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KE-24 - GIS&T Positions and Qualifications

Workforce needs tied to geospatial data continue to evolve.  Along with expansion in the absolute number of geospatial workers employed in the public and private sectors is greater diversity in the fields where their work has become important.  Together, these trends generate demand for new types of educational and professional development programs and opportunities. Colleges and universities have responded by offering structured academic programs ranging from minors and academic certificates to full GIS&T degrees.  Recent efforts also target experienced GIS&T professionals through technical certifications involving software applications and more comprehensive professional certifications designed to recognize knowledge, experience, and expertise.

KE-12 - GIS&T Project Planning and Management

GIS&T project planning and management falls under the broader category of project management (PM) in general and information technology (IT) PM in particular, providing a rich background and guidelines that are stewarded by associations and their certifications. The lifecycle of a project or its component phases involves a number of process groups involving a series of actions leading to a result that are sequenced in the following manner: initiating, planning, executing and controlling, and closing. Effective project planning and management requires understanding of its knowledge areas in the project management body of knowledge (PM BoK), which include integration, scope, time, cost, quality, human resource, communications, risk, procurement, and stakeholder management. Numerous tools and techniques are available to assist the project manager in planning, executing, and controlling these efforts, some of which are specific to GIS&T projects. The distinctiveness of GIS&T project planning and management lies in an understanding of the uniqueness, overlap and connections that exist between the PM BoK and the GIS&T BoK, both of which have achieved new levels of maturity in recent decades. 

AM-81 - GIS-Based Computational Modeling

GIS-based computational models are explored. While models vary immensely across disciplines and specialties, the focus is on models that simulate and forecast geographical systems and processes in time and space. The degree and means of integration of the many different models with GIS are covered, and the critical phases of modeling: design, implementation, calibration, sensitivity analysis, validation and error analysis are introduced. The use of models in simulations, an important purpose for implementing models within or outside of GIS, is discussed and the context of scenario-based planning explained. To conclude, a survey of model types is presented, with their application methods and some examples, and the goals of modeling are discussed.

AM-22 - Global Measures of Spatial Association

Spatial association broadly describes how the locations and values of samples or observations vary across space. Similarity in both the attribute values and locations of observations can be assessed using measures of spatial association based upon the first law of geography. In this entry, we focus on the measures of spatial autocorrelation that assess the degree of similarity between attribute values of nearby observations across the entire study region. These global measures assess spatial relationships with the combination of spatial proximity as captured in the spatial weights matrix and the attribute similarity as captured by variable covariance (i.e. Moran’s I) or squared difference (i.e. Geary’s C). For categorical data, the join count statistic provides a global measure of spatial association. Two visualization approaches for spatial autocorrelation measures include Moran scatterplots and variograms (also known as semi-variograms).

CP-23 - Google Earth Engine

Google Earth Engine (GEE) is a cloud-based platform for planetary scale geospatial data analysis and communication.  By placing more than 17 petabytes of earth science data and the tools needed to access, filter, perform, and export analyses in the same easy to use application, users are able to explore and scale up analyses in both space and time without any of the hassles traditionally encountered with big data analysis.  Constant development and refinement have propelled GEE into one of the most advanced and accessible cloud-based geospatial analysis platforms available, and the near real time data ingestion and interface flexibility means users can go from observation to presentation in a single window.

PD-13 - GPU Programming for GIS Applications

Graphics processing units (GPUs) are massively parallel computing environments with applications in graphics and general purpose programming. This entry describes GPU hardware, application domains, and both graphics and general purpose programming languages.

CP-06 - Graphics Processing Units (GPUs)

Graphics Processing Units (GPUs) represent a state-of-the-art acceleration technology for general-purpose computation. GPUs are based on many-core architecture that can deliver computing performance much higher than desktop computers based on Central Processing Units (CPUs). A typical GPU device may have hundreds or thousands of processing cores that work together for massively parallel computing. Basic hardware architecture and software standards that support the use of GPUs for general-purpose computation are illustrated by focusing on Nvidia GPUs and its software framework: CUDA. Many-core GPUs can be leveraged for the acceleration of spatial problem-solving.  

DC-19 - Ground Verification and Accuracy Assessment

Spatial products such as maps of land cover, soil type, wildfire, glaciers, and surface water have become increasingly available and used in science and policy decisions.  These maps are not without error, and it is critical that a description of quality accompany each product.  In the case of a thematic map, one aspect of quality is obtained by conducting a spatially explicit accuracy assessment in which the map class and reference class are compared on a per spatial unit basis (e.g., per 30m x 30m pixel).  The outcome of an accuracy assessment is a description of quality of the end-product map, in contrast to conducting an evaluation of map quality as part of the map production process.  The accuracy results can be used to decide if the map is of adequate quality for an intended application, as input to uncertainty analyses, and as information to improve future map products.

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