and interact with eachother and their environment
• Describe how multiple parameters (e.g., number of agents, variability of agents, random numberseeds for different series of random events or choices during each simulation, etc.) can be setwithin an agent-based model to change the model behaviour
• Explain how agent behaviors can be used to represent the effects actors have on each other andon their environment
• Generate multiple, different types of agents in a given system

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

• Describe a "bottom-up" simulation from an activity-perspective with changes in the locationsand/or activities the individual person (and/or vehicle) in space and time, in the activity patternsand space-time trajectories created by these activity patterns, and in the consequent emergentphenomena, such as traffic jams and land-use patterns
• Describe how measurements on the output of a model can be used to describe model behaviour
• Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification

• Compare and contrast how systematic errors and random errors affect measurement of distance
• Describe the causes of at least five different types of errors (e.g. positional, attribute, temporal, logical inconsistency and incompleteness)

• Describe the Modifiable Areal Unit Problem (MAUP) associated with aggregation of data collected at different scales and its affect on spatial autocorrelation
• Describe the Modifiable Areal Unit Problem (MAUP) and its affects on correlation, regression andClassification
• Describe the concept of Ecological Fallacy, and comment on its relationship with the ModifiableAreal Unit Problem (MAUP)

• Compare and contrast error propagation techniques (e.g., Taylor, Monte Carlo, etc.)
• Explain how some operations can exacerbate error while others dampen it (e.g., mean filter)

• Describe stochastic error models
• Exemplify stochastic error models used in GIScience

• Define fuzzy measures and give an example of a fuzzy measure
• Explain how numerical values can be mapped onto linguistic variables (e.g., “big,” “distant”)
• Explain how and why fuzzy measures can be used in geocomputation

• Compare and contrast Boolean and fuzzy logical operations
• Describe fuzzy aggregation operators
• Describe how an approach to map overlay analysis might be different if region boundaries were ‘fuzzy’ rather than crisp
• Exemplify one use of fuzzy aggregation operators
• Compare and contrast several operators for fuzzy aggregation, including those for intersect and union

• Develop a standardization criterion that recasts values into a statement of fuzzy set membership

• Explain why plane coordinates are sometimes preferable to geographic coordinates
• Explain what Universal Transverse Mercator (UTM) eastings and northings represent
• Associate UTM coordinates and zone specifications with corresponding position on a world map or globe
• Identify the map projection(s) upon which UTM coordinate systems are based, and explain the relationship between the projection(s) and the coordinate system grid
• Discuss the magnitude and cause of error associated with UTM coordinates
• Differentiate the characteristics and uses of the UTM coordinate system from the Military Grid Reference System (MGRS) and the World Geographic Reference System (GEOREF)
• Explain what State Plane Coordinates system (SPC) eastings and northings represent
• Associate SPC coordinates and zone specifications with corresponding position on a U.S. map or globe
• Identify the map projection(s) upon which SPC coordinate systems are based, and explain therelationship between the projection(s) and the coordinate system grid
• Discuss the magnitude and cause of error associated with SPC coordinates
• Recommend the most appropriate plane coordinate system for applications at different spatial extents and justify the recommendation
• Critique the U.S. Geological Survey’s choice of UTM as the standard coordinate system for the U.S. National Map
• Describe the characteristics of the “national grids” of countries other than the U.S.

• Explain the concept “quadtree”
• Describe the octahedral quarternary triangulated mesh georeferencing system proposed byDutton
• Discuss the advantages of hierarchical coordinates relative to geographic and plane coordinate systems

• Explain the concept “developable surface” and “reference globe” as conceptual ways of projecting the Earth’s surface
• Classify various map projection types by the three main classes of map projections based ondevelopable surfaces
• Classify various map projection types according to the geometric properties preserved
• Illustrate the graticule configurations for “other” projection classes, such as polyconic,pseudocylindrical, etc.
• Explain the mathematical basis by which latitude and longitude locations are projected into x andy coordinate space

• Define key terms such as "standard line," projection "case," latitude and longitude of origin
• Explain how the concepts of the tangent and secant cases relate to the idea of a standard line
• Identify the possible “aspects” of a projection and describe the graticule’s appearance in each aspect
• Identify the parameters that allow one to focus a projection on an area of interest
• Use GIS software to produce a graticule that matches a target graticule
• Implement a given map projection formulae in a software program that reads geographiccoordinates as input and produces projected (x, y) coordinates as output

• Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity
• Describe the different measurement levels on which thematic accuracy is based
• Describe the component measures and the utility of a misclassification matrix
• Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy
• Outline the SDTS and ISO TC211 standards for thematic accuracy

• Illustrate and explain the distinction between “resolution,” “precision,” and “accuracy”
• Illustrate and explain the distinctions between spatial resolution, thematic resolution, andtemporal resolution
• Discuss the implications of the sampling theorem (λ = 0.5 δ) to the concept of resolution
• Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument
• Explain how resampling affects the resolution of image data
• Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps

