Chapter 9: page: 160-67

Define Continuous and Discrete data?

• Continuous data: objects which have no definite boundary, generally no "empty" space and assumed to have three dimensions X,Y and Z e.g. elevation, temperature, and rainfall. The data is represented as surface in GIS

• Discrete data: objects which occupy a specific location in space at a given point in time e.g. road, river, and lot.and represented as point, line, or area feature in a GIS

Discuss the types of spatial pattern?

• Uniform - regular or evenly spaced points

• Clustered - objects are in close spatial proximity

• Random - no particular pattern (neither uniform nor clustered)

Spatial correlation - the spatial relationship between two variables (positively, negatively). It allows us to quantify spatial patterns

List two advantages of sampling?

1. Time minimization

Cost reduction

List the types of sampling strategies, illustrate by figures?

(see Figure 9.6 and 9.7, page 164 and 165)

• Random - all entities have equal probability of being selected

• Systematic (regular) - selection of entities based upon some systematic design e.g., every 10th tree in a transect, soil temperature collected every 100 feet

• Stratified - dividing the population into spatial subsets or thematic subsets before sampling e.g., X number of samples are to be taken from each of 4 plots

• Cluster

• Transect

• Contour

Define Interpolation and Extrapolation?

1. Interpolation: Used to predict missing values when we have values bounding, or on both sides of, the gap (surface fitting models)

– Linear

– Nonlinear (weighted distance)

2. Extrapolation: used when there are values on one side of the missing data, but none on the other side (sampling point data as estimates of areas rather than surfaces e.g sampling number of trees in small areas)