Chapter 13. Geographic Query and Analysis

  1. What is spatial analysis?
  2. How did spatial analysis help John Snow identify the source of cholera?
  3. How did Openshaw identify clusters of disease?
  4. What is an inductive spatial analysis approach?
  5. What is the deductive approach to spatial analysis?
  6. What is the normative approach to spatial analysis?
  7. Briefly define queries, spatial analytical measurements, spatial analytical transformations, descriptive spatial summaries, optimization techniques, and spatial analytical hypothesis testing?
  8. What are local, focal, global, and zonal operations?
  9. Interrogating a GIS database sometimes involves the use of a catalog view such as ArcCatalog.  What ways does this type of query system allow you to interrogate a database?
  10. What types of information can you query in a map view such as ArcMap?
  11. What types of information can you query in the table view?
  12. What is exploratory spatial data analysis?
  13. What does SQL have to do with querying a spatial database?
  14. What is an algorithm?
  15. What is a metric and what is the most common method?
  16. What is a great circle equation?
  17. Why are the lengths of polylines usually shorter than the objects they represent?
  18. Are measured areas also underestimates of the geographic objects they represent? Why or why not?
  19. Is there a difference between the length of a path on the Earth's surface and a planar projection? Why or why not?
  20. What algorithm can you use to measure the compactness of a polygon? What are its parameters?
  21. What are slope and aspect and what dataset would you usually use to create them?
  22. The spatial resolution used to calculate slope and aspect should always be specified.  Why?
  23. Slope can be calculated three different ways including as an angle ranging from 0 to 90 degrees, rise over run (run defined as the horizontal distance between two points, and rise over run (run defined as the hypotenuse of the right-angled triangle).  What are the implications of this?
  24. When a GIS calculates slope and aspect, it does so by estimating slope at each of the data points by comparing the elevation at that point to the elevations of surrounding points.  However, the number of surrounding points used in the calculation varies, as do the weights given to each of the surrounding points in the calculation.  What are the implications of this?
  25. What is a buffer?
  26. Give an example of why you might use a buffer transformation on a line.
  27. Raster buffers can be based on attributes of cells rather than simple Euclidean distances as with the vector model.  Explain.
  28. What is a point in polygon operation? Give an example of what it might be used for.
  29. Using discrete object polygon overlay can lead to large numbers of polygons.  Explain.
  30. In two vector datasets of the same area there will almost certainly be instances where lines in each dataset represent the same feature on the ground.  When overlaying them it leads to spurious polygons or slivers.  What are they and how can you avoid them by using a tolerance value?
  31. Raster overlay is simpler, but it produces a fundamentally different kind of result.  What is the basic concept behind raster overlay?
  32. What is spatial interpolation and what does it have to do with Tobler's Law?
  33. What is IDW and how do you calculate it?
  34. Explain the weights scheme in IDW.
  35. IDW interpolation may produce counterintuitive results in the areas of peaks and pits, and outside the area covered by the data points.  Why?
  36. Explain the general idea behind Kriging.
  37. What is a variogram?
  38. What is an isotropic variogram as opposed to an anisotropic variogram?
  39. Why is Kriging better than IDW?
  40. Interpolation and density measurements both begin with points and end with rasters.  However, transforms sample measurements from a field and the other transforms locations of discrete objects.  Which is which? Explain.
  41. There is no such thing as population density only population density at a spatial resolution.  Density estimation with a kernel allows the spatial resolution population density to be made explicit.  Explain.