Week 6 + Lab 2: Data Classification Flashcards

(42 cards)

1
Q

What is the term for this definition:

= the extent to which attributes (eg. land cover) correspond to their real world counterparts

a) accuracy
b) precision

A

a) accuracy

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2
Q

What is this an example of:
=29% vs 30%
=July 1987 vs August 2018

a) accuracy
b) precision

A

a) accuracy

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3
Q

What is the term for:
=the detail with which attributes are represented
= 29% vs 29.423408%
=July 1987 vs July 27th 1987 @ 10:33

a) accuracy
b) precision

A

b) precision

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4
Q

What is the term for this definition?
=inherent nature of feature

a) quantitative
b) qualitative

A

b) qualititative

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5
Q

True or false:

The Golf Course is an example of a Qualitative measurement?

A

True

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6
Q

What is the term for this definition:
=measured value

a) quantitative
b) qualitative

A

b) quanititative

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7
Q

True or False

The meters above sea level is an example of a Qualitative Measurement

A

False, it is Quantitative

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8
Q

What is the term for this description:

=descriptive, categorical

a) Nominal
b) ordinal
c) interval
d) ratio

A

a) Nominal

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9
Q

What is the term for this description:

=ranking: objects/events arranged as “greater than” or “less than”

a) Nominal
b) ordinal
c) interval
d) ratio

A

b) ordinal

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10
Q

What is the term for this description:

=order of + distance between observations
- no absolute zero as starting point

a) Nominal
b) ordinal
c) interval
d) ratio

A

c) interval

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11
Q

What is the term for this description:

=similar to “interval” but has a known absolute starting point

a) Nominal
b) ordinal
c) interval
d) ratio

A

d) ratio

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12
Q

What level of measurment only includes Mode for Math?

a) Nominal
b) ordinal
c) interval
d) ratio

A

a) nominal

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13
Q

what level of measurement only includes Median and Mode for Math?

a) Nominal
b) ordinal
c) interval
d) ratio

A

b) ordinal

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14
Q

What level of measurement includes these mathematical properties:
+, -, median, mode, mean

a) Nominal
b) ordinal
c) interval
d) ratio

A

c) interval

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15
Q

What level of measurement includes complete arithmetic possible (including x + /)

a) Nominal
b) ordinal
c) interval
d) ratio

A

d) ratio

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16
Q

What is this an example of:
=land cover
- wheat, soybean, water

a) Nominal
b) ordinal
c) interval
d) ratio

A

a) nominal

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17
Q

What is this an example of:
=Low, Med, High
- income
-wildfire risk

a) Nominal
b) ordinal
c) interval
d) ratio

18
Q

What is this an example of:

  • deg C/F
  • Time (CE)

a) Nominal
b) ordinal
c) interval
d) ratio

19
Q

What is this an example of:
- k temp scale
(600K is 2x as warm as 300K)
- number of people

a) Nominal
b) ordinal
c) interval
d) ratio

20
Q

What classification technique “splits data into classes of equal ranges”

a) equal interval
b) equal frequency (quantiles)
c) St. dev
d) natural breaks

A

a) equal intervals

21
Q

What classification technique “divides data into classes w/ equal numbers of observations in each class”?

a) equal interval
b) equal frequency (quantiles)
c) St. dev
d) natural breaks

A

b) equal frequency (quantiles)

22
Q
What classification technique "establishes class boundaries according to St. Dev from the mean"
- rule of thumb: 3 St. Dev above/below mean

a) equal interval
b) equal frequency (quantiles)
c) St. dev
d) natural breaks

23
Q
What Classification technique " ranks order data, places class breakpoints at breaks in data continuum"
- clusters similar values

a) equal interval
b) equal frequency (quantiles)
c) St. dev
d) natural breaks

A

d) natural breaks

24
Q

What classification technique is best used for continuous datasets?
- eg. temp, precipitation

a) equal interval
b) equal frequency (quantiles)
c) St. dev
d) natural breaks

A

a) equal interval

25
What classification technique is best used for evenly distributed data across a range? - excels at empahasizing the relative position of the data values - good for comparision a) equal interval b) equal frequency (quantiles) c) St. dev d) natural breaks
b) equal frequency (quantiles)
26
What classification technique is best used for data that conforms to a normal distribution? -shows departure from the overall average in (near) normally distributed datasets a) equal interval b) equal frequency (quantiles) c) St. dev d) natural breaks
c) St dev
27
What classification technique is best used with unevenly distributed data but not skeqwed towards either end of the distribution? - good to find similarities and dissimilarities between them? a) equal interval b) equal frequency (quantiles) c) St. dev d) natural breaks
d) natural breaks
28
What field stores - no decimals Range: -32 000 --> +32 000 a) short b) float c) long d) text
a) short
29
What field stores: -no decimals Range: -2 billion --> +2 billion a) short b) long c) float
b) long
30
What field stores: - decimal numbers -scientific notation Range: -1.2 x 10^38 --> +1.2 x 10^38 a) short b) long c) float
c) float
31
What field stores: - a series of alphanumeric symbols eg. street names, text descriptions a) long b) float c) text
c) text
32
Precision = 5, what is the correct number a) 55 55 b) 599 c) 99 999 d) 999.999
c) 99 999 Precision is the total number of digits for numeric fields --> number of characters
33
Scale = 3, what is the correct number? a) 3.33 b) .999 c) 3.93 d) 999
b) .333 Scale is the number of digits that are decimals
34
Precision is 5 and Scale is 3, what number is correct? a) 55.55 b) 99.999 c) 9.999 d) 99 999
b) 99.999
35
What is the term for this definition? =amount of variation in a data set from the mean? a) mean b) variance c) st. dev d) range
b) varience
36
What is the term for this definition? = the square root of varience? a) median b) range c) st. dev
c) st. dev
37
T/F A small St.Dev indicates that the data is grouped closel around the mean
True
38
T/F A small St. Dev indicates that the data is spread across a large range
False, that is Large St.Dev
39
Name the classification technique that has this problem =certain datasets may fall into 1 or 2 classes =skewed data and outliers can have empty classes a) Equal Interval b) Equal Frequency (quantiles) c) St. Dev d) Natural Breaks
a) equal interval
40
``` Name the classification technique that has this problem =features w/in the same class can have very different values =can make data seem like it has a wider difference -> small range data can be put into different classes ``` a) Equal Interval b) Equal Frequency (quantiles) c) St. Dev d) Natural Breaks
b) Equal frequency (quantiles)
41
Name the classification technique that has this problem =skewed data may get empty classes a) Equal Interval b) Equal Frequency (quantiles) c) St. Dev d) Natural Breaks
c) St. Dev
42
Name the classificaName the classification technique that has this problem - can make classes that have widely varying number ranges - can be difficult to compare 2+ maps (since class ranges are specific to each dataset) - may not see comparison a) Equal Interval b) Equal Frequency (quantiles) c) St. Dev d) Natural Breaks
d) Natural Breaks