Chapter 7. Generalization, Abstraction, and Metadata

1. What are metadata?
2. What are the six ways we can generalize field data.  Describe each.
3. How do interval-ratio level variables generalized by cell or irregular polygon lose information?
4. Why might a nominal-level irregular polygon representing a lake be less generalized than another land use/land cover category such as forest?
5. What is simplification?
6. What is aggregation?
7. What is the difference between classification and symbolization generalization of attributes?
8. What is the difference between database and cartographic generalization?
9. What is weeding and why might you do it?
10. What is a MMU?
11. What is the representative fraction?
12. You have a dataset was never in paper form and thus it only has a positional accuracy of a certain number of meters.  What conventions are in place to estimate a representative fraction to be estimated for this dataset?
13. What are some problems with using a MMU to describe the degree of generalization of a map?
14. What is spatial resolution?
15. How do you determine the spatial resolution of a raster dataset?
16. What is resampling?
17. Is resampling from courser to finer resolution likely to produce an accurate resolution at the finer scale? Explain.
18. Why is using the minimum distance between digitized vertices of a polyline to define vector resolution prblematic?
19. The effective spatial resolution of many vector datasets varies from one location to another.  Explain.
20. Why do we need metadata?
21. What are the 10 general FGDC CSDGM categories? Explain each.
22. What is light metadata and why might you choose to use it over FGDC standard metadata?
23. What is a geolibrary?