An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.
The primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.
In a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.
Vector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.
The storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.
Data can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.
Quantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.
Quantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.
In between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.