Centers of Academic Excellence in Geospatial Sciences Program … CAE... · 2016-08-16 · UNCLASSIFIED NGA/USGS Centers of Academic Excellence in Geospatial Sciences Program Guidance

UNCLASSIFIED

Public Release #16-561

UNCLASSIFIED

Centers of Academic Excellence in Geospatial Sciences

Program Guidance

Appendix IV-Charlie

Option 3 Scoring Sheet

Each item is limited to 3 examples for a maximum of 15 points each when asked for multiple

examples or entries.

Five (5) Program Requirements Section (Detailed in Appendix I)

Estimated Points

1. Outreach/Collaboration

1a. Shared curriculum. Point Value: 5 points each

1b. Reciprocity of credits. Point Value: 5 points each

1c. Sponsorship or participation in Geospatial Sciences (GS) competitions. Point Value: 5 points each

1d. Local or state government outreach. Point Value: 5 points each

1e. Community outreach. Point Value: 5 points each

SECTION POINT TOTAL

2. GS is Multidisciplinary within the Institution

2a. GS curriculum is taught in existing non-GS courses and non-GS students are introduced to GS knowledge and methods. Point Value: 5 points each

2b. Non-GS courses encourage papers or projects in GS topics or using GS methods and data. Point Value: 5 points each

SECTION POINT TOTAL

3. Student-based GS Research

3a. Program with GS focus has thesis, dissertation, student papers, or independent research project requirements.

UNCLASSIFIED

NGA/USGS Centers of Academic Excellence in Geospatial Sciences

Program Guidance

UNCLASSIFIED

Point Value: 5 points each

3b. List GS courses that require research papers or projects. Point Value: 5 points each

SECTION POINT TOTAL

4. Number of GS Faculty and Course Load

4a. Full-time employee or employees either faculty or member of the administration working in GS with overall responsibility for a GS program. Point Value: 5 points each

4b. Additional full-time GS faculty members (not previously listed) teaching GS courses within the department that sponsors the GS programs. Point Value: 5 points each

4c. Part-time, shared (inter-departmental, other institution, etc.), adjunct (industry expert, etc.) teaching GS courses within the department that sponsors GS programs. Point Value: 5 points each

SECTION POINT TOTAL

5. Active Faculty in Current GS Practice and Research

5a. Peer reviewed publications – papers on GS (inclusive of GEOINT) topics. Point Value: 5 points per paper

5b. Published books or chapters of books on GS topics. Point Value: 10 points per book / 5 point per chapter

5c. Faculty involved in writing grants and obtaining funding for GS (inclusive of GEOINT) education and/or research development or lab equipment. Point Value: 5 points per award

5d. Faculty members are subject matter experts in GS areas for professional certification or accreditation bodies and/or professional societies. Point Value: 10 points per certification/accreditation review/participation (e.g. leadership role on committee, conference panel, etc.)

5e. Faculty members are engaged in and/or initiate student participation (e.g. student’s present papers) or membership in GS professional societies. Point Value: 5 points per presentation or membership

5f. Faculty presents GS papers or thought leadership content at major Regional/National/International conferences and events. Point Value: 5 points per conference

SECTION POINT TOTAL

PROGRAM POINT TOTAL

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

Ten (10) Specialty Areas and Knowledge Unit Requirements Section

(Detailed in Appendix III)

Estimated Points

1. Geospatial Analysis Specialty Area (with Nine (9) Knowledge Units)

1. Geospatial Analytic Reasoning and Problem Solving Fundamentals Knowledge Unit

Satisfied when 7 Topics and all Learning Objectives are met (1 point each). TOPICS: 1. Perception and cognition associated with analytic reasoning and problem

solving.

2. Understanding meta-cognition and socio-cultural thought foundations. 3. Geographic and cultural influences that lead to analytic bias. 4. Analytical constraints associated with collective memory. 5. Establishing appropriate analytic queries related to problem-solving and

reporting requirements.

6. Making insightful judgments through sound analytical objectivity, healthy skepticism, and realistic pattern considerations.

7. Translating requirements into appropriate analytic questions. 8. Solving complex analytics questions through right-sizing project scope.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Discuss analytic bias as it relates to situational awareness, experience, and

cultural differences.

2. Identify common biases regarded as the main reasons for judgment errors in analysis.

3. Describe the impact of social media, collective memory, and false information on creating sound analytical interpretations and judgment.

4. Identify information and sources for checking analytical relevance and accuracy.

5. Recognize the difference between assumptions and inferences. 6. Identify viewpoints and sources of judgment that serve self-interests. 7. Solve a problem applying spatial analytic techniques. 8. List common approaches used to identify project scope and management.

LEARNING OBJECTIVES TOTAL KNOWLEDGE UNIT TOTAL

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

2. Foundations of Spatial Thinking Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Geography as a foundation for GIS. 2. Common-sense geographies. 3. The cultural and political influences affecting the perception and

understanding of geographic information and phenomena.

4. Tobler’s first law of geography. 5. Geographic contextualization and constraints for analysis and

interpretations.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Define the properties that make a phenomenon geographic. 2. Explore the history of geography and its role in Geospatial Information

Sciences and Technology.

3. Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial.

4. Describe the ways in which the elements of culture may influence the understanding and use of geographic information.

5. 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.

6. Evaluate the influences of political ideologies on the understanding of geographic information.


3. Geometric Measures Knowledge Unit Satisfied when all Topics and 7 Learning Objectives are met (1 point each). TOPICS: 1. Distances and lengths. 2. Direction, shape, area, volume, and time. 3. Proximity and distance decay. 4. Adjacency and connectivity.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe several different measures of distance between two points (e.g.

Euclidean, Manhattan, network, spherical, time, social, cost).

2. Describe operations that can be performed on qualitative representations of direction.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

3. Explain why the shape of an object might be important in analysis. 4. Explain how variations in the calculation of area may have real-world

implications (e.g. when calculating density.

5. Explain the rationale behind the use of different forms of distance decay functions.

6. Demonstrate how adjacency and connectivity can be recorded into matrices.

7. Explain how different map projections can introduce errors in measurement of distance, direction or area.

8. Explain how topology relates to adjacency and connectivity. LEARNING OBJECTIVES TOTAL

KNOWLEDGE UNIT TOTAL

4. Analysis of Workflow in Project Management Knowledge Unit Satisfied when the Topic and all Learning Objectives are met (1 point each). TOPIC: 1. Applying the scientific method to projects using geospatial and remote

sensing data.

