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-61 - Coordinate transformations
  • Cite appropriate applications of several coordinate transformation techniques (e.g., affine, similarity, Molodenski, Helmert)
  • Describe the impact of map projection transformation on raster and vector data
  • Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting
AM-57 - Data conversion
  • Identify the conceptual and practical difficulties associated with data model and format conversion
  • Convert a data set from the native format of one GIS product to another
  • Discuss the role of metadata in facilitating conversation of data models and data structures between systems
  • Describe a workflow for converting and implementing a data model in a GIS involving an Entity-Relationship (E-R) diagram and the Universal Modeling Language (UML)
AM-36 - Data mining approaches
  • Describe how data mining can be used for geospatial intelligence
  • Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component
  • Demonstrate how cluster analysis can be used as a data mining tool
  • Interpret patterns in space and time using Dorling and Openshaw’s geographical analysis machine (GAM) demonstration of disease incidence diffusion
  • Differentiate between data mining approaches used for spatial and non-spatial applications
  • Explain how spatial statistics techniques are used in spatial data mining
  • Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction
AM-19 - Exploratory data analysis (EDA)
  • Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots (including scatterplots, histograms, boxplots, q–q plots)
  • Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods
AM-41 - Flow modeling
  • Describe practical situations in which flow is conserved while splitting or joining at nodes of the network
  • Apply a maximum flow algorithm to calculate the largest flow from a source to a sink, using the edges of the network, subject to capacity constraints on the arcs and the conservation of flow
  • Explain how the concept of capacity represents an upper limit on the amount of flow through the network
  • Demonstrate how capacity is assigned to edges in a network using the appropriate data structure
AM-88 - Fuzzy aggregation operators
  • Compare and contrast Boolean and fuzzy logical operations
  • Compare and contrast several operators for fuzzy aggregation, including those for intersect and union
  • Exemplify one use of fuzzy aggregation operators
  • Describe how an approach to map overlay analysis might be different if region boundaries were fuzzy rather than crisp
  • Describe fuzzy aggregation operators
AM-78 - Genetic algorithms and artificial genomes
  • Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
  • Describe a cluster in a way that could be represented in a genome
  • Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
  • Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
  • Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
AM-77 - Genetic algorithms and global solutions
  • Describe the difficulty of finding globally optimal solutions for problems with many local optima
  • Explain how evolutionary algorithms may be used to search for solutions
  • Explain the important advantage that GA methods may offer to find diverse near-optimal solutions
  • Explain how a GA searches for solutions by using selection proportional to fitness, crossover, and (very low levels of) mutation to fitness criteria and crossover mutation to search for a globally optimal solution to a problem
  • Compare and contrast the effectiveness of multiple search criteria for finding the optimal solution with a simple greedy hill climbing approach
AM-22 - Global measures of spatial association
  • Describe the effect of the assumption of stationarity on global measures of spatial association
  • Justify, compute, and test the significance of the join count statistic for a pattern of objects
  • Compute the K function
  • Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
  • Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
  • Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
  • Explain how the K function provides a scale-dependent measure of dispersion
AM-56 - Impacts of transformations
  • Compare and contrast the impacts of different conversion approaches, including the effect on spatial components
  • Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)
  • Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity (e.g., converting a digital elevation model into a TIN)