Attribute Data Management Flashcards
lecture 12
What is attribute data?
descriptive and informs spatial data
Steps of attribute data managemnet?
- Input - lecture on data input
- Validation
- Manipulation
What is validation?
- 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
What is manipulation?
- The manipulation of attributes includes:
- Find
- Select by Attributes
- Add/Delete fields
- Calculate Geometry
- Join/Link
- Create Graph
- 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
When do we use line graphs?
Display data as a line: Example, Shows data changes over time
When do we use bar charts?
Uses bars to show the number/frequency of values falling within each class
Cumulative distribution graph
pie chart
what are types of Attribute Data?
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
Sorting attributes in GIS - software
- Text (Just letters/Words)
- Date
- Number
* Short integer (Short)
* Long integer (Long)
* Single-precision floating-point (Float)
* Double-precision floating-point (Double) - Binary large object (BLOB)
* BLOBS are typically images, audio or other multimedia objects
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.
- Short integer*: 1–4 (Oracle), 5 (DB2, Informix, SQL Server): 0
- Long integer: 5–10 (Oracle), 6-9 (DB2, Informix, SQL : 0
- Float: 1–6 :1–6
- Double: 7+: 0
measurement scale of attribute data?
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)
Nomianl/ qualitative attribute data?
▪ 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
Ordinal/ qualitative attribute data?
▪ 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)
interval/quantitative attribute data?
▪ 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.