TOPIC TOTAL LEARNING OBJECTIVES: 1. Discuss the scientific and algorithmic approaches to frame research

questions and develop projects.

2. Deconstruct a scientific hypothesis to identify possible strategies for testing. 3. Identify the sequence of operations and statistical/mathematical methods

appropriate for a specific application.

4. Develop a planned analytical procedure to solve a new unstructured problem.

5. Compare and contrast the relative merits of various tools and methods for procedure design, including flowcharting and pseudocode.

6. Select the appropriate environment (e.g. GIS software, software development environment) for implementing an analytical procedure.


5. Analysis of Topographic or Field-based Data Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Digital Elevation Models (DEMs). 2. Triangulated irregular networks (TINs). 3. DEM-derived surface calculations (e.g. slope, aspect, visibility).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Interpolation. 5. Friction surfaces.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Explain why the properties of spatial continuity are characteristic of spatial

surfaces.

2. Outline methods for calculating slope and aspect from a DEM. 3. Outline methods for calculating slope and aspect from a TIN. 4. Explain why different interpolation methods (e.g. inverse distance

weighted, bi-cubic spline fitting, kriging) produce different results, and suggest ways that they can be evaluated in the context of a specific problem.

5. Perform siting analyses using specified visibility, slope, and other surface-related constraints.

6. Explain how friction surfaces are enhanced by the use of impedance and barriers.

7. Apply the principles of friction surfaces in the calculation of least-cost paths. LEARNING OBJECTIVES TOTAL


6. Geostatistics and Spatial Econometrics Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Regionalized variable theory. 2. Spatial sampling for statistical analysis. 3. Principles of semi-variogram construction and modeling. 4. Weighted least squares method. 5. Principles of kriging and different types of kriging. 6. Spatial trend analysis. 7. Mathematical operations allowed at each level. 8. Spatial econometrics. 9. Spatial regression analysis and geographically weighted regression (GWR). 10. Spatial filtering.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Create spatial samples under a variety of requirements (e.g. coverage,

randomness, and transects).

2. Construct a semi-variogram and illustrate with a semi-variogram cloud.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

3. Apply the method of weighted least squares and maximum likelihood to fit semi- variogram models to datasets.

4. Conduct a spatial interpolation process using kriging from data description to final error map.

5. Apply kriging to appropriate datasets, and interpret the results. 6. Identify geospatial trends within datasets. 7. Explain and know how to test for spatial autocorrelation. 8. Discuss statistical levels and the operations allowed at each level. 9. Apply appropriate levels to data types. 10. Describe the metric content of the levels and know how to change among

levels.

11. Discuss how the statistical data level affects geospatial manipulations. 12. Describe the general types of spatial econometric models. 13. Demonstrate how the spatial weights matrix is fundamental in spatial

econometrics models.

14. Justify the choice of a particular spatial autoregressive model for a given application.

15. Apply a spatial autoregressive model to estimate spatial lags and spatial interactions among variables.

16. Identify modeling situations when and what spatial filtering will be useful. 17. Explain the principles of Geographically Weighted Regression (GWR), and

discuss what kinds of problems are most suited or not suited for GWR to model spatial relationships.

18. Perform an analysis using the GWR technique. LEARNING OBJECTIVES TOTAL


7. Network Analysis Knowledge Unit Satisfied when all Topics and 7 Learning Objectives are met (1 point each). TOPICS: 1. Defining a network: geospatial networks and social networks. 2. Graph theory. 3. Network metrics that describe connections, distributions, and

segmentation.

4. Methods of modeling networks (least-cost path, flow modeling, accessibility modeling).

5. Networks (e.g. hydrologic, transportation, telecommunications, transmission patterns of infectious diseases, social networks, natural disasters, etc.) used to define specific applications or industries.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

6. Understanding the inter-connectedness of networks, communities, patterns of behavior, etc. in analysis through defining and identifying network relationships.

7. Demonstrations of practical situations in which network fundamentals help define the analytical picture through relationship discovery.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe terminology related to network analysis. 2. Demonstrate how networks can be measured using the number of

elements in the network, the distances along network edges, and the network’s level of connectivity in a network.

3. Compute the optimum path between two points through a network using Dijkstra’s algorithm.

4. Apply a maximum flow algorithm to calculate the largest flow from a source to a sink.

5. Explain how the classic transportation problem can be structured as a linear program.

6. Explain several classic problems to which network analysis is applied. 7. Discuss methods for measuring different kinds of accessibility on a network. 8. Define how exploiting networks and network relationships can be applied

to address complex analysis problems.

9. Demonstrate how network analysis tools deepen analytic capabilities and enhance product outputs.


8. Optimization and Location-allocation Modeling Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Location modeling. 2. Linear and non-linear programming. 3. Integer programming. 4. Critical Path Method (CPM). 5. Location-allocation modeling. 6. Spatial Optimization.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Compare and contrast the concepts of discrete vs. continuous location

problems.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

2. Describe the structure of linear programs. 3. Implement linear programs for spatial allocation problems. 4. Assess the outcome of location-allocation models using other spatial

analysis techniques.

5. Describe the structure of linear programs. 6. Implement linear programs for spatial allocation problems. 7. Assess the outcome of location-allocation models using other spatial

analysis techniques.

8. Use location-allocation software to find service facilities that meet given sets of constraints.

9. Create working models to locate new or existing facilities for allocating resources.

10. Define optimal alternative methods, and the trade-offs among solutions. LEARNING OBJECTIVES TOTAL

KNOWLEDGE UNIT TOTAL 9. Spatial Data Integration Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Spatial integration (geometric integration, edge-matching, horizontal

integration, vertical integration, data fusion).

2. Attribute and semantic integration. 3. Temporal integration (time conversion, temporal lineage).

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe the methods used for data integration. 2. Provide methods for vertical and horizontal data integration. 3. Appraise when geometric and semantic integration are possible. 4. Discuss the practical limits of data integration based on data attribution,

resolution, and accuracy.


SPECIALTY AREA TOTAL

2. Cartographic Sciences and Geo-visualization Specialty Area (with four (4) Knowledge Units)

1. Foundations of Cartography Knowledge Unit Satisfied when 7 Topics and 7 Learning Objectives are met (1 point each). TOPICS:

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

1. History of cartography.

2. Understanding audience and purpose of a map.

3. Technological trends and transformations.

4. Map elements (legends, insets, neatlines, etc.), map scale, and map design.

5. Source materials for mapping, and scale of measurement.

6. Reference maps and thematic maps (and types of thematic maps: choroplethic maps, dasymetric maps, contour maps, etc.).

