Analytics and Modeling

This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.

Topics in this Knowledge Area are listed thematically below. Existing topics are linked directly to either their original (2006) or revised entries; forthcoming, future topics are italicized. 


Basic Spatial Operations Advanced Spatial Analysis Surface Analysis
Buffers Identifying & designing analytical procedures Calculating surface derivatives
Overlay Point pattern analysis Interpolation methods
Neighborhoods Cluster analysis Intervisibility
Map algebra Exploratory data analysis (EDA) Cost surfaces
  Analyzing multi-dimensional attributes  
Spatial Modeling Multi-criteria evaluation Network Analysis
Cartographic modeling Weighting schemes Least-cost (shortest) path analysis 
Components of models Spatial interaction Flow modeling
Coupling scientific models with GIS The spatial weights matrix The Classic Transportation Problem
Mathematical models Spatial interaction Other classic network problems
Spatial process models Space-scale algorithms Accessibility modeling
Using models to represent info & processes    
Workflow analysis and design Space-Time Analytics & Modeling Data Mining
  Computational movement analysis Data mining approaches
Data Manipulation Time geography Knowledge discovery
Approaches to point, line, area generalization   Pattern recognition
Coordinate transformations Spatial Statistics Geospatial data classification
Data conversion Global measures of spatial association Multi-layer feed-forward neural networks
Impacts of transformations Local measures of spatial association Rule learning
Raster resampling Spatial sampling for statistical analysis  
Vector-to-raster and raster-to-vector conversions Stochastic processes Spatial Simulation
  Outliers Simulation modeling
Analysis of Errors and Uncertainty  Bayesian methods Cellular automata modeling
Problems of currency, source, and scale Principles of semi-variogram construction Simulated annealing
Theory of error propagation Semi-variogram modeling Agent-based models
Propagation of error in geospatial modeling Kriging methods Adaptive agents
Fuzzy aggregation operators Principles of spatial econometrics Microsimulation & calibration of agent activities
  Spatial autoregressive models  
  Spatial filtering Spatial Optimization
  Kernels and density estimation Location-allocation modeling
  Spatial expansion & Geographically weighted regression Greedy heuristics
  Spatial distribution Interchange heuristics
  Mathematical models of uncertainty Genetic algorithms
  Non-linearity relationships and non-Gaussian distributions  
  Interchange with probability  


AM-42 - The Classic Transportation Problem
  • Describe 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
  • Explain why, if supply equals demand, there will always be a feasible solution to the classic transportation problem
AM-21 - The spatial weights matrix
  • Explain how different types of spatial weights matrices are defined and calculated
  • Discuss the appropriateness of different types of spatial weights matrices for various problems
  • Construct a spatial weights matrix for lattice, point, and area patterns
  • Explain the rationale used for each type of spatial weights matrix
AM-86 - Theory of error propagation
  • Describe stochastic error models
  • Exemplify stochastic error models used in GIScience
AM-49 - Using models to represent information and processes
  • Define a homomorphism as a mathematical property
  • Evaluate existing systems to determine whether they are adequate representations
  • Assess the data quality needed for a new application to be successful
  • Recognize the advantages and disadvantages of using models to study and manage the world as opposed to experimenting in the world directly
  • Describe the ways in which an existing model faithfully represents reality and the ways in which it does not
AM-59 - Vector-to-raster and raster-to-vector conversions
  • Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved
  • Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation
  • Illustrate the impact of vector/raster/vector conversions on the quality of a dataset
  • Convert vector data to raster format and back using GIS software
AM-89 - Weighting schemes
  • Evaluate a fuzzy weighting scheme in terms of uncertainty and error propagation
AM-55 - Workflow analysis and design
  • Compare and contrast various methods for modeling workflows, including narratives, flowcharts, and UML
  • Compare and contrast the relative merits of various software design methods, including traditional procedural designs, object-oriented design, the Rational Unified Process, Extreme Programming, and the Unified Software Development Process
  • Transform traditional workflows into computer-assisted workflows leveraging geospatial technologies to an appropriate degree
  • Discuss the degree to which structured and unstructured tasks can be automated
  • Differentiate between structured and unstructured tasks