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 Interaction Modelling Accessibility
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 Operations Modeling Surfaces Problems & 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-09 - Cluster analysis
  • Identify several cluster detection techniques and discuss their limitations
  • Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests
  • Perform a cluster detection analysis to detect “hot spots” in a point pattern
  • Discuss the characteristics of the various cluster detection techniques
AM-90 - Computational Movement Analysis

Figure 1. Group movement patterns as illustrated in this coordinated escape behavior of a group of mountain goat (Rubicapra rubicapra) evading approaching hikers on the Fuorcla Trupchun near the Italian/Swiss border are at the core of computational movement analysis. Once the trajectories of moving objects are collected and made accessible for computational processing, CMA aims at a better understanding of the characteristics of movement processes of animals, people or things in geographic space.

 

Computational Movement Analysis (CMA) develops and applies analytical computational tools aiming at a better understanding of movement data. CMA copes with the rapidly growing data streams capturing the mobility of people, animals, and things roaming geographic spaces. CMA studies how movement can be represented, modeled, and analyzed in GIS&T. The CMA toolbox includes a wide variety of approaches, ranging from database research, over computational geometry to data mining and visual analytics.

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-50 - Distance Operations

Distance is a central concept in geography, and consequently, there are various types of operations that leverage the concept of distance. This short article introduces common distance measures, the purpose of distance operations, different types of operations and considerations, as well as sample applications in the physical and social domains. Distance operations can be performed on both vector or raster data, but the operations and results may differ. While performing distance operations, it is important to remember how distance is conceptualized while performing the operation.

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 Evolutionary Computing

Genetic algorithms (GAs) are a family of search methods that have been shown to be effective in finding optimal or near-optimal solutions to a wide range of optimization problems. A GA maintains a population of solutions to the problem being solved and uses crossover, mutation, and selection operations to iteratively modify them. As the population evolves across generations, better solutions are created and inferior ones are selectively discarded. GAs usually run for a fixed number of iterations (generations) or until further improvements do not obtain. This contribution discusses the fundamental principles of genetic algorithms and uses Python code to illustrate how GAs can be developed for both numerical and spatial optimization problems. Computational experiments are used to demonstrate the effectiveness of GAs and to illustrate some nuances in GA design.

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