7. Data abstraction, classification, selection, generalization, symbolization.

8. Map reading and induction.

9. Projection as a map design issue.

10. Visual variables.

11. Color theory.

TOPICS TOTAL

LEARNING OBJECTIVES:

1. Describe how symbolization methods used in map-making affect viewer interpretation of the information being presented.

2. Discuss the impact that Web mapping via applications such as Google Earth has had on the practice of cartography.

3. Explain how emerging technologies in related fields (e.g., the stereoplotter, aerial and satellite imagery, GPS and LiDAR, the World Wide Web, immersive and virtual environments) have advanced cartography and visualization methods.

4. Explain how technological changes have affected cartographic design and production.

5. Evaluate the advantages and limitations of various technological approaches to mapping.

6. Select new technologies in related fields that have the most potential for use in cartography and visualization.

7. Explain the impact of advances in visualization methods on the evolution of cartography.

8. Describe how compilation, production, and distribution methods used in map-making have evolved.

9. Identify the map projections commonly used for specific types of maps. 10. Identify the most salient projection property of various generic mapping

goals and proper use of different types of thematic maps (e.g., choropleth map, navigation chart, flow map).

11. Explain why certain map projection properties have been associated with specific map types.

12. Select appropriate projections for world or regional scales that are suited to specific map purposes and phenomena with specific directional orientations or thematic areal aggregations.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

13. Determine the parameters needed to optimize the pattern of scale distortion that is associated with a given map projection for a particular mapping goal and area of interest.

14. Diagnose an inappropriate projection choice for a given map and suggest an alternative.

15. Construct a map projection suited to a given purpose and geographic location; re-create the same map using a different projection and describe what the different views communicate.

16. Identify the criteria used in the selection of data to be represented on a map.

17. Apply the concepts of classification, selection, and generalization of data for portrayal on a map.

18. Describe map projections in general, the types of projections, the distortions inherent to each type and how this relates to map design.


2. Mapping and Design Principals Knowledge Unit Satisfied when 7 Topics and 7 Learning Objectives are met (1 point each). TOPICS: 1. Raster and vector formats. 2. Modern and historic map production methods. 3. Map preparation (standard and custom products). 4. Typography and placement principles. 5. Data distribution methods. 6. Collaborative map design. 7. Information visualization techniques applied to geographic information. 8. Developing animated and interactive maps. 9. “Mapping mashup” construction and programming. 10. Sources of dynamic geographic information use. 11. Usability of dynamic maps.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Distinguish between raster and vector formats and how each is used in the

production of mapping and geospatial products.

2. Describe historical map production methods. 3. Discuss the principles of map preparation and production to include

projections, cartographic license and displacement, and rules for typography placement.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Explain modern and historic map production methods. 5. Prepare maps for standard and custom products. 6. Describe the impacts of conversion on practical use and visualization. 7. Discuss questions of locational and attribute accuracy. 8. Explain projection changes (forward and inverse). 9. Discuss appropriate algorithms and questions of data loss. 10. Explain methods used to distribute/disseminate map products and outputs

(e.g. interactive and on-line distribution, hand help devices, web services, social media sites such as Map Story).

11. Describe the principals for collaborative map design (e.g. VGI, Map Story, Wiki Maps).


3. Extraction and Generalization of Geospatial Data for Geographic Visualization and Cartography Knowledge Unit

Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Imagery types and their characteristics. 2. Proper selection and use of imagery to produce maps and geospatial

products.

3. Techniques for data extraction from imagery source. 4. Classification (per pixel). 5. Object-based image analysis. 6. Neural networks and learning classifiers. 7. Fuzzy maps.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Discuss imagery types. 2. Describe proper use of imagery types to support a range of mapping

applications.

3. Demonstrate the capability to extract content from an imagery source to support mapping outputs.

4. Explain the role of content specifications and standards in data extraction. LEARNING OBJECTIVES TOTAL


4. Integration of Geospatial Information Sources Knowledge Unit Satisfied when 7 Topics and all Learning Objectives are met (1 point each).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

TOPICS: 1. Emerging geospatial information sources (social media, open sources, VGI). 2. Data crowdsourcing fundamentals. 3. Pros/cons of using open source and social media data in mapping

applications.

4. Sources and methods for evaluating and incorporating open source and VGI.

5. Data transaction and update methods from hand-held and mobile devices. 6. Emerging techniques for integrating non-traditional geospatial data and

content for cartographic use.

7. CAD to GIS conversion, data interoperability in cartography. 8. Integrating floor plans/CAD diagrams into maps (Building Information

Modeling (BIM)).

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe non-traditional and emerging data types and their applicability to

mapping and product generation.

2. Explain the principles of crowdsourcing data (methods, pros/cons, the crowd to self- police content).

3. Examine methods for crowd ranking. (employing manual and automated techniques to understand the validity of open source data, applying automated methods for continuous crowd ranking to support the updating of geospatial data).

4. Explore methods for incorporating data from mobile devices into larger geospatial activities in near real-time (e.g. how to take data from Twitter feeds and media reports and incorporate to rapidly update geospatial data to support Humanitarian Assistance and Disaster Relief (HADR) missions).

5. Describe the limitations of using open source data and issues of data quality.

6. Develop fit for use products from open, community, and crowd-sourced data.


SPECIALTY AREA TOTAL 3. Remote Sensing / Imagery Science Specialty Area

(with Ten (10) Knowledge Units) 1. Remote Sensing Collection Platforms Knowledge Unit Satisfied when 7 Topics and 7 Learning Objectives are met (1 point each).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

TOPICS: 1. Basics of aerial photography. 2. High altitude and low altitude airborne platforms. 3. Basics of aircraft position / orientation measurement (i.e., GPS and Inertial

Navigation Systems (INS)).

4. Basic relationships between aircraft operation (i.e., flight speed) and collection parameters (i.e., sensor integration time).

5. Using aerial photography in geospatial information and production problem solving.

6. U.S. imaging satellite constellation. 7. Non-U.S. imaging satellites constellation. 8. Imaging satellite orbits (e.g. geosynchronous, sun synchronous, etc.). 9. Using imaging satellite types and orbits in geospatial information and

production problem solving.

10. Basic UAV aviation and safety. 11. UAV mission planning. 12. UAV data collection and processing.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe the basic theories of aerial photography. 2. Describe common applications for remote sensing using aerial photography. 3. Describe the basic terms related to aircraft flight path and image

parameters: Field of View (FOV), Instantaneous FOV (IFOV), Ground Instantaneous FOV (GIFOV) and their relationship.

