14 - Data exploration and classification

Question 1

The power of GIS resides in

Answer 1

its ability to process spatial data
and its associated attributes providing answers and solutions to
real life spatial issues

Question 2

Data exploration def

Answer 2

the process of examining data prior to formal structured data analysis

Question 3

Spatial data exploration

Answer 3

the process of examining the
attribute data of spatial features prior to a formal structured
data analysis

Question 4

data classification tool is based on what?

Answer 4

descriptive stats

Question 5

How is data exploration different to statistics?

Answer 5

1. Both spatial and attribute data involved
2. Media used in GIS is maps, graphs and tables

Question 6

Data visualization involves?

Answer 6

1. Rendering
2. Manipulation

Question 7

Rendering def

Answer 7

what to show in a graphic plot & what type of plot to make

Question 8

Manipulation def

Answer 8

how to operate on individual plots and how to organise multiple plots.
Organise graphs so that easy to interpret

Question 9

3 NB tasks when exploring data

Answer 9

1. Find patterns
2. Pose queries (characteristics and subsets)
3. Make comparisons

Question 10

Inferential stats def

Answer 10

generalizing from a sample to a population with calculated degree of certainty

Question 11

Descriptive stats def

Answer 11

are statistics which provide a statistical summary of a dataset - measures of central tendency or summary stats

Question 12

Descriptive stats (4)

Answer 12

1. Measures of central tendency
2. Measures of dispersion
3. Skewness
4. Kurtosis

Question 13

Measures of dispersion

Answer 13

1. Standard deviation
2. Variance
3. Z score
4. Range
5. Standard difference

Question 14

A low standard deviation indicates

Answer 14

the data points tend to be very close to the mean

Question 15

high standard deviation indicates

Answer 15

the data points are spread out over a large range of values

Question 16

Standard dev def

Answer 16

Shows how much variation or dispersion” exists from the average

Question 17

Variance

Answer 17

Measure of how far a set of numbers is spread out (Standard
deviation squared)

Question 18

Standard score (z score) def

Answer 18

The standardized or z score informs how many standard deviations a
reading is above or below the mean

Question 19

Classification def

Answer 19

the process of reducing a large number of individual quantitative values to a smaller number of ordered categories, each of which comprises a portion of the original data value range

Question 20

Classification types

Answer 20

1. Natural breaks
2. Equal interval classes
3. Geometric interval
4. Mean and standard deviation
5. Quantile
6. User defined

Question 21

Fundamental principle of classification

Answer 21

– ALWAYS mutually exclusive AND exhaustive
– Monochrome - 5-7 classes
– multi hue - 9 or less

Question 22

Considerations for classification

Answer 22

1. Available symbols
2. Communication goal
3. Complexity of spatial pattern

Question 23

Quantitative precision

Answer 23

• larger no. of classes
• represent a small range
  – too much info
  – indistinct symbols

Question 24

Immediate graphic impact

Answer 24

• small no. of classes
• graphically clear, imprecise quanti
  – overs simplification
  – class may have varying data values

Question 25

Natural breaks def

Answer 25

method seeks to reduce the variance within classes and
maximize the variance between classes

Question 26

Why is equal intervals not good for rectangular data distri?

Answer 26

• Since each bin will have an equal width, but the uniform distribution ensures that each bin will have approximately the same number of data points, the resulting visualization might misleadingly suggest that the data is more evenly spread than it actually is.
• In reality, the uniform distribution means each data point is equally likely, but equal interval binning does not convey any particular areas of interest within the data.

Question 27

Why is user defined intervals not good for skewed data?

Answer 27

Many classes will be empty and not
mapped

Question 28

Why is mean & std dev not good for skewed data?

Answer 28

For skewed data, the mean is pulled toward the skew, and the standard deviation may not accurately reflect the dispersion around the central values, resulting in misleading class boundaries