Dividing objects into points, lines, areas, and volumes is only the crudest of the many ways in which humans classify the things they find surrounding them in their environment. A topographic map may show areas of woodland, rivers, roads, contours, windmills, and several hundred other kinds of objects, each of them classifiable as points, lines, areas, or volumes. Objects may overlap — a river may pass through a wood, a road may cross a river, and all three may lie inside the area shown as “Montara, California”.
Spatial analysts think of multiple classes of objects as forming layers of information superimposed on each other (see Figure 2‑4, below). A study might be conducted on just one layer, analyzing the patterns formed by the objects in that layer and looking for clues as to the processes that created the pattern. In other cases the patterns of one layer might be analyzed in relation to other layers. For example, the pattern of cases of cancer in an area might be studied to see if it bears any relationship to the pattern of roads or electrical transmission lines in the area, or to the chemistry of drinking water.
Sometimes many different attributes are collected and published for the same sets of places (see Section 2.1.2, Attributes). Census agencies do this when they compile hundreds of attributes for census tracts, counties, or states. In such cases comparing layers is a simple matter of comparing the different attributes assigned to the same places. But more generally the objects in two layers may not be the same, creating difficulties for analysis. For example, suppose a study is being conducted of people’s perception of noise in a neighborhood near an airport. Monitoring devices have been positioned at points around the neighborhood, providing a layer of noise measurements. Residents have been interviewed, providing another layer of perceptual data. The analysis now moves to a comparison of the contents of the two layers, but it is hampered by the fact that the objects in the two layers are in different locations.
Source: Manifold GIS sample dataset, Montara CA