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. 

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).

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.

Grid operations are manipulation and analytical computations performed on raster data. Map Algebra is a language for organizing and implementing grid operations in Geographic Information Systems (GIS) software, and is typically categorized into Local, Focal, and Zonal functions, where each function typically ingests one or more grids and outputs a new grid. The value of a specific grid cell in the output grid for Local functions is determined from the value(s) of the analogous cell position(s) in the input grid(s), for Focal functions from the grid cell values drawn from a neighborhood around the specific output grid cell, and for Zonal functions from a set of grid cells specified in a separate zone grid. Individual functions within a category vary by applying a different arithmetic, statistical, or other type of operator to the function. Map Algebra also includes Global and Block function categories. Grid operations can be categorized as data manipulation procedures or within domain-specific applications, such as terrain analysis or image processing. Grid operations are employed in a variety of GIS-based analyses, but are particularly widely used for suitability modeling and environmental analyses.