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

 

AM-82 - Microsimulation and calibration of agent activities
  • Describe a “bottom-up” simulation from an activity-perspective with changes in the locations and/or activities the individual person (and/or vehicle) in space and time, in the activity patterns and space-time trajectories created by these activity patterns, and in the consequent emergent phenomena, such as traffic jams and land-use patterns
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Describe how measurements on the output of a model can be used to describe model behavior
AM-44 - Modelling Accessibility

Modelling accessibility involves combining ideas about destinations, distance, time, and impedances to measure the relative difficulty an individual or aggregate region faces when attempting to reach a facility, service, or resource. In its simplest form, modelling accessibility is about quantifying movement opportunity. Crucial to modelling accessibility is the calculation of the distance, time, or cost distance between two (or more) locations, which is an operation that geographic information systems (GIS) have been designed to accomplish. Measures and models of accessibility thus draw heavily on the algorithms embedded in a GIS and represent one of the key applied areas of GIS&T.

AM-13 - Multi-criteria evaluation
  • Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation
  • Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis
  • Differentiate between contributing factors and constraints in a multi-criteria application
  • Explain the legacy of multi-criteria evaluation in relation to cartographic modeling
  • Determine which method to use to combine criteria (e.g., linear, multiplication)
  • Create initial weights using the analytical hierarchy process (AHP)
  • Calibrate a linear combination model by adjusting weights using a test data set
AM-66 - Multi-layer feed-forward neural networks
  • Analyze the stability of the network using multiple runs with the same training data and architecture
  • Compare and contrast classification results when the architecture of the network and initial parameters are changed
  • Differentiate between feed-forward and recurrent architectures
  • Describe the architecture and components of a feed-forward neural network
AM-05 - Neighborhoods
  • Discuss the role of Voronoi polygons as the dual graph of the Delaunay triangulation
  • Explain how Voronoi polygons can be used to define neighborhoods around a set of points
  • Outline methods that can be used to establish non-overlapping neighborhoods of similarity in raster datasets
  • Create proximity polygons (Thiessen/Voronoi polygons) in point datasets
  • Write algorithms to calculate neighborhood statistics (minimum, maximum, focal flow) using a moving window in raster datasets
  • Explain how the range of map algebra operations (local, focal, zonal, and global) relate to the concept of neighborhoods
AM-43 - Other classic network problems
  • Describe several classic problems to which network analysis is applied (e.g., the traveling salesman problem, the Chinese postman problem)
  • Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally
AM-04 - Overlay
  • Explain why the process “dissolve and merge” often follows vector overlay operations
  • Outline the possible sources of error in overlay operations
  • Compare and contrast the concept of overlay as it is implemented in raster and vector domains
  • Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS
  • Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation
  • Formalize the operation called map overlay using Boolean logic
  • Explain what is meant by the term “planar enforcement”
  • Exemplify applications in which overlay is useful, such as site suitability analysis
AM-38 - Pattern recognition
  • Differentiate among machine learning, data mining, and pattern recognition
  • Explain the principles of pattern recognition
  • Apply a simple spatial mean filter to an image as a means of recognizing patterns
  • Construct an edge-recognition filter
  • Design a simple spatial mean filter
  • Explain the outcome of an artificial intelligence analysis (e.g., edge recognition), including a discussion of what the human did not see that the computer identified and vice versa
AM-07 - Point pattern analysis
  • List the conditions that make point pattern analysis a suitable process
  • Identify the various ways point patterns may be described
  • Identify various types of K-function analysis
  • Describe how Independent Random Process/Chi-Squared Result (IRP/CSR) may be used to make statistical statements about point patterns
  • Outline measures of pattern based on first and second order properties such as the mean center and standard distance, quadrat counts, nearest neighbor distance, and the more modern G, F, and K functions
  • Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis
  • Explain how distance-based methods of point pattern measurement can be derived from a distance matrix
  • Explain how proximity polygons (e.g., Thiessen polygons) may be used to describe point patterns
  • Explain how the K function provides a scale-dependent measure of dispersion
  • Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
AM-27 - Principles of semi-variogram construction
  • Identify and define the parameters of a semi-variogram (range, sill, nugget)
  • Demonstrate how semi-variograms react to spatial nonstationarity
  • Construct a semi-variogram and illustrate with a semi-variogram cloud
  • Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association

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