4. Describe the differences between roll, pitch, and yaw and their impact on resulting imagery.

5. Describe how platform speed and collection parameters influence image quality (i.e., blur, resolution, etc.).

6. Describe the difference between nadir-looking and "agile" satellites (i.e., Worldview-2).

7. Describe the full constellation of imaging satellites (US and non-US) in space, to include their uplink and downlink architectures.

8. Describe how a satellites orbit can affect when and what type of data can be collected.

9. Apply the knowledge of the global satellite constellation to solving geospatial problems such as disaster response/humanitarian relief, military operations support, global disease surveillance, crop surveillance, earth sciences (e.g. earthquakes, volcanoes, ice melt) and other areas.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

10. Describe UAV imagery collection and its applications ranging from precision agriculture, to disaster response/humanitarian relief/search and rescue, and homeland security and military applications.


2. Radiometry Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Basic radiometric and photometric terms. 2. Derivation of source propagation and sensor output equations. 3. How / why do specific materials detect photons at various wavelengths. 4. Ways to characterize radiometric performance of detectors. 5. Sensor calibration.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Comprehend the quantitative measurement of electromagnetic energy and

how it is applied to simple imaging systems.

2. Discuss radiometric and photometric terms. 3. Describe basic characteristics of detector materials and figures of merit. 4. Explain basic principles and approaches to radiometric sensor calibration.


3. Electro-optical (EO) Sensor Science Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Basic focal-plane and detector applications in remote sensing sensors. 2. Visible, shortwave-, midwave- and longwave- infrared measurement theory

and techniques.

3. Accounting for reflected and emitted energy as described in spectral signatures.

4. Atmospheric interactions, windows and absorption regions/bands. 5. Basic theory, application, and design of a broad range of sensors. 6. Basic passive EO systems and types.

TOPICS TOTAL LEARNING OBJECTIVES: 1. At a fundamental level, explain the process of passive EO signal generation,

(propagation, target interaction, signal receipt and recording).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

2. Discuss atmospheric effects on passive EO collections. 3. Describe reflected and emitted energy and spectral signature generation. 4. Explain sensor theory and application as specifically associated with passive

EO imaging.


4. Thermal Remote Sensing Knowledge Unit Satisfied when 7 Topics and all Learning Objectives are met (1 point each). TOPICS: 1. Principles of thermal remote sensing (Planck function, black body

radiation).

2. Atmospheric effects. 3. Spectral emissivity and kinetic temperature. 4. Factors affecting kinetic temperature. 5. Radiant temperature. 6. Solar heating, longwave upwelling and downwelling radiation. 7. Daytime vs. night-time acquisition. 8. Thermal data applications. 9. Measured radiance as a function of observed material temperature and

emissivity.

10. Methods to separate temperature and emissivity. 11. Thermal hyperpectral systems.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe the principles of thermal imaging systems and factors to consider

when processing thermal data.

2. Identify tools, processing techniques, and applications of thermal data. 3. Recognize thermal data benefits and limitations.


5. Basic Radar Science Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Wave-guiding and radiation as applicable to microwave antennas. 2. Radio Frequency/microwave measurement theory and techniques. 3. Basic theory, application, and design of a broad range of antennas.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Basic radar systems and types. TOPICS TOTAL

LEARNING OBJECTIVES: 1. At a fundamental level, explain the process of radar operation from signal

generation, to propagation, target interaction, signal receipt and recording.

2. Discuss atmospheric effects on radar operation. 3. Describe the use and purpose of signal chirping. 4. Explain antenna theory and application as specifically associated with radar

imaging.


6. LiDAR Data Collection and Processing Knowledge Unit Satisfied when 7 Topics and Learning Objectives are met (1 point each). TOPICS: 1. LiDAR data ingest/manipulation in 3D viewer. 2. Basic analysis of LiDAR data. 3. LiDAR data quality. 4. Types of LiDAR sensors. 5. Different forms of LiDAR data (multiple point returns, intensity data,

waveform data).

6. Introduction to LiDAR data analysis tools, principles, and applications (viewshed / line-of- sight analysis, DEM/DSM estimation).

7. LiDAR classification. 8. Application of LiDAR to real-world missions. 9. LiDAR systems for military applications.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Use LiDAR data in at least one software package. 2. Perform basic point cloud and raster based analysis. 3. Recognize basic LiDAR artifacts and the limitations of LiDAR data. 4. Describe the fundamental physics behind LiDAR collections. 5. Discuss the limitations of LiDAR collection (e.g. altitude, weather, dust). 6. Identify different LiDAR modalities (e.g. airborne, terrestrial, atmospheric). 7. Explain scientific, military, homeland security uses for LiDAR data. 8. Describe LiDAR systems employed by the military (e.g. the Buckeye System).

LEARNING OBJECTIVES TOTAL

UNCLASSIFIED


Program Guidance

UNCLASSIFIED


7. Remote Sensing Data Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Remote Sensing applications (classification, spectral signature analysis,

change detection, anomaly detection, target detection, spectral unmixing).

2. Mathematical frameworks for algorithm development (multivariate statistics, linear algebra and subspace geometry, spectral linear mixture model, basic signal detection theory).

3. Spectral Classification Algorithms (supervised and unsupervised, minimum distance to the mean, Mahalanobis distance, Gaussian maximum likelihood).

4. Spectral signature analysis algorithms (band ratio analysis such as NDVI, NDWI), geologic mineral analysis.

5. Spectral Detection algorithms (anomaly detection such as RX, change detection such as chronocrome, covariance equalization), target detection such as GLRT, spectral matched filter, ACE, CEM).

6. Linear spectral unmixing. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Explain the (semi-) automated applications of quantitative remote sensing

image analysis.

2. Describe the mathematical principles behind quantitative remote sensing image analysis.

3. Discuss the basics of spectral signature analysis. 4. Identify the limitations of quantitative remote sensing image analysis.


8. Digital Image Processing Knowledge Unit Satisfied when 7 Topics and all Learning Objectives are met (1 point each). TOPICS: 1. Radiometric and geometric correction. 2. Histogram manipulation, image enhancement and restoration. 3. Spatial and morphological filtering. 4. Image transformation and data/feature dimensionality reduction. 5. Basics of image/data compression. 6. Image classification and segmentation. 7. Basics of image storage format and representations.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

8. Image processing algorithms and techniques to support image enhancement; image filtering, resampling, interpolation.

9. Automatic and assisted feature recognition algorithms and their limitations. 10. Point and feature matching algorithms.