• Calculate, in terms of ground area, the uncertainty associated with decimal coordinates specifiedto three, four, and five decimal places
• Explain the concept of error propagation
• Explain, in general terms, the difference between single and double precision and impacts onerror propagation

• Distinguish between GIS, LIS, and CAD/CAM in the context of land records management
• Distinguish between topological fidelity and geometric accuracy in the context of a plat map
• Exemplify and compare deed descriptions in terms of how accurately they convey the geometry of a parcel
• Evaluate the difference in accuracy requirements for deeds systems versus registration system

• Outline a workflow that can be used to train a new employee to update a county road centrelines database using digital aerial imagery and standard GIS editing tools

• Design point, transect, and area sampling strategies for given applications
• Differentiate among random, systematic, stratified random, and stratified systematic unaligned sampling strategies
• Differentiate between situations in which one would use stratified random sampling andsystematic sampling

• Identify the fundamental principle of the sampling theorem for specifying a sampling rate orInterval
• Discuss what sampling intervals should be used to investigate some of the temporal patternsencountered in oceanography
• Propose a sampling strategy considering a variable range in autocorrelation distances for aVariable

• Compare common sensors—including LIDAR, and airborne panchromatic and multispectralcameras and scanners—in terms of spatial resolution, spectral sensitivity, ground coverage, andtemporal resolution

• Describe the elements of image interpretation
• Use photo interpretation keys to interpret features on aerial photographs
• Using a vertical aerial image, produce a map of land use/land cover classes
• 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

• Explain the relevance of the concept "parallax" in stereoscopic aerial imagery
• Outline the sequence of tasks involved in generating an orthoimage from a vertical aerialPhotograph
• Evaluate the advantages and disadvantages of photogrammetric methods and LIDAR forproduction of terrain elevation data
• Specify the technical components of an aerotriangulation system

• Describe the source data, instrumentation, and workflow involved in extracting vector data(features and elevations) from analog and digital stereoimagery
• Discuss the extent to which vector data extraction from aerial stereoimagery has been automated
• Discuss future prospects for automated feature extraction from aerial imagery

• Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, andtemporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal)Landsat, commercial high resolution (Ikonos and Quickbird); LIDAR and microwave (Radarsat;SIR-A & -B); hyperspectral (AVRIS, Hyperion)]
• Differentiate between “active” and “passive” sensors, citing examples of each
• Differentiate “push-broom” and “cross-track” scanning technologies
• Explain the principle of multibeam bathymetric mapping
• Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remoteSensing
• Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne orsatellite remote sensing for seafloor mapping
• Select the most appropriate remotely sensed data source for a given analytical task, study area,budget, and availability

• Differentiate supervised classification from unsupervised classification
• Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering
• 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 categoriesCalculate a set of filtered reflectance values for a given array of reflectance values and a digitalimage filtering algorithm
• Describe a situation in which filtered data are more useful than the original unfiltered data
• 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 ofWavelengths
• Describe an application of hyperspectral image data

• Explain how U.S. Geological Survey scientists and contractors assess the accuracy of theNational Land Cover Dataset
• Evaluate the thematic accuracy of a given soils map

• Distinguish between operational, organizational, and societal activities that rely upon geospatialInformation
• Identify practical problems in defining and measuring the value of geospatial information in landor other business decisions
• Describe the potential benefits of geospatial information in terms of efficiency, effectiveness, and equity
• Explain how cost-benefit analyses can be manipulated
• Compare and contrast the evaluation of benefits at different scales (e.g., national, regional /state, local)

• Describe recent models of the benefits of GI S&T applications
• Discuss the extent to which external costs and benefits enhance the economic case for GIS
• Explain how profit considerations have shaped the evolution of GI S&T
• Outline the elements of a business case that justifies an organizations’ investment in anenterprise geospatial information infrastructure

• 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, andbenefit from, GI S&T

• Differentiate among universal/deliberative, pluralist/representative, and participatory models ofcitizen participation in governing
• Compare the advantages and disadvantages of group participation and individual participation
• Describe the six “rungs” of increasing participation in governmental decision-making thatconstitute a “ladder” of public participation
• Describe the range of spatial scales at which community organizations operate
• Describe an example of “local knowledge” that is unlikely to be represented in the geospatial data maintained routinely by government agencies
• Defend or refute the argument that local knowledges are contested
• Explain how community organizations represent the interests of citizens, politicians, and planners
• Explain and respond to the assertion that “capturing local knowledge” can be exploitative
• Explain how legislation such as the Community Reinvestment Act of 1977 provides leverage tocommunity organizations

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• Distinguish among the various intellectual property rights, including copyright, patent, trademark, business methods, and other rights
• Differentiate geospatial information from other “works” protected under copyright law
• Explain how maps may be protected under U.S. copyright law
• Explain how databases may be protected under U.S. copyright law
• Describe advantages and disadvantages of “open” alternatives to copyright protection, such asthe Creative Commons
• Outline the intellectual property protection clause of a contract that a local government uses tolicense geospatial data to a community group

