Attribute Data Management

Question 1

What is attribute data?

Answer 1

descriptive and informs spatial data

Question 2

Steps of attribute data managemnet?

Answer 2

1. Input - lecture on data input
2. Validation
3. Manipulation

Question 3

What is validation?

Answer 3

• Data Validation entails checking the accuracy of you attribute data
  ▪ Wrong value in an attribute field?
  ▪ Missing attribute?
  ▪ Misspelled word or other typographical error?
• FIX: Sort attributes + Run Query

Question 4

What is manipulation?

Answer 4

• The manipulation of attributes includes:
• Find
• Select by Attributes
• Add/Delete fields
• Calculate Geometry
• Join/Link
• Create Graph
• Print
• Export

Manipulation of Attributes: Creating Different graphs
▪ Single vs multiple variables;
▪ Individual vs classes of values
* Data dictates which graph is best
* Numerous possibilities:
1. Line
2. Bar
3. Cumulative distribution
4. Scatterplot
5. Bubble plot

• Sort
• Edit/delete records
• Examine stats
  Calculate fields

Calculate geometry:
> Area
> Length

Question 5

When do we use line graphs?

Answer 5

Display data as a line: Example, Shows data changes over time

Question 6

When do we use bar charts?

Answer 6

Uses bars to show the number/frequency of values falling within each class

Question 7

Cumulative distribution graph

Question 8

pie chart

Question 9

what are types of Attribute Data?

Answer 9

data can be classified according to its:
* Type
* Measurement scale

Data type refers to how a GIS stores attribute data
* Common types include:
Types of Attribute Data
* Number
* Text
* Character
* Date
* Binary large objects (BLOB) e.g.
images, audio or multimedia

Question 10

Sorting attributes in GIS - software

Answer 10

1. Text (Just letters/Words)
2. Date
3. Number
   * Short integer (Short)
   * Long integer (Long)
   * Single-precision floating-point (Float)
   * Double-precision floating-point (Double)
4. Binary large object (BLOB)
   * BLOBS are typically images, audio or other multimedia objects

Question 11

types of attribute data: scale and precision table

Precision describes the number of digits that can be stored in the field, while scale describes the number of
decimal places. Negative numbers require additional precision to store the negative sign.

Answer 11

1. Short integer*: 1–4 (Oracle), 5 (DB2, Informix, SQL Server): 0
2. Long integer: 5–10 (Oracle), 6-9 (DB2, Informix, SQL : 0
3. Float: 1–6 :1–6
4. Double: 7+: 0

Question 12

measurement scale of attribute data?

Answer 12

Categorical + Qualitative:
1. Nominal
▪ No ranking/Used for naming
2. Ordinal
▪ Ranking with no number (Large > Medium > Small)

Numeric + Quantitative:
3. Interval
▪ Have known numerical intervals but no absolute zero (Temperature
°F/°C)
4. Ratio
▪ Same as interval but has absolute Zero ((Temperature Kelvin)

Question 13

Nomianl/ qualitative attribute data?

Answer 13

▪ Categories (“Names of items”)
▪ Subject/theme differences
▪ Grouped in categories based on qualitative data
▪ Not possible to measure the difference between two themes
▪ No rank
▪ For example:
▪ Road vs River
▪ Border or boundary
▪ Land vs. water
▪ Animal species

Question 14

Ordinal/ qualitative attribute data?

Answer 14

▪ Ordinal measurements describe
order
▪ Ranked categories, no inference to
spaces between rankings
▪ Class differences & rank/position
within class
▪ Grouped in categories based on
quantitative data
▪ For example:
▪ 1st, 2nd , 3rd etc
▪ Degree of soil erosion, e.g. light,
moderate, severe
▪ High/medium/low
▪ Ratings (1, 2, 3 stars)

Question 15

interval/quantitative attribute data?

Answer 15

▪ Ranked categories with known units
between rankings
▪ Operations of addition and subtraction
have meaning
▪ Based on quantitative data
▪ Zero does not mean no data
▪ For example:
▪ Temperature (Celcius )
▪ Ex. 40 degrees C is warmer than 30 or 20
and 0 means freezing point.

Question 16

ratio/ quantitative attribute data?

Answer 16

▪ Ranked categories with known units between intervals
▪ Operations of multiplication and division can be employed
▪ Based on quantitative data
▪ But…based on an absolute zero.
▪ Numerical values with the zero feature denotes an absence of a
feature
▪ For example:
▪ Precipitation
▪ Population
▪ Temperature (Kelvin)