AM-02 - Analytical approaches
New abstract will go here.
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-79 - Agent-based Modeling
Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.