Understanding geospatial data

Geospatial data is data that positions things on the Earth's surface. This is a deliberately vague definition that encompasses both the idea of location and shape. For example, a database of car accidents may include the latitude and longitude coordinates identifying where each accident occurred, and a file of county outlines would include both the position and shape of each county. Similarly, a GPS recording of a journey would include the position of the traveler over time, tracing out the path they took on their travels.

It is important to realize that geospatial data includes more than just the geospatial information itself. For example, the following outlines are not particularly useful by themselves:

Once you add appropriate metadata, however, these outlines make a lot more sense:

Geospatial data, therefore, includes both spatial information (locations and shapes) and non-spatial information (metadata) about each item being described.

Spatial information is usually represented as a series of coordinates, for example:

location = (-38.136734, 176.252300)
outline = ((-61.686,17.024),(-61.738,16.989),(-61.829,16.996) ...)

These numbers won't mean much to you directly, but once you plot these series of coordinates onto a map, the data suddenly becomes comprehensible:

There are two fundamental types of geospatial data:

The typical raster data formats you might encounter include:

For vector-format data, you may typically encounter the following formats:

Because your analysis can only be as good as the data you are analyzing, obtaining and using good-quality geospatial data is critical. Indeed, one of the big challenges in performing geospatial analysis is to get the right data for the job. Fortunately, there are several websites which provide free good-quality geospatial data. But if you're looking for a more obscure set of data, you may have trouble finding it. Of course, you do always have the choice of creating your own data from scratch, though this is an extremely time-consuming process.

We will return to the topic of geospatial data in Chapter 2, Geospatial Data, where we will examine what makes good geospatial data and how to obtain it.