MODULE 1: INTRODUCTION AND DATA STRUCTURE Spatial vs. Non-spatial Data Spatial Data Data that define a location. These are in the form of graphic primitives that are usually either points, lines, polygons or pixels. • Spatial data includes location, shape, size, and orientation. o For example, consider a particular square:
its center (the intersection of its diagonals) specifies its location
its shape is a square
the length of one of its sides specifies its size
the angle its diagonals make with, say, the x-axis specifies its orientation. • Spatial data includes spatial relationships. For example, the arrangement of ten bowling pins is spatial data. Non-spatial Data
Data that relate to a specific, precisely defined location. The data are often statistical but may be text, images or multi-media. These are linked in the GIS to spatial data that define the location. • Non-spatial data (also called attribute or characteristic data) is that information which is independent of all geometric considerations. o For example, a person’s height, mass, and age are non-spatial data because they are independent of the person’s location. o It’s interesting to note that, while mass is non-spatial data, weight is spatial data in the sense that something’s weight is very much dependent on its location.
It is possible to ignore the distinction between spatial and non-spatial data. However, there are fundamental differences between them: o spatial data are generally multi-dimensional and auto correlated. o non-spatial data are generally one-dimensional and independent.
These distinctions put spatial and non-spatial data into different philosophical camps with far-reaching implications for conceptual, processing, and storage issues. o For example, sorting is perhaps the most common and important non-spatial data processing function that is performed. o It is not obvious how to even sort locational data such that all points end up nearby their nearest neighbors.