Attribute has become the preferred term for any recorded characteristic or property of a place (see Table 1‑1 for a more formal definition). A place’s name is an obvious example of an attribute, but a vast array of other options has proven useful for various purposes. Some are measured, including elevation, temperature, or rainfall. Others are the result of classification, including soil type, land-use or land cover type, or rock type. Government agencies provide a host of attributes in the form of statistics, for places ranging in size from countries all the way down to neighborhoods and streets. The characteristics that people assign rightly or mistakenly to places, such as “expensive”, “exciting”, “smelly”, or “dangerous” are also examples of attributes. Attributes can be more than simple values or terms, and today it is possible to construct information systems that contain entire collections of images as attributes of hotels, or recordings of birdsong as attributes of natural areas. But while these are certainly feasible, they are beyond the bounds of most techniques of spatial analysis.
Within GIS the term attribute usually refers to records in a data table associated with individual features in a vector map or cells in a grid (raster or image file). Sample vector data attributes are illustrated in Figure 2‑1A where details of major wildfires recorded in Alaska are listed. Each row relates to a single polygon feature that identifies the spatial extent of the fire recorded. Most GIS packages do not display a separate attribute table for raster data, since each grid cell contains a single data item, which is the value at that point and can be readily examined. ArcGIS is somewhat unusual in that it provides an attribute table for raster data (see Figure 2‑1B).
Figure 2‑1 Attribute tables – spatial datasets
A. Alaskan fire dataset – polygon attributes
B. DEM dataset – raster file attribute table (ArcGIS)
Rows in this raster attribute table provide a count of the number of grid cells (pixels) in the raster that have a given value, e.g. 144 cells have a value of 453 meters. Furthermore, the linking between the attribute table visualization and mapped data enables all cells with elevation=453 to be selected and highlighted on the map.
Many terms have been adopted to describe attributes. From the perspective of spatial analysis the most useful divides attributes into scales or levels of measurement, as follows:
|•||Nominal. An attribute is nominal if it successfully distinguishes between locations, but without any implied ranking or potential for arithmetic. For example, a telephone number can be a useful attribute of a place, but the number itself generally has no numeric meaning. It would make no sense to add or divide telephone numbers, and there is no sense in which the number 9680244 is more or better than the number 8938049. Likewise, assigning arbitrary numerical values to classes of land type, e.g. 1=arable, 2=woodland, 3=marsh, 4=other is simply a convenient form of naming (the values are nominal). SITENAME in Figure 2‑1A is an example of a nominal attribute, as is OBJECTID, even though both happen to be numeric|
|•||Ordinal. An attribute is ordinal if it implies a ranking, in the sense that Class 1 may be better than Class 2, but as with nominal attributes no arithmetic operations make sense, and there is no implication that Class 3 is worse than Class 2 by the precise amount by which Class 2 is worse than Class 1. An example of an ordinal scale might be preferred locations for residences — an individual may prefer some areas of a city to others, but such differences between areas may be barely noticeable or quite profound. Note that although OBJECTID in Figure 2‑1A appears to be an ordinal variable it is not, because the IDs are provided as unique names only, and could equally well be in any order and use any values that provided uniqueness (and typically, in this example, are required to be integers)|
|•||Interval. The remaining three types of attributes are all quantitative, representing various types of measurements. Attributes are interval if differences make sense, as they do for example with measurements of temperature on the Celsius or Fahrenheit scales, or for measurements of elevation above sea level|
|•||Ratio. Attributes are ratio if it makes sense to divide one measurement by another. For example, it makes sense to say that one person weighs twice as much as another person, but it makes no sense to say that a temperature of 20 Celsius is twice as warm as a temperature of 10 Celsius, because while weight has an absolute zero Celsius temperature does not (but on an absolute scale of temperature, such as the Kelvin scale, 200 degrees can indeed be said to be twice as warm as 100 degrees). It follows that negative values cannot exist on a ratio scale. HA_BURNED and ACRES_BURN in Figure 2‑1A are examples of ratio attributes. Note that only one of these two attribute columns is required, since they are simple multiples of one another|
|•||Cyclic. Finally, it is not uncommon to encounter measurements of attributes that represent directions or cyclic phenomena, and to encounter the awkward property that two distinct points on the scale can be equal — for example, 0 and 360 degrees are equal. Directional data are cyclic (Figure 2‑2), as are calendar dates. Arithmetic operations are problematic with cyclic data, and special techniques are needed, such as the techniques used to overcome the Y2K problem, when the year after (19)99 was (20)00. For example, it makes no sense to average 1degree and 359degrees to get 180degrees, since the average of two directions close to north clearly is not south. Mardia and Jupp (1999) provide a comprehensive review of the analysis of directional or cyclic data (see further, Section 4.5.1, Directional analysis of linear datasets)|
Figure 2‑2 Cyclic attribute data — Wind direction, single location
While this terminology of measurement types is standard, spatial analysts find that another distinction is particularly important. This is the distinction between attributes that are termed spatially intensive and spatially extensive. Spatially extensive attributes include total population, measures of a place’s area or perimeter length, and total income — they are true only of the place as a whole.
Spatially intensive attributes include population density, average income, and percent unemployed, and if the place is homogeneous they will be true of any part of the place as well as of the whole. For many purposes it is necessary to keep spatially intensive and spatially extensive attributes apart, because they respond very differently when places are merged or split, and when many types of spatial analysis are conducted.
Since attributes are essentially measured or computed data items associated with a given location or set of locations, they are subject to the same issues as any conventional dataset: sampling error; measurement errors and limitations; mistakes and miscalculations; missing values; temporal and thematic errors and similar issues. Metadata accompanying spatial datasets should assist in assessing the quality of such attribute data, but at least the same level of caution should be applied to spatial attribute data as with any other form of data that one might wish to use or analyze.