TOPIC TOTAL LEARNING OBJECTIVES: 1. Describe the steps necessary to prepare raster images for analysis. 2. Apply various forms of pixel and histogram manipulation to extract

information from image.

3. Apply methods to classify an image into various features and classes. 4. Explain the concepts of digital counts, image histogram processing, and

compression.

5. Demonstrate basic proficiency in the computational manipulation of imagery.


9. Computational Radiometry Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Understanding of imaging system modeling (e.g. NIIRS, general image

quality equation).

2. Understanding of material and optical properties. 3. Understanding of atmospheric modeling. 4. Scene construction basics and geometry modeling.

TOPIC TOTAL LEARNING OBJECTIVES: 1. Explain the process to produce synthetic imagery covering various regions

of the electromagnetic spectrum.

2. Use synthetic scenes to test image system designs. 3. Use synthetic scenes to evaluate image exploitation algorithms.


10. Imagery Time Series Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Understanding of basic temporal signal analysis methods such as Harmonic

Analysis of Time Series (HANTS) and Savitsky-Golay Filter, to include.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

2. Using the Iterative Fourier transform to model pixel-wise observations. 3. Decomposing complex temporal signals into series of simple sinusoidal

waves.

4. Replacing outliers and noisy data with values from the Fourier series. 5. Appling least-squares polynomial regression and fitting successive subsets

of adjacent data points.

6. Understanding various time scales of phenomenology in remotely sensed imagery, such as daily vs. annual (i.e., seasonal) cycles.

7. Understanding various time scales of remote sensing systems, such as video rate vs. daily observation vs. Landsat revisit rate.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe the algorithms used for temporal signal decomposition. 2. Explain the various timescales of interest in remote sensing systems. 3. Explain the observable phenomenologies in temporal remote sensing

systems.



4. Photogrammetry Specialty Area (with Five (5) Knowledge Units)

1. Photogrammetric Theory Knowledge Unit Satisfied when at least 7 Topics and all Learning Objectives are met (1 point each). TOPICS: 1. The importance of photogrammetry in geospatial applications. 2. Photogrammetric interior orientation (focal length, principal point, image

coordinate systems, transformations, and fiducials).

3. Photogrammetric exterior orientation (location, orientation, and transformations).

4. Photogrammetric optics, ray tracing, lens/telescope design and lens distortion modeling.

5. Camera, sensor and platform coordinate systems and associated transformations for satellite, airborne, and UAV platforms.

6. Sensor models (modeling ground to image and image to ground projections for various sensors and platforms and colinearity equations).

7. Rigorous vs. replacement sensor models, generic sensor models, and community sensor model (CSM).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

8. Approximations to sensor models (polynomial [RPC, RSM], DLT, orthographic and accuracy/performance characteristics).

9. Single image resection to recover camera model. 10. Relative orientation, multi-image intersection. 11. Camera calibration. 12. Perspective geometry. 13. Block adjustment/triangulation of multiple photos to recover imaging and

ground parameters, including interior and exterior orientations.

14. Stereoscopy, parallax, and relief displacement. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Discuss sensor modeling and accuracy characterization. 2. Explain how to determine interior and exterior orientation of sensors on

satellite, airborne and UAV platforms and how they are used in photogrammetric operations.

3. Describe a variety of multi-image photogrammetric techniques and their application to camera calibration, exterior orientation, and image exploitation.

4. Discuss optics theory and application. 5. Compare and contrast the similarities and differences among the

photogrammetric exploitation of imagery data from satellite, airborne and UAV platforms.


2. Photogrammetric Application Knowledge Unit Satisfied when at least 7 Topics and all Learning Objectives are met (1 point each). TOPICS: 1. Image measurement techniques and autocorrelation. 2. Monoscopic ray intersection. 3. Stereoscopic/multiscopic ray intersection. 4. Triangulation, single sensor and multi-sensor block adjustment. 5. Perspective, orthographic, and epipolar rectification. 6. Terrain/surface/object models (types, formats, how constructed, how to

use, accuracy).

7. Automated and manual terrain extraction techniques. 8. Line of sight extractions from multiscopic imagery. 9. Image registration.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

10. Algorithms and techniques for measuring object properties from imagery (dimensions, shape, locations, orientation).

11. Algorithms, techniques and limitations of using solar information for measuring object properties from imagery.

12. Algorithms, techniques and limitations of using shadow information for measuring object properties from imagery.

13. CAD modeling and fusing CAD models with imagery to include draping imagery over urban 3D models.

14. Image simulation techniques. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Discuss algorithms to generate terrain models and their various formats. 2. Explain how image rays can be used to determine object dimensions and

orientations.

3. Describe how to build CAD models of objects and project them into imagery for multiple types of terrain (e.g. earth surface, urban areas, etc.).

4. Explain the theory and application of rectification. LEARNING OBJECTIVES TOTAL


3. Close Range Photogrammetry Knowledge Unit Satisfied when at least 7 Topics and all Learning Objectives are met (1 point each).

TOPICS: 1. Characteristics of handheld cameras (point and click, single lens reflex, and

mobile devices).

2. Close-range camera calibration. 3. Perspective geometry and single photo perspective photogrammetric

techniques.

4. Recovering camera model from vanishing perspective. 5. Block adjustment/triangulation of multiple photos to recover imaging and

ground parameters, including interior and exterior orientations.

6. Stereoscopy, parallax, and relief displacement. 7. Algorithms and techniques for measuring object properties from close-

range imagery (dimensions, shape, locations, orientation).

8. Algorithms, techniques and limitations of using solar information for measuring object properties from close-range imagery.

9. Incorporation of photogrammetric results into 3D visualization products, e.g., point clouds, surface models, and engineering models.

10. Perspective geometry and characteristics of immersive imagery (e.g. digital street view images, Google Earth, etc).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe the characteristics of close-range cameras. 2. Explain how image rays can be used to determine object dimensions and

orientations.

3. Discuss perspective geometry and how to use it in building camera models. 4. Apply relevant techniques of computer vision to close-range

photogrammetry.

5. Apply relevant techniques of measurement to determine object dimensions and orientations in immersive imagery (e.g. digital street view images, Google Earth).


4. Mathematics, Statistics, and Optimization for Imagery Applications Knowledge Unit

Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Understanding statements of absolute and relative accuracy for

geopositioning, distance, azimuth, and various object properties.

