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 in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized


Conceptual Frameworks Data Exploration & Spatial Statistics Network & Location Analysis
Basic Primitives Spatial Sampling for Spatial Analysis Intro to Network & Location Analysis
Spatial Relationships Exploratory Spatial Data Analysis (ESDA) Network Route & Tour Problems
Neighborhoods Kernels & Density Estimation Location & Service Area Problems
First & Second Laws of Geography Spatial Interaction Modelling Accessibility
Spatial Statistics Cartographic Modeling Location-allocation Modeling
Methodological Context Multi-criteria Evaluation The Classic Transportation Problem
Spatial Analysis as a Process Grid-based Statistics and Metrics Space-Time Analysis & Modeling
Geospatial Analysis & Model Building Landscape Metrics Time Geography
Changing Context of GIScience Point Pattern Analysis Capturing Spatio-Temporal Dynamics in Computational Modeling 
Data Manipulation Hot-spot and Cluster Analysis GIS-Based Computational Modeling
Point, Line, and Area Generalization Global Measures of Spatial Association Computational Movement Analysis
Coordinate transformations Local Indicators of Spatial Autocorrelation Accounting for Errors in Space-Time Modeling
Raster resampling Simple Regression & Trend Surface Analysis Geocomputational Methods & Models
Vector-to-raster and raster-to-vector conversions Spatial Filtering Models Spatial Process Models
Transaction Management Geographically Weighted Regression Cellular Automata
  Spatial Autoregressive & Bayesian Methods Agent-based Modeling
Building Blocks   Simulation Modeling
Spatial & Spatiotemporal Data Models Surface & Field Analysis Artificial Neural Networks
Length & Area Operations Modeling Surfaces Genetic Algorithms & Evolutionary Computing 
Polyline & Polygon Operations Surface Geometry Big Data & Geospatial Analysis
Overlay & Combination Operations Intervisibility Problems & with Large Spatial Databases
Areal Interpolation Digital Elevation Models & Terrain Metrics Pattern Recognition & Matching
Classification & Clustering Watersheds & Drainage Artificial Intelligence Approaches
Aggregation of Spatial Entities Gridding, Interpolation, and Contouring Intro to Spatial Data Mining
Boundaries & Zone Membership Deterministic Interpolation Models Rule Learning for Spatial Data Mining
Tesselations & Triangulations Inverse Distance Weighting Machine Learning Approaches
Spatial Queries Radial Basis & Spline Functions CyberGIS
Distance Operations Triangulation Analysis of Errors & Uncertainty
Buffers Polynomial Functions Problems of Currency, Source, and Scale
Directional Operations Core Concepts in Geostatistics Problems of Scale & Zoning
Grid Operations & Map Algebra Kriging Interpolation Theory of Error Propagation
    Propagation of Error in Geospatial Modeling
    Fuzzy Aggregation Operators
    Mathematical Models of Uncertainty


AM-89 - Weighting schemes
  • Evaluate a fuzzy weighting scheme in terms of uncertainty and error propagation