• 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 contracts, licenses, and other mechanisms for sharing geospatial data
• Outline the terms of a licensing agreement with a local engineering consulting firm that amanager of a county government GIS office would employ if charged to recoup revenue throughsale and licensure of county data

• Describe a method that allows users within an organization to access data, including methods ofdata sharing, version control, and maintenance
• Describe how internal spatial data sources can be handled during an implementation process
• Describe how spatial data and GI S&T can be integrated into a work flow process
• Develop a plan for user feedback and self-evaluation procedures
• Evaluate how external spatial data sources can be incorporated into the business process
• Evaluate internal spatial databases for continuing adequacy
• Evaluate the efficiency and effectiveness of an existing enterprise GI system
• Evaluate the needs for spatial data sources including currency, accuracy and access, specificallyaddressing issues related to financial costs, sharing arrangements, online/realtime, andtransactional processes across an organization
• Illustrate how a business process analysis can be used to periodically review systemRequirements
• List improvements that may be made to the design of an existing GI system

• Describe various approaches to the long-term funding of a GI system in an organization
• Describe methods to evaluate the return on investment (ROI) of a GI system within anOrganization
• Develop a budget for ongoing re-design and system improvement
• Discuss the advantages and disadvantages of maintenance contracts for software, hardware, and data across an enterprise
• Evaluate the adequacy of current investments in capital (e.g., facilities, hardware, software) and labor for a GI system
• Justify changes to the investment in an enterprise GI system, including both cutbacks andincreased expenses

• Describe how using standards can affect implementation of a GI System
• Describe effective methods to get stakeholders to create, adopt, or develop and maintainmetadata for shared datasets
• 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 GISystem implementation

• Demonstrate how the way people do their jobs can affect system management
• Describe how system management includes understanding people
• Describe methods for articulating user needs to internal technical support staff

• Describe the stages of two different models of implementing a GI system within an organization
• Describe different organizational models for coordinating GI S&T participants and stakeholders
• Compare and contrast centralized, federated, and distributed models for managing information infrastructures
• Describe the roles and relationships of GI S&T support staff
• Exemplify how to make GI S&T relevant to top management

• Describe the differences between licensing, certification and accreditation in relation to GI S&T positions and qualifications in the U.S., Europe and South Africa
• Discuss the status of professional and academic certification (registration) in GI S&T
• Discuss how a code of ethics might be applied within an organization
• Explain why it has been difficult for many agencies and organizations to define positions androles for GI S&T professionals
• Identify the qualifications needed for a particular GI S&T position
• Identify the standard occupational codes that are relevant to GI S&T

• Compare and contrast training methods utilized in a non-profit to those employed in a localgovernment agency
• Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations
• Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GI S&T workforce
• Find or create training resources appropriate for GI S&T workforce in a local governmentOrganization
• Identify the particular skills necessary for users to perform tasks in three different workforcedomains (e.g., small city, medium county agency, a business, or others)
• Illustrate methods that are effective in providing opportunities for education and training when implementing a GI system in a small city
• Teach necessary skills for users to successfully perform tasks in an enterprise GI system

• Demonstrate understanding of the conceptual underpinnings and purposes of work integrated learning
• Investigate current WIL practice and opportunities in an organisation or sector
• Develop WIL policies, procedures and plans for an organisation
• Provide WIL advice and support
• Promote WIL practices

• Compare and contrast the impact effect of time for developing consensus-based standards withimmediate operational needs
• Explain how resistance to change affects the adoption of standards in an organizationcoordinating a GI system
• Explain how a business case analysis can be used to justify the expense of implementingconsensus-based standards
• Identify organizations that focus on developing standards related to GI S&T
• Identify standards that are used in GI S&T

• Explain how an understanding of use of current and proposed technology in other organizationscan aid in implementing a GI system

• Describe the rationale for and against sharing data among organizations
• Describe methods used by organizations to facilitate data sharing
• Describe the barriers to information sharing

• Assess the status of openness in the GI S&T field
• Differentiate “open standards,” “open source,” and “open systems”
• Discuss the advantages and disadvantages of adopting open systems in the context of a localGovernment
• In the role of a consultant or chief information officer, respond to a client’s or colleague’s question about the future prospects of open standards and systems in GI S&T

• Assess the effect of restricting data in the context of the availability of alternate sources of data
• Exemplify areas where post-9/11 changes in policies have restricted or expanded data access

• Describe the advantages and disadvantages to an organization in using GIS portal informationfrom other organizations
• Describe how inter-organization GIS portals may impact or influence issues related to socialequity, privacy and data access
• Discuss how distributed GI S&T may affect the nature of organizations and relationships amongInstitutions
• Suggest the possible societal and ethical implications of distributed GI S&T