gis lesson 1
問題一覧
1
divides the range of attribute values into equal sized sub ranges, allowing one to
specify the number of intervals while the algorithm determines where the breaks
should be
equal interval
2
emphasizes the amount of an attribute value relative to other values
equal interval
3
best applied to familiar data ranges such as percentages and temperature
equal interval
4
GOOD: presenting information to a non-technical audience
easier to interpret especially for familiar values like percentages
equal interval
5
DISADVANTAGE: values may cluster in the histogram, may have many
features in one class and none in another
equal interval
6
each class contains an equal number of features
quantile
7
well suited to linearly (i.e. evenly) distributed data
quantile
8
similar features can be placed in adjacent classes, or features with widely different
values can be put in the same class
quantile
9
distortion can be minimized by increasing the number of classes
quantile
10
GOOD: emphasizing relative position (e.g. which countries are in the top 20% for
income)
quantile
11
DISADVANTAGE: features with similar values may end up in different classes,
exaggerating their Differences reverse can also happen: wide range of values can end
up in the same classes, minimizing differences
quantile
12
classes are based on natural groupings inherent in the data
natural breaks
13
identifies break points by picking the class breaks that best group similar values and
maximizes differences between classes
natural breaks
14
boundaries are set where there are relatively big jumps in the data values
natural breaks
15
GOOD: mapping values that are not evenly distributed in the histogram
natural breaks
16
DISADVANTAGE: class ranges are tailored to one data set, so difficult to compare maps
can be difficult to choose optimum number of classes especially if data are
evenly distributed
natural breaks
17
creates class breaks based on class intervals that have geometric series
geometric int
18
the algorithm minimizes the sum of squares of the number of elements in each class
geometric int
19
ensures that each class range has approximately the same number of values with each
class and the change between intervals is fairly consistent
geometric int
20
GOOD: designed to accommodate continuous data creates a balance between
highlighting changes in the middle values and the extreme values, thereby visually
appealing and cartographically comprehensive
geometric int
21
DISADVANTAGE: especially if data are evenly distributed
geometric int
22
shows how much a feature’s attribute value varies from the mean
standard dev
23
class breaks are created based on the calculated mean and the standard deviations
from the mean
standard dev
24
two-color ramp helps emphasize values above (shown in blue) and below (shown in red)
the mean
standard dev
25
GOOD: seeing which features are above or below an average value data has a normal
distribution
standard dev
26
DISADVANTAGE: map doesn’t show actual values, only how far values are from the
mean very high or low values can skew the mean
standard dev
gis lesson 2
gis lesson 2
poo · 47問 · 2年前gis lesson 2
gis lesson 2
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nrc topic 6
poo · 26問 · 2年前nrc topic 6
nrc topic 6
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gis lesson 3
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gis lesson 3
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gis lesson 4
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gis lesson 4
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fmh
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fmh
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tbm
poo · 36問 · 2年前tbm
tbm
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ethics ch2
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ethics ch2
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ethics ch1.5-6
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ethics ch1.5-6
19問 • 1年前ch1
ch1
poo · 16問 · 1年前ch1
ch1
16問 • 1年前ch1
ch1
poo · 28問 · 1年前ch1
ch1
28問 • 1年前topic 1
topic 1
poo · 7問 · 1年前topic 1
topic 1
7問 • 1年前問題一覧
1
divides the range of attribute values into equal sized sub ranges, allowing one to
specify the number of intervals while the algorithm determines where the breaks
should be
equal interval
2
emphasizes the amount of an attribute value relative to other values
equal interval
3
best applied to familiar data ranges such as percentages and temperature
equal interval
4
GOOD: presenting information to a non-technical audience
easier to interpret especially for familiar values like percentages
equal interval
5
DISADVANTAGE: values may cluster in the histogram, may have many
features in one class and none in another
equal interval
6
each class contains an equal number of features
quantile
7
well suited to linearly (i.e. evenly) distributed data
quantile
8
similar features can be placed in adjacent classes, or features with widely different
values can be put in the same class
quantile
9
distortion can be minimized by increasing the number of classes
quantile
10
GOOD: emphasizing relative position (e.g. which countries are in the top 20% for
income)
quantile
11
DISADVANTAGE: features with similar values may end up in different classes,
exaggerating their Differences reverse can also happen: wide range of values can end
up in the same classes, minimizing differences
quantile
12
classes are based on natural groupings inherent in the data
natural breaks
13
identifies break points by picking the class breaks that best group similar values and
maximizes differences between classes
natural breaks
14
boundaries are set where there are relatively big jumps in the data values
natural breaks
15
GOOD: mapping values that are not evenly distributed in the histogram
natural breaks
16
DISADVANTAGE: class ranges are tailored to one data set, so difficult to compare maps
can be difficult to choose optimum number of classes especially if data are
evenly distributed
natural breaks
17
creates class breaks based on class intervals that have geometric series
geometric int
18
the algorithm minimizes the sum of squares of the number of elements in each class
geometric int
19
ensures that each class range has approximately the same number of values with each
class and the change between intervals is fairly consistent
geometric int
20
GOOD: designed to accommodate continuous data creates a balance between
highlighting changes in the middle values and the extreme values, thereby visually
appealing and cartographically comprehensive
geometric int
21
DISADVANTAGE: especially if data are evenly distributed
geometric int
22
shows how much a feature’s attribute value varies from the mean
standard dev
23
class breaks are created based on the calculated mean and the standard deviations
from the mean
standard dev
24
two-color ramp helps emphasize values above (shown in blue) and below (shown in red)
the mean
standard dev
25
GOOD: seeing which features are above or below an average value data has a normal
distribution
standard dev
26
DISADVANTAGE: map doesn’t show actual values, only how far values are from the
mean very high or low values can skew the mean
standard dev