Characteristics of Geospatial Data
Locations are typically expressed in either geographical coordinates (in degrees latitude and longitude) or as Universal Transverse Mercator coordinates (expressed in metres East and metres North). Interestingly, objects on the Earth’s surface not only occupy locations; they also have attributes associated with them. Attributes are “non-spatial data that are associated with a geospatial location” (Shellito, 2016, p. 37). For example, the type of tree shown in the figure above is an example of a non-spatial attribute associated with the tree. It is important to note that attribute data are typically measured on one of four scales (also known as the level of data measurement): nominal, ordinal, interval, and ratio.
Level of Data Measurement | Description | Example(s) |
Nominal | Data are in categories that may contain numbers, but these types of data represent differences in kind only. | Land-use/land-cover types (Class 1 = agriculture, Class 2 = commercial, Class 3 = residential) |
Ordinal | Data are used to represent a ranking; it does not deal with the numbers that may be associated with a ranking. | Top ten cities ranked from warmest to coolest air temperature. |
Interval | Differences between numbers are meaningful and significant, but there is no fixed zero point. | Temperature in degrees Celsius or degrees Fahrenheit |
Ratio | Differences between numbers are meaningful and significant; there is a fixed zero point. | Height, weight, age, area, cost |
The distinction between different levels of data measurement is very important since types of data analyses can only be performed on data measured on a particular level or scale.
Geospatial data are also organized according to a type of data model, either raster or vector. In other words, geospatial data are represented as either grid cells or as a series of lines, points, areas, and/or polygons. A raster-based data model is a “conceptualization of representing geospatial data with a series of equally spaced and sized grid cells. Grid cells are a single square unit of a raster” (Shellito, 2017, p. 288). Remote-sensing data are typically raster-based data. The term, pixel (derived from the words picture and element) is used to describe a single square unit of a raster image. The spatial resolution of the raster (or the size of the pixel) is typically expressed in metres.
A vector-based data model is a “conceptualization of the world that represents geospatial data as a series of vector objects (points, lines, areas, and/or polygons)” (Shellito, 2016, p. 127). Using this model, vector objects (points, lines, and polygons) are “used to model real-world phenomena using the vector data model” (Shellito, 2016, p. 127). The diagram below illustrates the difference between raster- and vector-based models.
It is not uncommon for a researcher (student) to create their own raster- and/or vector-based geospatial data to solve an environmental sustainability challenge. There are many different types of geospatial software (both free and/or commercially available software) that allows you to create your own geospatial data (e.g., ESRI’s ArcGIS).
