2021 QUARTER 02

A B C D E F G H I J K L M N O P R S T U V W
DC-19 - Ground Verification and Accuracy Assessment

Spatial products such as maps of land cover, soil type, wildfire, glaciers, and surface water have become increasingly available and used in science and policy decisions.  These maps are not without error, and it is critical that a description of quality accompany each product.  In the case of a thematic map, one aspect of quality is obtained by conducting a spatially explicit accuracy assessment in which the map class and reference class are compared on a per spatial unit basis (e.g., per 30m x 30m pixel).  The outcome of an accuracy assessment is a description of quality of the end-product map, in contrast to conducting an evaluation of map quality as part of the map production process.  The accuracy results can be used to decide if the map is of adequate quality for an intended application, as input to uncertainty analyses, and as information to improve future map products.

DM-11 - Hierarchical data models
  • Illustrate the quadtree model
  • Describe the advantages and disadvantages of the quadtree model for geographic database representation and modeling
  • Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, rtrees, pyramids)
  • Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data
  • Implement a format for encoding quadtrees in a data file
CP-03 - High performance computing
  • Describe how the power increase in desktop computing has expanded the analytic methods that can be used for GIS&T
  • Exemplify how the power increase in desktop computing has expanded the analytic methods that can be used for GIS&T
DC-36 - Historical Maps in GIS

The use of historical maps in coordination with GIS aids scholars who are approaching a geographical study in which an historical approach is required or is interested in the geographical relationships between different historical representations of the landscape in cartographic document.  Historical maps allow the comparison of spatial relationships of past phenomena and their evolution over time and permit both qualitative and quantitative diachronic analysis. In this chapter, an explanation of the use of historical maps in GIS for the study of landscape and environment is offered. After a short theoretical introduction on the meaning of the term “historical map,” the reader will find the key steps in using historic maps in a GIS, a brief overview on the challenges in interpretation of historical maps, and some example applications.

DM-52 - Horizontal datums
  • Discuss appropriate applications of the various datum transformation options
  • Explain the difference between NAD 27 and NAD 83 in terms of ellipsoid parameters
  • Outline the historical development of horizontal datums
  • Explain the difference in coordinate specifications for the same position when referenced to NAD 27 and NAD 83
  • Explain the rationale for updating NAD 27 to NAD 83
  • Explain why all GPS data are originally referenced to the WGS 84 datum
  • Identify which datum transformation options are available and unavailable in a GIS software package
  • Define “horizontal datum” in terms of the relationship between a coordinate system and an approximation of the Earth’s surface
  • Describe the limitations of a Molodenski transformation and in what circumstances a higher parameter transformation such as Helmert may be appropriate
  • Determine the impact of a datum transformation from NAD 27 to NAD 83 for a given location using a conversion routine maintained by the U.S. National Geodetic Survey
  • Explain the methodology employed by the U.S. National Geodetic Survey to transform control points from NAD 27 to NAD 83
  • Perform a Molodenski transformation manually
  • Use GIS software to perform a datum transformation
GS-22 - Implications of distributed GIS&T
  • Describe the advantages and disadvantages to an organization in using GIS portal information from other organizations
  • Describe how inter-organization GIS portals may impact or influence issues related to social equity, privacy and data access
  • Discuss how distributed GIS&T may affect the nature of organizations and relationships among institutions
  • Suggest the possible societal and ethical implications of distributed GIS&T
AM-17 - Intervisibility
  • Define “intervisibility”
  • Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by DEMs
  • Perform siting analyses using specified visibility, slope, and other surface related constraints
  • Explain the sources and impact of errors that affect intervisibility analyses
PD-32 - JavaScript for GIS

JavaScript (which has no connection to the Java computer language) is a popular high-level programming languages used to develop user interfaces in web pages. The principle goal of using JavaScript for programming web and mobile GIS applications is to build front-end applications that make use of spatial data and GIS principles, and in many cases, have embedded, interactive maps. It is considered much easier to program than Java or C languages for adding automation, animation, and interactivity into web pages and applications. JavaScript uses the leading browsers as runtime environments (RTE) and thus benefits from rapid and continuously evolving browser support for all web and mobile applications.

AM-08 - Kernels and Density Estimation

Kernel density estimation is an important nonparametric technique to estimate density from point-based or line-based data. It has been widely used for various purposes, such as point or line data smoothing, risk mapping, and hot spot detection. It applies a kernel function on each observation (point or line) and spreads the observation over the kernel window. The kernel density estimate at a location will be the sum of the fractions of all observations at that location. In a GIS environment, kernel density estimation usually results in a density surface where each cell is rendered based on the kernel density estimated at the cell center. The result of kernel density estimation could vary substantially depending on the choice of kernel function or kernel bandwidth, with the latter having a greater impact. When applying a fixed kernel bandwidth over all of the observations, undersmoothing of density may occur in areas with only sparse observation while oversmoothing may be found in other areas. To solve this issue, adaptive or variable bandwidth approaches have been suggested.

AM-29 - Kriging Interpolation

Kriging is an interpolation method that makes predictions at unsampled locations using a linear combination of observations at nearby sampled locations. The influence of each observation on the kriging prediction is based on several factors: 1) its geographical proximity to the unsampled location, 2) the spatial arrangement of all observations (i.e., data configuration, such as clustering of observations in oversampled areas), and 3) the pattern of spatial correlation of the data. The development of kriging models is meaningful only when data are spatially correlated.. Kriging has several advantages over traditional interpolation techniques, such as inverse distance weighting or nearest neighbor: 1) it provides a measure of uncertainty attached to the results (i.e., kriging variance); 2) it accounts for direction-dependent relationships (i.e., spatial anisotropy); 3) weights are assigned to observations based on the spatial correlation of data instead of assumptions made by the analyst for IDW; 4) kriging predictions are not constrained to the range of observations used for interpolation, and 5) data measured over different spatial supports can be combined and change of support, such as downscaling or upscaling, can be conducted.

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