2. Error propagation theory and its application to geopositioning, relative mensuration, and measured object properties.

3. Statistical representation and analysis of sensor or image product absolute and relative accuracy performance.

4. Optimization theory using least squares techniques (general least squares, constrained, unified least squares, sequential, least squares filtering).

5. Linear algebra (matrix representation, linear transformations, equation solution).

6. Numerical analysis (numerical considerations, iteration, numerical approximation).

7. Projective geometry. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Identify general concepts of statistics and their application to spatial

information.

2. Discuss statistical graphing and analysis of absolute and relative accuracy performance for sensors and their derived products.

3. Explain theory and application of least squares optimization techniques. 4. Describe how to model 3D space.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED


5. Digital Photogrammetry Knowledge Unit Satisfied when at least 7 Topics and all Learning Objectives are met (1 point each). TOPICS: 1. Image processing algorithms and techniques to support image

enhancement (image filtering, resampling, interpolation).

2. Automatic and assisted feature recognition algorithms and their limitations. 3. Point and feature-matching algorithms. 4. Computer vision (camera calibration, image formation, 3D shape

reconstruction, object recognition, feature detection, motion estimation, feature matching, transformations, computational photography).

5. Digital signal processing. 6. Digital scanning algorithms, techniques, and accuracy. 7. Incorporation of photogrammetric results into 3D visualization products

(e.g., point clouds, surface models, engineering models).

8. Implications for photogrammetry in immersive 3D environments (e.g. Oculus Rift, holograms, first-person video, anaglyphs).

TOPICS TOTAL LEARNING OBJECTIVES: 1. Identify overall image processing algorithms for remotes sensing

applications.

2. Describe theory and algorithms of computer vision and apply them to photogrammetric problems.

3. Describe theory and algorithms of immersive 3D technologies such as Oculus Rift, holograms, first person video, anaglyphs, etc. and apply them to photogrammetric problems.



5. Information Science Specialty Area (with Three (3) Knowledge Units)

1. Spatial Applications of Big Data Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Big data for spatial applications. 2. Big data analytics for spatial applications.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

3. Methods for spatial data analysis in big data. 4. Using data science principles in cartographic design. 5. Application of data science principles to cartography. 6. Future and emerging areas of data science inquiry for spatial applications. 7. QA/QC of geospatial data in big data applications.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Explain the concepts and principles of big data analytics for spatial

applications (meaning, methods, and outcomes).

2. Demonstrate the use of big data analytics concepts in geospatial analysis. 3. Describe QA/QC methods for geospatial data in big data applications and

discuss the implications for analysis and analytics results.

4. Describe the future directions for big data analytics in geospatial applications.

5. Discuss approaches to geospatial metadata/data tagging in big data applications, why metadata/data tagging is important and challenges to managing geospatial data in big data applications.


2. Advanced Spatial Analysis Through Programming Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Common scripting languages for spatial applications (e.g. Python, etc.). 2. Common programming languages for spatial applications (e.g. IDL,

C++/JAVA, ERDAS Imagine Spatial Modeler).

3. Cloud-based programming for spatial applications. 4. Creating spatial applications for mobile and web-based platforms.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe the basics of scripting and programming languages used for spatial

applications (desktop, mobile, web-based applications).

2. Explain the basics of programming and coding algorithmic routines in expanding existing applications and/or developing new functionality and capabilities to address hard-problem challenges with niche solutions.

3. Discuss the basics of how programming and scripting languages are used with common geospatial software packages (e.g. ENVI, ESRI, ERDAS Imagine).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Demonstrate the applied application of programming for spatial applications (e.g. automate the QC of 40 maps).

5. Demonstrate the applied use of cloud-based technologies (e.g. MapReduce, Hadoop) for geospatial applications.


3. Spatial Query Operations and Query Languages Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Set theory. 2. Application of query operations/query languages to GIS and spatial data

analysis (e.g. Structured Query Language (SQL), non-SQL, SPARQL, JSON, JAVA, HTML).

3. Attribute queries vs. spatial queries. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Explain how set theory relates to spatial queries. 2. Perform a logic (set theoretic) query using GIS software. 3. Define basic terms of query processing (e.g. SQL, primary and foreign keys,

table join).

4. Demonstrate multiple query language techniques (e.g. SQL, non-SQL, SPARQL, JSON, JAVA, HTML) to retrieve elements from a GIS.

5. Compare and contrast attribute queries and spatial queries. 6. Construct a query statement to search for a specific spatial or temporal

relationship; compare/contrast the use of different query languages for spatio-temporal data searches and describe when to choose one language over another.


SPECIALTY AREA TOTAL 6. Aeronautical Analysis Specialty Area

(with Four (4) Knowledge Units) 1. Airspace Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Domestic airspace boundary formulation, limitations and characteristics. 2. Foreign airspace boundary formulation, limitations and characteristics.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

3. International airspace structures, regulations and policy. 4. Domestic and international piloting procedures in airspace of the world.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Identify the various types and characteristics of worldwide airspace

structures.

2. Describe US military mission requirements in using worldwide airspace structures.

3. Determine if, how, and when domestic/international airspace is safe for US military use.


2. Airway Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Domestic airway limitations and characteristics. 2. Foreign airway limitations and characteristics. 3. International airway structures, regulations and policy. 4. Domestic and international piloting procedures on airways of the world.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Identify the various types and characteristics of worldwide airway

structures.

2. Describe US military mission requirements in using worldwide airway structures.

3. Determine if, how and when a domestic/international airway is safe for US military use.


3. Airfield Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Airfields logistics. 2. Airfield infrastructure. 3. U.S. military aircraft usage of domestics/international airfield services. 4. Common signs of airfield upgrades and expansion.

TOPICS TOTAL

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

LEARNING OBJECTIVES: 1. Identify common and uncommon forms of domestic and international

airfield infrastructure.

2. Describe the variety and complexity of US military mission requirements in using worldwide airfields.

3. Determine if, how and when domestic/international airfields are safe and/or suitable for US military use.


4. Flight Procedure Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Domestic flight procedure limitations and characteristics. 2. Foreign flight procedure limitations and characteristics. 3. International flight procedure formulation, regulations and policy. 4. Domestic and international piloting procedures of the world.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Discuss the various worldwide flight procedures. 2. Describe US military mission requirements in using worldwide flight

procedures.

3. Determine if, how and when domestic/international flight procedures are safe for US military use.


SPECIALTY AREA TOTAL 7. Navigation and Location Specialty Area

(with Two (2) Knowledge Units) 1. Geodesy Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Geometric geodesy (ellipsoid characteristics, geometry, WGS84). 2. Gravity modeling and earth gravity models; the geoid and geoid separation,

mean sea level approximation.

