“Attribute values may be defined with respect to nominal, ordinal, interval, or ratio scales of measurement. It is important to recognize the scales of measurement used in GIS data as this determines t”
In this module, we try to categorize data and explore the types of operation that can be applied to a dataset.
Nominal Data: it serves only to identify or distinguish one entity from another. A nominal data set is just a set of names, except the names, take the form of numbers.
Give four examples of nominal data.
Binary scale: A special case of nominal scale when there is only two dichotomous (division into two parts) possible outcomes example: wet/dry land.
Give four examples of binary scale data.
Ordinal Scale: Ordinal scale requires some ranking criterion; e.g., desirable residential areas. It requires an asymmetric relationship between objects
Give four examples of ordinal scale data.
Interval: Interval scale is a "true" metric scale. Ordering as well as "distance" is implied.
Ratio: Ratio scale is another "true" metric scale. It has an inherent (or "true") zero and can, therefore, be used to compare ratios: for example, a 50-year old person has spent twice as many years on this planet as a 25-year old.
When dealing with attributes it is important to understand what classification category they fall under. This helps in applying the right symbolization. For example, when dealing with discrete data only categorized classification can be used whilst when dealing with continuous data categorized and graduated classification can be used. Knowing the attribute category enables a user to efficiently use the appropriate algorithm.
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