In GIS, every spatial feature, whether a point, line, polygon, or cell, has descriptive data attached to it. These pieces of descriptive information are called attributes, and they’re stored in what’s known as an attribute table. Think of an attribute table as a spreadsheet that describes the what behind the where:

• Each row corresponds to a geographic feature (e.g., a particular building, river, or archaeological site)
• Each column represents a variable or characteristic (e.g., name, category, elevation, date built, material)

If you’re familiar with a database or Excel spreadsheet, this structure will feel intuitive. In GIS, the spatial and tabular data are linked. When you click a point on a map, you can see all its associated attributes. Imagine, for example, a point feature class of monuments around the world. One might associate the following characteristics, or attributes, with those monuments: name, type, the year it was built, the primary material it consists of, its height, and, because we’re in a GIS, the latitude and longitude of each of the monuments (see Table 1 for an example of the attribute table of this hypothetical feature class). Because this data was captured along with the locations, one could symbolize the various monuments by height or material, query monuments built before 1900, create labels showing names dynamically on the map, select by height or era, etc. The essential point to remember here is that the nature of spatial analysis that can be done in a GIS depends on the data associated with the features being studied.

id	Name	Type	Year Built	Material	Latitude	Longitude	Height (m)
1	Arc de Triomphe	Memorial	1836	Stone	48.8738	2.2950	50
2	Lincoln Memorial	Memorial	1922	Marble	38.889248	-77.050636	30
3	Eiffel Tower	Tower	1889	Iron	48.858222	2.2945	330
4	Hagia Sophia	Holy Site	537	Stone	41.008333	28.98	55
5	CN Tower	Tower	1976	Concrete and Steel	43.642556	-79.387083	553.3
6	Space Needle	Tower	1962	Steel	47.6204	-122.3491	184
7	St. Peter’s Basilica	Holy Site	1626	Stone	41.902222	12.453333	136.6

Applying a Humanities Lens: Mapping what Matters

When you’re designing your dataset, ask

• Which attributes actually matter for the question(s) I’m trying to ask?
• Do my categories or fields reflect historical realities or modern assumptions?
• Am I leaving space for (and how might I represent) ambiguity, uncertainly, or multivocality?