Spatial Thinking in Planning Practice: An Introduction to GIS

9 following table and demonstrated using an example of a marathon race: Nominal Ordinal Interval Ratio Runner ID Order !nished Time of day !nished Total race time 238 1 10:10am 2:30 143 2 10:11am 2:31 14 3 10:13am 2:33 . . . . . . . . . . . . . . . . 301 450 18:10pm 10:30 Figure 1.7. Levels of Measurement. Adopted from GIS Commons. http://giscommons.org/chapter-2-input/ Nominal data use characters or numbers to establish identity or categories within a series. In a marathon race, the numbers pinned to the runners’ jerseys are nominal numbers (!rst column in the !gure above). "ey iden- tify runners, but the numbers do not indicate the order or even a predicted race outcome. Besides races, tele- phone numbers are a good example. It signi!es the unique identity of a telephone. "e phone number 961-8224 is not more than 961-8049. Place names (and those of people) are nominal too. You may prefer the sound of one name, but they serve only to distinguish themselves from each other. Nominal characters and numbers do not suggest a rank order or relative value; they identify and categorize. Nominal data are usually coded as char- acter (string) data in a GIS database. Although census data originate as individual counts, much of what is counted is individuals’ membership in nominal categories. Race, ethnicity, marital status, mode of transportation to work (car, bus, subway, railroad...), and type of heating fuel (gas, fuel oil, coal, electricity...) are measured as numbers of observations assigned to unranked categories. Using nominal data we can use the Census Bureau’s !rst atlas to depict the minority groups with the largest percentage of population in each U.S. state (Figure 1.8). Colors were chosen to di#erentiate the groups through a qualitative color scheme to show di#erences between the classes, but not to imply any quanti- tative ordering. "us, although numerical data were used to determine which category each state is in, the map depicts the resulting nominal categories rather than the underlying numerical data. Figure 1.8. Highest percent minority group by state. Source: US Census (2000). Chapter 1: De!ning a Geographic Information System