Identify the key academic disciplines that contributed to the development of GIS&T
Evaluate the role that the Quantitative Revolution in geography played in the development of GIS
Describe the major research foci in GIS and related fields in the 1970s, 1980s, 1990s, and 2000s
Evaluate the importance of the NCGIA and UCGIS in coalescing GIScience as a sub-field of GIS&T
Discuss the contributions of early academic centers of GIS&T research and development (e.g., Harvard Laboratory for Computer Graphics, UK Experimental Cartography Unit)
Compare and contrast agent-based models and cellular automata as approaches for modeling spatial processes
Describe how agent-based models use object-oriented programming constructs of inheritance and encapsulation to represent the behavior of heterogeneous and interactive and adaptive actors
Describe how multiple, different types of agents in a given system behave and interact with each other and their environment
Generate multiple, different types of agents in a given system
Describe how multiple parameters (e.g., number of agents, variability of agents, random number seeds for different series of random events or choices during each simulation) can be set within an agent-based model to change the model behavior
Explain how agent behaviors can be used to represent the effects actors have on each other and on their environment
Design simple experiments with an agent-based model
Design and implement a simple agent-based model using appropriate commercial or open source development tools
Conduct simple experiments with an agent-based model, analyze results, and evaluate their statistical significance with respect to degrees of freedom, sensitivity analyses, and uncertainty in the model
Describe how measurements on various inputs and outputs of a model can be used to describe model behavior and to relate model outcomes to various initial conditions
Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
Determine if an agent-based model has been run enough times with enough different random number seeds for rigorous inference of its results
Differentiate supervised classification from unsupervised classification
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 of wavelengths
Describe an application of hyperspectral image data
Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
Describe a situation in which filtered data are more useful than the original unfiltered data
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 categories
Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling
Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations
Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis
Differentiate between geostatistics and spatial statistics
Discuss situations when it is desirable to adopt a spatial approach to the analysis of data
Explain what is added to spatial analysis to make it spatio-temporal analysis
Explain what is special (i.e., difficult) about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems
Outline the sequence of tasks required to complete the analytical process for a given spatial problem
Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis
FC-31 - Academic origins