2018 QUARTER 02

FC-09 - Relationships between space and time
  • Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as “after” or “longer”
  • Describe different types of movement and change
  • Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time
  • Identify various types of geographic interactions in space and time
  • Compare and contrast the characteristics of spatial and temporal dimensions
CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.

KE-05 - Requirements analysis
  • Describe the need for user-centered requirements analysis
  • Create requirements reports for individual potential applications in terms of the data, procedures, and output needed
  • Assess the relative importance and immediacy of potential applications
  • Synthesize the needs of individual users and tasks into enterprise-wide needs
  • Differentiate between the responsibilities of the proposed system and those that remain with the user
  • Illustrate how a business process analysis can be used to identify requirements during a GIS implementation
  • Describe how spatial data and GIS&T can be integrated into a workflow process
  • Evaluate how external spatial data sources can be incorporated into the business process
  • Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development (JAD)
  • Document existing and potential tasks in terms of workflow and information flow
FC-21 - Resolution
  • Illustrate and explain the distinction between resolution, precision, and accuracy
  • Discuss the implications of the sampling theorem (? = 0.5 d) to the concept of resolution
  • Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument
  • Explain how resampling affects the resolution of image data
  • Discuss the advantages and potential problems associated with the use of minimum mapping unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps
  • Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution
  • Illustrate the impact of grid cell resolution on the information that can be portrayed
  • Relate the concept of grid cell resolution to the more general concept of “support” and granularity
  • Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software
  • Evaluate the ease of measuring resolution in different types of tessellations
AM-68 - Rule Learning for Spatial Data Mining

Recent research has identified rule learning as a promising technique for geographic pattern mining and knowledge discovery to make sense of the big spatial data avalanche (Koperski & Han, 1995; Shekhar et al., 2003). Rules conveying associative implications regarding locations, as well as semantic and spatial characteristics of analyzed spatial features, are especially of interest. This overview considers fundamentals and recent advancements in two approaches applied on spatial data: spatial association rule learning and co-location rule learning.

DC-08 - Sample intervals
  • Identify the fundamental principle of the sampling theorem for specifying a sampling rate or interval
  • Discuss what sampling intervals should be used to investigate some of the temporal patterns encountered in oceanography
  • Propose a sampling strategy considering a variable range in autocorrelation distances for a variable
DC-06 - Sample size selection
  • Determine the minimum number and distribution of point samples for a given study area and a
  • Determine minimum homogeneous ground area for a particular application
  • Describe how spatial autocorrelation influences selection of sample size and sample statistics
  • Assess the practicality of statistically reliable sampling in a given situation
  • given statistical test of thematic accuracy
CV-04 - Scale and Generalization
Scale and generalization are two fundamental, related concepts in geospatial data. Scale has multiple meanings depending on context, both within geographic information science and in other disciplines. Typically it refers to relative proportions between objects in the real world and their representations. Generalization is the act of modifying detail, usually reducing it, in geospatial data. It is often driven by a need to represent data at coarsened resolution, being typically a consequence of reducing representation scale. Multiple computations and graphical modication processes can be used to achieve generalization, each introducing increased abstraction to the data, its symbolization, or both.
AM-28 - Semi-variogram modeling
  • List the possible sources of error in a selected and fitted model of an experimental semi-variogram
  • Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
  • Explain the necessity of defining a semi-variogram model for geographic data
  • Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
  • Describe some commonly used semi-variogram models
FC-11 - Set Theory
  • Describe set theory
  • Explain how logic theory relates to set theory
  • Perform a logic (set theoretic) query using GIS software
  • Explain how set theory relates to spatial queries