Attribute-based Operations

Objectives of Lecture:

1. More about representation (left out of last lecture) Overheads, demos
2. Operations based on attributes (Chapter 4), beginning of Part 2
3. ArcMap (Spatial Analyst) for Attribute Operations (Exercise 2)

Operations that REDUCE information

These operations reduce the information content by collapsing many categories to one [or to fewer...] (Group, Isolate - which does it in the BACKGROUND category) or by collapsing higher level measurements into ordinal (Classify). The red text above identifies the external information required to make this happen.
There are many rules for classification (remember the 360 lecture on the topic).

• Equal Intervals (depends solely on range of attribute; applies only to Interval or higher)
• Quantiles (depends on counting the objects; depends on meaasurement framework)
• Jenk's Iterative (minimizes within class variance; depends on Interval or higher)
• "Natural Breaks" (an aesthetic judgement about gaps)
• Thresholds with external meaning (freezing point of water, gain/loss, legal requirements, etc.)

Scale reduces information only if the resolution of the scale is altered (an issue of representation), otherwise a scaling changes no information.

Even these simple operations produce results that influence the apparent measurement framework. Aggregation occurs when the aspatial groupings of attributes require a geometric process to remove boundaries which are now internal to one category. Often called 'dissolve' or 'drop-line' aggregation.

Isolation of a single category can change from an exhaustive categorical coverage to an isolated object view, but the decisions made on the original coverage provide the geometric representation.

Operations that INCREASE information

Increase in information content comes from external assumptions or judgements. With a source of preferences, you can order categories. With a set of scores, you can assign numbers to categories. With a non-linear function, you can rescale numbers. These are not inherent in the attribute reference systems you have to start with. They come from understanding something outside the numbers you have. (These are the holes in Stevens' system!)

Examples: King County use of Public Benefit Rating System (look at 20.36.100, page 82-3 of the 122 page document) | Ecology Wetland Rating System ; (69 page .pdf; look at pages beginning at 33 for the form, 44 for the defintions of Classes) ; applied to Eastern Washington | Timber Site Index |

Operations on Attibutes

Operations on attribute values take one set of attributes and give you new "columns". How this is implemented depends on the data representation.

Vector implementation with attribute tables make these distinct, but in raster systems, pairs of values invoke overlay methods (Chapter 5).


Crosstabulate, Difference, Rate, Density, Add, Subtract, Proportion, etc.
If you have pairs of values for the same set of objects (representation), you can generate new values that depend on PAIRS of values.

Indirect Measurement through LOOKUP Tables

The 'attribute' provides a foreign key to a lookup table of attribute values. This process is called JOIN in a relational database package. The Washington State Timber Site Index is assigned from a soil class to create the Land Grade Classes for taxation purposes (indirect measurement a few times in a row...) [WAC 458-40-530].