2016 QUARTER 04

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

Describe the effect of the assumption of stationarity on global measures of spatial association
Justify, compute, and test the significance of the join count statistic for a pattern of objects
Compute the K function
Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
Explain how the K function provides a scale-dependent measure of dispersion

Explain how GPS receivers calculate coordinate data
Discuss the relationship of GPS to the Global Satellite Navigation System
Explain “selective availability,” why it was discontinued in 2000, and what alternatives are available to the U.S. Department of Defense
Explain the relationship of the U.S. Global Positioning System with comparable systems sponsored by Russia and the European Union and the Global Navigation Satellite System
Discuss the role of GPS in location-based services (LBS)
Specify the features of a GPS receiver that is able to achieve geometric accuracies on the order of centimeters without post-processing
Explain the relevance of the concept of trilateration to both GPS positioning and control surveying
Perform differential correction of GPS data using reference data from a CORS station
List, define, and rank the sources of error associated with GPS positioning
Distinguish between horizontal and vertical accuracies when using coarse acquisition codes/standard positioning service (C-codes) and precision acquisition codes/precise positioning service (P-codes)

Demonstrate how to implement a greedy heuristic process
Identify problems for which the greedy heuristic also produces the optimal solution (e.g., Kruskal’s algorithm for minimum spanning tree, the fractional Knapsack problem)

Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)
Explain the advantage of wavelet compression
Evaluate the relative merits of grid compression methods for storage
Differentiate between lossy and lossless compression methods

Explain how grid representations embody the field-based view
Differentiate among a lattice, a tessellation, and a grid
Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation
Identify the national framework datasets based on a grid model

Evaluate the thematic accuracy of a given soils map
Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset

Search form