Geographic Information Systems

GEOL-4611   Fall 2014

Review for Lecture Test #2

 

 

Chapter 4 – Data Types and Sources

1) Compare and contrast primary and secondary data that are used in GIS projects.  Give examples of each. 

2) Describe the theory and process behind Global Positioning Systems.  Describe the factors that control the resolution of GPS units.  What are the basic requirements of a GPS unit needed to obtain resolution of 3 meters or less?  Why is greater resolution usually not required for most projects?  Describe how sub-centimeter resolution can be obtained from GPS.     

3) Describe how relative location measurements are collected.  How are these measurements converted to real-world coordinates in a GIS project?  Compare and     contrast the following: level, transit, theodolite and total station.

4) List examples of primary geochemical, geotechnical and geophysical field data.  Explain how data dictionaries are used to collect observational field data.

5) Describe how satellites collect primary spatial data.  Describe the type of information collected by Landsat satellites and how this data is used.

6) Describe how aerial photos are converted into orthophotos.  What is the resolution, scale and update period for DOQQs? 

7) Explain why geophysical data is often classified as secondary data.  Compare and    contrast raw, free air and Bouguer gravity data

8) Describe how paper maps are converted to digital copies.  Describe the major problems associate with these conversions.  How is precision used in describing         these conversions?  Compare and contrast precision and map accuracy.   

9) Define rubber sheeting and semi-automatic line following. Describe how these techniques are used in paper to digital map conversions.

10) Define the following terms: Ephemeris, data dictionary, WAAS, transformation, DOQQ, audit trail, snapping distance, fuzzy tolerance, topology, and metadata.

 

Chapter 5 - Attribute Data Types and Analysis

1) Compare and contrast spatial and nonspatial attribute data.  Give examples of spatial attribute data other than location data.  Define and give examples of temporal and thematic nonspatial data.

2) Define and give examples of the following measurements that are commonly used with nonspatial data: ratios, intervals, ordinal, ranked, categorical, probability and binary measurements.

3) Describe how attribute data are stored in Geographic Information Systems.  Define the following: fields, items, join, relate and SDE.

4) What is a relational database?  How is the FID used in these data structures?  List and describe the three required properties of a relational database.

5) What is normalization?  Describe how normalization commonly results in data redundancy.  How can linking reduce or remove redundancy and make a database

     more efficient?

6) Define and describe hierarchical or definition tree databases. How are they used?

7) Define and give examples of the following statistical measurements: mean, median, variance and standard deviation.  Be able to calculate each measurement for a          given database.   

8) What is the Z-score and how is it used?  Describe and give an example.

9) Define and give an example of a distribution analysis.  Compare and contrast the following distributions: normal, random, and bimodal.  How is correlation between    two or more types of data measured and described?

10) Describe how attribute data is recoded and reclassified using Lookup and Breakpoint Table Conversions.

 

Chapter 6 - Spatial Data Analysis

1) Define and describe the following types of locational searches used for spatial data: simple area searches, buffer operations and overlay operations.  Give examples when each would be used.  Are they used for vector or raster models?  Explain.

2) What is a query operation and when is it used?  What is SQL and how is it used in query operations?

3) Explain how the following measurements are used to describe the distribution of vector data: mean center, standard distance and line or polygon feature statistics.

4) Define and describe covariance and autocorrelation.

5) What are point-to-area transformations and when are they used?  Describe the following point-to-area transformation techniques and provided illustrations of each:

     Grid Method, Thiessen Polygons, Moving Window, Zone of Influence, Kriging, Delaunay Triangulation and Inverse Distance Weighting.

6) Describe how the randomness of spatial data can be measured.  List and describe two ways in which spatial models can be tested.

7) Define and describe the following filtering techniques: smoothing, hill shading and directional filters.  Are they used for vector or raster data?  Explain.

8) Define and describe common techniques used in the correlation of two (binary) map layers.  What is a predictive map?  Explain the difference between correlation with ordinal and nominal data.

9) Define, compare and contrast predictive and prescription models.

10) Define and give examples of the following modeling techniques: Boolean Logic, Index Overlays, Fuzzy Logic, and Bayesian Method.  What are the three basic operators used in Boolean Logic and how do they correspond to the operations used in ArcMap?    

11) Define the following terms: buffer, overlay, spatial autocorrelation, ground truthing, residual mapping, weighting factor, prior probability, and posterior probability.