Statistics

Question 1

Mode

Answer 1

The most common value in a dataset.

Can be computed using all levels of data (e.g., nominal, ordinal, interval, and

Question 2

Positive Spatial Autocorrelation

Answer 2

Describes patterns where nearby or neighboring values are more similar than distant values.

Question 3

Negative Spatial Autocorrelation

Answer 3

Describes patterns where neighboring values are dissimilar. (Not common)

Question 4

Why could negative spatial autocorrelation be useful?

Answer 4

When mapping the effects of some artificial (anthropogenic) influence on a pattern.

Question 5

Quadrat

Answer 5

A user defined geographic area that is usually square or rectangular.

Question 6

Quadrat analysis

Answer 6

Used to determine the uniformity of points distributed in a number of quadrats.

Question 7

Point Pattern Analysis

Answer 7

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Question 8

Nearest Neighbor analysis

Answer 8

Creates an index based on the distance of each object to its closest neighboring object.

Question 9

What does Nearest Neighbor analysis determine?

Answer 9

Determines whether the spatial distribution of the locations is random or non-random, and is expressed as an index of the ratio of the observed distance between points divided by the expected distance.

Question 10

What does Nearest Neighbor analysis determine?

Answer 10

Determines whether the spatial distribution of the locations is random or non-random, and is expressed as an index of the ratio of the observed distance between points divided by the expected distance.

Question 11

Nearest Neighbor Ration

Answer 11

Provides a useful measure of the pattern in a single value.
This ratio is simply the observed nearest-neighbor distance divided by the expected distance for a random distribution:

Where distance observed is the mean nearest-neighbour distance and Dist Ran is the expected distance for a random point pattern.