Spatial Data

Fundamental properties are inherent to the nature of attributes as they are distributed across the earth’s surface. There is a fundamental continuity (structure) to attributes in space that derives from the underlying processes that shape the human and physical geographical world. Continuity is also a fundamental property of attributes observed in time. If we know the level of an attribute at one position in space (time) we can make an informed estimate of its level at adjacent locations (points in time). Spatial autocorrelation, in statistical terms, is a second order property of an attribute distributed in geographic space. In addition there may be a mean or first-order component of variation represented by a linear, quadratic, cubic (etc.) trend. We can think of these as two different scales of spatial variation although the distinction may be hard to make and quantify in practice. As Cressie (1991) remarks: ‘What is one person’s (spatial) covariance may be another persons mean structure’ (p. 25). It has often been remarked that spatial variation is heterogeneous. This type of decomposition (plus a white noise element to capture highly localized heterogeneity) is one way of formally capturing that heterogeneity using what are termed ‘global’ models. Another approach is to only analyze spatial subsets, that is allow model structure to vary locally.

(Haining, R. 2009. The Special Nature of Spatial Data (Chapter 2). Spatial Analysis (Handbook). Ed. A.S. Foteringham and P.A. Rogerson. Sage Publications. 6 p.)

Types of GIS Spatial Data

In GIS, spatial data is classified as three main types: point, line, and polygon.

A point is a convenient visual symbol (an X, dot or other graphic), but it does not reflect the real dimensions of the feature. Points may indicate specific locations (such as a given address, or the occurrence of an event) and/or which are usually too small to depict properly at the chosen scale features (such as a building).

A line is a one-dimensional feature with a starting and an ending point. Lines represent linear features, either real (e.g., roads or streams) as in Figure 2.2, or fictitious (e.g., administrative boundaries).

A polygon is an enclosed area, a two-dimensional feature with at least three sides (and therefore with an area). For example, it may represent a parcel of land, agricultural fields, or a political district.

(Fundamentals of GIS Data, Chapter 2, p.1 accessed at http://igre.emich.edu/wsatraining/TManual/Chapter2/Chap2.pdf)

What makes the analysis of spatial data special is the fact that it has always played a central role in the quantitative scientific tradition in geography. In general terms, spatial analysis can be considered to be the formal quantitative study of phenomena that manifest themselves in spare. This implies a focus on location, area, distance and interaction, e.g., as expressed in Tobler's (1979) First Law of Geography, where "everything is related to everything else, but near things are more related than distant things." In order to interpret what "near" and "distant" mean in a particular context, observations on the phenomenon of interest need to be referenced in space, e.g., in terms of points, lines or areal units.

The wide array of philosophical and methodological dilemmas that confront the analysis of spatial data necessitates an eclectic perspective. Many different ways of looking at a data set or at a model specification should be compared, and sensitivity analysis should play a central role. If different approaches yield the same conclusions, one can be fairly confident that meaningful insights have been gained. On the other hand, if the statistical findings turn out to be very sensitive to the approach taken, there is likely to be something wrong with the data and/or with the model and not much faith should be put in the precise quantitative results.

The characteristics of errors that affect observations of spatial data clearly motivate the need for a specialized methodology of spatial statistics and spatial econometrics.

(Anselin, L. 1989. What’s Special about Spatial Data? Spring 1989 Symposium on Spatial Statistics, Past, Present and Future, Department of Geography, Syracuse University.)