All Topics

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

 

Methodological Context Surface & Field Analyses Space-Time Analysis & Modeling
Geospatial Analysis & Model Building Modeling Surfaces Time Geography
Changing Context of GIScience Gridding, Interpolation, and Contouring Capturing Spatio-Temporal Dynamics in Computational Modeling 
Building Blocks Inverse Distance Weighting GIS-Based Computational Modeling
Overlay & Combination Operations Radial Basis & Spline Functions Computational Movement Analysis
Areal Interpolation Polynomial Functions Volumes and Space-Time Volumes
Aggregation of Spatial Entitles Kriging Interpolation  
Classification & Clustering LiDAR Point Cloud Analysis Geocomputational Methods & Models
Boundaries & Zone Membership Intervisibility, Line-of-Sight, and Viewsheds Cellular Automata
Spatial Queries Digital Elevation Models & Terrain Metrics Agent-based Modeling
Buffers TIN-based Models and Terrain Metrics Simulation Modeling
Grid Operations & Map Algebra Watersheds & Drainage Artificial Neural Networks
Data Exploration & Spatial Statistics 3D Parametric Surfaces Genetic Algorithms & Evolutionary Computing 
Spatial Statistics Network & Location Analysis Big Data & Geospatial Analysis
Spatial Sampling for Spatial Analysis Intro to Network & Location Analysis Problems & with Large Spatial Databases
Exploratory Spatial Data Analysis (ESDA) Location & Service Area Problems Pattern Recognition & Matching
Point Pattern Analysis Network Route & Tour Problems Artificial Intelligence Approaches
Kernels & Density Estimation Modelling Accessibility Intro to Spatial Data Mining
Spatial Interaction Location-allocation Modeling Rule Learning for Spatial Data Mining
Cartographic Modeling The Classic Transportation Problem Machine Learning Approaches
Multi-criteria Evaluation   CyberGIS and Cyberinfrastructure
Grid-based Statistics and Metrics   Analysis of Errors & Uncertainty
Landscape Metrics   Error-based Uncertainty
Hot-spot and Cluster Analysis   Conceptual Models of Error & Uncertainty
Global Measures of Spatial Association   Spatial Data Uncertainty
Local Indicators of Spatial Autocorrelation   Problems of Scale & Zoning
Simple Regression & Trend Surface Analysis   Thematic Accuracy & Assessment
Geographically Weighted Regression   Stochastic Simulation & Monte Carlo Methods
Spatial Autoregressive & Bayesian Methods   Mathematical Models of Uncertainty
Spatial Filtering Models   Fuzzy Aggregation Operators

 

A B C D E F G I K L M O P R S T V W
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.

AM-93 - Artificial Intelligence Approaches

Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.

AM-25 - Bayesian methods
  • Define “prior and posterior distributions” and “Markov-Chain Monte Carlo”
  • Explain how the Bayesian perspective is a unified framework from which to view uncertainty
  • Compare and contrast Bayesian methods and classical “frequentist” statistical methods
AM-03 - Buffers

This short article introduces the definition of buffer and explains how buffers are created for single or multiple geographic features of different geometric types. It also discusses how buffers are generated differently in vector and raster data models and based on the concept of cost.

AM-64 - Calculating surface derivatives
  • List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe
  • Outline how higher order derivatives of height can be interpreted
  • Explain how slope and aspect can be represented as the vector field given by the first derivative of height
  • Explain why the properties of spatial continuity are characteristic of spatial surfaces
  • Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each
  • Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model
  • Outline a number of different methods for calculating slope from a DEM
AM-80 - Capturing Spatiotemporal Dynamics in Computational Modeling

We live in a dynamic world that includes various types of changes at different locations over time in natural environments as well as in human societies. Modern sensing technology, location-aware technology and mobile technology have made it feasible to collect spatiotemporal tracking data at a high spatial and temporal granularity and at affordable costs. Coupled with powerful information and communication technologies, we now have much better data and computing platforms to pursue computational modeling of spatiotemporal dynamics. Researchers have attempted to better understand various kinds of spatiotemporal dynamics in order to predict, or even control, future changes of certain phenomena. A simple approach to representing spatiotemporal dynamics is by adding time (t) to the spatial dimensions (x,y,z) of each feature. However, spatiotemporal dynamics in the real world are more complex than a simple representation of (x,y,z,t) that describes the location of a feature at a given time. This article presents selected concepts, computational modeling approaches, and sample applications that provide a foundation to computational modeling of spatiotemporal dynamics. We also indicate why the research of spatiotemporal dynamics is important to geographic information systems (GIS) and geographic information science (GIScience), especially from a temporal GIS perspective.

AM-12 - Cartographic modeling
  • Describe the difference between prescriptive and descriptive cartographic models
  • Develop a flowchart of a cartographic model for a site suitability problem
  • Discuss the origins of cartographic modeling with reference to the work of Ian McHarg
AM-69 - Cellular Automata

Cellular automata (CA) are simple models that can simulate complex processes in both space and time. A CA consists of six defining components: a framework, cells, a neighborhood, rules, initial conditions, and an update sequence. CA models are simple, nominally deterministic yet capable of showing phase changes and emergence, map easily onto the data structures used in geographic information systems, and are easy to implement and understand. This has contributed to their popularity for applications such as measuring land use changes and monitoring disease spread, among many others.

AM-09 - Classification and Clustering

Classification and clustering are often confused with each other, or used interchangeably. Clustering and classification are distinguished by whether the number and type of classes are known beforehand (classification), or if they are learned from the data (clustering). The overarching goal of classification and clustering is to place observations into groups that share similar characteristics while maximizing the separation of the groups that are dissimilar to each other. Clusters are found in environmental and social applications, and classification is a common way of organizing information. Both are used in many areas of GIS including spatial cluster detection, remote sensing classification, cartography, and spatial analysis. Cartographic classification methods present a simplified way to examine some classification and clustering methods, and these will be explored in more depth with example applications.

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