3. Earth coordinate systems and associated transformations (ECI, ECF, spherical, ellipsoidal).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Local coordinate systems and associated transformations (spherical, ENU, geographic projection systems [UTM, state plane]).

5. Absolute and relative survey coordinate and accuracy information. 6. Ellipsoid height, geoid height, and orthographic height. 7. Universal time and earth orientation parameters.

TOPICS TOTAL LEARNING OBJECTIVES: 8. Discuss the general theory of geodesy and gravity modeling (relations of

gravitational models and geoid, the effects of gravitational distribution on the height datum problem).

9. Identify the various spatial coordinate systems and how to transform between them.

10. Explain what the geoid is, and how it relates to mean sea level. 11. Discuss the importance of ground survey information and the use of the

word “control” to describe a surveyed point or feature.

12. Describe the differences between ellipsoid height, geoid height, MSL height, and orthographic heights.


2. Fundamentals of the Global Positioning System (GPS) and the Global Navigation Satellite (GNSS) Knowledge Unit

Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. How GPS works. 2. Orbits and signals. 3. Accuracy and error analysis. 4. GPS modernization and GNSS. 5. GNSS today and into the future.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe a GNSS system (hardware, software and control of system) and

relate it to GPS.

2. Describe a plan for collecting GPS data (hardware, software, process) for different types of applications.

3. Describe what can affect the accuracy or cause errors for data collected by a GPS.

4. Demonstrate how to collect accurate GPS data useful in different applications.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

5. Discuss the historic development of GNSS and describe possible future uses and trends.


SPECIALTY AREA TOTAL 8. Maritime Specialty Area

(with X Knowledge Units) (Knowledge Unit Name Here) Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS:

TOPICS TOTAL LEARNING OBJECTIVES:


SPECIALTY AREA TOTAL 9. Human Geography Specialty Area

(with 3 Knowledge Units) 1. Human Geography Fundamentals Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. People and their environments. 2. Cultural bias. 3. Human Geography (HG), Human Terrain Analysis (HTA), and Socio-Cultural

Analysis (SCA).

4. Know the various types of human geography projects. 5. Human Geography and the intelligence community (IC). 6. Impacts of Human Geography and policy.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Explain how humans interact with their environment. 2. Compare and contrast the terms “spatial”, “spatial-temporal”, and “spatial

analysis”.

3. Define the role of cognitive bias in creation or maintenance of human geography data.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Distinguish among the basic concepts of Human Geography (HG), Human Terrain Analysis (HTA), and Socio-Cultural Analysis (SCA).

5. Describe the capabilities and limitations of intelligence disciplines (e.g. open source intelligence (OSINT), Human Intelligence (HUMINT), Signals Intelligence (SIGINT), Geospatial Intelligence (GEOINT)) as they relate to human geography.

6. Define how human geography can impact policy maker decisions. 7. Identify academic resources that human geography can leverage.


2. Cultural/Regional Expertise Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Impacts of human geography issues. 2. Human population characteristics and distribution. 3. Material and non-material cultures. 4. Acculturation, assimilation, and globalization. 5. Religion, creed, values, and norms. 6. Concepts of social identity (e.g. race, ethnicity, and language). 7. Cultural political organizations. 8. Population demography and migration. 9. Demographic Transition Model. 10. Epidemiologic Transition Model.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe some of the major human geography issues in a region that can influence

information requirements for governmental and non-governmental organizations.

2. Define the characteristics and distribution of human populations that would be of interest to policy makers.

3. Describe factors that determine why things and people are located in distributed in certain patterns.

4. Describe the differences between material and nonmaterial culture. 5. Understand the terms cultural landscape, cultural diffusion, vernacular regions,

and mental maps.

6. Understand what, how, and why acculturation, assimilation, and globalization take place.

7. Describe how major religious systems describe and organize themselves. 8. Explain the distinctions between universalizing religions, ethnic religions, and

traditional religions.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

9. Compare and contrast fundamental tenets of major religions (e.g. Christianity, Islam, Hinduism, and Buddhism, etc.) and describe some of the world’s most notable inter- and intra-faith conflicts.

10. Describe terms of identity (ethnicity race, tribe, nationality, citizenship and language).

11. Explain basic organizational principles of tribes, clans and patrilineal or matrilineal descent.

12. Explain how administrative boundaries may or may not correctly portray the broad spatial patterns found in human geography.

13. Describe the broad spatial patters of language families across the globe, with emphasis on monolingual and multilingual areas.

14. Understand linguistic terms that are important to Human Geography, including official language, standard language, lingua franca, dialect, pidgin language and creole language.

15. Explain with examples, differences between translation and transliteration. 16. Contrast the terms nation, state, nation-state, multinational state, and country as

defined by political scientists.

17. Describe generally understood effects of imperialism and colonialism across the globe today.

18. Explain basic population parameters and processes (e.g. birth and death rates, fertility, life expectancy, natural increase, infant mortality, carrying capacity, overpopulation, etc.).

19. Understand how basic demographic parameters and processes relate to broad social measures, including economic development, education, gender relationships, political power, etc.

20. Explain the Demographic Transition Model and give examples of its relevance to Human Geography.

21. Describe basic factors that can lead to migration, particularly in terms of push and pull effects and migration models including migrant status, voluntary migration, forced migration, refugee status, chain migration, internal migration, emigration, and diaspora.

22. Describe the Epidemiologic Transition Model and its effects on populations, cultures and demographics.


3. Human Geography Data Foundation and Management Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Human Geography data, data sources and search engines. 2. Human Geography data assessment, standards and metadata. 3. Human Geography data schema.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

4. Human Geography data enhancement. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Describe potential sources of Human Geography data and information. 2. Explain how to assess/evaluate Human Geography data sources. 3. Define common commercial databases and search engines from which data

pertinent to Human Geography can be obtained.

4. Explain the importance of assessment and how to source Human Geography information (e.g. from databases, written reports, and other written material).

5. Define the purpose of geospatial standards for Human Geography data creation and use.

6. Describe how to transform data and information from unstructured to structured formats.

7. Comprehend the importance of metadata tagging for Human Geography datasets. 8. Describe basic IT terminology and concepts as it relates to Human Geography data,

databases, and data stewardship.

9. Explain the function of a data schema in the structuring of geospatial data. 10. Describe how to attribute data to facilitate discovery and reuse (populate HG

metadata).

