All Topics

A B C D E F G H I J K L M N O P R S T U V W
FC-16 - Area and Region
  • List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes
  • Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation
  • Outline an algorithm to find the area of a polygon using the coordinates of its vertices
  • Explain how variations in the calculation of area may have real world implications, such as calculating density
  • Delineate regions using properties, spatial relationships, and geospatial technologies
  • Exemplify regions found at different scales
  • Explain the relationship between regions and categories
  • Identify the kinds of phenomena commonly found at the boundaries of regions
  • Explain why general-purpose regions rarely exist
  • Differentiate among different types of regions, including functional, cultural, physical, administrative, and others
  • Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)
  • Use established analysis methods that are based on the concept of region (e.g., landscape ecology)
  • Explain the nature of the Modifiable Areal Unit Problem (MAUP)
DM-81 - Array Databases

Array Databases are a class of No-SQL databases that store, manage, and analyze data whose natural structures are arrays. With the growth of large volumes of spatial data (i.e., satellite imagery) there is a pressing need to have new ways to store and manipulate array data. Currently, there are several databases and platforms that have extended their initial architectures to support for multidimensional arrays. However, extending a platform to support a multidimensional array comes at a performance cost, when compared to Array Databases who specialize in the storage, retrieval, and processing of n-dimensional data.

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.

CP-04 - Artificial Intelligence Tools and Platforms for GIS

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

GS-21 - Balancing security and open access to geospatial information
  • Discuss the way that a legal regime balances the need for security of geospatial data with the desire for open access
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
CV-19 - Big Data Visualization

As new information and communication technologies have altered so many aspects of our daily lives over the past decades, they have simultaneously stimulated a shift in the types of data that we collect, produce, and analyze. Together, this changing data landscape is often referred to as "big data." Big data is distinguished from "small data" not only by its high volume but also by the velocity, variety, exhaustivity, resolution, relationality, and flexibility of the datasets. This entry discusses the visualization of big spatial datasets. As many such datasets contain geographic attributes or are situated and produced within geographic space, cartography takes on a pivotal role in big data visualization. Visualization of big data is frequently and effectively used to communicate and present information, but it is in making sense of big data – generating new insights and knowledge – that visualization is becoming an indispensable tool, making cartography vital to understanding geographic big data. Although visualization of big data presents several challenges, human experts can use visualization in general, and cartography in particular, aided by interfaces and software designed for this purpose, to effectively explore and analyze big data.

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.

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