11. Describe how to create multiple Human Geography vector data sets within a GIS platform following a given data schema.

12. Describe how to construct a geospatial dataset from a set of static maps, graphics, tabular and text reports.

13. Describe how to modify existing geospatial data – both geometry and attribution. 14. Describe the importance of data management and data enhancement prior to

analysis.



10. Information Technology / Data Science Specialty Area (with 5 Knowledge Units)

1. Research Design and Application for Data and Analysis Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Fundamentals and historical context of data analysis and the data science

pipeline.

2. Components of data sets. 3. Different data structures. 4. Common data-representation schemes and structures.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

5. Scope the resources required for a data science project. 6. Know what analyses are possible given a particular data set, including both the

state of the art of the field and inherent limitations.

7. Making reproducible research and processes. 8. Basic statistical understanding including probability distributions, hypothesis

testing, and linear regression, and causality.

9. Types of data science questions i.e. Descriptive, Exploratory, Inferential, and so on.

10. Design of experiment. 11. Sampling. 12. Critical thinking and logic.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Discuss what data represents. 2. Describe the components of data. 3. Identify common data structures used for collection for analytic problems. 4. Discuss common data-representation schemes and structures: unstructured and

semi-structured data: text, web logs, and html.

5. Explain the resources required to develop and complete a data science project with a timeline and cost estimate.

6. Describe best practices of reproducible data analysis. 7. Identify various experimental designs and describe the benefits and constraints of

each.

8. Explain various sampling schemes. 9. Describe common critical thinking techniques.


2. Data Storage and Preparation Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Data acquisition. 2. Dealing with Big Data sets: ETL, SQL, non-SQL, data nodes, data fusion/integration,

data transformation.

3. Data cleaning. 4. Data Recording. 5. Understand specialized systems and algorithms that have been developed to work

with data at scale, including MapReduce and other software; core techniques in

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

distributed systems; characteristics of HPC and cloud platforms; and important scalable algorithms for graphs, streams and text.

6. Data Munging/Mining: PCA, feature Extraction, binding, unbiased estimators, handing missing variables and outliers, normalization, dimensionality reduction, denosing, sampling.

7. Tidy Data. 8. CRISP-DM. 9. Data base structures and trade-offs.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Describe how to access data from a variety of sources including relational

databases, NoSQL data stores, web-based APIS.

2. Demonstrate programming skills in R, Hadoop and other languages to mine massive amounts of information.

3. Prepare clean data. 4. Show how to reformat/recode data for analysis. 5. Apply dimensionality reduction techniques to big data sets. 6. Explain CRISP-DM data mining construct. 7. Explain different data base structures and the benefits and draw backs of each. 8. Describe the tidy data concepts and employ it to produce a clean data set.


3. Exploring and Analyzing Data Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Exploratory analysis and inferential hypothesis testing through the basics of

statistical analysis.

2. Data analyses using comparisons between batches, analysis of variance and linear and logistic regression. Evaluation of assumptions; data transformation; reliability of statistical measures; resampling methods; validation of assumptions; interpretation; causation versus correlation.

3. Principals of Bayesian Statistics. 4. Spatial Statistics. 5. Time-Series Analysis. 6. Programming for data analysis (e.g. SAS, R or Python) to include data frames,

vectors, matrices, reading and writing data, sub-setting, REGEX, functions and factor analysis.

7. Texting mining/NLP: corpus, text analysis, TF/IDF, SVM, feature extraction, sentiment analysis.

UNCLASSIFIED


Program Guidance

UNCLASSIFIED

TOPICS TOTAL LEARNING OBJECTIVES: 1. Applying statistical methods and regression techniques to make sense out of data

sets both large and small.

2. Demonstrate how to apply Bayesian statistics to solve problems. 3. Employ time series analysis to temporal and spatio-temporal data. 4. Employ spatial statistics to spatial and spatio-temporal data. 5. Use various statistical packages or programs to conduct data analysis. 6. Apply text mining techniques to unstructured textual data.


4. Machine Learning and Statistical Models Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Introduction of the theory and application of statistical machine learning Topics

include supervised versus unsupervised learning; and regression, classification, clustering, and dimensionality reduction.

2. Deep Learning techniques, especially CNN and computer vision. 3. Collaborative Filtering/Recommendation Engines. 4. Model Evaluation. 5. Machine leaning applications. 6. Open-source programming tools and techniques available for implementing

machine learning.

TOPICS TOTAL LEARNING OBJECTIVES: 1. Identify potential applications of machine learning. 2. Describe the differences in type of analyses enabled by regression, classification,

clustering, and dimensionality reduction.

3. Select the appropriate machine learning technique. 4. Explain the differences between machine learning and deep learning and describe

the structure of deep learning techniques.

5. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.

6. Asses the model quality with relevant error metrics. 7. Use a fitted model to analyze new data. 8. Build an end-to-end application that uses machine learning at its core. 9. Implement these techniques in Python or R (or in the language of your choice,

though Python or R is highly recommended).

UNCLASSIFIED


Program Guidance

UNCLASSIFIED


5. Data Visualization and Communication Knowledge Unit Satisfied when all Topics and Learning Objectives are met (1 point each). TOPICS: 1. Types of infographics: decision trees, neural networks, survey plots, timelines,

bubble charts, scatterplots, tree maps, histograms, boxplots, etc.

2. Communicating quantitative information through storytelling to impact the organization.

3. Understand the design and presentation of digital information using modern visualization software (e.g. Tableau, ggplot2, D3.js, matplotlib, Qlikview).

4. Identify common design principles for visualizations (e.g. Edward Tufte’s The Visual Display of Quantitative Information).

5. Presenting appropriate data visualization for specific customers. TOPICS TOTAL

LEARNING OBJECTIVES: 1. Design and critique visualizations. 2. Prepare infographics and dashboards in at least one program (e.g. MATLAB,

Tableau, etc.) and programming language (e.g. R, Python, etc.).

3. Construct streamlined analyses and highlight their implications efficiently using visualizations.

4. Produce effective visualizations that harness the human brain’s innate perceptual and cognitive tendencies.

5. Explore methods of presenting complex information to enhance comprehension and analysis; and the incorporation of visualization techniques into human-computer interfaces.

6. Explain the state-of-the-art in privacy, ethics, governance around big data and data science.



Documents

Centers of Academic Excellence in Geospatial Sciences Program … CAE... · 2016-08-16 · UNCLASSIFIED NGA/USGS Centers of Academic Excellence in Geospatial Sciences Program Guidance